From 1820b60ac86f74b2e1aedcb8f3542943613f9d10 Mon Sep 17 00:00:00 2001 From: Philip Loche Date: Tue, 21 Oct 2025 17:39:51 +0200 Subject: [PATCH 1/7] Reorganized example files --- .gitignore | 2 +- .readthedocs.yml | 4 +- docs/generate_examples/conf.py | 31 +- docs/src/examples/ase/ase.zip | Bin 0 -> 6176 bytes docs/src/examples/ase/ase_jupyter.zip | Bin 0 -> 10186 bytes docs/src/examples/ase/ethanol_reduced_100.xyz | 1100 +++++++++++++ .../images/thumb/sphx_glr_run_ase_thumb.png | Bin 0 -> 41946 bytes docs/src/examples/ase/index.rst | 61 + .../src/examples}/ase/options.yaml | 0 docs/src/examples/ase/run_ase.codeobj.json | 926 +++++++++++ docs/src/examples/ase/run_ase.ipynb | 266 ++++ .../src/examples}/ase/run_ase.py | 0 docs/src/examples/ase/run_ase.py.md5 | 1 + docs/src/examples/ase/run_ase.rst | 403 +++++ docs/src/examples/ase/run_ase.zip | Bin 0 -> 16212 bytes {examples => docs/src/examples}/ase/train.sh | 0 docs/src/examples/basic_usage/basic_usage.zip | Bin 0 -> 4718 bytes 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docs/src/getting-started/override.rst | 2 +- docs/src/index.rst | 2 +- .../tutorials/advanced_tutorials/index.rst | 17 - .../tutorials/beginner_tutorials/index.rst | 16 - docs/src/tutorials/index.rst | 14 - .../.gitignore | 2 + examples/0-beginner/00-basic-usage.sh | 125 ++ examples/0-beginner/01-data_preparation.py | 242 +++ .../0-beginner/02-fine-tuning.py | 49 +- examples/0-beginner/03-train_from_scratch.py | 151 ++ examples/0-beginner/04-parity_plot.py | 146 ++ examples/0-beginner/05-run_ase.py | 207 +++ examples/0-beginner/GALLERY_HEADER.rst | 7 + examples/0-beginner/carbon_reduced_100.xyz | 1 + .../ethanol_reduced_100.xyz | 0 .../ethanol_reduced_100_predicted.xyz | 1100 +++++++++++++ examples/0-beginner/eval-basic.yaml | 6 + examples/0-beginner/eval-scratch.yaml | 8 + examples/0-beginner/options-ase.yaml | 15 + examples/0-beginner/options-basic.yaml | 18 + examples/0-beginner/options-memmap.yaml | 38 + examples/0-beginner/options-scratch.yaml | 22 + .../qm9_reduced_100.xyz | 0 examples/1-advanced/.gitignore | 1 + .../1-advanced/00-transfer-learning.py | 28 +- examples/1-advanced/01-llpr.py | 121 ++ examples/1-advanced/02-zbl.py | 147 ++ .../1-advanced/03-fitting-generic-targets.py | 2 + examples/1-advanced/04-flashmd.py | 120 ++ examples/1-advanced/05-multi-gpu.py | 90 ++ examples/1-advanced/GALLERY_HEADER.rst | 8 + .../ethanol_reduced_100.xyz | 0 .../1-advanced/options-distributed-soap.yaml | 1 + examples/1-advanced/options-distributed.yaml | 1 + examples/1-advanced/options-flashmd.yaml | 43 + examples/1-advanced/options-llpr.yaml | 20 + examples/1-advanced/options-no-llpr.yaml | 21 + examples/1-advanced/options-no-zbl.yaml | 22 + examples/1-advanced/options_zbl.yaml | 22 + .../{llpr => 1-advanced}/qm9_reduced_100.xyz | 0 examples/GALLERY_HEADER.rst | 6 + examples/README.rst | 4 - examples/ase/README.rst | 2 - examples/basic_usage/README.rst | 2 - examples/beginner/README.rst | 2 - examples/llpr/README.rst | 2 - examples/multi-gpu/README.rst | 3 - examples/multi-gpu/ethanol_reduced_100.xyz | 1 - .../soap-bpnn/options-distributed.yaml | 1 - .../multi-gpu/soap-bpnn/submit-distributed.sh | 1 - .../programmatic/data_preparation/README.rst | 2 - .../data_preparation/carbon_reduced_100.xyz | 1 - .../programmatic/data_preparation/data.xyz | 1201 +++++++++++++++ .../data_preparation/qm9_reduced_100.xyz | 1 - .../data_preparation/qm9_reduced_100.zip | Bin 0 -> 861146 bytes .../qm9_reduced_100_all_at_once.zip | Bin 0 -> 861146 bytes examples/programmatic/flashmd/README.rst | 2 - .../use_architectures_outside/README.rst | 2 - .../qm9_reduced_100.xyz | 1 - examples/train_from_scratch/README.rst | 2 - .../ethanol_reduced_100.xyz | 1 - examples/validation/README.rst | 2 - examples/zbl/README.rst | 2 - examples/zbl/ethanol_reduced_100.xyz | 1 - .../gap/tests/ethanol_reduced_100.xyz | 1 - tox.ini | 9 +- 194 files changed, 27513 insertions(+), 161 deletions(-) create mode 100644 docs/src/examples/ase/ase.zip create mode 100644 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(100%) rename {examples => docs/src/examples}/zbl/train.sh (100%) mode change 100755 => 100644 create mode 100644 docs/src/examples/zbl/zbl.zip create mode 100644 docs/src/examples/zbl/zbl_jupyter.zip delete mode 100644 docs/src/tutorials/advanced_tutorials/index.rst delete mode 100644 docs/src/tutorials/beginner_tutorials/index.rst delete mode 100644 docs/src/tutorials/index.rst rename examples/{programmatic/data_preparation => 0-beginner}/.gitignore (61%) create mode 100755 examples/0-beginner/00-basic-usage.sh create mode 100644 examples/0-beginner/01-data_preparation.py rename docs/src/advanced-concepts/fine-tuning.rst => examples/0-beginner/02-fine-tuning.py (80%) create mode 100644 examples/0-beginner/03-train_from_scratch.py create mode 100644 examples/0-beginner/04-parity_plot.py create mode 100644 examples/0-beginner/05-run_ase.py create mode 100644 examples/0-beginner/GALLERY_HEADER.rst create mode 120000 examples/0-beginner/carbon_reduced_100.xyz rename examples/{ase => 0-beginner}/ethanol_reduced_100.xyz (100%) create mode 100644 examples/0-beginner/ethanol_reduced_100_predicted.xyz create mode 100644 examples/0-beginner/eval-basic.yaml create mode 100644 examples/0-beginner/eval-scratch.yaml create mode 100644 examples/0-beginner/options-ase.yaml create mode 100644 examples/0-beginner/options-basic.yaml create mode 100644 examples/0-beginner/options-memmap.yaml create mode 100644 examples/0-beginner/options-scratch.yaml rename examples/{basic_usage => 0-beginner}/qm9_reduced_100.xyz (100%) create mode 100644 examples/1-advanced/.gitignore rename docs/src/advanced-concepts/transfer-learning.rst => examples/1-advanced/00-transfer-learning.py (73%) create mode 100644 examples/1-advanced/01-llpr.py create mode 100644 examples/1-advanced/02-zbl.py rename docs/src/advanced-concepts/fitting-generic-targets.rst => examples/1-advanced/03-fitting-generic-targets.py (99%) create mode 100644 examples/1-advanced/04-flashmd.py create mode 100644 examples/1-advanced/05-multi-gpu.py create mode 100644 examples/1-advanced/GALLERY_HEADER.rst rename examples/{llpr => 1-advanced}/ethanol_reduced_100.xyz (100%) create mode 120000 examples/1-advanced/options-distributed-soap.yaml create mode 120000 examples/1-advanced/options-distributed.yaml create mode 100644 examples/1-advanced/options-flashmd.yaml create mode 100644 examples/1-advanced/options-llpr.yaml create mode 100644 examples/1-advanced/options-no-llpr.yaml create mode 100644 examples/1-advanced/options-no-zbl.yaml create mode 100644 examples/1-advanced/options_zbl.yaml rename examples/{llpr => 1-advanced}/qm9_reduced_100.xyz (100%) create mode 100644 examples/GALLERY_HEADER.rst delete mode 100644 examples/README.rst delete mode 100644 examples/ase/README.rst delete mode 100644 examples/basic_usage/README.rst delete mode 100644 examples/beginner/README.rst delete mode 100644 examples/llpr/README.rst delete mode 100644 examples/multi-gpu/README.rst delete mode 120000 examples/multi-gpu/ethanol_reduced_100.xyz delete mode 120000 examples/multi-gpu/soap-bpnn/options-distributed.yaml delete mode 120000 examples/multi-gpu/soap-bpnn/submit-distributed.sh delete mode 100644 examples/programmatic/data_preparation/README.rst delete mode 120000 examples/programmatic/data_preparation/carbon_reduced_100.xyz create mode 100644 examples/programmatic/data_preparation/data.xyz delete mode 120000 examples/programmatic/data_preparation/qm9_reduced_100.xyz create mode 100644 examples/programmatic/data_preparation/qm9_reduced_100.zip create mode 100644 examples/programmatic/data_preparation/qm9_reduced_100_all_at_once.zip delete mode 100644 examples/programmatic/flashmd/README.rst delete mode 100644 examples/programmatic/use_architectures_outside/README.rst delete mode 120000 examples/programmatic/use_architectures_outside/qm9_reduced_100.xyz delete mode 100644 examples/train_from_scratch/README.rst delete mode 120000 examples/train_from_scratch/ethanol_reduced_100.xyz delete mode 100644 examples/validation/README.rst delete mode 100644 examples/zbl/README.rst delete mode 120000 examples/zbl/ethanol_reduced_100.xyz delete mode 120000 src/metatrain/gap/tests/ethanol_reduced_100.xyz diff --git a/.gitignore b/.gitignore index 38a17a91e5..209292ff09 100644 --- a/.gitignore +++ b/.gitignore @@ -175,7 +175,7 @@ examples/basic_usage/*.xyz extensions/ # sphinx gallery -docs/src/examples +docs/src/generated_examples/ *execution_times* # JavaScript diff --git a/.readthedocs.yml b/.readthedocs.yml index 4d6792f547..7dae109215 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -13,9 +13,7 @@ build: rust: "1.75" jobs: pre_build: - - set -e && cd examples/ase && bash train.sh - - set -e && cd examples/llpr && bash train.sh - - set -e && cd examples/zbl && bash train.sh + - set -e && for f in $(find examples -name '*.sh'); do cd $(dirname $f); bash $(basename $f); cd -; done # Build documentation in the docs/ directory with Sphinx sphinx: diff --git a/docs/generate_examples/conf.py b/docs/generate_examples/conf.py index ffdccc12f7..39b920e5c7 100644 --- a/docs/generate_examples/conf.py +++ b/docs/generate_examples/conf.py @@ -15,34 +15,13 @@ sphinx_gallery_conf = { "filename_pattern": r"/*\.py", "copyfile_regex": r".*\.(pt|sh|xyz|yaml)", - "ignore_pattern": r"train\.sh", "example_extensions": {".py", ".sh"}, "default_thumb_file": os.path.join(ROOT, "docs/src/logo/metatrain-512.png"), - "examples_dirs": [ - os.path.join(ROOT, "examples", "ase"), - os.path.join(ROOT, "examples", "llpr"), - os.path.join(ROOT, "examples", "zbl"), - os.path.join(ROOT, "examples", "programmatic", "use_architectures_outside"), - os.path.join(ROOT, "examples", "programmatic", "data_preparation"), - os.path.join(ROOT, "examples", "programmatic", "flashmd"), - os.path.join(ROOT, "examples", "basic_usage"), - os.path.join(ROOT, "examples", "train_from_scratch"), - os.path.join(ROOT, "examples", "validation"), - os.path.join(ROOT, "examples", "multi-gpu"), - ], - "gallery_dirs": [ - os.path.join(ROOT, "docs", "src", "examples", "ase"), - os.path.join(ROOT, "docs", "src", "examples", "llpr"), - os.path.join(ROOT, "docs", "src", "examples", "zbl"), - os.path.join(ROOT, "docs", "src", "examples", "programmatic", "use_architectures_outside"), - os.path.join(ROOT, "docs", "src", "examples", "programmatic", "data_preparation"), - os.path.join(ROOT, "docs", "src", "examples", "programmatic", "flashmd"), - os.path.join(ROOT, "docs", "src", "examples", "basic_usage"), - os.path.join(ROOT, "docs", "src", "examples", "train_from_scratch"), - os.path.join(ROOT, "docs", "src", "examples", "validation"), - os.path.join(ROOT, "docs", "src", "examples", "multi-gpu"), - ], + "examples_dirs": "../../examples", + "gallery_dirs": "generated_examples", "min_reported_time": 5, "matplotlib_animations": True, - "image_scrapers": ( ChemiscopeScraper(), ), + "image_scrapers": ["matplotlib", ChemiscopeScraper()], + 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+ +.. only:: html + + .. image:: /examples/ase/images/thumb/sphx_glr_run_ase_thumb.png + :alt: + + :ref:`sphx_glr_examples_ase_run_ase.py` + +.. raw:: html + +
Running molecular dynamics with ASE
+
+ + +.. thumbnail-parent-div-close + +.. raw:: html + + + + +.. toctree:: + :hidden: + + /examples/ase/run_ase + + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-gallery + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download all examples as source code: ase.zip ` + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download all examples in Jupyter notebooks: ase_jupyter.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/examples/ase/options.yaml b/docs/src/examples/ase/options.yaml similarity index 100% rename from examples/ase/options.yaml rename to docs/src/examples/ase/options.yaml diff --git a/docs/src/examples/ase/run_ase.codeobj.json b/docs/src/examples/ase/run_ase.codeobj.json new file mode 100644 index 0000000000..44011ab7cd --- /dev/null +++ b/docs/src/examples/ase/run_ase.codeobj.json @@ -0,0 +1,926 @@ +{ + "Analysis": [ + { + "is_class": true, + "is_explicit": false, + "module": "ase.geometry.analysis", + "module_short": "ase.geometry.analysis", + "name": "Analysis" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.geometry", + "module_short": "ase.geometry", + "name": "Analysis" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Analysis" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase.geometry.analysis", + "module_short": "ase.geometry.analysis", + "name": "Analysis" + } + ], + "MetatomicCalculator": [ + { + "is_class": true, + "is_explicit": false, + "module": "metatomic.torch.ase_calculator", + "module_short": "metatomic.torch.ase_calculator", + "name": "MetatomicCalculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "metatomic.torch", + "module_short": "metatomic.torch", + "name": "MetatomicCalculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "metatomic", + "module_short": "metatomic", + "name": "MetatomicCalculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.calculators.calculator", + "module_short": "ase.calculators.calculator", + "name": "Calculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.calculators", + "module_short": "ase.calculators", + "name": "Calculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Calculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.calculators.calculator", + "module_short": "ase.calculators.calculator", + "name": "BaseCalculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.calculators", + "module_short": "ase.calculators", + "name": "BaseCalculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "BaseCalculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.calculators.abc", + "module_short": "ase.calculators.abc", + "name": "GetPropertiesMixin" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.calculators", + "module_short": "ase.calculators", + "name": "GetPropertiesMixin" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "GetPropertiesMixin" + }, + { + "is_class": true, + "is_explicit": false, + "module": "abc", + "module_short": "abc", + "name": "ABC" + }, + { + "is_class": false, + "is_explicit": false, + "module": "metatomic.torch.ase_calculator", + "module_short": "metatomic.torch.ase_calculator", + "name": "MetatomicCalculator" + } + ], + "ana_train": [ + { + "is_class": false, + "is_explicit": false, + "module": "ase.geometry.analysis", + "module_short": "ase.geometry.analysis", + "name": "Analysis" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase.geometry", + "module_short": "ase.geometry", + "name": "Analysis" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Analysis" + } + ], + "ana_train.get_rdf": [ + { + "is_class": false, + "is_explicit": false, + "module": "ase.geometry.analysis", + "module_short": "ase.geometry.analysis", + "name": "Analysis.get_rdf" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase.geometry", + "module_short": "ase.geometry", + "name": "Analysis.get_rdf" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Analysis.get_rdf" + } + ], + "ana_traj": [ + { + "is_class": false, + "is_explicit": false, + "module": "ase.geometry.analysis", + "module_short": "ase.geometry.analysis", + "name": "Analysis" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase.geometry", + "module_short": "ase.geometry", + "name": "Analysis" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Analysis" + } + ], + "ana_traj.get_rdf": [ + { + "is_class": false, + "is_explicit": false, + "module": "ase.geometry.analysis", + "module_short": "ase.geometry.analysis", + "name": "Analysis.get_rdf" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase.geometry", + "module_short": "ase.geometry", + "name": "Analysis.get_rdf" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Analysis.get_rdf" + } + ], + "animation": [ + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib.animation", + "module_short": "matplotlib.animation", + "name": "FuncAnimation" + }, + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib", + "module_short": "matplotlib", + "name": "FuncAnimation" + } + ], + "ase.geometry.analysis.Analysis": [ + { + "is_class": false, + "is_explicit": true, + "module": "ase.geometry.analysis", + "module_short": "ase.geometry.analysis", + "name": "Analysis" + } + ], + "ase.geometry.analysis.Analysis.get_rdf": [ + { + "is_class": false, + "is_explicit": true, + "module": "ase.geometry.analysis.Analysis", + "module_short": "ase.geometry.analysis.Analysis", + "name": "get_rdf" + } + ], + "ase.io.read": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + } + ], + "ase.md.VelocityVerlet": [ + { + "is_class": true, + "is_explicit": false, + "module": "ase.md.verlet", + "module_short": "ase.md", + "name": "VelocityVerlet" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.md", + "module_short": "ase.md", + "name": "VelocityVerlet" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "VelocityVerlet" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.md.md", + "module_short": "ase.md.md", + "name": "MolecularDynamics" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.md", + "module_short": "ase.md", + "name": "MolecularDynamics" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "MolecularDynamics" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.optimize.optimize", + "module_short": "ase.optimize.optimize", + "name": "Dynamics" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.optimize", + "module_short": "ase.optimize", + "name": "Dynamics" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Dynamics" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.utils", + "module_short": "ase.utils", + "name": "IOContext" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "IOContext" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase.md", + "module_short": "ase.md", + "name": "VelocityVerlet" + } + ], + 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"matplotlib.pyplot", + "name": "show" + } + ], + "plt.xlabel": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib.pyplot", + "module_short": "matplotlib.pyplot", + "name": "xlabel" + } + ], + "plt.ylabel": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib.pyplot", + "module_short": "matplotlib.pyplot", + "name": "ylabel" + } + ], + "potential_energy": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "ndarray" + } + ], + "potential_energy.mean": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "builtin_function_or_method" + } + ], + "rdf_train": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "list" + } + ], + "rdf_train_mean": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "ndarray" + } + ], + "rdf_traj": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "list" + } + ], + "rdf_traj_mean": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "ndarray" + } + ], + "step": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "int" + } + ], + "total_energy": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "ndarray" + } + ], + "total_energy.mean": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "builtin_function_or_method" + } + ], + "train_frames": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "list" + } + ], + "trajectory": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "list" + } + ], + "trajectory.append": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "builtin_function_or_method" + } + ] +} diff --git a/docs/src/examples/ase/run_ase.ipynb b/docs/src/examples/ase/run_ase.ipynb new file mode 100644 index 0000000000..61d76e3cb0 --- /dev/null +++ b/docs/src/examples/ase/run_ase.ipynb @@ -0,0 +1,266 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Running molecular dynamics with ASE\n\nThis tutorial demonstrates how to use an already trained and exported model to run an\nASE simulation of a single ethanol molecule in vacuum. We use a model that was trained\nusing the `architecture-soap-bpnn` architecture on 100 ethanol systems\ncontaining energies and forces. You can obtain the :download:`dataset file\n` used in this example from our website. The dataset is a\nsubset of the [rMD17 dataset](https://iopscience.iop.org/article/10.1088/2632-2153/abba6f/meta).\n\nThe model was trained using the following training options.\n\n.. literalinclude:: options.yaml\n :language: yaml\n\nYou can train the same model yourself with\n\n.. literalinclude:: train.sh\n :language: bash\n\nA detailed step-by-step introduction on how to train a model is provided in\nthe `label_basic_usage` tutorial.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we start by importing the necessary libraries, including the integration of ASE\ncalculators for metatensor atomistic models.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import ase.md\nimport ase.md.velocitydistribution\nimport ase.units\nimport ase.visualize.plot\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom ase.geometry.analysis import Analysis\nfrom metatomic.torch.ase_calculator import MetatomicCalculator" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting up the simulation\n\nNext, we initialize the simulation by extracting the initial positions from the\ndataset file which we initially trained the model on.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "train_frames = ase.io.read(\"ethanol_reduced_100.xyz\", \":\")\natoms = train_frames[0].copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below we show the initial configuration of a single ethanol molecule in vacuum.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ase.visualize.plot.plot_atoms(atoms)\n\nplt.xlabel(\"\u00c5\")\nplt.ylabel(\"\u00c5\")\n\nplt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our initial coordinates do not include velocities. We initialize the velocities\naccording to a Maxwell-Boltzmann Distribution at 300 K.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ase.md.velocitydistribution.MaxwellBoltzmannDistribution(atoms, temperature_K=300)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now register our exported model as the energy calculator to obtain energies and\nforces.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "atoms.calc = MetatomicCalculator(\"model.pt\", extensions_directory=\"extensions/\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we define the integrator which we use to obtain new positions and velocities\nbased on our energy calculator. We use a common timestep of 0.5 fs.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "integrator = ase.md.VelocityVerlet(atoms, timestep=0.5 * ase.units.fs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run the simulation\n\nWe now have everything ready to run the MD simulation at constant energy (NVE). To\nkeep the execution time of this tutorial small we run the simulations only for 100\nsteps. If you want to run a longer simulation you can increase the ``n_steps``\nvariable.\n\nDuring the simulation loop we collect data about the simulation for later analysis.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n_steps = 100\n\npotential_energy = np.zeros(n_steps)\nkinetic_energy = np.zeros(n_steps)\ntotal_energy = np.zeros(n_steps)\ntrajectory = []\n\nfor step in range(n_steps):\n # run a single simulation step\n integrator.run(1)\n\n trajectory.append(atoms.copy())\n potential_energy[step] = atoms.get_potential_energy()\n kinetic_energy[step] = atoms.get_kinetic_energy()\n total_energy[step] = atoms.get_total_energy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyse the results\n\n### Energy conservation\n\nFor a first analysis, we plot the evolution of the mean of the kinetic, potential, and\ntotal energy which is an important measure for the stability of a simulation.\n\nAs shown below we see that both the kinetic, potential, and total energy\nfluctuate but the total energy is conserved over the length of the simulation.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "plt.plot(potential_energy - potential_energy.mean(), label=\"potential energy\")\nplt.plot(kinetic_energy - kinetic_energy.mean(), label=\"kinetic energy\")\nplt.plot(total_energy - total_energy.mean(), label=\"total energy\")\n\nplt.xlabel(\"step\")\nplt.ylabel(\"energy / kcal/mol\")\nplt.legend()\n\nplt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Inspect the systems\n\nEven though the total energy is conserved, we also have to verify that the ethanol\nmolecule is stable and the bonds did not break.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "animation = ase.visualize.plot.animate(trajectory, interval=100, save_count=None)\nplt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Carbon-hydrogen radial distribution function\n\nAs a final analysis we also calculate and plot the carbon-hydrogen radial distribution\nfunction (RDF) from the trajectory and compare this to the RDF from the training set.\n\nTo use the RDF code from ase we first have to define a unit cell for our systems.\nWe choose a cubic one with a side length of 10 \u00c5.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "for atoms in train_frames:\n atoms.cell = 10 * np.ones(3)\n atoms.pbc = True\n\nfor atoms in trajectory:\n atoms.cell = 10 * np.ones(3)\n atoms.pbc = True" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now can initilize the :py:class:`ase.geometry.analysis.Analysis` objects and\ncompute the the RDF using the :py:meth:`ase.geometry.analysis.Analysis.get_rdf`\nmethod.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ana_traj = Analysis(trajectory)\nana_train = Analysis(train_frames)\n\nrdf_traj = ana_traj.get_rdf(rmax=5, nbins=50, elements=[\"C\", \"H\"], return_dists=True)\nrdf_train = ana_train.get_rdf(rmax=5, nbins=50, elements=[\"C\", \"H\"], return_dists=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We extract the bin positions from the returned values and and averege the RDF over the\nwhole trajectory and dataset, respectively.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "bins = rdf_traj[0][1]\nrdf_traj_mean = np.mean([rdf_traj[i][0] for i in range(n_steps)], axis=0)\nrdf_train_mean = np.mean([rdf_train[i][0] for i in range(n_steps)], axis=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plotting the RDF verifies that the hydrogen bonds are stable, confirming that we\nperformed an energy-conserving and stable simulation.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "plt.plot(bins, rdf_traj_mean, label=\"trajectory\")\nplt.plot(bins, rdf_train_mean, label=\"training set\")\n\nplt.legend()\nplt.xlabel(\"r / \u00c5\")\nplt.ylabel(\"radial distribution function\")\n\nplt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/examples/ase/run_ase.py b/docs/src/examples/ase/run_ase.py similarity index 100% rename from examples/ase/run_ase.py rename to docs/src/examples/ase/run_ase.py diff --git a/docs/src/examples/ase/run_ase.py.md5 b/docs/src/examples/ase/run_ase.py.md5 new file mode 100644 index 0000000000..31f5391e73 --- /dev/null +++ b/docs/src/examples/ase/run_ase.py.md5 @@ -0,0 +1 @@ +ac05d2993cd2bb401fd7750e31d970c6 \ No newline at end of file diff --git a/docs/src/examples/ase/run_ase.rst b/docs/src/examples/ase/run_ase.rst new file mode 100644 index 0000000000..b1401d1108 --- /dev/null +++ b/docs/src/examples/ase/run_ase.rst @@ -0,0 +1,403 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "examples/ase/run_ase.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code. + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_examples_ase_run_ase.py: + + +Running molecular dynamics with ASE +=================================== + +This tutorial demonstrates how to use an already trained and exported model to run an +ASE simulation of a single ethanol molecule in vacuum. We use a model that was trained +using the :ref:`architecture-soap-bpnn` architecture on 100 ethanol systems +containing energies and forces. You can obtain the :download:`dataset file +` used in this example from our website. The dataset is a +subset of the `rMD17 dataset +`_. + +The model was trained using the following training options. + +.. literalinclude:: options.yaml + :language: yaml + +You can train the same model yourself with + +.. literalinclude:: train.sh + :language: bash + +A detailed step-by-step introduction on how to train a model is provided in +the :ref:`label_basic_usage` tutorial. + +.. GENERATED FROM PYTHON SOURCE LINES 28-30 + +First, we start by importing the necessary libraries, including the integration of ASE +calculators for metatensor atomistic models. + +.. GENERATED FROM PYTHON SOURCE LINES 31-42 + +.. code-block:: Python + + + import ase.md + import ase.md.velocitydistribution + import ase.units + import ase.visualize.plot + import matplotlib.pyplot as plt + import numpy as np + from ase.geometry.analysis import Analysis + from metatomic.torch.ase_calculator import MetatomicCalculator + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 43-48 + +Setting up the simulation +------------------------- + +Next, we initialize the simulation by extracting the initial positions from the +dataset file which we initially trained the model on. + +.. GENERATED FROM PYTHON SOURCE LINES 49-53 + +.. code-block:: Python + + + train_frames = ase.io.read("ethanol_reduced_100.xyz", ":") + atoms = train_frames[0].copy() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 54-55 + +Below we show the initial configuration of a single ethanol molecule in vacuum. + +.. GENERATED FROM PYTHON SOURCE LINES 56-65 + +.. code-block:: Python + + + ase.visualize.plot.plot_atoms(atoms) + + plt.xlabel("Ã…") + plt.ylabel("Ã…") + + plt.show() + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 66-68 + +Our initial coordinates do not include velocities. We initialize the velocities +according to a Maxwell-Boltzmann Distribution at 300 K. + +.. GENERATED FROM PYTHON SOURCE LINES 69-72 + +.. code-block:: Python + + + ase.md.velocitydistribution.MaxwellBoltzmannDistribution(atoms, temperature_K=300) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 73-75 + +We now register our exported model as the energy calculator to obtain energies and +forces. + +.. GENERATED FROM PYTHON SOURCE LINES 76-79 + +.. code-block:: Python + + + atoms.calc = MetatomicCalculator("model.pt", extensions_directory="extensions/") + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 80-82 + +Finally, we define the integrator which we use to obtain new positions and velocities +based on our energy calculator. We use a common timestep of 0.5 fs. + +.. GENERATED FROM PYTHON SOURCE LINES 83-87 + +.. code-block:: Python + + + integrator = ase.md.VelocityVerlet(atoms, timestep=0.5 * ase.units.fs) + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 88-97 + +Run the simulation +------------------ + +We now have everything ready to run the MD simulation at constant energy (NVE). To +keep the execution time of this tutorial small we run the simulations only for 100 +steps. If you want to run a longer simulation you can increase the ``n_steps`` +variable. + +During the simulation loop we collect data about the simulation for later analysis. + +.. GENERATED FROM PYTHON SOURCE LINES 98-116 + +.. code-block:: Python + + + + n_steps = 100 + + potential_energy = np.zeros(n_steps) + kinetic_energy = np.zeros(n_steps) + total_energy = np.zeros(n_steps) + trajectory = [] + + for step in range(n_steps): + # run a single simulation step + integrator.run(1) + + trajectory.append(atoms.copy()) + potential_energy[step] = atoms.get_potential_energy() + kinetic_energy[step] = atoms.get_kinetic_energy() + total_energy[step] = atoms.get_total_energy() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 117-128 + +Analyse the results +------------------- + +Energy conservation +################### + +For a first analysis, we plot the evolution of the mean of the kinetic, potential, and +total energy which is an important measure for the stability of a simulation. + +As shown below we see that both the kinetic, potential, and total energy +fluctuate but the total energy is conserved over the length of the simulation. + +.. GENERATED FROM PYTHON SOURCE LINES 129-141 + +.. code-block:: Python + + + + plt.plot(potential_energy - potential_energy.mean(), label="potential energy") + plt.plot(kinetic_energy - kinetic_energy.mean(), label="kinetic energy") + plt.plot(total_energy - total_energy.mean(), label="total energy") + + plt.xlabel("step") + plt.ylabel("energy / kcal/mol") + plt.legend() + + plt.show() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 142-147 + +Inspect the systems +################### + +Even though the total energy is conserved, we also have to verify that the ethanol +molecule is stable and the bonds did not break. + +.. GENERATED FROM PYTHON SOURCE LINES 148-152 + +.. code-block:: Python + + + animation = ase.visualize.plot.animate(trajectory, interval=100, save_count=None) + plt.show() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 153-161 + +Carbon-hydrogen radial distribution function +############################################ + +As a final analysis we also calculate and plot the carbon-hydrogen radial distribution +function (RDF) from the trajectory and compare this to the RDF from the training set. + +To use the RDF code from ase we first have to define a unit cell for our systems. +We choose a cubic one with a side length of 10 Ã…. + +.. GENERATED FROM PYTHON SOURCE LINES 162-171 + +.. code-block:: Python + + + for atoms in train_frames: + atoms.cell = 10 * np.ones(3) + atoms.pbc = True + + for atoms in trajectory: + atoms.cell = 10 * np.ones(3) + atoms.pbc = True + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 172-175 + +We now can initilize the :py:class:`ase.geometry.analysis.Analysis` objects and +compute the the RDF using the :py:meth:`ase.geometry.analysis.Analysis.get_rdf` +method. + +.. GENERATED FROM PYTHON SOURCE LINES 176-183 + +.. code-block:: Python + + + ana_traj = Analysis(trajectory) + ana_train = Analysis(train_frames) + + rdf_traj = ana_traj.get_rdf(rmax=5, nbins=50, elements=["C", "H"], return_dists=True) + rdf_train = ana_train.get_rdf(rmax=5, nbins=50, elements=["C", "H"], return_dists=True) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 184-186 + +We extract the bin positions from the returned values and and averege the RDF over the +whole trajectory and dataset, respectively. + +.. GENERATED FROM PYTHON SOURCE LINES 187-192 + +.. code-block:: Python + + + bins = rdf_traj[0][1] + rdf_traj_mean = np.mean([rdf_traj[i][0] for i in range(n_steps)], axis=0) + rdf_train_mean = np.mean([rdf_train[i][0] for i in range(n_steps)], axis=0) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 193-195 + +Plotting the RDF verifies that the hydrogen bonds are stable, confirming that we +performed an energy-conserving and stable simulation. + +.. GENERATED FROM PYTHON SOURCE LINES 196-205 + +.. code-block:: Python + + + plt.plot(bins, rdf_traj_mean, label="trajectory") + plt.plot(bins, rdf_train_mean, label="training set") + + plt.legend() + plt.xlabel("r / Ã…") + plt.ylabel("radial distribution function") + + plt.show() + + + + + + + + +.. _sphx_glr_download_examples_ase_run_ase.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: run_ase.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: run_ase.py ` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: run_ase.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/ase/run_ase.zip b/docs/src/examples/ase/run_ase.zip new file mode 100644 index 0000000000000000000000000000000000000000..074a0cf90b7fc095beb556e85cee490919d5a9a0 GIT binary patch literal 16212 zcmdU0+iv5=8FrE$00O-W(AxoxpmJc-cHC`KAr7$F0}?EDyG<5p3)7HCF=%0s&MJ-3X6hS`sec$`KmNJo3mZ?-kb;=7C&n3^2sAOkJHDeFIdEy;j z{o{GBW=YAax>7~LQx=O%$+9YVB}z6^XRK1JE(PO~@w5;;o-@2mq=@k-X5u_oMTHNU zibYC`fhFE}IG&YB2E?jFNv0-@+3qFGXjsHzLGQ!1YCjB_bZSob-@`TKi&?%Z--Rw66CNXZH} zA%H@PVw&K_bf<|bB2fnHuc~Gd?ybhO%p4l4GnpzL4^LuVff*H>B&qN|vv(R5BCaD5 zk8oaaK7aH130)_q8&*VSan7?GB$yN`W2!FLnHZPAKVYwLr3NGxa<8n%1RjK=+nyB9 zAMgLZS>b&)tE#*l?(QZkFQY`rNCfyZP{nkY7gZ9aVt0Qp*x&o~)7?*gcks#1C;JZ$ zcKLYBe>d69M8!Wp83n|8VF}%#F)(guJW*+?&h#guKmDO{;%AA?LBLX=Uhp)LQCi1h zICO94JWD;s*f8aCTJxzGGX2PFXsY?CZ&vcGL1GSylp>vI0bYH8-WQa!1>j?jN8Uq7 zFIbg=;NUPsVZKAZLH(*w;ERz&*@(5lfm;fI=y{<|lUTFMYeg*OW08(gVd!7>sXw@S}8?VEPZ$zc3Ugt*2 zoGAh|cP{$TOnf2E4G$sk6-*+oz5s&wibVy7L|JQO*Roteok>BAibINUUZdA!D%ja9 ziDo1sc1zkCRFXMkN0kH{wS0~y1r(%Yhnk&<3djZz{0r=jzs>xizvU5SXipc!(cWX7LRh{Kr{SZ(WpKywpV$4N4+TSA}4FKtCyg6rl?mS`GUnY-Wq0DxfynQ^kr&2%)t^%t|@bOS=@iqv2%x7Jzva zX)y3A3O?#NKR**`y7Prft2Y^!l0EKd3bc(Kz?D1$dyIOmRH(p0aX`8-3=y^&d{7SS zNuF!;>=019e1RX7U`!#VI9(LlS1kBi@`~V>c3tz1g`j&_m(_J^xT<@Fw7K5KmwNT+zA{al_7#*RRXOc;|~YA7d&7SZ5XX>1r|o1 zvN-tG8pLl!k&4PO$8JBQy&r4c)0Q!qls$Pw6t-X-K#FDaq794X>5QL(Yp0@^BP4+N zutAlHoQR{(A9o`sNE}oV{zO*Iy$3J8eX@m+LE)^|0(aDMaE>rgdkwm}i5p;HU0_#c z;0tLw1!%OPjm}EY6=sMW3*r_4CBi{-zM3$&WW--Mx{2&qs^k;`+3wbBC{BX%;V#5f zKtDN=BMs){1OQLr>i9SnfhNS`x@hcX2TZC|PWVL#Iv{1*YBD}nH4g8gs}Y4W1Qv}W zfjWBjJaCpS;pGZqOZIzYrR5O5D0m|ZRSxXZE$=nt6Rz{JC6%f!hXEV=ozV&$eRS-3 zL`N-pi24e|Kf;|iq_Bl;ni#nWhdU&oU3y8YE&aLR7Xcxy}A6OS3d;@I3jaIV@^c0MbeRq)|OM18f~!wb|xs&H^H{O=Qiw- zm7#4)3~->nO{0d!kWsJ>+1FtnDGC6UFzH%IwcJ*GoFGu16Lj*SO-^KV;bFKlI%eOD*f;HMqVUv7P3E7l5+MR{-h?7r=BsF5=TA z1v#v41VS3J5QA8ovCCe=OYS1aw(nChCAG5vn)t+pCtt}jC)=eNZ_~_+#q`OkAXBXB zso`@MN7iMRmR+8fifQKoTMYIk6GV`jIpjabfc?DEp?K%TD6yz-9FJe4@DxCNA%%Tp1g^sT9Z8(QaB?J zU5yf{l|_mTzJC04%fW+xyD?x5%WX8p83iINLXfI$48=pXq5-H>K#68bQN@Nu^%!X(SYj$dWPET!tMU7L z?57_g0pzf>cK~C1DTDD>Hd>?u$+sdLf+Gdga&TZAhgp!1DVls$)M6Q;b&xlP=M)Ur zqqx|F8_2X=b~eoC!zkrtiJH)=R5NID%o7C1gpRKDfMCS88a9)-&~T!yfDl;ZqqD1^ zBDbwZMLeNkht{iDYZf1o+JG$0=^ZsfmL~Ok5WT-3kv8jtnE}iOhF$($bJn29`1#?3 zZ6?QwEDs+*6-6pCL_OuLgaAtF-Xqy6LN_TP=P)7NpK_FhMBU2=@2TD=m|iGCCZlJfJUJVe4~H-KRmfJ)@i z+rnyT_YywWRTjvGXlqa9NoVh!88tSY439DP+vMb7I5HDq!#$i)2?CiWv^)c)5v&;J zwqv6KT1Ygv&Y~9rkeAdhn+OT&T4B_;b*{ek3|%MR+hGaR-QsLvzp{yr+||JeWgzh3-iV}t(vs#8%<^0^#;k0975vs(H?cBCU>dPG0^ z@u-^T0~A-(q4AtV5O2T#!96Wunj_j_fN4OX++@fZud3XKGL*Mcmk;H` zTPe(kvbCA-Qj`xRq;gGF`B1LEEFa2#UH+(7mp>l7Yh8XTFsyG1f!D6f)Ap+>^Pz++ zE|lhJ-`f}GFFFOLAdx7~FP=d4dD^qa3VkTaft*}coe!mNHS}2jwyL>)t-hVQKXk#B zwy!Sx6M3(3T?#f^116f{k%{ABvt*s7&XRTQo24ijMHFgyjM5osNEp8 z()RS&0b6AjdmW1q2?;e#bkz+NI$H`#WOk zt@K($xrSEjy_Sf!`G(RF+5a1LiSI}T5YsL9po#oe-`Ch}4(0lr&B&+M+G`G_Yc&%Q zZlTkBuSL7pBd43VHeJ)%ThS#4->+PmUG+MmZf%od*(tIA<}mK7WO3z}_RO0C$`eLc7ar7}>uRz?VAtAXC)}>#C;q=`Hm*0Ys2=FVFFso6 z#)nJe0WMd%!GJ*M_a_AUu)6AW`HVp5>$O(!G?azBj!czkL?! zQc!TO#hs!wQ=!Ehb6FT==Mpq6OIl*}M=^_Ve9pgoJBvb1iT|!`VlgXufEmtnF z>Wxqtd1@x8kgK?Aic2?^C$$#luJkT9c-6^p+C+s8x8KYPeVV?ysF!uw5LO5B%9IC# z0(u_6Um7g%UyDMDl!o|dn95ER_?TVHQ$8Qz3(k53>m|NBgMsCPc7mb#qA7OyxqjQe zYj&CE2Syd_l!D)E93_ZIx@S_hoh3z~iV=Mqs`Co74-H66ju9sAEWE<79(`L1>kpO& zBIp}{BiAb+TdU&$^A`r@j5KGpM~ zqZAV#dWF3}hmtM+{z0&R5d6W?3zyL}^W_+!8%8N`z;6k4_h5vfLq$;cydPdZd++@Z zKe+hI?B-kU8yo-p8~ueZx!A9^clWIt*=zTGjjzQw`}6?c%V$6T1-*VB|CRXt@6YMe Fe*q$4Rrvq_ literal 0 HcmV?d00001 diff --git a/examples/ase/train.sh b/docs/src/examples/ase/train.sh old mode 100755 new mode 100644 similarity index 100% rename from examples/ase/train.sh rename to docs/src/examples/ase/train.sh diff --git a/docs/src/examples/basic_usage/basic_usage.zip b/docs/src/examples/basic_usage/basic_usage.zip new file mode 100644 index 0000000000000000000000000000000000000000..29173744cf9d766480cb629534615cf1c3358a9b GIT binary patch literal 4718 zcmb7IO^+ML5tZX42=?Z{m)r^x3`oLqsN@j9jAKB89oP_7Sx0LmMh-j8nI@; 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+ +.. only:: html + + .. image:: /examples/basic_usage/images/thumb/sphx_glr_usage_thumb.png + :alt: + + :ref:`sphx_glr_examples_basic_usage_usage.sh` + +.. raw:: html + +
Basic Usage
+
+ + +.. thumbnail-parent-div-close + +.. raw:: html + + + + +.. toctree:: + :hidden: + + /examples/basic_usage/usage + + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-gallery + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download all examples as source code: basic_usage.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/examples/basic_usage/options.yaml b/docs/src/examples/basic_usage/options.yaml similarity index 100% rename from examples/basic_usage/options.yaml rename to docs/src/examples/basic_usage/options.yaml diff --git a/docs/src/examples/basic_usage/output.xyz b/docs/src/examples/basic_usage/output.xyz new file mode 100644 index 0000000000..37cb4a2653 --- /dev/null +++ b/docs/src/examples/basic_usage/output.xyz @@ -0,0 +1,1201 @@ +5 +Properties=species:S:1:pos:R:3 energy=-40.47401479185843 pbc="F F F" +C -0.01269814 1.08580416 0.00800100 +H 0.00215042 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A=127.83497 B=24.85872 C=23.97872 mu=1.5258 alpha=16.97 epsHOMO=-0.2653 epsLUMO=0.0784 epsgap=0.3437 =83.794 zpve=0.051208 U0=-115.679136 U=-115.675816 H=-115.674872 G=-115.701876 Cv=8.751 pbc="F F F" +C -0.00828817 1.39046978 -0.00560069 +O -0.00797038 -0.02504537 0.02030606 +H 1.00658338 1.81556366 0.00348335 +H -0.54657475 1.79916975 -0.87390126 +H -0.52288871 1.72555240 0.89907326 +H 0.44142019 -0.33354425 -0.77152059 +7 +Properties=species:S:1:pos:R:3 A=160.28041 B=8.59323 C=8.59321 mu=0.7156 alpha=28.78 epsHOMO=-0.2609 epsLUMO=0.0613 epsgap=0.3222 =177.1963 zpve=0.05541 U0=-116.609549 U=-116.60555 H=-116.604606 G=-116.633775 Cv=12.482 pbc="F F F" +C -0.01782102 1.46435788 0.01009397 +C 0.00208816 0.00950777 0.00201200 +C 0.01834085 -1.19180518 -0.00450508 +H 0.99782210 1.87425349 0.00260606 +H -0.54220434 1.85801178 -0.86721192 +H -0.52533306 1.84834356 0.90148138 +H 0.03231658 -2.25314823 -0.01025999 +6 +Properties=species:S:1:pos:R:3 A=159.03567 B=9.22327 C=9.22324 mu=3.8266 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epsgap=0.2845 =145.3078 zpve=0.045279 U0=-169.860788 U=-169.856903 H=-169.855958 G=-169.885594 Cv=10.89 pbc="F F F" +N -0.02589986 1.34614561 0.00889391 +C 0.04646728 -0.01174341 0.00120425 +O 1.07183520 -0.65258782 -0.01113313 +H 0.82535497 1.88504948 0.00373769 +H -0.90837680 1.82679638 0.01891992 +H -0.96144100 -0.47500376 0.00807391 +11 +Properties=species:S:1:pos:R:3 A=29.45018 B=8.37701 C=7.42076 mu=0.0597 alpha=34.75 epsHOMO=-0.323 epsLUMO=0.0949 epsgap=0.4179 =227.1361 zpve=0.103182 U0=-119.052475 U=-119.047927 H=-119.046983 G=-119.078157 Cv=14.84 pbc="F F F" +C -0.03113825 1.54081582 0.03192126 +C 0.01215347 0.01092235 -0.01603259 +C 0.72169129 -0.52583353 -1.26230570 +H 0.97955987 1.96459116 0.03098367 +H -0.55840223 1.94831192 -0.83816075 +H -0.54252252 1.90153531 0.93005671 +H 0.51522791 -0.36840234 0.88231134 +H -1.01070641 -0.38456999 0.02051783 +H 1.75851210 -0.17376585 -1.30871516 +H 0.74087192 -1.62024959 -1.27516511 +H 0.22023351 -0.19051179 -2.17729020 +9 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1.26478161 0.64923984 -0.00655044 +O -0.00024018 -0.00771006 0.00204055 +H -0.31763326 1.88586598 0.93475751 +H -0.33527583 1.89576673 -0.90397578 +H 1.83246682 0.56256848 -0.93193290 +H 1.85008258 0.55267938 0.90680147 +10 +Properties=species:S:1:pos:R:3 A=10.12193 B=8.49011 C=4.89615 mu=2.7362 alpha=35.53 epsHOMO=-0.2431 epsLUMO=-0.0087 epsgap=0.2344 =292.4367 zpve=0.083382 U0=-193.08834 U=-193.082969 H=-193.082024 G=-193.116476 Cv=16.893 pbc="F F F" +C -0.00310189 1.48050328 -0.17259867 +C -0.04527202 -0.02887059 0.00139493 +C 1.29705800 -0.72194415 0.16889042 +O -1.08643172 -0.64273343 0.00630393 +H 0.59403529 1.74668579 -1.05225043 +H -1.01540407 1.87034023 -0.28382265 +H 0.48099600 1.95070605 0.69099376 +H 1.82512144 -0.32470731 1.04328658 +H 1.14892507 -1.79597689 0.28554393 +H 1.93682635 -0.52949692 -0.69995403 +9 +Properties=species:S:1:pos:R:3 A=10.78886 B=9.27509 C=5.14977 mu=3.6367 alpha=31.83 epsHOMO=-0.2436 epsLUMO=0.0347 epsgap=0.2783 =267.6148 zpve=0.07319 U0=-209.159302 U=-209.15402 H=-209.153076 G=-209.187468 Cv=16.561 pbc="F F F" +C 0.00679889 1.49969736 -0.02567030 +C -0.02740385 -0.01752051 -0.14894811 +N 1.17826667 -0.63324298 0.05691721 +O -1.03721965 -0.63980649 -0.40727572 +H 0.91390280 1.92923755 -0.46100067 +H -0.87080896 1.91124186 -0.52345176 +H -0.02826124 1.78517271 1.03149125 +H 1.98405139 -0.13175861 0.38674906 +H 1.19049421 -1.63919563 0.10006209 +8 +Properties=species:S:1:pos:R:3 A=11.1475 B=10.36388 C=5.42091 mu=3.4869 alpha=28.07 epsHOMO=-0.2495 epsLUMO=0.0556 epsgap=0.3051 =244.2308 zpve=0.063824 U0=-225.221461 U=-225.217075 H=-225.216131 G=-225.247724 Cv=15.292 pbc="F F F" +N 0.03605270 1.36077873 -0.12416403 +C -0.02591138 -0.02076560 0.00200645 +N 1.21968479 -0.62334220 0.11963222 +O -1.06822866 -0.64174597 0.00865573 +H 0.80749439 1.83455148 0.32144915 +H -0.85825775 1.80373500 0.02243770 +H 1.99927148 -0.17303054 -0.33602496 +H 1.18204327 -1.62147901 -0.02109234 +14 +Properties=species:S:1:pos:R:3 A=7.75166 B=7.74847 C=4.48668 mu=0.0897 alpha=45.46 epsHOMO=-0.3167 epsLUMO=0.0843 epsgap=0.401 =355.0621 zpve=0.131146 U0=-158.342346 U=-158.336603 H=-158.335658 G=-158.370016 Cv=20.273 pbc="F F F" +C -0.03215888 1.54021598 0.01074456 +C 0.03381741 0.00745852 0.00180716 +C 0.71375572 -0.50856404 -1.27296949 +C 0.73848999 -0.52220886 1.25750907 +H 0.97522736 1.97414341 0.00528362 +H -0.56196769 1.92232326 -0.86835418 +H -0.54817239 1.91268414 0.90208039 +H -0.99752525 -0.37310578 0.00977297 +H 1.75070021 -0.15586784 -1.33269631 +H 0.73446691 -1.60328288 -1.29939696 +H 0.19367011 -0.16085589 -2.17195508 +H 0.23611990 -0.18382017 2.17005260 +H 0.75964083 -1.61713153 1.27226101 +H 1.77635866 -0.16985318 1.30053019 +12 +Properties=species:S:1:pos:R:3 A=8.67553 B=8.00568 C=4.75542 mu=1.4259 alpha=38.58 epsHOMO=-0.2612 epsLUMO=0.074 epsgap=0.3351 =318.3721 zpve=0.107673 U0=-194.267232 U=-194.261748 H=-194.260804 G=-194.294663 Cv=19.052 pbc="F F F" +C -0.03315871 1.54782566 -0.00438815 +C -0.01108534 0.01859095 0.01676117 +C 0.70938384 -0.53596711 1.23978444 +O -1.33219841 -0.51712554 0.05236057 +H 0.98215692 1.95715900 -0.03182225 +H -0.56388100 1.92087623 -0.88845865 +H -0.54082548 1.93340945 0.88575489 +H 0.51042122 -0.33767241 -0.88819176 +H 0.22240492 -0.18785860 2.15650245 +H 0.68242024 -1.62887638 1.23305141 +H 1.75434301 -0.21246950 1.25524188 +H -1.81818681 -0.15577435 -0.69553962 +6 +Properties=species:S:1:pos:R:3 A=0 B=4.4259726 C=4.4259726 mu=0 alpha=38.52 epsHOMO=-0.2599 epsLUMO=-0.0214 epsgap=0.2386 =278.6264 zpve=0.037354 U0=-153.459846 U=-153.455442 H=-153.454498 G=-153.482621 Cv=15.312 pbc="F F F" +C 0.68098021 0.00000000 0.00000000 +C -0.68098021 0.00000000 0.00000000 +C -1.88766603 0.00000000 0.00000000 +C 1.88766603 0.00000000 0.00000000 +H -2.94960000 0.00000000 0.00000000 +H 2.94960000 0.00000000 0.00000000 +5 +Properties=species:S:1:pos:R:3 A=0 B=4.5793222 C=4.5793222 mu=3.792 alpha=32.66 epsHOMO=-0.3102 epsLUMO=-0.0543 epsgap=0.2559 =260.1896 zpve=0.027259 U0=-169.557758 U=-169.553764 H=-169.55282 G=-169.581024 Cv=12.93 pbc="F F F" +C 0.01510390 0.00000000 1.00000000 +C 1.38233149 0.00000000 1.00000000 +C -1.18862525 0.00000000 1.00000000 +H -2.25177201 0.00000000 1.00000000 +N 2.54296187 0.00000000 1.00000000 +4 +Properties=species:S:1:pos:R:3 A=0 B=4.73269 C=4.73269 mu=0.0023 alpha=27.7 epsHOMO=-0.3696 epsLUMO=-0.0926 epsgap=0.277 =242.9308 zpve=0.015951 U0=-185.648533 U=-185.644825 H=-185.64388 G=-185.667652 Cv=10.398 pbc="F F F" +N 0.01745734 -1.16134217 -0.00415342 +C 0.00253212 -0.00344278 0.00179939 +C -0.01611419 1.37220987 0.00934873 +N -0.03264302 2.53008276 0.01609090 +6 +Properties=species:S:1:pos:R:3 A=68.5171 B=4.8345 C=4.51586 mu=2.7824 alpha=31.14 epsHOMO=-0.2777 epsLUMO=-0.0735 epsgap=0.2042 =268.3921 zpve=0.037208 U0=-190.624631 U=-190.620363 H=-190.619419 G=-190.650543 Cv=13.049 pbc="F F F" +O -0.06776120 -0.04233838 0.00282489 +C -0.00562945 1.16382879 0.00743862 +C 1.22985272 1.91339997 0.00028781 +C 2.23625322 2.57430970 -0.00529277 +H -0.91670701 1.79467546 0.01781352 +H 3.13429662 3.14370267 -0.01037656 +5 +Properties=species:S:1:pos:R:3 A=67.88408 B=5.00823 C=4.66413 mu=2.3112 alpha=26.25 epsHOMO=-0.3166 epsLUMO=-0.11 epsgap=0.2066 =251.0007 zpve=0.02654 U0=-206.721858 U=-206.717875 H=-206.716931 G=-206.747625 Cv=11.329 pbc="F F F" +O -0.04055978 -0.03799144 0.00266513 +C -0.01768870 1.16193251 0.00746008 +C 1.23315534 1.93381336 0.00043371 +N 2.19857378 2.57055097 -0.00483320 +H -0.92627534 1.78959196 0.01764859 +6 +Properties=species:S:1:pos:R:3 A=56.25048 B=4.77441 C=4.40088 mu=0.002 alpha=26.12 epsHOMO=-0.2668 epsLUMO=-0.1113 epsgap=0.1555 =266.8164 zpve=0.036943 U0=-227.798785 U=-227.79457 H=-227.793626 G=-227.825074 Cv=12.147 pbc="F F F" +O 0.00337122 -0.03146586 0.00157206 +C -0.01713174 1.17081409 0.00676444 +C 1.26555783 1.99719410 -0.00007703 +O 1.24507069 3.19946589 0.00681275 +H -0.94690483 1.77678142 0.01852668 +H 2.19534410 1.39121374 -0.01006739 +10 +Properties=species:S:1:pos:R:3 A=80.28766 B=3.3688 C=3.3688 mu=0 alpha=42.32 epsHOMO=-0.2412 epsLUMO=0.0684 epsgap=0.3096 =400.2236 zpve=0.083896 U0=-155.908941 U=-155.90318 H=-155.902236 G=-155.937641 Cv=17.447 pbc="F F F" +C -0.01787959 1.46676893 0.01011180 +C 0.00186644 0.01072913 0.00211109 +C 0.01819704 -1.19272861 -0.00450723 +C 0.03795170 -2.64876793 -0.01251193 +H 0.86460312 1.87589979 0.51479719 +H -0.03217624 1.87210581 -1.00791002 +H -0.90233115 1.85185524 0.53003953 +H -0.83477011 -3.06339007 0.50451436 +H 0.03276422 -3.04315564 -1.03491223 +H 0.93213486 -3.03931642 0.48626606 +10 +Properties=species:S:1:pos:R:3 A=27.39459 B=4.53005 C=4.08342 mu=0.7067 alpha=40.09 epsHOMO=-0.2592 epsLUMO=0.0566 epsgap=0.3157 =333.9589 zpve=0.084338 U0=-155.897345 U=-155.892291 H=-155.891347 G=-155.924226 Cv=17.13 pbc="F F F" +C -0.03095840 1.54775025 0.03167945 +C 0.01485375 0.00962471 -0.02082154 +C 0.69099073 -0.49954575 -1.20857624 +C 1.25222275 -0.89514467 -2.19527879 +H 0.97899300 1.96621609 0.03373834 +H -0.56082354 1.95002902 -0.83580745 +H -0.54576708 1.88225519 0.93709062 +H 0.51920135 -0.37425398 0.87496224 +H -1.00644802 -0.39030838 0.01340632 +H 1.74594541 -1.25604187 -3.06299415 +9 +Properties=species:S:1:pos:R:3 A=28.0016 B=4.67752 C=4.21765 mu=3.9233 alpha=35.38 epsHOMO=-0.3213 epsLUMO=0.034 epsgap=0.3553 =314.5335 zpve=0.07419 U0=-172.006141 U=-172.001467 H=-172.000523 G=-172.032826 Cv=14.988 pbc="F F F" +C -0.02522005 1.54773054 0.02133370 +C 0.01183939 0.00999278 -0.01556702 +C 0.68915114 -0.50836846 -1.20331488 +N 1.22565733 -0.90360846 -2.14737482 +H 0.98571620 1.96212990 0.02370153 +H -0.55522192 1.94599662 -0.84711771 +H -0.53962556 1.88294354 0.92536604 +H 0.52481910 -0.38226048 0.86933355 +H -1.00453542 -0.39832109 0.00530071 +8 +Properties=species:S:1:pos:R:3 A=32.19893 B=4.76299 C=4.30696 mu=4.4361 alpha=31.81 epsHOMO=-0.2683 epsLUMO=0.0173 epsgap=0.2855 =295.6635 zpve=0.063305 U0=-188.042067 U=-188.037478 H=-188.036534 G=-188.06863 Cv=14.488 pbc="F F F" +N -0.03608099 1.48762533 0.03678072 +C -0.01039471 0.02517605 -0.01506608 +C -1.37180956 -0.52108920 0.02739272 +N -2.44026695 -0.95956444 0.03370519 +H 0.91729752 1.83547547 0.05202682 +H -0.47162178 1.85677205 -0.80275569 +H 0.48946161 -0.40880401 -0.89715168 +H 0.52827150 -0.34533338 0.86469547 +8 +Properties=species:S:1:pos:R:3 A=35.11615 B=4.66117 C=4.22354 mu=1.7211 alpha=33.1 epsHOMO=-0.2595 epsLUMO=0.0277 epsgap=0.2872 =300.0993 zpve=0.060632 U0=-191.810916 U=-191.806025 H=-191.805081 G=-191.837634 Cv=15.855 pbc="F F F" +O -0.02013197 1.45738137 0.01010195 +C 0.00168478 0.03579980 0.00224736 +C -1.36366691 -0.47481101 0.01255056 +C -2.47902835 -0.91970202 0.02082451 +H 0.89446911 1.75736553 0.00327363 +H 0.51979028 -0.35175413 -0.88980302 +H 0.53670458 -0.36127661 0.88000894 +H -3.46773502 -1.30737009 0.02818120 +7 +Properties=species:S:1:pos:R:3 A=35.93172 B=4.83443 C=4.37827 mu=4.6788 alpha=28.56 epsHOMO=-0.3018 epsLUMO=0.0022 epsgap=0.3039 =280.6659 zpve=0.050262 U0=-207.916786 U=-207.912215 H=-207.911271 G=-207.943384 Cv=13.845 pbc="F F F" +O -0.03738221 1.44547841 0.01010769 +C 0.01292906 0.03441661 0.00209165 +C -1.35747452 -0.48874523 0.01239738 +N -2.42135277 -0.93685184 0.02016053 +H 0.86898057 1.76972949 0.00417577 +H 0.51692713 -0.36329790 -0.89215814 +H 0.53387616 -0.37278749 0.88223921 +10 +Properties=species:S:1:pos:R:3 A=26.14564 B=4.2893 C=4.12773 mu=2.6741 alpha=35.83 epsHOMO=-0.25 epsLUMO=-0.0205 epsgap=0.2295 =333.3276 zpve=0.084175 U0=-193.075202 U=-193.070116 H=-193.069171 G=-193.102798 Cv=15.954 pbc="F F F" +C 0.16879340 1.52513042 -0.15747056 +C -0.18737625 0.06194763 0.14679374 +C 0.50917645 -0.43996215 1.39125850 +O 1.18191079 -1.43565585 1.45816384 +H 1.23126511 1.63137254 -0.39632693 +H -0.40744668 1.89431681 -1.00969246 +H -0.04931034 2.17266260 0.69857432 +H -1.26905160 -0.01668067 0.32854433 +H 0.06279062 -0.60250479 -0.68474036 +H 0.35384841 0.20663370 2.28871052 +9 +Properties=species:S:1:pos:R:3 A=19.98749 B=6.11589 C=4.82584 mu=3.7071 alpha=32.78 epsHOMO=-0.2516 epsLUMO=0.0335 epsgap=0.2851 =279.7863 zpve=0.074166 U0=-209.144909 U=-209.139976 H=-209.139032 G=-209.172305 Cv=15.058 pbc="F F F" +C 0.01474916 1.47888256 -0.03509326 +N 0.18708077 0.03831317 -0.02906851 +C 0.45703661 -0.66858895 1.10069001 +O 0.58457996 -0.19876202 2.21119705 +H 0.76025289 1.96736104 -0.67198126 +H -0.98462423 1.75984483 -0.38485103 +H 0.14269849 1.82387718 0.99149397 +H 0.10386349 -0.46937726 -0.89461379 +H 0.55047568 -1.75253452 0.88868242 +8 +Properties=species:S:1:pos:R:3 A=48.62351 B=4.67298 C=4.38187 mu=3.92 alpha=29.47 epsHOMO=-0.2814 epsLUMO=0.0074 epsgap=0.2888 =293.917 zpve=0.061327 U0=-229.013797 U=-229.009003 H=-229.008059 G=-229.041086 Cv=13.885 pbc="F F F" +C 0.10653781 1.43512049 0.03336100 +O 0.42686564 0.04716929 0.09309945 +C 0.50714862 -0.63652665 -1.06318297 +O 0.76927014 -1.79955017 -1.11010475 +H 0.92349251 2.00385139 0.48465728 +H -0.03757815 1.76927183 -1.00057425 +H -0.81269569 1.60801916 0.59873287 +H 0.30458460 -0.00231902 -1.94965553 +8 +Properties=species:S:1:pos:R:3 A=36.49282 B=4.31175 C=4.04493 mu=1.7341 alpha=28.53 epsHOMO=-0.2537 epsLUMO=-0.0341 epsgap=0.2196 =303.8129 zpve=0.060508 U0=-228.992613 U=-228.987769 H=-228.986825 G=-229.019918 Cv=14.78 pbc="F F F" +O 0.19375856 1.37170995 0.31398297 +C 0.04992637 0.01405481 -0.04526239 +C -1.40888025 -0.39635680 -0.20601574 +O -1.77729462 -1.37233860 -0.80361689 +H -0.01723186 1.91269132 -0.45337758 +H 0.60440478 -0.26519766 -0.95205725 +H 0.46740406 -0.57521679 0.78176270 +H -2.11835055 0.30075962 0.29870515 +14 +Properties=species:S:1:pos:R:3 A=23.44662 B=3.60588 C=3.38683 mu=0 alpha=45.71 epsHOMO=-0.317 epsLUMO=0.0937 epsgap=0.4107 =426.2996 zpve=0.131708 U0=-158.340943 U=-158.33517 H=-158.334226 G=-158.36894 Cv=19.668 pbc="F F F" +C -0.03007624 1.55804912 0.02980774 +C 0.00707277 0.02834877 -0.00712529 +C 0.71320792 -0.52989340 -1.24765581 +C 0.75036895 -2.05959366 -1.28458957 +H 0.98195330 1.97845113 0.02800797 +H -0.55582433 1.96212343 -0.84270157 +H -0.54083762 1.92692755 0.92493697 +H 0.50928672 -0.34909049 0.89361319 +H -1.01733481 -0.36542299 0.03207393 +H 0.21098948 -0.15245770 -2.14839302 +H 1.73761331 -0.13611625 -1.28685672 +H 1.27616854 -2.46366410 -0.41210980 +H -0.26165699 -2.48000447 -1.28273552 +H 1.26108491 -2.42846444 -2.17974803 +12 +Properties=species:S:1:pos:R:3 A=26.59789 B=3.79186 C=3.53779 mu=1.3402 alpha=38.61 epsHOMO=-0.2619 epsLUMO=0.081 epsgap=0.3429 =382.8628 zpve=0.108241 U0=-194.261089 U=-194.255495 H=-194.254551 G=-194.28893 Cv=18.431 pbc="F F F" +C -0.00379012 1.52196790 0.01027996 +C -0.03589372 -0.00778254 0.00225671 +C -1.45219016 -0.56653711 0.01284386 +O -1.36799785 -1.98394604 0.00453606 +H 1.02477576 1.89411231 0.00239793 +H -0.51286674 1.93764614 -0.86653888 +H -0.49584080 1.92801344 0.90121721 +H 0.48140784 -0.39530552 -0.88316064 +H 0.49828589 -0.40486256 0.87330348 +H -1.98734220 -0.20399494 0.90666062 +H -2.00431485 -0.19459473 -0.86666460 +H -2.26171819 -2.33824298 0.01034748 +12 +Properties=species:S:1:pos:R:3 A=28.29567 B=4.14901 C=3.88349 mu=1.0363 alpha=39.34 epsHOMO=-0.2503 epsLUMO=0.0925 epsgap=0.3428 =368.9331 zpve=0.107895 U0=-194.254127 U=-194.248585 H=-194.247641 G=-194.281899 Cv=17.888 pbc="F F F" +C -0.00519551 1.50868224 0.03308760 +C 0.01316580 -0.01037819 0.01988643 +O 0.69758944 -0.46344140 1.16868979 +C 0.76688491 -1.86508183 1.24602669 +H 1.01445476 1.90423799 0.02996018 +H -0.52990866 1.89184059 -0.84726911 +H -0.51263765 1.87805843 0.92875424 +H -1.01599507 -0.40998729 0.00714282 +H 0.51093143 -0.38369435 -0.89219395 +H -0.23415841 -2.32451489 1.28977780 +H 1.30129276 -2.29853663 0.38486095 +H 1.30990287 -2.11811998 2.16013206 +10 +Properties=species:S:1:pos:R:3 A=14.79671 B=5.6956 C=4.58846 mu=0.0075 alpha=31.42 epsHOMO=-0.2594 epsLUMO=0.0584 epsgap=0.3179 =297.8398 zpve=0.085172 U0=-230.183076 U=-230.177723 H=-230.176779 G=-230.211195 Cv=16.837 pbc="F F F" +O -0.01418735 1.42642242 -0.05423651 +C -0.00391839 0.00717714 0.03496062 +C -1.42317019 -0.55560600 -0.01256639 +O -2.11229413 -0.10563875 -1.17228313 +H -0.18987882 1.64154705 -0.97555148 +H 0.59659504 -0.43315430 -0.77409095 +H 0.47311116 -0.24903733 0.98747216 +H -1.39494859 -1.64975279 -0.06283188 +H -1.96497417 -0.27216915 0.90131972 +H -2.35925991 0.80944396 -1.00614597 +12 +Properties=species:S:1:pos:R:3 A=15.61472 B=6.31363 C=5.54514 mu=0.1136 alpha=41.96 epsHOMO=-0.2727 epsLUMO=0.1012 epsgap=0.3738 =298.6061 zpve=0.109284 U0=-157.116735 U=-157.11209 H=-157.111146 G=-157.143262 Cv=16.49 pbc="F F F" +C -0.03602245 1.52092949 0.01569161 +C 0.01938487 0.01004403 0.04814953 +C 1.30891671 -0.71251995 -0.24920416 +C 0.75347423 -0.70267714 1.15564676 +H 0.90105691 1.95389291 0.38283338 +H -0.19640365 1.89437369 -1.00208569 +H -0.84685273 1.90588930 0.64451322 +H -0.88416809 -0.48001889 -0.30566335 +H 1.26900901 -1.63903068 -0.81150371 +H 2.19010738 -0.11131209 -0.44927513 +H 1.26248862 -0.09487455 1.89690028 +H 0.33540836 -1.62248918 1.54980695 +10 +Properties=species:S:1:pos:R:3 A=18.21931 B=6.63877 C=5.92459 mu=1.812 alpha=35.01 epsHOMO=-0.2633 epsLUMO=0.1052 epsgap=0.3685 =267.2979 zpve=0.085275 U0=-193.039603 U=-193.035186 H=-193.034242 G=-193.065979 Cv=14.764 pbc="F F F" +C -0.01646343 1.51830705 0.00771445 +C -0.02743227 0.01657694 -0.10889741 +C 0.77296553 -0.70131886 -1.10863115 +O 1.17212293 -0.66702969 0.26278829 +H 0.91865980 1.92914217 -0.38285880 +H -0.85158014 1.95456506 -0.55109460 +H -0.11487702 1.82596470 1.05397777 +H -0.93582598 -0.47350600 0.24682793 +H 0.44443922 -1.67201495 -1.47810622 +H 1.40538284 -0.13600339 -1.79271491 +11 +Properties=species:S:1:pos:R:3 A=16.58914 B=7.18798 C=6.11415 mu=1.1353 alpha=39.02 epsHOMO=-0.2304 epsLUMO=0.0968 epsgap=0.3271 =270.5508 zpve=0.097671 U0=-173.147782 U=-173.143343 H=-173.142399 G=-173.174073 Cv=15.298 pbc="F F F" +C -0.05275541 1.47419574 0.00999108 +N 0.01940516 0.02189947 0.06334866 +C 1.32511048 -0.56446850 -0.21420725 +C 0.78051759 -0.54057993 1.17242760 +H 0.86784202 1.97176076 0.36297285 +H -0.23845155 1.79095005 -1.02142866 +H -0.88683448 1.81938959 0.62950398 +H 1.31992909 -1.48186782 -0.79602038 +H 2.15202820 0.11020204 -0.43285248 +H 1.24563570 0.14996098 1.87499245 +H 0.37013358 -1.44020447 1.62233365 +10 +Properties=species:S:1:pos:R:3 A=16.79477 B=6.71015 C=5.86781 mu=1.3894 alpha=34.64 epsHOMO=-0.239 epsLUMO=0.0775 epsgap=0.3166 =263.966 zpve=0.085106 U0=-193.034988 U=-193.030356 H=-193.029411 G=-193.061689 Cv=15.679 pbc="F F F" +O 0.21577583 1.35386859 -0.10695107 +C 0.01628099 -0.02832718 -0.02915694 +C -0.39081465 -0.71497009 1.24315669 +C -1.35118138 -0.64280380 0.06273589 +H -0.43642338 1.79062849 0.45006761 +H 0.74981167 -0.52404050 -0.65624767 +H 0.05959375 -1.66840483 1.49729237 +H -0.61369353 -0.08848006 2.10170640 +H -2.19821932 0.03058430 0.15411451 +H -1.56395912 -1.54640426 -0.49826515 +12 +Properties=species:S:1:pos:R:3 A=10.66107 B=10.66107 C=6.30452 mu=0 alpha=41.83 epsHOMO=-0.2982 epsLUMO=0.0956 epsgap=0.3938 =268.4432 zpve=0.110511 U0=-157.115484 U=-157.111322 H=-157.110378 G=-157.141657 Cv=14.696 pbc="F F F" +C -0.02542957 1.54032578 -0.04215944 +C 1.52564399 1.50419989 0.04121276 +C 1.42146727 0.06146790 0.60844885 +C -0.02638833 -0.01070956 0.04935088 +H -0.46591372 1.99639894 0.84962894 +H -0.47900239 2.00028207 -0.92365389 +H 1.98248278 1.51375334 -0.95303183 +H 2.02191365 2.25986579 0.65522129 +H 2.14291679 -0.67956358 0.25531480 +H 1.43105853 0.05751479 1.70261879 +H -0.79053440 -0.48530296 0.66996573 +H -0.05233245 -0.47238446 -0.94236394 +10 +Properties=species:S:1:pos:R:3 A=12.06545 B=11.77731 C=6.71997 mu=1.7978 alpha=34.56 epsHOMO=-0.2424 epsLUMO=0.0859 epsgap=0.3283 =236.9759 zpve=0.086675 U0=-193.034094 U=-193.029968 H=-193.029024 G=-193.060777 Cv=12.915 pbc="F F F" +C -0.03489789 1.55259455 -0.08507691 +C 1.49349183 1.45459700 0.10114677 +O 1.42725884 0.02348990 0.27254886 +C -0.00634758 0.02236494 0.10944630 +H -0.56579210 2.11301721 0.68591527 +H -0.36628352 1.89393380 -1.06692327 +H 2.10031724 1.73525720 -0.76898349 +H 1.90023090 1.95506611 0.98896748 +H -0.51987849 -0.35595179 1.00235948 +H -0.31970062 -0.57567421 -0.75559275 +12 +Properties=species:S:1:pos:R:3 A=8.63483 B=4.11977 C=2.88825 mu=0.6875 alpha=45.37 epsHOMO=-0.2392 epsLUMO=0.0192 epsgap=0.2584 =452.5112 zpve=0.100501 U0=-248.375248 U=-248.368823 H=-248.367879 G=-248.405354 Cv=21.616 pbc="F F F" +C -0.10255370 1.49907127 -0.00133642 +C -0.00519774 -0.00343114 -0.02723470 +C -0.00688536 -0.76890740 1.26434744 +N 0.08186799 -0.69202791 -1.10087306 +O 0.07439067 0.13215394 -2.24195258 +H 0.78342093 1.94690862 -0.46354050 +H -0.96150430 1.83614082 -0.59021965 +H -0.19854173 1.87462989 1.01931607 +H -0.92620012 -0.57297946 1.82982479 +H 0.07256154 -1.84064782 1.07516863 +H 0.82937178 -0.45553340 1.90133089 +H 0.13975924 -0.51254299 -2.95389260 +10 +Properties=species:S:1:pos:R:3 A=9.17136 B=9.04195 C=4.5531 mu=1.8689 alpha=43.14 epsHOMO=-0.2029 epsLUMO=0.0499 epsgap=0.2528 =303.9808 zpve=0.082433 U0=-210.101789 U=-210.097816 H=-210.096872 G=-210.12818 Cv=14.821 pbc="F F F" +N -0.00829694 1.35362838 0.00995971 +C 1.28033254 1.82457421 -0.00022459 +C 2.12216639 0.73698898 -0.01445600 +C 1.30849904 -0.43109580 -0.01284154 +C -0.00342782 -0.01836884 0.00235736 +H -0.83243234 1.92772668 0.02122995 +H 1.48471820 2.88312630 0.00354313 +H 3.20066918 0.77343077 -0.02488416 +H 1.64810599 -1.45539683 -0.02180394 +H -0.92551647 -0.57697054 0.00839402 +9 +Properties=species:S:1:pos:R:3 A=9.77329 B=9.41192 C=4.7946 mu=3.6193 alpha=39.13 epsHOMO=-0.2253 epsLUMO=0.0332 epsgap=0.2585 =283.6817 zpve=0.071145 U0=-226.160842 U=-226.157088 H=-226.156144 G=-226.187104 Cv=13.371 pbc="F F F" +N -0.00996090 1.35636172 0.01000855 +C 1.29535422 1.79575578 -0.00047017 +C 2.05550519 0.65637438 -0.01422927 +N 1.25385745 -0.46275132 -0.01260894 +C 0.02301170 -0.00706382 0.00200146 +H -0.83195718 1.93618436 0.02132575 +H 1.54553961 2.84344998 0.00289054 +H 3.13177434 0.57569766 -0.02524155 +H -0.87667864 -0.60414531 0.00750794 +9 +Properties=species:S:1:pos:R:3 A=9.50228 B=9.29497 C=4.69874 mu=0.5571 alpha=39.2 epsHOMO=-0.2246 epsLUMO=0.0199 epsgap=0.2445 =289.005 zpve=0.069883 U0=-229.969129 U=-229.965414 H=-229.96447 G=-229.995393 Cv=13.358 pbc="F F F" +O -0.03255819 1.35065130 0.00967376 +C 1.25319237 1.79167710 -0.00030351 +C 2.11664681 0.74375623 -0.01441494 +C 1.30397796 -0.43852387 -0.01283316 +C 0.01629568 -0.00777185 0.00191294 +H 1.37951089 2.86161135 0.00427433 +H 3.19376406 0.79865501 -0.02458604 +H 1.63858893 -1.46383271 -0.02156949 +H -0.93736295 -0.50899925 0.00843891 +8 +Properties=species:S:1:pos:R:3 A=10.10926 B=9.69372 C=4.94857 mu=1.5081 alpha=35.17 epsHOMO=-0.2509 epsLUMO=0.001 epsgap=0.2519 =269.2444 zpve=0.058593 U0=-246.02915 U=-246.025614 H=-246.024669 G=-246.055309 Cv=12.04 pbc="F F F" +O -0.03808298 1.35643965 0.00975193 +C 1.26496982 1.76855319 -0.00061744 +C 2.04955750 0.66630033 -0.01406047 +N 1.24227075 -0.46732066 -0.01237541 +C 0.04019820 0.00561577 0.00183569 +H 1.43513907 2.83095265 0.00340823 +H 3.12532898 0.59783015 -0.02480757 +H -0.89387707 -0.53342154 0.00796935 +17 +Properties=species:S:1:pos:R:3 A=4.4178 B=4.41753 C=4.41717 mu=0.0003 alpha=56.01 epsHOMO=-0.3145 epsLUMO=0.0737 epsgap=0.3882 =486.2719 zpve=0.158836 U0=-197.632222 U=-197.625241 H=-197.624297 G=-197.661411 Cv=26.084 pbc="F F F" +C -0.01859029 1.53486266 0.01039904 +C 0.00210320 -0.00413672 0.00196980 +C 0.74643954 -0.51390760 1.24887638 +C -1.44190166 -0.53673615 0.01311457 +C 0.72234971 -0.50049606 -1.26445632 +H 0.99776928 1.94420529 0.00435474 +H -0.54181883 1.92841415 -0.86819911 +H -0.52758402 1.91888376 0.90148530 +H 0.25549576 -0.17602728 2.16833067 +H 0.77815389 -1.60892372 1.27088904 +H 1.77964786 -0.14975834 1.26944035 +H -1.99871895 -0.18888794 -0.86408066 +H -1.45956909 -1.63227235 0.00649916 +H -1.98088827 -0.19979639 0.90561580 +H 1.75482473 -0.13554018 -1.30118083 +H 0.75396989 -1.59524036 -1.29822280 +H 0.21358221 -0.15343900 -2.17067181 +15 +Properties=species:S:1:pos:R:3 A=4.69474 B=4.66938 C=4.47909 mu=1.384 alpha=49.04 epsHOMO=-0.2601 epsLUMO=0.0664 epsgap=0.3265 =449.0573 zpve=0.134977 U0=-233.560626 U=-233.553779 H=-233.552834 G=-233.589759 Cv=25.128 pbc="F F F" +C -0.00855999 1.54270147 0.00152717 +C 0.00506780 0.00702635 0.01856160 +C 0.76137731 -0.51839196 1.24766443 +C -1.42012774 -0.54933183 -0.02116008 +O 0.62590477 -0.48363745 -1.17657057 +H 1.01361406 1.94020776 0.00281128 +H -0.51459669 1.90702466 -0.89705781 +H -0.52214815 1.94882514 0.87918344 +H 0.28130613 -0.20165548 2.17943598 +H 0.79965506 -1.61131746 1.23022549 +H 1.79096916 -0.14072935 1.26083003 +H -1.93854829 -0.19910724 -0.91828281 +H -1.39937632 -1.64259062 -0.04563833 +H -1.98850725 -0.23059656 0.85726520 +H 1.53111188 -0.15488046 -1.19210222 +9 +Properties=species:S:1:pos:R:3 A=10.21876 B=4.04324 C=2.94978 mu=2.8579 alpha=42.02 epsHOMO=-0.2654 epsLUMO=-0.0575 epsgap=0.2079 =416.7799 zpve=0.065175 U0=-229.927277 U=-229.921622 H=-229.920677 G=-229.955843 Cv=18.723 pbc="F F F" +C -0.00693851 1.49559567 -0.01063358 +C -0.02986170 -0.01594623 0.06248673 +O -0.08815239 -0.63215062 1.10334812 +C 0.02336981 -0.70256747 -1.22062379 +C 0.06738132 -1.26058341 -2.28623129 +H 0.90429806 1.83281887 -0.51655384 +H -0.85338524 1.85556926 -0.60552819 +H -0.05237764 1.91099807 0.99695949 +H 0.10466868 -1.77036047 -3.21832617 +8 +Properties=species:S:1:pos:R:3 A=10.16063 B=4.15847 C=3.0056 mu=3.3351 alpha=37.08 epsHOMO=-0.3007 epsLUMO=-0.0889 epsgap=0.2118 =397.7757 zpve=0.05451 U0=-246.027383 U=-246.022024 H=-246.02108 G=-246.055769 Cv=16.963 pbc="F F F" +C -0.01171097 1.49973776 -0.01213268 +C -0.02919740 -0.00338745 0.07151218 +O -0.09164028 -0.64592927 1.08732241 +C 0.03831104 -0.69690258 -1.23748557 +N 0.09136286 -1.22982650 -2.26228068 +H 0.90093132 1.83370670 -0.51814928 +H -0.85663933 1.84864400 -0.61618135 +H -0.06416540 1.92131542 0.99203818 +8 +Properties=species:S:1:pos:R:3 A=10.68423 B=4.3198 C=3.07993 mu=5.1815 alpha=38.31 epsHOMO=-0.2739 epsLUMO=-0.0438 epsgap=0.2301 =384.5574 zpve=0.056619 U0=-242.19573 U=-242.190591 H=-242.189646 G=-242.223513 Cv=17.465 pbc="F F F" +N -0.05232108 1.34991769 0.15535538 +C 0.08549378 -0.01476951 0.02668890 +N 1.14555215 -0.71993906 -0.03966037 +C -1.19426556 -0.71152088 0.01802151 +N -2.22747610 -1.22790543 0.00132148 +H 0.75474469 1.91998737 -0.04411334 +H -0.94458527 1.76600473 -0.05932293 +H 1.98312429 -0.13415500 -0.02878551 +9 +Properties=species:S:1:pos:R:3 A=9.08854 B=4.42607 C=3.03169 mu=0.9461 alpha=36.51 epsHOMO=-0.2538 epsLUMO=-0.0964 epsgap=0.1574 =399.222 zpve=0.064992 U0=-267.10335 U=-267.097658 H=-267.096714 G=-267.132534 Cv=17.806 pbc="F F F" +C -0.43126066 1.30586664 -0.12758585 +C 0.25285715 -0.01026988 0.13138867 +O 0.69872099 -0.36890333 1.19604690 +C 0.38978943 -0.96773680 -1.06226208 +O -0.01611268 -0.71564293 -2.16583718 +H 0.10006565 1.85324490 -0.91332597 +H -1.44270433 1.13069361 -0.50923371 +H -0.46757554 1.89548158 0.78836479 +H 0.90329886 -1.91558845 -0.79531972 +9 +Properties=species:S:1:pos:R:3 A=10.08927 B=4.54115 C=3.13487 mu=2.7707 alpha=38.09 epsHOMO=-0.2561 epsLUMO=-0.064 epsgap=0.192 =384.4827 zpve=0.067668 U0=-263.278851 U=-263.273589 H=-263.272645 G=-263.306835 Cv=17.972 pbc="F F F" +N -0.16032929 1.08528962 -0.45398652 +C 0.18649127 -0.08762327 0.14758021 +N 1.28979627 -0.46427423 0.67927030 +C -0.94204384 -1.08440264 0.20423621 +O -2.01802205 -0.89007687 -0.30583149 +H 0.54574437 1.73837930 -0.74689246 +H -1.04684920 1.09570857 -0.93607796 +H 2.01738195 0.24991828 0.59881811 +H -0.67740148 -2.01003995 0.74746441 +13 +Properties=species:S:1:pos:R:3 A=7.92874 B=3.8272 C=2.82581 mu=0.6578 alpha=51.2 epsHOMO=-0.2589 epsLUMO=0.0571 epsgap=0.316 =481.9854 zpve=0.112471 U0=-195.186772 U=-195.180446 H=-195.179502 G=-195.215658 Cv=22.569 pbc="F F F" +C -0.01763434 1.54859468 -0.00253584 +C -0.01719180 0.00735789 0.03269604 +C 0.75818463 -0.52820155 1.25278862 +C -1.38213303 -0.51768438 0.00775516 +C -2.50796392 -0.94005629 0.00478552 +H 1.00944396 1.92639613 -0.02632830 +H -0.54514614 1.92217052 -0.88393459 +H -0.51243000 1.95339892 0.88560642 +H 0.49366179 -0.34904155 -0.87265890 +H 0.28968458 -0.19345372 2.18334679 +H 0.77833392 -1.62102506 1.25783727 +H 1.78952492 -0.16181727 1.23574954 +H -3.49998244 -1.31810165 -0.00723572 +12 +Properties=species:S:1:pos:R:3 A=7.90009 B=3.95466 C=2.88643 mu=3.9512 alpha=46.23 epsHOMO=-0.318 epsLUMO=0.0365 epsgap=0.3545 =460.694 zpve=0.102281 U0=-211.295796 U=-211.289821 H=-211.288877 G=-211.324525 Cv=20.467 pbc="F F F" +C -0.00242350 1.55035837 0.01056187 +C -0.01856600 0.01004352 -0.02261770 +C -1.45045318 -0.55766178 0.01293347 +C 0.70125749 -0.48583168 -1.20172764 +N 1.26190946 -0.87126766 -2.13619740 +H 1.01959496 1.93673215 0.00349668 +H -0.52972844 1.96141639 -0.85498386 +H -0.50134251 1.90346839 0.91755862 +H 0.52481877 -0.36222456 0.85525462 +H -2.02427903 -0.21428716 -0.85251869 +H -1.44438296 -1.65026927 0.00754107 +H -1.95805494 -0.21725882 0.91996760 +11 +Properties=species:S:1:pos:R:3 A=8.30095 B=3.97615 C=2.93764 mu=2.7429 alpha=42.87 epsHOMO=-0.2704 epsLUMO=0.0278 epsgap=0.2983 =440.0738 zpve=0.091554 U0=-227.338075 U=-227.332253 H=-227.331309 G=-227.366638 Cv=19.946 pbc="F F F" +C -0.01064295 1.54128691 0.00722087 +C -0.00203811 0.00756015 0.03863463 +N 0.76268677 -0.47101822 1.19250856 +C -1.38541954 -0.52099256 -0.00194097 +N -2.45680847 -0.95636072 0.00694969 +H 1.02043523 1.90106589 0.00392365 +H -0.52873161 1.91193452 -0.88031480 +H -0.51849536 1.94150431 0.89013197 +H 0.50418828 -0.35805500 -0.86276072 +H 0.30070681 -0.18364934 2.05155305 +H 0.78148992 -1.48670299 1.20428183 +11 +Properties=species:S:1:pos:R:3 A=8.55919 B=3.89952 C=2.91869 mu=1.3582 alpha=44.03 epsHOMO=-0.2665 epsLUMO=0.0336 epsgap=0.3001 =444.6452 zpve=0.088908 U0=-231.108368 U=-231.102292 H=-231.101348 G=-231.137061 Cv=21.306 pbc="F F F" +C -0.00754278 1.55566144 0.08650633 +C 0.01207541 0.02096553 0.04935916 +O -1.29150251 -0.52785128 0.22126386 +C 0.65093362 -0.47718651 -1.17430507 +C 1.13902673 -0.86577343 -2.20205518 +H 1.00824243 1.95969458 0.04929733 +H -0.56272245 1.95192010 -0.76978085 +H -0.49328161 1.88929438 1.00704123 +H 0.57811967 -0.34464907 0.91458864 +H -1.78516532 -0.35903262 -0.58909400 +H 1.58078877 -1.22365060 -3.09919447 +10 +Properties=species:S:1:pos:R:3 A=8.55908 B=4.01671 C=2.97882 mu=3.269 alpha=39.28 epsHOMO=-0.3051 epsLUMO=0.0115 epsgap=0.3166 =424.3395 zpve=0.078602 U0=-247.214861 U=-247.209162 H=-247.208218 G=-247.243338 Cv=19.252 pbc="F F F" +C -0.00992462 1.54951908 0.00732200 +C 0.00740485 0.01722020 0.05031270 +O 0.72085722 -0.49630479 1.15864583 +C -1.36995095 -0.52355929 0.00894432 +N -2.45081572 -0.93233138 0.00716912 +H 1.01892314 1.91630447 0.02724149 +H -0.50261490 1.90969362 -0.89997951 +H -0.54800426 1.95377611 0.87053664 +H 0.53602846 -0.36494195 -0.82970945 +H 0.27702643 -0.20644534 1.96421773 +13 +Properties=species:S:1:pos:R:3 A=7.66289 B=3.70925 C=2.79937 mu=2.6921 alpha=46.58 epsHOMO=-0.2469 epsLUMO=-0.0188 epsgap=0.2281 =482.0475 zpve=0.112328 U0=-232.364952 U=-232.358577 H=-232.357633 G=-232.394589 Cv=21.434 pbc="F F F" +C -0.02629647 1.53877116 -0.00783747 +C 0.01468904 0.00095310 0.00166037 +C 0.74737862 -0.53388859 1.24420471 +C -1.40211264 -0.53859257 -0.01557820 +O -1.87620455 -1.23582351 -0.87399559 +H 0.98823696 1.94625080 0.03705522 +H -0.50725090 1.92153422 -0.91285565 +H -0.57756514 1.92360883 0.85796386 +H 0.50584680 -0.36421091 -0.90619124 +H 0.22958380 -0.23909403 2.16431052 +H 0.81692736 -1.62559499 1.22973438 +H 1.76267153 -0.12846566 1.29050755 +H -2.00861510 -0.22792742 0.87121666 +11 +Properties=species:S:1:pos:R:3 A=8.31096 B=4.07963 C=2.92918 mu=2.8354 alpha=39.16 epsHOMO=-0.255 epsLUMO=-0.0279 epsgap=0.2271 =432.1489 zpve=0.088443 U0=-268.287661 U=-268.281505 H=-268.280561 G=-268.316982 Cv=20.344 pbc="F F F" +C -0.05483118 1.46032843 -0.17068647 +C 0.07105714 -0.03524485 0.11225841 +O 0.58204486 -0.33316656 1.40176256 +C -1.27639279 -0.74383027 0.03051185 +O -2.11855775 -0.49643175 -0.79163412 +H 0.93158072 1.93091398 -0.23353204 +H -0.57713995 1.61290939 -1.11835562 +H -0.62444315 1.94776119 0.62573670 +H 0.69687521 -0.50071620 -0.67159471 +H 1.45603316 0.06066659 1.47858584 +H -1.40723217 -1.53388627 0.80211181 +12 +Properties=species:S:1:pos:R:3 A=8.9331 B=4.19405 C=2.96046 mu=3.7163 alpha=44.42 epsHOMO=-0.2424 epsLUMO=0.0327 epsgap=0.2751 =441.85 zpve=0.10227 U0=-248.430371 U=-248.424309 H=-248.423365 G=-248.459383 Cv=19.918 pbc="F F F" +C -0.09776499 1.44917268 0.04561984 +N 0.01043053 0.00373818 -0.00070600 +C 0.05323028 -0.71432112 1.25264891 +C 0.06728716 -0.64067225 -1.20060778 +O 0.03636432 -0.11006515 -2.29109228 +H 0.75650660 1.88747109 0.57647421 +H -0.11648577 1.81632103 -0.98049172 +H -1.01641106 1.75521548 0.56151415 +H -0.85453556 -0.53657675 1.84390168 +H 0.13297722 -1.78696336 1.05851351 +H 0.91637348 -0.40737846 1.85761345 +H 0.14762240 -1.73801339 -1.06805139 +11 +Properties=species:S:1:pos:R:3 A=10.13982 B=3.81346 C=2.86965 mu=2.9514 alpha=39.33 epsHOMO=-0.2699 epsLUMO=-0.0262 epsgap=0.2437 =440.1727 zpve=0.088924 U0=-268.301176 U=-268.295084 H=-268.29414 G=-268.331307 Cv=19.824 pbc="F F F" +C -0.01708808 1.44281136 0.03225501 +C -0.05544542 -0.06560572 -0.02375999 +O -0.17379913 -0.76196233 0.96223129 +C 0.06068107 -0.73615179 -1.38825252 +O 0.00877771 -2.12574390 -1.27749486 +H 0.92062950 1.81463913 -0.39522215 +H -0.83050523 1.86542281 -0.56780350 +H -0.10911975 1.78049045 1.06493675 +H -0.75015067 -0.35235060 -2.03056074 +H 1.00312898 -0.40389029 -1.85592942 +H -0.09063255 -2.29450960 -0.32608019 +13 +Properties=species:S:1:pos:R:3 A=9.55216 B=3.57844 C=2.7341 mu=2.6168 alpha=46.19 epsHOMO=-0.2423 epsLUMO=-0.0072 epsgap=0.2351 =489.8518 zpve=0.112006 U0=-232.377706 U=-232.371073 H=-232.370129 G=-232.408256 Cv=21.716 pbc="F F F" +C -0.04384102 1.54099793 -0.03913814 +C 0.02765132 0.01885817 0.04778773 +C 0.76814644 -0.48842218 1.28000847 +C 0.87850208 -1.99746312 1.42393323 +O 1.24179142 0.26651340 2.09788702 +H 0.95796454 1.97690198 -0.06816012 +H -0.58447717 1.85356031 -0.93690369 +H -0.55173626 1.95737759 0.83440529 +H -0.97692824 -0.42709877 0.05353178 +H 0.51853486 -0.40806507 -0.83809409 +H 1.39924980 -2.42678188 0.56053177 +H 1.42001187 -2.24449367 2.33776426 +H -0.11750602 -2.45360651 1.45141965 +12 +Properties=species:S:1:pos:R:3 A=9.78802 B=3.78441 C=2.84416 mu=3.499 alpha=42.54 epsHOMO=-0.2438 epsLUMO=0.0355 epsgap=0.2793 =457.447 zpve=0.101847 U0=-248.448467 U=-248.441988 H=-248.441044 G=-248.478935 Cv=21.374 pbc="F F F" +C -0.00220144 1.53344329 -0.05960369 +C -0.03235837 0.01206167 0.06840265 +C 0.69666086 -0.48196462 1.31828322 +N 0.98104007 -1.82034162 1.31984698 +O 1.00392124 0.24606232 2.24136749 +H 1.02111190 1.89525169 -0.19182672 +H -0.59707204 1.86206427 -0.91645604 +H -0.39728839 1.99920712 0.84545201 +H -1.06936083 -0.34552935 0.13040806 +H 0.39947165 -0.46906818 -0.81777020 +H 0.63281951 -2.43917647 0.60874785 +H 1.35708014 -2.22217793 2.16303522 +12 +Properties=species:S:1:pos:R:3 A=10.01185 B=3.84195 C=2.87635 mu=3.5402 alpha=43.61 epsHOMO=-0.2418 epsLUMO=0.0387 epsgap=0.2805 =458.9803 zpve=0.101735 U0=-248.443503 U=-248.436899 H=-248.435955 G=-248.474272 Cv=20.967 pbc="F F F" +C 0.00619941 1.45933202 -0.03893859 +N -0.06411616 0.01422963 -0.12046901 +C -1.20595394 -0.68196796 0.16117120 +C -1.08982704 -2.19284899 0.02095775 +O -2.24482911 -0.14443077 0.49994418 +H 0.75053989 1.78372520 0.69715573 +H 0.25692001 1.90395263 -1.00876215 +H -0.97856026 1.81121438 0.27082194 +H 0.75820982 -0.49543660 -0.39464024 +H -0.09909703 -2.53543104 -0.28970003 +H -1.34097671 -2.65395851 0.97972885 +H -1.82903100 -2.53483935 -0.70813076 +11 +Properties=species:S:1:pos:R:3 A=10.44045 B=4.04692 C=2.98547 mu=3.5648 alpha=39.51 epsHOMO=-0.2436 epsLUMO=0.0599 epsgap=0.3034 =428.404 zpve=0.091713 U0=-264.504487 U=-264.498452 H=-264.497508 G=-264.533633 Cv=20.274 pbc="F F F" +C 0.00922630 1.45021814 -0.03194543 +N -0.01429488 -0.00054646 -0.01782667 +C -1.21576579 -0.67252403 0.04421896 +N -1.09733352 -2.04644898 -0.15997741 +O -2.28599299 -0.12893711 0.24196027 +H 0.89659038 1.81885022 0.49120949 +H 0.00233950 1.86388504 -1.04914155 +H -0.88562684 1.79943346 0.48353298 +H 0.75257305 -0.47506631 -0.46719265 +H -0.24882333 -2.48772194 0.16375654 +H -1.93419610 -2.53898393 0.11482880 +12 +Properties=species:S:1:pos:R:3 A=9.69683 B=4.1533 C=3.01703 mu=1.1876 alpha=43.57 epsHOMO=-0.2595 epsLUMO=0.0352 epsgap=0.2948 =443.1687 zpve=0.102062 U0=-248.416462 U=-248.410358 H=-248.409414 G=-248.445651 Cv=20.256 pbc="F F F" +C -0.09160695 1.37540046 -0.00729426 +O -0.06964534 -0.04831581 -0.11148812 +C 0.68114625 -0.56142673 -1.10732458 +C 0.60405298 -2.06701550 -1.08766892 +N 1.32645533 0.19266343 -1.89967731 +H 0.91463916 1.76843655 0.16257460 +H -0.48562826 1.82624615 -0.92235216 +H -0.73971430 1.60007423 0.84110605 +H -0.43430065 -2.39242162 -1.20325205 +H 1.20627110 -2.50049977 -1.88745640 +H 0.95651337 -2.44976670 -0.12501838 +H 1.83504115 -0.36104361 -2.58486055 +11 +Properties=species:S:1:pos:R:3 A=10.20392 B=4.16834 C=3.07281 mu=1.7569 alpha=39.33 epsHOMO=-0.2685 epsLUMO=0.0174 epsgap=0.2859 =427.6606 zpve=0.089436 U0=-268.32127 U=-268.315051 H=-268.314106 G=-268.351214 Cv=19.501 pbc="F F F" +C 0.07295254 1.38840919 0.06442046 +O 0.21049114 -0.03740028 0.04612736 +C 0.50102805 -0.57224288 -1.15975346 +C 0.62218715 -2.07378323 -1.06030684 +O 0.63922154 0.08224418 -2.16157325 +H 1.00029720 1.87062570 -0.25532064 +H -0.73338957 1.70782089 -0.60080454 +H -0.15751280 1.65154060 1.09692954 +H -0.31330001 -2.50202851 -0.68941906 +H 0.85646588 -2.48430275 -2.04157352 +H 1.40651685 -2.34055496 -0.34634301 +10 +Properties=species:S:1:pos:R:3 A=10.68182 B=4.39532 C=3.17969 mu=2.329 alpha=35.5 epsHOMO=-0.2669 epsLUMO=0.0575 epsgap=0.3244 =398.5908 zpve=0.079271 U0=-284.385189 U=-284.379361 H=-284.378417 G=-284.414085 Cv=19.084 pbc="F F F" +C -0.02645771 1.38305753 0.02085660 +O 0.04139566 -0.04439503 -0.00277452 +C 0.66775338 -0.56001625 -1.09000344 +N 0.72605063 -1.92219983 -0.98822954 +O 1.12952381 0.09648143 -1.99322180 +H 0.97529779 1.82044307 0.02387306 +H -0.57085698 1.76214621 -0.84790425 +H -0.55410722 1.63835731 0.94043594 +H 0.12662696 -2.38401590 -0.32566929 +H 0.97230070 -2.41798421 -1.82732573 +10 +Properties=species:S:1:pos:R:3 A=10.20696 B=3.93135 C=2.9219 mu=4.5676 alpha=35.87 epsHOMO=-0.246 epsLUMO=0.0273 epsgap=0.2733 =418.5967 zpve=0.078222 U0=-284.360325 U=-284.354148 H=-284.353204 G=-284.390143 Cv=20.089 pbc="F F F" +N -0.08115607 1.31807026 0.01276742 +C 0.05442259 -0.04477733 0.08155388 +O 1.12163074 -0.59783950 0.20940553 +C -1.28687786 -0.79011642 0.06178435 +O -1.04605114 -2.13954195 -0.23957158 +H 0.76623366 1.85991128 -0.04483425 +H -0.93021346 1.74567447 -0.31565615 +H -1.74559696 -0.65521334 1.05526700 +H -1.96026061 -0.31149222 -0.67026256 +H -1.85637296 -2.63017646 -0.07625478 +10 +Properties=species:S:1:pos:R:3 A=10.14389 B=4.0714 C=3.02223 mu=5.3004 alpha=35.19 epsHOMO=-0.2527 epsLUMO=0.0208 epsgap=0.2735 =408.0279 zpve=0.080317 U0=-284.372483 U=-284.367172 H=-284.366228 G=-284.40095 Cv=17.931 pbc="F F F" +N 0.01838100 0.93057357 0.04943058 +H -0.70674506 0.56116798 -0.55550134 +H 0.68248307 0.18430002 0.21487099 +H 0.21928084 2.79858558 -0.32670585 +C -0.54586914 1.44688585 1.30274586 +C -0.80466975 2.96030569 1.21052835 +O -0.21468822 3.53648884 0.15531687 +O -1.45295676 3.56338618 2.02143528 +H -1.47662579 0.96512183 1.62072169 +H 0.17781209 1.30324286 2.11157176 +13 +Properties=species:S:1:pos:R:3 A=6.6508 B=4.19317 C=3.39441 mu=2.2854 alpha=42.01 epsHOMO=-0.2597 epsLUMO=0.0631 epsgap=0.3228 =438.3028 zpve=0.11345 U0=-269.479234 U=-269.472993 H=-269.472049 G=-269.508213 Cv=22.072 pbc="F F F" +C -0.02775167 1.53421433 0.00651151 +C 0.08343246 0.01218277 0.05258986 +O 0.95856392 -0.48200710 -0.96694113 +C 0.69767574 -0.49516451 1.35948812 +O 2.02247062 -0.02842530 1.52222186 +H 0.94333763 1.98841434 0.21841589 +H -0.37320516 1.87354041 -0.97716908 +H -0.74773817 1.89256394 0.74926853 +H -0.91671848 -0.43697036 -0.06040528 +H 0.76726710 -0.01412177 -1.78502043 +H 0.11650002 -0.13351914 2.21392431 +H 0.65814685 -1.59563445 1.36274474 +H 2.46378566 -0.22762476 0.68698431 +17 +Properties=species:S:1:pos:R:3 A=7.26511 B=3.32874 C=2.54491 mu=0.0618 alpha=56.26 epsHOMO=-0.3085 epsLUMO=0.085 epsgap=0.3934 =565.8412 zpve=0.159632 U0=-197.629387 U=-197.622325 H=-197.621381 G=-197.659365 Cv=25.169 pbc="F F F" +C 0.05879035 1.56174169 -0.01945240 +C -0.01544024 0.03182306 0.00308332 +C -1.44103237 -0.54862028 0.05028629 +C -1.40777007 -2.07231030 -0.13012010 +C -2.18400529 -0.17659759 1.34114303 +H 1.09435638 1.90233607 -0.11900072 +H -0.50689566 1.97217319 -0.86388098 +H -0.34407951 2.00628082 0.89579522 +H 0.48926758 -0.36011214 -0.88953902 +H 0.55254938 -0.35139446 0.86269887 +H -2.00031575 -0.12153682 -0.79559684 +H -0.85501131 -2.55065810 0.68775438 +H -2.41787513 -2.49543628 -0.13720127 +H -0.91928762 -2.35363802 -1.06914014 +H -3.19100197 -0.60699559 1.35137051 +H -1.65197294 -0.56010584 2.22063481 +H -2.28793759 0.90575132 1.46079572 +15 +Properties=species:S:1:pos:R:3 A=8.11598 B=3.43678 C=2.66869 mu=1.3894 alpha=49.45 epsHOMO=-0.2617 epsLUMO=0.0733 epsgap=0.335 =521.8605 zpve=0.136091 U0=-233.555951 U=-233.549143 H=-233.548199 G=-233.585602 Cv=23.99 pbc="F F F" +C -0.03841227 1.55621396 0.00935837 +C 0.01907135 0.02716079 -0.02074203 +C 0.73141506 -0.52709328 -1.25454422 +C 0.83975868 -2.05206973 -1.22872655 +O -0.01226470 -0.08514394 -2.38973758 +H 0.96845664 1.98863424 0.03526234 +H -0.54377006 1.93824134 -0.88074407 +H -0.57652955 1.91383386 0.89256698 +H 0.53166867 -0.34991486 0.87240999 +H -0.99727186 -0.38638654 -0.00185289 +H 1.74909346 -0.09943025 -1.28826184 +H 1.34143968 -2.42487137 -2.12957624 +H 1.41822012 -2.39229108 -0.36340871 +H -0.15616765 -2.50443859 -1.18308875 +H 0.43090825 -0.40901616 -3.17979642 +15 +Properties=species:S:1:pos:R:3 A=7.55335 B=3.78323 C=2.80566 mu=1.0758 alpha=49.8 epsHOMO=-0.2476 epsLUMO=0.086 epsgap=0.3336 =507.9614 zpve=0.135681 U0=-233.545899 U=-233.539034 H=-233.53809 G=-233.57582 Cv=23.573 pbc="F F F" +C -0.00857605 1.38097833 0.09454670 +O -0.02255106 -0.00739461 -0.13062576 +C -1.30243341 -0.61997805 -0.02088309 +C -1.18687286 -1.97885751 -0.70303918 +C -1.75587824 -0.74662760 1.43714508 +H 1.00321550 1.73258087 -0.12443496 +H -0.71511468 1.90838275 -0.56674280 +H -0.24909065 1.64967237 1.13384681 +H -2.03737978 -0.00432241 -0.56884198 +H -0.44792779 -2.59945605 -0.18596870 +H -2.14751041 -2.50225525 -0.69102581 +H -0.86524212 -1.85860679 -1.74077836 +H -2.73321482 -1.23674908 1.49205117 +H -1.03621350 -1.34483828 2.00544671 +H -1.84898410 0.22935483 1.92198087 +15 +Properties=species:S:1:pos:R:3 A=6.14183 B=5.17679 C=3.79297 mu=0.1068 alpha=52.92 epsHOMO=-0.2633 epsLUMO=0.0893 epsgap=0.3526 =439.1643 zpve=0.137025 U0=-196.409349 U=-196.403398 H=-196.402453 G=-196.437676 Cv=22.258 pbc="F F F" +C -0.03913204 1.50456305 0.04978913 +C 0.00985856 -0.01113880 0.01248619 +C -1.24167981 -0.66663044 -0.53989979 +C 1.33803387 -0.66895748 -0.27400430 +C 0.75126442 -0.72221586 1.11862571 +H 0.88633040 1.92490040 0.45579358 +H -0.18183878 1.92045743 -0.95489291 +H -0.86832076 1.85815718 0.67439648 +H -1.43570890 -0.34344387 -1.56976411 +H -1.15326757 -1.75757800 -0.54433832 +H -2.12221416 -0.40570820 0.05951361 +H 1.34398374 -1.58235208 -0.86005084 +H 2.20196897 -0.03329084 -0.43929326 +H 1.21949576 -0.12246018 1.89246580 +H 0.36153319 -1.67153717 1.47171932 +13 +Properties=species:S:1:pos:R:3 A=6.49534 B=5.52329 C=3.9108 mu=1.8235 alpha=45.7 epsHOMO=-0.2596 epsLUMO=0.091 epsgap=0.3505 =405.521 zpve=0.112851 U0=-232.335768 U=-232.329981 H=-232.329037 G=-232.364091 Cv=20.719 pbc="F F F" +C -0.03970330 1.50954998 -0.04112233 +C -0.03023556 -0.00335329 -0.06498361 +C -1.24613189 -0.68356648 0.52531190 +C 0.78937201 -0.71129193 -1.06027559 +O 1.22546798 -0.60059036 0.29712283 +H 0.88992772 1.90948478 -0.45296558 +H -0.87952723 1.90350108 -0.62376642 +H -0.14448576 1.87590604 0.98604429 +H -2.15218727 -0.41012616 -0.02629495 +H -1.13412584 -1.77005431 0.49747032 +H -1.38586631 -0.38055453 1.56882757 +H 0.50712690 -1.71386874 -1.38085018 +H 1.37088104 -0.14369322 -1.78640853 +13 +Properties=species:S:1:pos:R:3 A=6.57648 B=5.31603 C=3.98453 mu=1.3822 alpha=45.85 epsHOMO=-0.2494 epsLUMO=0.0776 epsgap=0.327 =401.4888 zpve=0.113237 U0=-232.33341 U=-232.327734 H=-232.32679 G=-232.36147 Cv=21.17 pbc="F F F" +C -0.03714052 1.53215920 0.01332310 +C 0.01344335 0.02117019 0.01922005 +O -1.17476104 -0.59735284 -0.40931260 +C 1.30744798 -0.69902106 -0.27389650 +C 0.70844749 -0.71373504 1.12226318 +H 0.89026861 1.96240279 0.40276135 +H -0.17846151 1.91561262 -1.00518522 +H -0.87011232 1.88955201 0.62730953 +H -1.28352265 -0.40225391 -1.34690641 +H 1.23973813 -1.61642115 -0.84783774 +H 2.20543993 -0.11523820 -0.44744474 +H 1.19539159 -0.13889834 1.90240284 +H 0.24735439 -1.63670241 1.45378480 +10 +Properties=species:S:1:pos:R:3 A=12.18399 B=5.1384 C=3.78868 mu=2.5732 alpha=40.19 epsHOMO=-0.263 epsLUMO=0.0277 epsgap=0.2907 =352.0378 zpve=0.080558 U0=-247.201165 U=-247.196699 H=-247.195755 G=-247.22841 Cv=15.51 pbc="F F F" +N -0.04445152 1.25623533 0.31681915 +C -0.03914559 0.04913710 -0.01670558 +C -1.08749619 -1.04164446 -0.11488033 +C 0.09869688 -1.90754844 -0.58225894 +O 0.99647508 -0.76306629 -0.44028010 +H 0.88935980 1.66481350 0.24916893 +H -1.54324114 -1.32235812 0.83572172 +H -1.86910036 -0.85713839 -0.85325910 +H 0.39739753 -2.72227350 0.08158721 +H 0.06945080 -2.25418345 -1.61796084 +11 +Properties=species:S:1:pos:R:3 A=10.79571 B=4.78732 C=3.54292 mu=2.7119 alpha=42.77 epsHOMO=-0.2415 epsLUMO=-0.0194 epsgap=0.2222 =379.6371 zpve=0.090544 U0=-231.15578 U=-231.150804 H=-231.149859 G=-231.184401 Cv=17.076 pbc="F F F" +O -0.00101746 0.01846628 -0.20114742 +C -0.01405811 1.19658059 0.00205306 +C -1.13542911 2.22979148 0.21957980 +C -0.03500884 3.29097637 0.53052223 +C 1.08469664 2.25947795 0.18994331 +H -1.71642814 2.41772662 -0.68901078 +H -1.83392757 1.98415059 1.02444244 +H -0.05534741 4.17585264 -0.10670963 +H -0.02541477 3.61399048 1.57278917 +H 1.81074389 2.03288534 0.97578973 +H 1.63600801 2.46255380 -0.73376244 +10 +Properties=species:S:1:pos:R:3 A=12.23436 B=4.97895 C=3.70561 mu=3.6671 alpha=39.73 epsHOMO=-0.25 epsLUMO=0.038 epsgap=0.288 =355.0934 zpve=0.080442 U0=-247.225618 U=-247.220897 H=-247.219953 G=-247.253218 Cv=16.1 pbc="F F F" +O 0.03184134 -0.03461682 0.00849836 +C -0.02440334 1.16440533 0.02160811 +C -1.14541322 2.23712809 0.03012901 +C 0.00928805 3.27945878 0.04557758 +N 0.93843838 2.14652109 0.03554396 +H -1.77030529 2.23779730 -0.86428437 +H -1.77632809 2.21765386 0.92007934 +H 0.07170564 3.91863516 -0.84072193 +H 0.06622603 3.89826625 0.94657952 +H 1.94616840 2.09535713 0.03855945 +9 +Properties=species:S:1:pos:R:3 A=12.44643 B=5.23328 C=3.86599 mu=3.9339 alpha=35.4 epsHOMO=-0.2788 epsLUMO=0.0089 epsgap=0.2878 =336.0792 zpve=0.068574 U0=-267.106213 U=-267.101929 H=-267.100985 G=-267.13332 Cv=14.483 pbc="F F F" +O 0.04424004 0.03933243 -0.33071104 +C -0.04854479 1.17416230 0.00410076 +C -1.14211656 2.23611850 0.15338361 +C 0.02332538 3.12357010 0.61681625 +O 0.95663808 2.01190122 0.42603746 +H -1.62573755 2.51482212 -0.78401102 +H -1.90009213 1.99553386 0.90028663 +H 0.28368077 3.96172751 -0.03229287 +H 0.00782250 3.43942655 1.66162725 +10 +Properties=species:S:1:pos:R:3 A=11.54185 B=4.90368 C=3.63309 mu=2.5257 alpha=39.34 epsHOMO=-0.2437 epsLUMO=-0.0258 epsgap=0.2179 =359.152 zpve=0.07965 U0=-247.190194 U=-247.185543 H=-247.184599 G=-247.217688 Cv=16.083 pbc="F F F" +O 0.00053891 0.00665475 -0.09743049 +C -0.01371990 1.19213881 0.03139936 +C -1.10668681 2.26612284 0.17247913 +N -0.03543445 3.25123475 0.48533693 +C 1.05328615 2.29521506 0.14383904 +H -1.65047970 2.42796175 -0.76886054 +H -1.83606786 2.11370530 0.97567410 +H -0.05434600 4.07193609 -0.10727051 +H 1.80754206 2.16277968 0.92736250 +H 1.56737937 2.47130277 -0.81152688 +9 +Properties=species:S:1:pos:R:3 A=12.18264 B=4.94228 C=3.68162 mu=0.8477 alpha=35.94 epsHOMO=-0.2647 epsLUMO=-0.0352 epsgap=0.2295 =344.5473 zpve=0.066989 U0=-267.068488 U=-267.063976 H=-267.063032 G=-267.095887 Cv=15.115 pbc="F F F" +O -0.00031692 0.00676321 -0.17139774 +C -0.01417098 1.18672865 -0.00980279 +C -1.08283120 2.27653381 0.15574094 +O -0.03856115 3.26460423 0.27513986 +C 1.02857270 2.30515693 0.12774311 +H -1.72923855 2.43647507 -0.71671274 +H -1.70219898 2.19346725 1.05800754 +H 1.67366781 2.23923293 1.01324179 +H 1.64705068 2.48224548 -0.76148355 +15 +Properties=species:S:1:pos:R:3 A=9.63416 B=4.10254 C=3.28665 mu=0.095 alpha=52.94 epsHOMO=-0.2896 epsLUMO=0.0927 epsgap=0.3823 =455.0215 zpve=0.138424 U0=-196.407957 U=-196.40245 H=-196.401505 G=-196.436159 Cv=20.299 pbc="F F F" +C -0.47005934 1.44206547 -0.06684548 +C 0.14287306 0.05485144 0.03747984 +C 1.37254693 -0.30295273 -0.84679168 +C 1.95947994 -1.09364561 0.35326039 +C 0.99887433 -0.30768787 1.28563099 +H 0.29210839 2.21844829 0.06844100 +H -0.93614604 1.60094605 -1.04612799 +H -1.24148161 1.59707353 0.69627593 +H -0.63517341 -0.70560132 -0.10055095 +H 1.19338376 -0.84118177 -1.78129390 +H 1.96867777 0.58857708 -1.07004184 +H 1.69873597 -2.15500718 0.30521100 +H 3.03253510 -1.01077235 0.54148058 +H 1.48290386 0.58242428 1.70211026 +H 0.51304296 -0.84979686 2.10121062 +13 +Properties=species:S:1:pos:R:3 A=9.93129 B=4.56362 C=3.63704 mu=1.6826 alpha=45.72 epsHOMO=-0.241 epsLUMO=0.0915 epsgap=0.3325 =407.6909 zpve=0.11459 U0=-232.33123 U=-232.325877 H=-232.324933 G=-232.359589 Cv=18.582 pbc="F F F" +C -0.32308228 1.53751451 0.01947624 +C 0.11847214 0.08900172 -0.00523140 +C 0.87489284 -0.43763303 -1.24721738 +C 1.97841048 -0.80657483 -0.23740356 +O 1.27344926 -0.15419108 0.83794040 +H 0.49211985 2.19658795 -0.29527664 +H -1.17615835 1.68923490 -0.65134808 +H -0.62776963 1.82747912 1.03013205 +H -0.71410200 -0.56389187 0.29597305 +H 0.41033314 -1.26269706 -1.78910031 +H 1.15560811 0.34596269 -1.95459519 +H 2.09501012 -1.88349764 -0.05606083 +H 2.96970344 -0.36924147 -0.40408009 +13 +Properties=species:S:1:pos:R:3 A=10.13697 B=4.32512 C=3.50912 mu=1.8995 alpha=45.45 epsHOMO=-0.2419 epsLUMO=0.0814 epsgap=0.3233 =414.1575 zpve=0.114694 U0=-232.325947 U=-232.320548 H=-232.319604 G=-232.354576 Cv=18.443 pbc="F F F" +C -0.18371124 1.54668909 -0.01671129 +C 0.04513382 0.04386724 0.02270494 +C 1.20301104 -0.54034580 -0.81983464 +O 1.80061686 -1.15530615 0.33924991 +C 0.83391983 -0.54313754 1.21648003 +H 0.75102609 2.09256015 0.15344911 +H -0.58319248 1.86155707 -0.98694169 +H -0.89843724 1.85917126 0.75244153 +H -0.89506736 -0.48768372 -0.14843995 +H 0.93560472 -1.27857588 -1.58543917 +H 1.86392938 0.21379924 -1.27081649 +H 1.29414565 0.20948993 1.87273952 +H 0.31517042 -1.28326856 1.83756393 +13 +Properties=species:S:1:pos:R:3 A=10.09703 B=4.26865 C=3.40487 mu=1.4604 alpha=45.55 epsHOMO=-0.256 epsLUMO=0.0801 epsgap=0.3361 =413.9118 zpve=0.115063 U0=-232.333258 U=-232.328097 H=-232.327153 G=-232.361103 Cv=19.023 pbc="F F F" +O 0.54622847 1.03270424 -0.58398083 +C -0.00112269 -0.11036621 0.01898238 +C 0.13945950 -0.34310429 1.54777117 +C -1.26863784 -0.99876517 1.51632654 +C -1.53844651 -0.26418236 0.17423510 +H 0.20332379 1.80433874 -0.12071219 +H 0.41296647 -0.95857955 -0.53560402 +H 0.98890738 -0.92719626 1.90921867 +H 0.11786549 0.61323034 2.08356053 +H -1.20828813 -2.08191064 1.38036699 +H -1.94143614 -0.79119746 2.35014116 +H -2.00495682 0.71307933 0.34581493 +H -2.09160311 -0.78230202 -0.61249171 +11 +Properties=species:S:1:pos:R:3 A=10.85394 B=4.52867 C=3.62328 mu=2.4158 alpha=38.58 epsHOMO=-0.2465 epsLUMO=0.0604 epsgap=0.3069 =374.4087 zpve=0.091005 U0=-268.248371 U=-268.243234 H=-268.24229 G=-268.276572 Cv=17.344 pbc="F F F" +O 0.07721714 1.41738724 -0.14439016 +C 0.01675156 0.02484791 0.03233817 +C 0.11193823 -0.54392541 1.47000744 +O -1.16387804 -1.20093191 1.32441605 +C -1.41930178 -0.52040930 0.07750673 +H 1.00022214 1.67889479 -0.21697466 +H 0.66036495 -0.50380879 -0.68180327 +H 0.91806589 -1.25385265 1.68878628 +H 0.09146208 0.24427487 2.23499232 +H -2.16139683 0.28183960 0.18222436 +H -1.72867456 -1.20941157 -0.71550887 +15 +Properties=species:S:1:pos:R:3 A=7.14221 B=4.37083 C=3.30265 mu=0.1023 alpha=52.92 epsHOMO=-0.2632 epsLUMO=0.094 epsgap=0.3573 =465.3301 zpve=0.137341 U0=-196.406419 U=-196.40034 H=-196.399396 G=-196.435152 Cv=22.005 pbc="F F F" +C -0.05189850 1.55217435 -0.10318438 +C 0.01226648 0.04690675 0.03747520 +C 1.30115692 -0.70661553 -0.19725724 +C 0.70431939 -0.64893239 1.19003562 +C 1.39750324 0.09481280 2.31078011 +H 0.87404209 2.03543171 0.22162228 +H -0.21641357 1.83695278 -1.14852958 +H -0.87157646 1.97673684 0.48797046 +H -0.88508206 -0.45596941 -0.31580786 +H 1.27663658 -1.64149022 -0.74697967 +H 2.19424067 -0.11404121 -0.37569359 +H 0.21672461 -1.56377934 1.51902400 +H 1.91317356 0.99051175 1.95271747 +H 0.68738009 0.40901468 3.08446972 +H 2.14934633 -0.54151807 2.79111543 +13 +Properties=species:S:1:pos:R:3 A=7.36997 B=4.78662 C=3.53123 mu=1.3092 alpha=45.84 epsHOMO=-0.2518 epsLUMO=0.0822 epsgap=0.334 =418.298 zpve=0.113827 U0=-232.32907 U=-232.323327 H=-232.322383 G=-232.357426 Cv=20.702 pbc="F F F" +C -0.07992786 1.55914739 -0.03875994 +C 0.00042206 0.05111569 0.04346551 +C 1.31748624 -0.66230034 -0.20269965 +C 0.71624326 -0.61854924 1.18128126 +O 1.28134126 0.21474798 2.15341739 +H 0.80207677 2.01979988 0.41147972 +H -0.15235410 1.89225261 -1.07968732 +H -0.95836082 1.93564781 0.49643179 +H -0.88615174 -0.46939753 -0.30918496 +H 1.32384464 -1.60086657 -0.74666557 +H 2.18619315 -0.03316813 -0.37068758 +H 0.28509951 -1.54025035 1.57291630 +H 2.12931661 -0.16234387 2.40895930 +14 +Properties=species:S:1:pos:R:3 A=7.53724 B=4.74514 C=3.54049 mu=1.2483 alpha=49.79 epsHOMO=-0.2199 epsLUMO=0.0931 epsgap=0.313 =432.5081 zpve=0.125556 U0=-212.438188 U=-212.432286 H=-212.431342 G=-212.466725 Cv=20.993 pbc="F F F" +C -0.17034561 1.55765735 -0.10065470 +C 0.01075256 0.05574638 -0.05225833 +C 0.71794239 -0.67802425 -1.14204150 +N 1.29811574 -0.59777508 0.19386866 +C 2.50047290 0.20256923 0.36428818 +H 0.60876703 2.05717913 -0.68119301 +H -1.13238698 1.79957181 -0.56489127 +H -0.17350949 1.98946323 0.90659028 +H -0.81010441 -0.46148051 0.44427396 +H 0.39136106 -1.66274618 -1.46771171 +H 1.16668484 -0.06908735 -1.92776126 +H 2.58340476 1.06745445 -0.31262255 +H 2.55143652 0.56892747 1.39502638 +H 3.37185012 -0.43769461 0.19201862 +13 +Properties=species:S:1:pos:R:3 A=8.15202 B=4.38313 C=3.43318 mu=1.8159 alpha=46.02 epsHOMO=-0.2573 epsLUMO=0.0984 epsgap=0.3557 =432.2224 zpve=0.113178 U0=-232.334436 U=-232.32857 H=-232.327626 G=-232.363064 Cv=20.451 pbc="F F F" +C -0.03345961 1.54674842 -0.13373520 +C -0.04966080 0.05914273 0.10861437 +O 1.17261123 -0.65888877 -0.09925231 +C 0.62653087 -0.62548063 1.22492030 +C 1.43849738 0.05647689 2.29618065 +H 0.84261573 2.02417904 0.30895947 +H -0.02020603 1.75197977 -1.20938503 +H -0.93419215 2.01073729 0.28317577 +H -0.92262410 -0.44789053 -0.31001063 +H 0.19593793 -1.58039168 1.53656778 +H 1.84118250 1.01302927 1.95813013 +H 0.82514514 0.22925358 3.18745348 +H 2.28152757 -0.57796414 2.58961605 diff --git a/docs/src/examples/basic_usage/usage.rst b/docs/src/examples/basic_usage/usage.rst new file mode 100644 index 0000000000..a65bd19678 --- /dev/null +++ b/docs/src/examples/basic_usage/usage.rst @@ -0,0 +1,189 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "examples/basic_usage/usage.sh" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code. + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_examples_basic_usage_usage.sh: + +.. _label_basic_usage: + +Basic Usage +=========== + +``metatrain`` is designed for a direct usage from the command line (cli). The program +is registered via the abbreviation ``mtt`` to your command line. The general help of +``metatrain`` can be accessed using + +.. GENERATED FROM PYTHON SOURCE LINES 10-11 + +.. code-block:: Bash + + mtt --help + +.. GENERATED FROM PYTHON SOURCE LINES 12-52 + +We now demonstrate how to ``train`` and ``evaluate`` a model from the command line. +For this example we use the :ref:`architecture-soap-bpnn` architecture and a subset of +the `QM9 dataset `_. You can obtain the +dataset for this example here: :download:`qm9_reduced_100.xyz +<../../../../examples/basic_usage/qm9_reduced_100.xyz>`. + + +Training +-------- + +To train models, ``metatrain`` uses a dynamic override strategy for your training +options. We allow a dynamical composition and override of the default architecture +with either your custom ``options.yaml`` and even command line override grammar. For +reference and reproducibility purposes ``metatrain`` always writes the fully expanded, +including the overwritten option to ``options_restart.yaml``. The restart options file +is written into a subfolder named with the current *date* and *time* inside the +``output`` directory of your current training run. + +The sub-command to start a model training is + +.. code-block:: bash + + mtt train + +To train a model you have to define your options. This includes the specific +architecture you want to use and the data including the training systems and target +values + +The default model and training hyperparameter for each model are listed in their +corresponding documentation page. We will use these minimal options to run an example +training using the default hyperparameters of an SOAP BPNN model + +.. literalinclude:: ../../../../examples/basic_usage/options.yaml + :language: yaml + +For each training run a new output directory in the format +``outputs/YYYY-MM-DD/HH-MM-SS`` based on the current *date* and *time* is created. We +use this output directory to store checkpoints, the restart ``options_restart.yaml`` +file as well as the log files. To start the training, create an ``options.yaml`` file +in the current directory and type + +.. GENERATED FROM PYTHON SOURCE LINES 55-57 + +.. code-block:: Bash + + + mtt train options.yaml + +.. GENERATED FROM PYTHON SOURCE LINES 58-62 + +The functions saves the final model `model.pt` to the current output folder for later +evaluation. An `extensions/` folder, which contains the compiled extensions for the +model, might also be saved depending on the architecture. All command line flags of +the train sub-command can be listed via + +.. GENERATED FROM PYTHON SOURCE LINES 66-67 + +.. code-block:: Bash + + mtt train --help + +.. GENERATED FROM PYTHON SOURCE LINES 68-96 + +After the training has finished, the ``mtt train`` command generates the +``model.ckpt`` (final checkpoint) and ``model.pt`` (exported model) files in the +current directory, as well as in the ``output/YYYY-MM-DD/HH-MM-SS`` directory. + +Evaluation +---------- + +The sub-command to evaluate an already trained model is + +.. code-block:: bash + + mtt eval + +Besides the trained ``model``, you will also have to provide a file containing the +system and possible target values for evaluation. The system section of this +``eval.yaml`` is exactly the same as for a dataset in the ``options.yaml`` file. + +.. literalinclude:: ../../../../examples/basic_usage/eval.yaml + :language: yaml + +Note that the ``targets`` section is optional. If the ``targets`` section is present, +the function will calculate and report RMSE values of the predictions with respect to +the real values as loaded from the ``targets`` section. You can run an evaluation by +typing + +We now evaluate the model on the training dataset, where the first arguments specifies +trained model and the second an option file containing the path of the dataset for +evaulation. The extensions of the model, if any, can be specified via the ``-e`` flag. + +.. GENERATED FROM PYTHON SOURCE LINES 99-100 + +.. code-block:: Bash + + mtt eval model.pt eval.yaml -e extensions/ + +.. GENERATED FROM PYTHON SOURCE LINES 101-105 + +The evaluation command predicts those properties the model was trained against; here +``"U0"``. The predictions together with the systems have been written in a file named +``output.xyz`` in the current directory. The written file starts with the following +lines + +.. GENERATED FROM PYTHON SOURCE LINES 108-109 + +.. code-block:: Bash + + head -n 20 output.xyz + +.. GENERATED FROM PYTHON SOURCE LINES 110-111 + +All command line flags of the eval sub-command can be listed via + +.. GENERATED FROM PYTHON SOURCE LINES 114-115 + +.. code-block:: Bash + + mtt eval --help + +.. GENERATED FROM PYTHON SOURCE LINES 115-123 + +An important parameter of ``mtt eval`` is the ``-b`` (or ``--batch-size``) option, +which allows you to specify the batch size for the evaluation. + +Molecular simulations +--------------------- + +The trained model can also be used to run molecular simulations. +You can find how in the :ref:`tutorials` section. + + +.. _sphx_glr_download_examples_basic_usage_usage.sh: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Bash source code: usage.sh ` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: usage.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/examples/basic_usage/usage.sh b/docs/src/examples/basic_usage/usage.sh old mode 100755 new mode 100644 similarity index 100% rename from examples/basic_usage/usage.sh rename to docs/src/examples/basic_usage/usage.sh diff --git a/docs/src/examples/basic_usage/usage.zip b/docs/src/examples/basic_usage/usage.zip new file mode 100644 index 0000000000000000000000000000000000000000..29173744cf9d766480cb629534615cf1c3358a9b GIT binary patch literal 4718 zcmb7IO^+ML5tZX42=?Z{m)r^x3`oLqsN@j9jAKB89oP_7Sx0LmMh-j8nI@; 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+ +.. only:: html + + .. image:: /examples/llpr/images/thumb/sphx_glr_llpr_thumb.png + :alt: + + :ref:`sphx_glr_examples_llpr_llpr.py` + +.. raw:: html + +
Computing LLPR uncertainties
+
+ + +.. thumbnail-parent-div-close + +.. raw:: html + + + + +.. toctree:: + :hidden: + + /examples/llpr/llpr + + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-gallery + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download all examples as source code: llpr.zip ` + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download all examples in Jupyter notebooks: llpr_jupyter.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/llpr/llpr.codeobj.json b/docs/src/examples/llpr/llpr.codeobj.json new file mode 100644 index 0000000000..bd7f237401 --- /dev/null +++ b/docs/src/examples/llpr/llpr.codeobj.json @@ -0,0 +1,728 @@ +{ + "LogNorm": [ + { + "is_class": true, + "is_explicit": false, + "module": "matplotlib.colors", + "module_short": "matplotlib.colors", + "name": "LogNorm" + }, + { + "is_class": true, + "is_explicit": false, + "module": "matplotlib", + "module_short": "matplotlib", + "name": "LogNorm" + }, + { + "is_class": true, + "is_explicit": false, + "module": "matplotlib.colors", + "module_short": "matplotlib.colors", + "name": "Normalize" + }, + { + "is_class": true, + "is_explicit": false, + "module": "matplotlib", + "module_short": "matplotlib", + "name": "Normalize" + }, + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib.colors", + "module_short": "matplotlib.colors", + "name": "LogNorm" + } + ], + "MetatomicCalculator": [ + { + "is_class": true, + "is_explicit": false, + "module": "metatomic.torch.ase_calculator", + "module_short": "metatomic.torch.ase_calculator", + "name": "MetatomicCalculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "metatomic.torch", + "module_short": "metatomic.torch", + "name": "MetatomicCalculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "metatomic", + "module_short": "metatomic", + "name": "MetatomicCalculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.calculators.calculator", + "module_short": "ase.calculators.calculator", + "name": "Calculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.calculators", + "module_short": "ase.calculators", + "name": "Calculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Calculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.calculators.calculator", + "module_short": "ase.calculators.calculator", + "name": "BaseCalculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.calculators", + "module_short": "ase.calculators", + "name": "BaseCalculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "BaseCalculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.calculators.abc", + "module_short": "ase.calculators.abc", + "name": "GetPropertiesMixin" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.calculators", + "module_short": "ase.calculators", + "name": "GetPropertiesMixin" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "GetPropertiesMixin" + }, + { + "is_class": true, + "is_explicit": false, + "module": "abc", + "module_short": "abc", + "name": "ABC" + }, + { + "is_class": false, + "is_explicit": false, + "module": "metatomic.torch.ase_calculator", + "module_short": "metatomic.torch.ase_calculator", + "name": "MetatomicCalculator" + } + ], + "ModelOutput": [ + { + "is_class": false, + "is_explicit": false, + "module": "torch", + "module_short": "torch", + "name": "ScriptClass" + }, + { + "is_class": false, + "is_explicit": false, + "module": "metatomic.torch", + "module_short": "metatomic.torch", + "name": "ModelOutput" + } + ], + "ase.io.read": [ + { + "is_class": false, + "is_explicit": 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"module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib.pyplot", + "module_short": "matplotlib.pyplot", + "name": "colorbar" + } + ], + "plt.get_cmap": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib.pyplot", + "module_short": "matplotlib.pyplot", + "name": "get_cmap" + } + ], + "plt.show": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib.pyplot", + "module_short": "matplotlib.pyplot", + "name": "show" + } + ], + "predictions": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "dict" + } + ], + "structure": [ + { + "is_class": false, + "is_explicit": false, + "module": "ase.atoms", + "module_short": "ase", + "name": "Atoms" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Atoms" + } + ], + "structures": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "list" + } + ], + "uncertainties": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "ndarray" + } + ] +} diff --git a/docs/src/examples/llpr/llpr.ipynb b/docs/src/examples/llpr/llpr.ipynb new file mode 100644 index 0000000000..5c74493b0f --- /dev/null +++ b/docs/src/examples/llpr/llpr.ipynb @@ -0,0 +1,68 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Computing LLPR uncertainties\n\nThis tutorial demonstrates how to train a model with uncertainties using metatrain.\nIt involves the computation of the uncertainties on ethanol molecules, using the\nlast-layer prediction rigidity ([LLPR](LLPR_)) approximation. Both total and local\n([LPR](LPR_)) uncertainties are computed\n\n\nThe baseline model was trained using the following training options, where the training\nset consists of 100 structures from the QM9 dataset.\n\n.. literalinclude:: options.yaml\n :language: yaml\n\nOnce a model is trained, you can add LLPR uncertainties to it by launching a training\nrun with the \"llpr\" architecture, on the same data. In this case, the training options\nto add LLPR uncertainties are as follows:\n\n.. literalinclude:: options-llpr.yaml\n :language: yaml\n\nAdding LLPR uncertainties is very cheap compared to training a model, as it only\ninvolves one pass through the training data (equivalent to one epoch of training).\n\nYou can repeat the same training yourself with\n\n.. literalinclude:: train.sh\n :language: bash\n\nA detailed step-by-step introduction on how to train a model is provided in\nthe `label_basic_usage` tutorial.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As an example, we will compute the energies and uncertainties of the LLPR model on a\nfew ethanol structures.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import ase.io\nimport matplotlib.pyplot as plt\nfrom ase.visualize.plot import plot_atoms\nfrom matplotlib.colors import LogNorm\nfrom metatomic.torch import ModelOutput\nfrom metatomic.torch.ase_calculator import MetatomicCalculator\n\n\n# load 5 ethanol structures\nstructures = ase.io.read(\"ethanol_reduced_100.xyz\", \":5\")\n\n# load the model as an ASE calculator\ncalc = MetatomicCalculator(\n \"model-llpr.pt\", extensions_directory=\"extensions/\", device=\"cpu\"\n)\n\n# the uncertainties are available throguh the ``run_model`` method of the calculator\npredictions = calc.run_model(\n structures,\n {\n \"energy\": ModelOutput(per_atom=False),\n \"energy_uncertainty\": ModelOutput(per_atom=False),\n },\n)\n\n# print the energies and uncertainties\n\nenergies = predictions[\"energy\"].block().values.squeeze().numpy()\nuncertainties = predictions[\"energy_uncertainty\"].block().values.squeeze().numpy()\n\nprint(energies)\nprint(uncertainties)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also obtain per-atom uncertainties (local prediction rigidity, LPR). As an\nexample, we will compute the uncertainties on an ethanol structure.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "structure = structures[0]\npredictions = calc.run_model(\n structure,\n {\n # here, we use per_atom=True to request per-atom uncertainties\n \"energy_uncertainty\": ModelOutput(per_atom=True),\n },\n)\nlocal_uncertainty = predictions[\"energy_uncertainty\"].block().values.squeeze().numpy()\nlocal_uncertainty = local_uncertainty * 1000.0 # convert from eV to meV\n\nnorm = LogNorm(vmin=min(local_uncertainty), vmax=max(local_uncertainty))\ncolormap = plt.get_cmap(\"viridis\")\ncolors = colormap(norm(local_uncertainty))\nax = plot_atoms(structure, colors=colors, rotation=\"180x,0y,0z\")\ncustom_ticks = [0.7, 0.8, 0.9, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5]\ncbar = plt.colorbar(\n plt.cm.ScalarMappable(norm=norm, cmap=colormap),\n ax=ax,\n label=\"Local energy uncertainty (meV)\",\n ticks=custom_ticks,\n)\ncbar.ax.set_yticklabels([f\"{tick}\" for tick in custom_ticks])\ncbar.minorticks_off()\nax.set_xticks([])\nax.set_yticks([])\nplt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/examples/llpr/llpr.py b/docs/src/examples/llpr/llpr.py similarity index 100% rename from examples/llpr/llpr.py rename to docs/src/examples/llpr/llpr.py diff --git a/docs/src/examples/llpr/llpr.py.md5 b/docs/src/examples/llpr/llpr.py.md5 new file mode 100644 index 0000000000..d9979d99e5 --- /dev/null +++ b/docs/src/examples/llpr/llpr.py.md5 @@ -0,0 +1 @@ +76970f9c741099e3b7ea8946bb48d8e0 \ No newline at end of file diff --git a/docs/src/examples/llpr/llpr.rst b/docs/src/examples/llpr/llpr.rst new file mode 100644 index 0000000000..aed660fd85 --- /dev/null +++ b/docs/src/examples/llpr/llpr.rst @@ -0,0 +1,183 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "examples/llpr/llpr.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code. + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_examples_llpr_llpr.py: + +.. _llprexample: + +Computing LLPR uncertainties +============================ + +This tutorial demonstrates how to train a model with uncertainties using metatrain. +It involves the computation of the uncertainties on ethanol molecules, using the +last-layer prediction rigidity (`LLPR `_) approximation. Both total and local +(`LPR `_) uncertainties are computed + +.. _LLPR: https://arxiv.org/html/2403.02251v1 +.. _LPR: https://pubs.acs.org/doi/10.1021/acs.jctc.3c00704 + +The baseline model was trained using the following training options, where the training +set consists of 100 structures from the QM9 dataset. + +.. literalinclude:: options.yaml + :language: yaml + +Once a model is trained, you can add LLPR uncertainties to it by launching a training +run with the "llpr" architecture, on the same data. In this case, the training options +to add LLPR uncertainties are as follows: + +.. literalinclude:: options-llpr.yaml + :language: yaml + +Adding LLPR uncertainties is very cheap compared to training a model, as it only +involves one pass through the training data (equivalent to one epoch of training). + +You can repeat the same training yourself with + +.. literalinclude:: train.sh + :language: bash + +A detailed step-by-step introduction on how to train a model is provided in +the :ref:`label_basic_usage` tutorial. + +.. GENERATED FROM PYTHON SOURCE LINES 40-42 + +As an example, we will compute the energies and uncertainties of the LLPR model on a +few ethanol structures. + +.. GENERATED FROM PYTHON SOURCE LINES 43-78 + +.. code-block:: Python + + + import ase.io + import matplotlib.pyplot as plt + from ase.visualize.plot import plot_atoms + from matplotlib.colors import LogNorm + from metatomic.torch import ModelOutput + from metatomic.torch.ase_calculator import MetatomicCalculator + + + # load 5 ethanol structures + structures = ase.io.read("ethanol_reduced_100.xyz", ":5") + + # load the model as an ASE calculator + calc = MetatomicCalculator( + "model-llpr.pt", extensions_directory="extensions/", device="cpu" + ) + + # the uncertainties are available throguh the ``run_model`` method of the calculator + predictions = calc.run_model( + structures, + { + "energy": ModelOutput(per_atom=False), + "energy_uncertainty": ModelOutput(per_atom=False), + }, + ) + + # print the energies and uncertainties + + energies = predictions["energy"].block().values.squeeze().numpy() + uncertainties = predictions["energy_uncertainty"].block().values.squeeze().numpy() + + print(energies) + print(uncertainties) + + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + [-154.97468067 -154.97642503 -154.97632212 -154.97600337 -154.97451059] + [0.0097369 0.01553008 0.02451612 0.01447105 0.01356071] + + + + +.. GENERATED FROM PYTHON SOURCE LINES 79-81 + +We can also obtain per-atom uncertainties (local prediction rigidity, LPR). As an +example, we will compute the uncertainties on an ethanol structure. + +.. GENERATED FROM PYTHON SOURCE LINES 82-110 + +.. code-block:: Python + + + structure = structures[0] + predictions = calc.run_model( + structure, + { + # here, we use per_atom=True to request per-atom uncertainties + "energy_uncertainty": ModelOutput(per_atom=True), + }, + ) + local_uncertainty = predictions["energy_uncertainty"].block().values.squeeze().numpy() + local_uncertainty = local_uncertainty * 1000.0 # convert from eV to meV + + norm = LogNorm(vmin=min(local_uncertainty), vmax=max(local_uncertainty)) + colormap = plt.get_cmap("viridis") + colors = colormap(norm(local_uncertainty)) + ax = plot_atoms(structure, colors=colors, rotation="180x,0y,0z") + custom_ticks = [0.7, 0.8, 0.9, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5] + cbar = plt.colorbar( + plt.cm.ScalarMappable(norm=norm, cmap=colormap), + ax=ax, + label="Local energy uncertainty (meV)", + ticks=custom_ticks, + ) + cbar.ax.set_yticklabels([f"{tick}" for tick in custom_ticks]) + cbar.minorticks_off() + ax.set_xticks([]) + ax.set_yticks([]) + plt.show() + + + + + + + + +.. _sphx_glr_download_examples_llpr_llpr.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: llpr.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: llpr.py ` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: llpr.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/llpr/llpr.zip b/docs/src/examples/llpr/llpr.zip new file mode 100644 index 0000000000000000000000000000000000000000..46bdfa75339844db082667a71e1500958b666a9a GIT binary patch literal 3789 zcmb_fU5_NS60ZPG;#cG$Z~Vaggq-WD>W|$X!b4_M)vogO_5C>a_}0~vN3Xv5fc_qR^^fb;L(6;hoBTK_iEiY}MjHtuQBB#dG?oh6M z1>vi$2@heyU;gu_fB*8vg9miI1{31KdXprH5PV8cYAws!s4-(7+p=zgt`_|A^6GEA 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=19.0002 zpve=0.021375 U0=-76.404702 U=-76.401867 H=-76.400922 G=-76.422349 Cv=6.002 pbc="F F F" +O -0.03436050 0.97753957 0.00760159 +H 0.06476649 0.02057220 0.00153463 +H 0.87179037 1.30079240 0.00069313 +4 +Properties=species:S:1:pos:R:3 A=0 B=35.6100361 C=35.6100361 mu=0 alpha=16.28 epsHOMO=-0.2845 epsLUMO=0.0506 epsgap=0.3351 =59.5248 zpve=0.026841 U0=-77.308427 U=-77.305527 H=-77.304583 G=-77.327429 Cv=8.574 pbc="F F F" +C 0.59953949 0.00000000 1.00000000 +C -0.59953949 0.00000000 1.00000000 +H -1.66163859 0.00000000 1.00000000 +H 1.66163859 0.00000000 1.00000000 +3 +Properties=species:S:1:pos:R:3 A=0 B=44.593883 C=44.593883 mu=2.8937 alpha=12.99 epsHOMO=-0.3604 epsLUMO=0.0191 epsgap=0.3796 =48.7476 zpve=0.016601 U0=-93.411888 U=-93.40937 H=-93.408425 G=-93.431246 Cv=6.278 pbc="F F F" +C -0.01332393 1.13246572 0.00827589 +N 0.00231072 -0.01915859 0.00192873 +H -0.02780270 2.19894930 0.01415379 +4 +Properties=species:S:1:pos:R:3 A=285.48839 B=38.9823 C=34.29892 mu=2.1089 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=193.1659 zpve=0.079754 U0=-154.972731 U=-154.968412 H=-154.967467 G=-154.998148 Cv=13.546 pbc="F F F" +C -0.00860504 1.50203829 -0.00681217 +C 0.01099310 -0.01764877 -0.01377035 +O 0.68088841 -0.44041803 -1.19313210 +H 1.01153334 1.89662030 -0.01920155 +H -0.53159862 1.88076109 -0.88974659 +H -0.51674572 1.87611771 0.88710739 +H 0.52377121 -0.38912295 0.88824082 +H -1.02027544 -0.40507261 0.01690670 +H 0.69529573 -1.40179568 -1.20148495 +9 +Properties=species:S:1:pos:R:3 A=39.37691 B=10.04033 C=8.90353 mu=1.1502 alpha=28.13 epsHOMO=-0.2525 epsLUMO=0.091 epsgap=0.3435 =187.1015 zpve=0.079534 U0=-154.960361 U=-154.956045 H=-154.9551 G=-154.985747 Cv=12.934 pbc="F F F" +C -0.01482147 1.39241234 0.00567104 +O -0.00471537 -0.01360660 0.01459658 +C 0.63794893 -0.55329676 -1.11358232 +H 1.00528358 1.81015807 0.00465631 +H -0.54689560 1.79343530 -0.87251051 +H -0.53002895 1.72291969 0.91101696 +H 0.13993792 -0.25599333 -2.05098375 +H 1.69265275 -0.23868382 -1.17477743 +H 0.59959435 -1.64180161 -1.02407616 +9 +Properties=species:S:1:pos:R:3 A=20.15852 B=20.15302 C=12.5891 mu=0.0005 alpha=30.82 epsHOMO=-0.2888 epsLUMO=0.1042 epsgap=0.393 =155.8145 zpve=0.081231 U0=-117.824798 U=-117.821426 H=-117.820482 G=-117.849087 Cv=11.041 pbc="F F F" +C -0.01193280 1.51433198 0.01031700 +C 1.30299118 0.77886562 -0.00617842 +C 0.00867174 0.00767042 0.00201032 +H -0.30541464 2.01702117 0.92533239 +H -0.32275498 2.02680156 -0.89347839 +H 1.88493396 0.79145020 -0.92119393 +H 1.90226631 0.78165391 0.89766373 +H -0.27088043 -0.51289125 0.91137474 +H -0.28821983 -0.50310409 -0.90740824 +7 +Properties=species:S:1:pos:R:3 A=25.77385 B=22.12109 C=14.18066 mu=1.7675 alpha=24.04 epsHOMO=-0.2682 epsLUMO=0.1042 epsgap=0.3724 =129.891 zpve=0.057289 U0=-153.742562 U=-153.73941 H=-153.738466 G=-153.766642 Cv=9.176 pbc="F F F" +C 0.01537720 1.41764380 0.00956287 +C 1.26478161 0.64923984 -0.00655044 +O -0.00024018 -0.00771006 0.00204055 +H -0.31763326 1.88586598 0.93475751 +H -0.33527583 1.89576673 -0.90397578 +H 1.83246682 0.56256848 -0.93193290 +H 1.85008258 0.55267938 0.90680147 +10 +Properties=species:S:1:pos:R:3 A=10.12193 B=8.49011 C=4.89615 mu=2.7362 alpha=35.53 epsHOMO=-0.2431 epsLUMO=-0.0087 epsgap=0.2344 =292.4367 zpve=0.083382 U0=-193.08834 U=-193.082969 H=-193.082024 G=-193.116476 Cv=16.893 pbc="F F F" +C -0.00310189 1.48050328 -0.17259867 +C -0.04527202 -0.02887059 0.00139493 +C 1.29705800 -0.72194415 0.16889042 +O -1.08643172 -0.64273343 0.00630393 +H 0.59403529 1.74668579 -1.05225043 +H -1.01540407 1.87034023 -0.28382265 +H 0.48099600 1.95070605 0.69099376 +H 1.82512144 -0.32470731 1.04328658 +H 1.14892507 -1.79597689 0.28554393 +H 1.93682635 -0.52949692 -0.69995403 +9 +Properties=species:S:1:pos:R:3 A=10.78886 B=9.27509 C=5.14977 mu=3.6367 alpha=31.83 epsHOMO=-0.2436 epsLUMO=0.0347 epsgap=0.2783 =267.6148 zpve=0.07319 U0=-209.159302 U=-209.15402 H=-209.153076 G=-209.187468 Cv=16.561 pbc="F F F" +C 0.00679889 1.49969736 -0.02567030 +C -0.02740385 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U=-158.336603 H=-158.335658 G=-158.370016 Cv=20.273 pbc="F F F" +C -0.03215888 1.54021598 0.01074456 +C 0.03381741 0.00745852 0.00180716 +C 0.71375572 -0.50856404 -1.27296949 +C 0.73848999 -0.52220886 1.25750907 +H 0.97522736 1.97414341 0.00528362 +H -0.56196769 1.92232326 -0.86835418 +H -0.54817239 1.91268414 0.90208039 +H -0.99752525 -0.37310578 0.00977297 +H 1.75070021 -0.15586784 -1.33269631 +H 0.73446691 -1.60328288 -1.29939696 +H 0.19367011 -0.16085589 -2.17195508 +H 0.23611990 -0.18382017 2.17005260 +H 0.75964083 -1.61713153 1.27226101 +H 1.77635866 -0.16985318 1.30053019 +12 +Properties=species:S:1:pos:R:3 A=8.67553 B=8.00568 C=4.75542 mu=1.4259 alpha=38.58 epsHOMO=-0.2612 epsLUMO=0.074 epsgap=0.3351 =318.3721 zpve=0.107673 U0=-194.267232 U=-194.261748 H=-194.260804 G=-194.294663 Cv=19.052 pbc="F F F" +C -0.03315871 1.54782566 -0.00438815 +C -0.01108534 0.01859095 0.01676117 +C 0.70938384 -0.53596711 1.23978444 +O -1.33219841 -0.51712554 0.05236057 +H 0.98215692 1.95715900 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1.38233149 0.00000000 1.00000000 +C -1.18862525 0.00000000 1.00000000 +H -2.25177201 0.00000000 1.00000000 +N 2.54296187 0.00000000 1.00000000 +4 +Properties=species:S:1:pos:R:3 A=0 B=4.73269 C=4.73269 mu=0.0023 alpha=27.7 epsHOMO=-0.3696 epsLUMO=-0.0926 epsgap=0.277 =242.9308 zpve=0.015951 U0=-185.648533 U=-185.644825 H=-185.64388 G=-185.667652 Cv=10.398 pbc="F F F" +N 0.01745734 -1.16134217 -0.00415342 +C 0.00253212 -0.00344278 0.00179939 +C -0.01611419 1.37220987 0.00934873 +N -0.03264302 2.53008276 0.01609090 +6 +Properties=species:S:1:pos:R:3 A=68.5171 B=4.8345 C=4.51586 mu=2.7824 alpha=31.14 epsHOMO=-0.2777 epsLUMO=-0.0735 epsgap=0.2042 =268.3921 zpve=0.037208 U0=-190.624631 U=-190.620363 H=-190.619419 G=-190.650543 Cv=13.049 pbc="F F F" +O -0.06776120 -0.04233838 0.00282489 +C -0.00562945 1.16382879 0.00743862 +C 1.22985272 1.91339997 0.00028781 +C 2.23625322 2.57430970 -0.00529277 +H -0.91670701 1.79467546 0.01781352 +H 3.13429662 3.14370267 -0.01037656 +5 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=400.2236 zpve=0.083896 U0=-155.908941 U=-155.90318 H=-155.902236 G=-155.937641 Cv=17.447 pbc="F F F" +C -0.01787959 1.46676893 0.01011180 +C 0.00186644 0.01072913 0.00211109 +C 0.01819704 -1.19272861 -0.00450723 +C 0.03795170 -2.64876793 -0.01251193 +H 0.86460312 1.87589979 0.51479719 +H -0.03217624 1.87210581 -1.00791002 +H -0.90233115 1.85185524 0.53003953 +H -0.83477011 -3.06339007 0.50451436 +H 0.03276422 -3.04315564 -1.03491223 +H 0.93213486 -3.03931642 0.48626606 +10 +Properties=species:S:1:pos:R:3 A=27.39459 B=4.53005 C=4.08342 mu=0.7067 alpha=40.09 epsHOMO=-0.2592 epsLUMO=0.0566 epsgap=0.3157 =333.9589 zpve=0.084338 U0=-155.897345 U=-155.892291 H=-155.891347 G=-155.924226 Cv=17.13 pbc="F F F" +C -0.03095840 1.54775025 0.03167945 +C 0.01485375 0.00962471 -0.02082154 +C 0.69099073 -0.49954575 -1.20857624 +C 1.25222275 -0.89514467 -2.19527879 +H 0.97899300 1.96621609 0.03373834 +H -0.56082354 1.95002902 -0.83580745 +H -0.54576708 1.88225519 0.93709062 +H 0.51920135 -0.37425398 0.87496224 +H -1.00644802 -0.39030838 0.01340632 +H 1.74594541 -1.25604187 -3.06299415 +9 +Properties=species:S:1:pos:R:3 A=28.0016 B=4.67752 C=4.21765 mu=3.9233 alpha=35.38 epsHOMO=-0.3213 epsLUMO=0.034 epsgap=0.3553 =314.5335 zpve=0.07419 U0=-172.006141 U=-172.001467 H=-172.000523 G=-172.032826 Cv=14.988 pbc="F F F" +C -0.02522005 1.54773054 0.02133370 +C 0.01183939 0.00999278 -0.01556702 +C 0.68915114 -0.50836846 -1.20331488 +N 1.22565733 -0.90360846 -2.14737482 +H 0.98571620 1.96212990 0.02370153 +H -0.55522192 1.94599662 -0.84711771 +H -0.53962556 1.88294354 0.92536604 +H 0.52481910 -0.38226048 0.86933355 +H -1.00453542 -0.39832109 0.00530071 +8 +Properties=species:S:1:pos:R:3 A=32.19893 B=4.76299 C=4.30696 mu=4.4361 alpha=31.81 epsHOMO=-0.2683 epsLUMO=0.0173 epsgap=0.2855 =295.6635 zpve=0.063305 U0=-188.042067 U=-188.037478 H=-188.036534 G=-188.06863 Cv=14.488 pbc="F F F" +N -0.03608099 1.48762533 0.03678072 +C -0.01039471 0.02517605 -0.01506608 +C -1.37180956 -0.52108920 0.02739272 +N -2.44026695 -0.95956444 0.03370519 +H 0.91729752 1.83547547 0.05202682 +H -0.47162178 1.85677205 -0.80275569 +H 0.48946161 -0.40880401 -0.89715168 +H 0.52827150 -0.34533338 0.86469547 +8 +Properties=species:S:1:pos:R:3 A=35.11615 B=4.66117 C=4.22354 mu=1.7211 alpha=33.1 epsHOMO=-0.2595 epsLUMO=0.0277 epsgap=0.2872 =300.0993 zpve=0.060632 U0=-191.810916 U=-191.806025 H=-191.805081 G=-191.837634 Cv=15.855 pbc="F F F" +O -0.02013197 1.45738137 0.01010195 +C 0.00168478 0.03579980 0.00224736 +C -1.36366691 -0.47481101 0.01255056 +C -2.47902835 -0.91970202 0.02082451 +H 0.89446911 1.75736553 0.00327363 +H 0.51979028 -0.35175413 -0.88980302 +H 0.53670458 -0.36127661 0.88000894 +H -3.46773502 -1.30737009 0.02818120 +7 +Properties=species:S:1:pos:R:3 A=35.93172 B=4.83443 C=4.37827 mu=4.6788 alpha=28.56 epsHOMO=-0.3018 epsLUMO=0.0022 epsgap=0.3039 =280.6659 zpve=0.050262 U0=-207.916786 U=-207.912215 H=-207.911271 G=-207.943384 Cv=13.845 pbc="F F F" +O -0.03738221 1.44547841 0.01010769 +C 0.01292906 0.03441661 0.00209165 +C -1.35747452 -0.48874523 0.01239738 +N -2.42135277 -0.93685184 0.02016053 +H 0.86898057 1.76972949 0.00417577 +H 0.51692713 -0.36329790 -0.89215814 +H 0.53387616 -0.37278749 0.88223921 +10 +Properties=species:S:1:pos:R:3 A=26.14564 B=4.2893 C=4.12773 mu=2.6741 alpha=35.83 epsHOMO=-0.25 epsLUMO=-0.0205 epsgap=0.2295 =333.3276 zpve=0.084175 U0=-193.075202 U=-193.070116 H=-193.069171 G=-193.102798 Cv=15.954 pbc="F F F" +C 0.16879340 1.52513042 -0.15747056 +C -0.18737625 0.06194763 0.14679374 +C 0.50917645 -0.43996215 1.39125850 +O 1.18191079 -1.43565585 1.45816384 +H 1.23126511 1.63137254 -0.39632693 +H -0.40744668 1.89431681 -1.00969246 +H -0.04931034 2.17266260 0.69857432 +H -1.26905160 -0.01668067 0.32854433 +H 0.06279062 -0.60250479 -0.68474036 +H 0.35384841 0.20663370 2.28871052 +9 +Properties=species:S:1:pos:R:3 A=19.98749 B=6.11589 C=4.82584 mu=3.7071 alpha=32.78 epsHOMO=-0.2516 epsLUMO=0.0335 epsgap=0.2851 =279.7863 zpve=0.074166 U0=-209.144909 U=-209.139976 H=-209.139032 G=-209.172305 Cv=15.058 pbc="F F F" +C 0.01474916 1.47888256 -0.03509326 +N 0.18708077 0.03831317 -0.02906851 +C 0.45703661 -0.66858895 1.10069001 +O 0.58457996 -0.19876202 2.21119705 +H 0.76025289 1.96736104 -0.67198126 +H -0.98462423 1.75984483 -0.38485103 +H 0.14269849 1.82387718 0.99149397 +H 0.10386349 -0.46937726 -0.89461379 +H 0.55047568 -1.75253452 0.88868242 +8 +Properties=species:S:1:pos:R:3 A=48.62351 B=4.67298 C=4.38187 mu=3.92 alpha=29.47 epsHOMO=-0.2814 epsLUMO=0.0074 epsgap=0.2888 =293.917 zpve=0.061327 U0=-229.013797 U=-229.009003 H=-229.008059 G=-229.041086 Cv=13.885 pbc="F F F" +C 0.10653781 1.43512049 0.03336100 +O 0.42686564 0.04716929 0.09309945 +C 0.50714862 -0.63652665 -1.06318297 +O 0.76927014 -1.79955017 -1.11010475 +H 0.92349251 2.00385139 0.48465728 +H -0.03757815 1.76927183 -1.00057425 +H -0.81269569 1.60801916 0.59873287 +H 0.30458460 -0.00231902 -1.94965553 +8 +Properties=species:S:1:pos:R:3 A=36.49282 B=4.31175 C=4.04493 mu=1.7341 alpha=28.53 epsHOMO=-0.2537 epsLUMO=-0.0341 epsgap=0.2196 =303.8129 zpve=0.060508 U0=-228.992613 U=-228.987769 H=-228.986825 G=-229.019918 Cv=14.78 pbc="F F F" +O 0.19375856 1.37170995 0.31398297 +C 0.04992637 0.01405481 -0.04526239 +C -1.40888025 -0.39635680 -0.20601574 +O -1.77729462 -1.37233860 -0.80361689 +H -0.01723186 1.91269132 -0.45337758 +H 0.60440478 -0.26519766 -0.95205725 +H 0.46740406 -0.57521679 0.78176270 +H -2.11835055 0.30075962 0.29870515 +14 +Properties=species:S:1:pos:R:3 A=23.44662 B=3.60588 C=3.38683 mu=0 alpha=45.71 epsHOMO=-0.317 epsLUMO=0.0937 epsgap=0.4107 =426.2996 zpve=0.131708 U0=-158.340943 U=-158.33517 H=-158.334226 G=-158.36894 Cv=19.668 pbc="F F F" +C -0.03007624 1.55804912 0.02980774 +C 0.00707277 0.02834877 -0.00712529 +C 0.71320792 -0.52989340 -1.24765581 +C 0.75036895 -2.05959366 -1.28458957 +H 0.98195330 1.97845113 0.02800797 +H -0.55582433 1.96212343 -0.84270157 +H -0.54083762 1.92692755 0.92493697 +H 0.50928672 -0.34909049 0.89361319 +H -1.01733481 -0.36542299 0.03207393 +H 0.21098948 -0.15245770 -2.14839302 +H 1.73761331 -0.13611625 -1.28685672 +H 1.27616854 -2.46366410 -0.41210980 +H -0.26165699 -2.48000447 -1.28273552 +H 1.26108491 -2.42846444 -2.17974803 +12 +Properties=species:S:1:pos:R:3 A=26.59789 B=3.79186 C=3.53779 mu=1.3402 alpha=38.61 epsHOMO=-0.2619 epsLUMO=0.081 epsgap=0.3429 =382.8628 zpve=0.108241 U0=-194.261089 U=-194.255495 H=-194.254551 G=-194.28893 Cv=18.431 pbc="F F F" +C -0.00379012 1.52196790 0.01027996 +C -0.03589372 -0.00778254 0.00225671 +C -1.45219016 -0.56653711 0.01284386 +O -1.36799785 -1.98394604 0.00453606 +H 1.02477576 1.89411231 0.00239793 +H -0.51286674 1.93764614 -0.86653888 +H -0.49584080 1.92801344 0.90121721 +H 0.48140784 -0.39530552 -0.88316064 +H 0.49828589 -0.40486256 0.87330348 +H -1.98734220 -0.20399494 0.90666062 +H -2.00431485 -0.19459473 -0.86666460 +H -2.26171819 -2.33824298 0.01034748 +12 +Properties=species:S:1:pos:R:3 A=28.29567 B=4.14901 C=3.88349 mu=1.0363 alpha=39.34 epsHOMO=-0.2503 epsLUMO=0.0925 epsgap=0.3428 =368.9331 zpve=0.107895 U0=-194.254127 U=-194.248585 H=-194.247641 G=-194.281899 Cv=17.888 pbc="F F F" +C -0.00519551 1.50868224 0.03308760 +C 0.01316580 -0.01037819 0.01988643 +O 0.69758944 -0.46344140 1.16868979 +C 0.76688491 -1.86508183 1.24602669 +H 1.01445476 1.90423799 0.02996018 +H -0.52990866 1.89184059 -0.84726911 +H -0.51263765 1.87805843 0.92875424 +H -1.01599507 -0.40998729 0.00714282 +H 0.51093143 -0.38369435 -0.89219395 +H -0.23415841 -2.32451489 1.28977780 +H 1.30129276 -2.29853663 0.38486095 +H 1.30990287 -2.11811998 2.16013206 +10 +Properties=species:S:1:pos:R:3 A=14.79671 B=5.6956 C=4.58846 mu=0.0075 alpha=31.42 epsHOMO=-0.2594 epsLUMO=0.0584 epsgap=0.3179 =297.8398 zpve=0.085172 U0=-230.183076 U=-230.177723 H=-230.176779 G=-230.211195 Cv=16.837 pbc="F F F" +O -0.01418735 1.42642242 -0.05423651 +C -0.00391839 0.00717714 0.03496062 +C -1.42317019 -0.55560600 -0.01256639 +O -2.11229413 -0.10563875 -1.17228313 +H -0.18987882 1.64154705 -0.97555148 +H 0.59659504 -0.43315430 -0.77409095 +H 0.47311116 -0.24903733 0.98747216 +H -1.39494859 -1.64975279 -0.06283188 +H -1.96497417 -0.27216915 0.90131972 +H -2.35925991 0.80944396 -1.00614597 +12 +Properties=species:S:1:pos:R:3 A=15.61472 B=6.31363 C=5.54514 mu=0.1136 alpha=41.96 epsHOMO=-0.2727 epsLUMO=0.1012 epsgap=0.3738 =298.6061 zpve=0.109284 U0=-157.116735 U=-157.11209 H=-157.111146 G=-157.143262 Cv=16.49 pbc="F F F" +C -0.03602245 1.52092949 0.01569161 +C 0.01938487 0.01004403 0.04814953 +C 1.30891671 -0.71251995 -0.24920416 +C 0.75347423 -0.70267714 1.15564676 +H 0.90105691 1.95389291 0.38283338 +H -0.19640365 1.89437369 -1.00208569 +H -0.84685273 1.90588930 0.64451322 +H -0.88416809 -0.48001889 -0.30566335 +H 1.26900901 -1.63903068 -0.81150371 +H 2.19010738 -0.11131209 -0.44927513 +H 1.26248862 -0.09487455 1.89690028 +H 0.33540836 -1.62248918 1.54980695 +10 +Properties=species:S:1:pos:R:3 A=18.21931 B=6.63877 C=5.92459 mu=1.812 alpha=35.01 epsHOMO=-0.2633 epsLUMO=0.1052 epsgap=0.3685 =267.2979 zpve=0.085275 U0=-193.039603 U=-193.035186 H=-193.034242 G=-193.065979 Cv=14.764 pbc="F F F" +C -0.01646343 1.51830705 0.00771445 +C -0.02743227 0.01657694 -0.10889741 +C 0.77296553 -0.70131886 -1.10863115 +O 1.17212293 -0.66702969 0.26278829 +H 0.91865980 1.92914217 -0.38285880 +H -0.85158014 1.95456506 -0.55109460 +H -0.11487702 1.82596470 1.05397777 +H -0.93582598 -0.47350600 0.24682793 +H 0.44443922 -1.67201495 -1.47810622 +H 1.40538284 -0.13600339 -1.79271491 +11 +Properties=species:S:1:pos:R:3 A=16.58914 B=7.18798 C=6.11415 mu=1.1353 alpha=39.02 epsHOMO=-0.2304 epsLUMO=0.0968 epsgap=0.3271 =270.5508 zpve=0.097671 U0=-173.147782 U=-173.143343 H=-173.142399 G=-173.174073 Cv=15.298 pbc="F F F" +C -0.05275541 1.47419574 0.00999108 +N 0.01940516 0.02189947 0.06334866 +C 1.32511048 -0.56446850 -0.21420725 +C 0.78051759 -0.54057993 1.17242760 +H 0.86784202 1.97176076 0.36297285 +H -0.23845155 1.79095005 -1.02142866 +H -0.88683448 1.81938959 0.62950398 +H 1.31992909 -1.48186782 -0.79602038 +H 2.15202820 0.11020204 -0.43285248 +H 1.24563570 0.14996098 1.87499245 +H 0.37013358 -1.44020447 1.62233365 +10 +Properties=species:S:1:pos:R:3 A=16.79477 B=6.71015 C=5.86781 mu=1.3894 alpha=34.64 epsHOMO=-0.239 epsLUMO=0.0775 epsgap=0.3166 =263.966 zpve=0.085106 U0=-193.034988 U=-193.030356 H=-193.029411 G=-193.061689 Cv=15.679 pbc="F F F" +O 0.21577583 1.35386859 -0.10695107 +C 0.01628099 -0.02832718 -0.02915694 +C -0.39081465 -0.71497009 1.24315669 +C -1.35118138 -0.64280380 0.06273589 +H -0.43642338 1.79062849 0.45006761 +H 0.74981167 -0.52404050 -0.65624767 +H 0.05959375 -1.66840483 1.49729237 +H -0.61369353 -0.08848006 2.10170640 +H -2.19821932 0.03058430 0.15411451 +H -1.56395912 -1.54640426 -0.49826515 +12 +Properties=species:S:1:pos:R:3 A=10.66107 B=10.66107 C=6.30452 mu=0 alpha=41.83 epsHOMO=-0.2982 epsLUMO=0.0956 epsgap=0.3938 =268.4432 zpve=0.110511 U0=-157.115484 U=-157.111322 H=-157.110378 G=-157.141657 Cv=14.696 pbc="F F F" +C -0.02542957 1.54032578 -0.04215944 +C 1.52564399 1.50419989 0.04121276 +C 1.42146727 0.06146790 0.60844885 +C -0.02638833 -0.01070956 0.04935088 +H -0.46591372 1.99639894 0.84962894 +H -0.47900239 2.00028207 -0.92365389 +H 1.98248278 1.51375334 -0.95303183 +H 2.02191365 2.25986579 0.65522129 +H 2.14291679 -0.67956358 0.25531480 +H 1.43105853 0.05751479 1.70261879 +H -0.79053440 -0.48530296 0.66996573 +H -0.05233245 -0.47238446 -0.94236394 +10 +Properties=species:S:1:pos:R:3 A=12.06545 B=11.77731 C=6.71997 mu=1.7978 alpha=34.56 epsHOMO=-0.2424 epsLUMO=0.0859 epsgap=0.3283 =236.9759 zpve=0.086675 U0=-193.034094 U=-193.029968 H=-193.029024 G=-193.060777 Cv=12.915 pbc="F F F" +C -0.03489789 1.55259455 -0.08507691 +C 1.49349183 1.45459700 0.10114677 +O 1.42725884 0.02348990 0.27254886 +C -0.00634758 0.02236494 0.10944630 +H -0.56579210 2.11301721 0.68591527 +H -0.36628352 1.89393380 -1.06692327 +H 2.10031724 1.73525720 -0.76898349 +H 1.90023090 1.95506611 0.98896748 +H -0.51987849 -0.35595179 1.00235948 +H -0.31970062 -0.57567421 -0.75559275 +12 +Properties=species:S:1:pos:R:3 A=8.63483 B=4.11977 C=2.88825 mu=0.6875 alpha=45.37 epsHOMO=-0.2392 epsLUMO=0.0192 epsgap=0.2584 =452.5112 zpve=0.100501 U0=-248.375248 U=-248.368823 H=-248.367879 G=-248.405354 Cv=21.616 pbc="F F F" +C -0.10255370 1.49907127 -0.00133642 +C -0.00519774 -0.00343114 -0.02723470 +C -0.00688536 -0.76890740 1.26434744 +N 0.08186799 -0.69202791 -1.10087306 +O 0.07439067 0.13215394 -2.24195258 +H 0.78342093 1.94690862 -0.46354050 +H -0.96150430 1.83614082 -0.59021965 +H -0.19854173 1.87462989 1.01931607 +H -0.92620012 -0.57297946 1.82982479 +H 0.07256154 -1.84064782 1.07516863 +H 0.82937178 -0.45553340 1.90133089 +H 0.13975924 -0.51254299 -2.95389260 +10 +Properties=species:S:1:pos:R:3 A=9.17136 B=9.04195 C=4.5531 mu=1.8689 alpha=43.14 epsHOMO=-0.2029 epsLUMO=0.0499 epsgap=0.2528 =303.9808 zpve=0.082433 U0=-210.101789 U=-210.097816 H=-210.096872 G=-210.12818 Cv=14.821 pbc="F F F" +N -0.00829694 1.35362838 0.00995971 +C 1.28033254 1.82457421 -0.00022459 +C 2.12216639 0.73698898 -0.01445600 +C 1.30849904 -0.43109580 -0.01284154 +C -0.00342782 -0.01836884 0.00235736 +H -0.83243234 1.92772668 0.02122995 +H 1.48471820 2.88312630 0.00354313 +H 3.20066918 0.77343077 -0.02488416 +H 1.64810599 -1.45539683 -0.02180394 +H -0.92551647 -0.57697054 0.00839402 +9 +Properties=species:S:1:pos:R:3 A=9.77329 B=9.41192 C=4.7946 mu=3.6193 alpha=39.13 epsHOMO=-0.2253 epsLUMO=0.0332 epsgap=0.2585 =283.6817 zpve=0.071145 U0=-226.160842 U=-226.157088 H=-226.156144 G=-226.187104 Cv=13.371 pbc="F F F" +N -0.00996090 1.35636172 0.01000855 +C 1.29535422 1.79575578 -0.00047017 +C 2.05550519 0.65637438 -0.01422927 +N 1.25385745 -0.46275132 -0.01260894 +C 0.02301170 -0.00706382 0.00200146 +H -0.83195718 1.93618436 0.02132575 +H 1.54553961 2.84344998 0.00289054 +H 3.13177434 0.57569766 -0.02524155 +H -0.87667864 -0.60414531 0.00750794 +9 +Properties=species:S:1:pos:R:3 A=9.50228 B=9.29497 C=4.69874 mu=0.5571 alpha=39.2 epsHOMO=-0.2246 epsLUMO=0.0199 epsgap=0.2445 =289.005 zpve=0.069883 U0=-229.969129 U=-229.965414 H=-229.96447 G=-229.995393 Cv=13.358 pbc="F F F" +O -0.03255819 1.35065130 0.00967376 +C 1.25319237 1.79167710 -0.00030351 +C 2.11664681 0.74375623 -0.01441494 +C 1.30397796 -0.43852387 -0.01283316 +C 0.01629568 -0.00777185 0.00191294 +H 1.37951089 2.86161135 0.00427433 +H 3.19376406 0.79865501 -0.02458604 +H 1.63858893 -1.46383271 -0.02156949 +H -0.93736295 -0.50899925 0.00843891 +8 +Properties=species:S:1:pos:R:3 A=10.10926 B=9.69372 C=4.94857 mu=1.5081 alpha=35.17 epsHOMO=-0.2509 epsLUMO=0.001 epsgap=0.2519 =269.2444 zpve=0.058593 U0=-246.02915 U=-246.025614 H=-246.024669 G=-246.055309 Cv=12.04 pbc="F F F" +O -0.03808298 1.35643965 0.00975193 +C 1.26496982 1.76855319 -0.00061744 +C 2.04955750 0.66630033 -0.01406047 +N 1.24227075 -0.46732066 -0.01237541 +C 0.04019820 0.00561577 0.00183569 +H 1.43513907 2.83095265 0.00340823 +H 3.12532898 0.59783015 -0.02480757 +H -0.89387707 -0.53342154 0.00796935 +17 +Properties=species:S:1:pos:R:3 A=4.4178 B=4.41753 C=4.41717 mu=0.0003 alpha=56.01 epsHOMO=-0.3145 epsLUMO=0.0737 epsgap=0.3882 =486.2719 zpve=0.158836 U0=-197.632222 U=-197.625241 H=-197.624297 G=-197.661411 Cv=26.084 pbc="F F F" +C -0.01859029 1.53486266 0.01039904 +C 0.00210320 -0.00413672 0.00196980 +C 0.74643954 -0.51390760 1.24887638 +C -1.44190166 -0.53673615 0.01311457 +C 0.72234971 -0.50049606 -1.26445632 +H 0.99776928 1.94420529 0.00435474 +H -0.54181883 1.92841415 -0.86819911 +H -0.52758402 1.91888376 0.90148530 +H 0.25549576 -0.17602728 2.16833067 +H 0.77815389 -1.60892372 1.27088904 +H 1.77964786 -0.14975834 1.26944035 +H -1.99871895 -0.18888794 -0.86408066 +H -1.45956909 -1.63227235 0.00649916 +H -1.98088827 -0.19979639 0.90561580 +H 1.75482473 -0.13554018 -1.30118083 +H 0.75396989 -1.59524036 -1.29822280 +H 0.21358221 -0.15343900 -2.17067181 +15 +Properties=species:S:1:pos:R:3 A=4.69474 B=4.66938 C=4.47909 mu=1.384 alpha=49.04 epsHOMO=-0.2601 epsLUMO=0.0664 epsgap=0.3265 =449.0573 zpve=0.134977 U0=-233.560626 U=-233.553779 H=-233.552834 G=-233.589759 Cv=25.128 pbc="F F F" +C -0.00855999 1.54270147 0.00152717 +C 0.00506780 0.00702635 0.01856160 +C 0.76137731 -0.51839196 1.24766443 +C -1.42012774 -0.54933183 -0.02116008 +O 0.62590477 -0.48363745 -1.17657057 +H 1.01361406 1.94020776 0.00281128 +H -0.51459669 1.90702466 -0.89705781 +H -0.52214815 1.94882514 0.87918344 +H 0.28130613 -0.20165548 2.17943598 +H 0.79965506 -1.61131746 1.23022549 +H 1.79096916 -0.14072935 1.26083003 +H -1.93854829 -0.19910724 -0.91828281 +H -1.39937632 -1.64259062 -0.04563833 +H -1.98850725 -0.23059656 0.85726520 +H 1.53111188 -0.15488046 -1.19210222 +9 +Properties=species:S:1:pos:R:3 A=10.21876 B=4.04324 C=2.94978 mu=2.8579 alpha=42.02 epsHOMO=-0.2654 epsLUMO=-0.0575 epsgap=0.2079 =416.7799 zpve=0.065175 U0=-229.927277 U=-229.921622 H=-229.920677 G=-229.955843 Cv=18.723 pbc="F F F" +C -0.00693851 1.49559567 -0.01063358 +C -0.02986170 -0.01594623 0.06248673 +O -0.08815239 -0.63215062 1.10334812 +C 0.02336981 -0.70256747 -1.22062379 +C 0.06738132 -1.26058341 -2.28623129 +H 0.90429806 1.83281887 -0.51655384 +H -0.85338524 1.85556926 -0.60552819 +H -0.05237764 1.91099807 0.99695949 +H 0.10466868 -1.77036047 -3.21832617 +8 +Properties=species:S:1:pos:R:3 A=10.16063 B=4.15847 C=3.0056 mu=3.3351 alpha=37.08 epsHOMO=-0.3007 epsLUMO=-0.0889 epsgap=0.2118 =397.7757 zpve=0.05451 U0=-246.027383 U=-246.022024 H=-246.02108 G=-246.055769 Cv=16.963 pbc="F F F" +C -0.01171097 1.49973776 -0.01213268 +C -0.02919740 -0.00338745 0.07151218 +O -0.09164028 -0.64592927 1.08732241 +C 0.03831104 -0.69690258 -1.23748557 +N 0.09136286 -1.22982650 -2.26228068 +H 0.90093132 1.83370670 -0.51814928 +H -0.85663933 1.84864400 -0.61618135 +H -0.06416540 1.92131542 0.99203818 +8 +Properties=species:S:1:pos:R:3 A=10.68423 B=4.3198 C=3.07993 mu=5.1815 alpha=38.31 epsHOMO=-0.2739 epsLUMO=-0.0438 epsgap=0.2301 =384.5574 zpve=0.056619 U0=-242.19573 U=-242.190591 H=-242.189646 G=-242.223513 Cv=17.465 pbc="F F F" +N -0.05232108 1.34991769 0.15535538 +C 0.08549378 -0.01476951 0.02668890 +N 1.14555215 -0.71993906 -0.03966037 +C -1.19426556 -0.71152088 0.01802151 +N -2.22747610 -1.22790543 0.00132148 +H 0.75474469 1.91998737 -0.04411334 +H -0.94458527 1.76600473 -0.05932293 +H 1.98312429 -0.13415500 -0.02878551 +9 +Properties=species:S:1:pos:R:3 A=9.08854 B=4.42607 C=3.03169 mu=0.9461 alpha=36.51 epsHOMO=-0.2538 epsLUMO=-0.0964 epsgap=0.1574 =399.222 zpve=0.064992 U0=-267.10335 U=-267.097658 H=-267.096714 G=-267.132534 Cv=17.806 pbc="F F F" +C -0.43126066 1.30586664 -0.12758585 +C 0.25285715 -0.01026988 0.13138867 +O 0.69872099 -0.36890333 1.19604690 +C 0.38978943 -0.96773680 -1.06226208 +O -0.01611268 -0.71564293 -2.16583718 +H 0.10006565 1.85324490 -0.91332597 +H -1.44270433 1.13069361 -0.50923371 +H -0.46757554 1.89548158 0.78836479 +H 0.90329886 -1.91558845 -0.79531972 +9 +Properties=species:S:1:pos:R:3 A=10.08927 B=4.54115 C=3.13487 mu=2.7707 alpha=38.09 epsHOMO=-0.2561 epsLUMO=-0.064 epsgap=0.192 =384.4827 zpve=0.067668 U0=-263.278851 U=-263.273589 H=-263.272645 G=-263.306835 Cv=17.972 pbc="F F F" +N -0.16032929 1.08528962 -0.45398652 +C 0.18649127 -0.08762327 0.14758021 +N 1.28979627 -0.46427423 0.67927030 +C -0.94204384 -1.08440264 0.20423621 +O -2.01802205 -0.89007687 -0.30583149 +H 0.54574437 1.73837930 -0.74689246 +H -1.04684920 1.09570857 -0.93607796 +H 2.01738195 0.24991828 0.59881811 +H -0.67740148 -2.01003995 0.74746441 +13 +Properties=species:S:1:pos:R:3 A=7.92874 B=3.8272 C=2.82581 mu=0.6578 alpha=51.2 epsHOMO=-0.2589 epsLUMO=0.0571 epsgap=0.316 =481.9854 zpve=0.112471 U0=-195.186772 U=-195.180446 H=-195.179502 G=-195.215658 Cv=22.569 pbc="F F F" +C -0.01763434 1.54859468 -0.00253584 +C -0.01719180 0.00735789 0.03269604 +C 0.75818463 -0.52820155 1.25278862 +C -1.38213303 -0.51768438 0.00775516 +C -2.50796392 -0.94005629 0.00478552 +H 1.00944396 1.92639613 -0.02632830 +H -0.54514614 1.92217052 -0.88393459 +H -0.51243000 1.95339892 0.88560642 +H 0.49366179 -0.34904155 -0.87265890 +H 0.28968458 -0.19345372 2.18334679 +H 0.77833392 -1.62102506 1.25783727 +H 1.78952492 -0.16181727 1.23574954 +H -3.49998244 -1.31810165 -0.00723572 +12 +Properties=species:S:1:pos:R:3 A=7.90009 B=3.95466 C=2.88643 mu=3.9512 alpha=46.23 epsHOMO=-0.318 epsLUMO=0.0365 epsgap=0.3545 =460.694 zpve=0.102281 U0=-211.295796 U=-211.289821 H=-211.288877 G=-211.324525 Cv=20.467 pbc="F F F" +C -0.00242350 1.55035837 0.01056187 +C -0.01856600 0.01004352 -0.02261770 +C -1.45045318 -0.55766178 0.01293347 +C 0.70125749 -0.48583168 -1.20172764 +N 1.26190946 -0.87126766 -2.13619740 +H 1.01959496 1.93673215 0.00349668 +H -0.52972844 1.96141639 -0.85498386 +H -0.50134251 1.90346839 0.91755862 +H 0.52481877 -0.36222456 0.85525462 +H -2.02427903 -0.21428716 -0.85251869 +H -1.44438296 -1.65026927 0.00754107 +H -1.95805494 -0.21725882 0.91996760 +11 +Properties=species:S:1:pos:R:3 A=8.30095 B=3.97615 C=2.93764 mu=2.7429 alpha=42.87 epsHOMO=-0.2704 epsLUMO=0.0278 epsgap=0.2983 =440.0738 zpve=0.091554 U0=-227.338075 U=-227.332253 H=-227.331309 G=-227.366638 Cv=19.946 pbc="F F F" +C -0.01064295 1.54128691 0.00722087 +C -0.00203811 0.00756015 0.03863463 +N 0.76268677 -0.47101822 1.19250856 +C -1.38541954 -0.52099256 -0.00194097 +N -2.45680847 -0.95636072 0.00694969 +H 1.02043523 1.90106589 0.00392365 +H -0.52873161 1.91193452 -0.88031480 +H -0.51849536 1.94150431 0.89013197 +H 0.50418828 -0.35805500 -0.86276072 +H 0.30070681 -0.18364934 2.05155305 +H 0.78148992 -1.48670299 1.20428183 +11 +Properties=species:S:1:pos:R:3 A=8.55919 B=3.89952 C=2.91869 mu=1.3582 alpha=44.03 epsHOMO=-0.2665 epsLUMO=0.0336 epsgap=0.3001 =444.6452 zpve=0.088908 U0=-231.108368 U=-231.102292 H=-231.101348 G=-231.137061 Cv=21.306 pbc="F F F" +C -0.00754278 1.55566144 0.08650633 +C 0.01207541 0.02096553 0.04935916 +O -1.29150251 -0.52785128 0.22126386 +C 0.65093362 -0.47718651 -1.17430507 +C 1.13902673 -0.86577343 -2.20205518 +H 1.00824243 1.95969458 0.04929733 +H -0.56272245 1.95192010 -0.76978085 +H -0.49328161 1.88929438 1.00704123 +H 0.57811967 -0.34464907 0.91458864 +H -1.78516532 -0.35903262 -0.58909400 +H 1.58078877 -1.22365060 -3.09919447 +10 +Properties=species:S:1:pos:R:3 A=8.55908 B=4.01671 C=2.97882 mu=3.269 alpha=39.28 epsHOMO=-0.3051 epsLUMO=0.0115 epsgap=0.3166 =424.3395 zpve=0.078602 U0=-247.214861 U=-247.209162 H=-247.208218 G=-247.243338 Cv=19.252 pbc="F F F" +C -0.00992462 1.54951908 0.00732200 +C 0.00740485 0.01722020 0.05031270 +O 0.72085722 -0.49630479 1.15864583 +C -1.36995095 -0.52355929 0.00894432 +N -2.45081572 -0.93233138 0.00716912 +H 1.01892314 1.91630447 0.02724149 +H -0.50261490 1.90969362 -0.89997951 +H -0.54800426 1.95377611 0.87053664 +H 0.53602846 -0.36494195 -0.82970945 +H 0.27702643 -0.20644534 1.96421773 +13 +Properties=species:S:1:pos:R:3 A=7.66289 B=3.70925 C=2.79937 mu=2.6921 alpha=46.58 epsHOMO=-0.2469 epsLUMO=-0.0188 epsgap=0.2281 =482.0475 zpve=0.112328 U0=-232.364952 U=-232.358577 H=-232.357633 G=-232.394589 Cv=21.434 pbc="F F F" +C -0.02629647 1.53877116 -0.00783747 +C 0.01468904 0.00095310 0.00166037 +C 0.74737862 -0.53388859 1.24420471 +C -1.40211264 -0.53859257 -0.01557820 +O -1.87620455 -1.23582351 -0.87399559 +H 0.98823696 1.94625080 0.03705522 +H -0.50725090 1.92153422 -0.91285565 +H -0.57756514 1.92360883 0.85796386 +H 0.50584680 -0.36421091 -0.90619124 +H 0.22958380 -0.23909403 2.16431052 +H 0.81692736 -1.62559499 1.22973438 +H 1.76267153 -0.12846566 1.29050755 +H -2.00861510 -0.22792742 0.87121666 +11 +Properties=species:S:1:pos:R:3 A=8.31096 B=4.07963 C=2.92918 mu=2.8354 alpha=39.16 epsHOMO=-0.255 epsLUMO=-0.0279 epsgap=0.2271 =432.1489 zpve=0.088443 U0=-268.287661 U=-268.281505 H=-268.280561 G=-268.316982 Cv=20.344 pbc="F F F" +C -0.05483118 1.46032843 -0.17068647 +C 0.07105714 -0.03524485 0.11225841 +O 0.58204486 -0.33316656 1.40176256 +C -1.27639279 -0.74383027 0.03051185 +O -2.11855775 -0.49643175 -0.79163412 +H 0.93158072 1.93091398 -0.23353204 +H -0.57713995 1.61290939 -1.11835562 +H -0.62444315 1.94776119 0.62573670 +H 0.69687521 -0.50071620 -0.67159471 +H 1.45603316 0.06066659 1.47858584 +H -1.40723217 -1.53388627 0.80211181 +12 +Properties=species:S:1:pos:R:3 A=8.9331 B=4.19405 C=2.96046 mu=3.7163 alpha=44.42 epsHOMO=-0.2424 epsLUMO=0.0327 epsgap=0.2751 =441.85 zpve=0.10227 U0=-248.430371 U=-248.424309 H=-248.423365 G=-248.459383 Cv=19.918 pbc="F F F" +C -0.09776499 1.44917268 0.04561984 +N 0.01043053 0.00373818 -0.00070600 +C 0.05323028 -0.71432112 1.25264891 +C 0.06728716 -0.64067225 -1.20060778 +O 0.03636432 -0.11006515 -2.29109228 +H 0.75650660 1.88747109 0.57647421 +H -0.11648577 1.81632103 -0.98049172 +H -1.01641106 1.75521548 0.56151415 +H -0.85453556 -0.53657675 1.84390168 +H 0.13297722 -1.78696336 1.05851351 +H 0.91637348 -0.40737846 1.85761345 +H 0.14762240 -1.73801339 -1.06805139 +11 +Properties=species:S:1:pos:R:3 A=10.13982 B=3.81346 C=2.86965 mu=2.9514 alpha=39.33 epsHOMO=-0.2699 epsLUMO=-0.0262 epsgap=0.2437 =440.1727 zpve=0.088924 U0=-268.301176 U=-268.295084 H=-268.29414 G=-268.331307 Cv=19.824 pbc="F F F" +C -0.01708808 1.44281136 0.03225501 +C -0.05544542 -0.06560572 -0.02375999 +O -0.17379913 -0.76196233 0.96223129 +C 0.06068107 -0.73615179 -1.38825252 +O 0.00877771 -2.12574390 -1.27749486 +H 0.92062950 1.81463913 -0.39522215 +H -0.83050523 1.86542281 -0.56780350 +H -0.10911975 1.78049045 1.06493675 +H -0.75015067 -0.35235060 -2.03056074 +H 1.00312898 -0.40389029 -1.85592942 +H -0.09063255 -2.29450960 -0.32608019 +13 +Properties=species:S:1:pos:R:3 A=9.55216 B=3.57844 C=2.7341 mu=2.6168 alpha=46.19 epsHOMO=-0.2423 epsLUMO=-0.0072 epsgap=0.2351 =489.8518 zpve=0.112006 U0=-232.377706 U=-232.371073 H=-232.370129 G=-232.408256 Cv=21.716 pbc="F F F" +C -0.04384102 1.54099793 -0.03913814 +C 0.02765132 0.01885817 0.04778773 +C 0.76814644 -0.48842218 1.28000847 +C 0.87850208 -1.99746312 1.42393323 +O 1.24179142 0.26651340 2.09788702 +H 0.95796454 1.97690198 -0.06816012 +H -0.58447717 1.85356031 -0.93690369 +H -0.55173626 1.95737759 0.83440529 +H -0.97692824 -0.42709877 0.05353178 +H 0.51853486 -0.40806507 -0.83809409 +H 1.39924980 -2.42678188 0.56053177 +H 1.42001187 -2.24449367 2.33776426 +H -0.11750602 -2.45360651 1.45141965 +12 +Properties=species:S:1:pos:R:3 A=9.78802 B=3.78441 C=2.84416 mu=3.499 alpha=42.54 epsHOMO=-0.2438 epsLUMO=0.0355 epsgap=0.2793 =457.447 zpve=0.101847 U0=-248.448467 U=-248.441988 H=-248.441044 G=-248.478935 Cv=21.374 pbc="F F F" +C -0.00220144 1.53344329 -0.05960369 +C -0.03235837 0.01206167 0.06840265 +C 0.69666086 -0.48196462 1.31828322 +N 0.98104007 -1.82034162 1.31984698 +O 1.00392124 0.24606232 2.24136749 +H 1.02111190 1.89525169 -0.19182672 +H -0.59707204 1.86206427 -0.91645604 +H -0.39728839 1.99920712 0.84545201 +H -1.06936083 -0.34552935 0.13040806 +H 0.39947165 -0.46906818 -0.81777020 +H 0.63281951 -2.43917647 0.60874785 +H 1.35708014 -2.22217793 2.16303522 +12 +Properties=species:S:1:pos:R:3 A=10.01185 B=3.84195 C=2.87635 mu=3.5402 alpha=43.61 epsHOMO=-0.2418 epsLUMO=0.0387 epsgap=0.2805 =458.9803 zpve=0.101735 U0=-248.443503 U=-248.436899 H=-248.435955 G=-248.474272 Cv=20.967 pbc="F F F" +C 0.00619941 1.45933202 -0.03893859 +N -0.06411616 0.01422963 -0.12046901 +C -1.20595394 -0.68196796 0.16117120 +C -1.08982704 -2.19284899 0.02095775 +O -2.24482911 -0.14443077 0.49994418 +H 0.75053989 1.78372520 0.69715573 +H 0.25692001 1.90395263 -1.00876215 +H -0.97856026 1.81121438 0.27082194 +H 0.75820982 -0.49543660 -0.39464024 +H -0.09909703 -2.53543104 -0.28970003 +H -1.34097671 -2.65395851 0.97972885 +H -1.82903100 -2.53483935 -0.70813076 +11 +Properties=species:S:1:pos:R:3 A=10.44045 B=4.04692 C=2.98547 mu=3.5648 alpha=39.51 epsHOMO=-0.2436 epsLUMO=0.0599 epsgap=0.3034 =428.404 zpve=0.091713 U0=-264.504487 U=-264.498452 H=-264.497508 G=-264.533633 Cv=20.274 pbc="F F F" +C 0.00922630 1.45021814 -0.03194543 +N -0.01429488 -0.00054646 -0.01782667 +C -1.21576579 -0.67252403 0.04421896 +N -1.09733352 -2.04644898 -0.15997741 +O -2.28599299 -0.12893711 0.24196027 +H 0.89659038 1.81885022 0.49120949 +H 0.00233950 1.86388504 -1.04914155 +H -0.88562684 1.79943346 0.48353298 +H 0.75257305 -0.47506631 -0.46719265 +H -0.24882333 -2.48772194 0.16375654 +H -1.93419610 -2.53898393 0.11482880 +12 +Properties=species:S:1:pos:R:3 A=9.69683 B=4.1533 C=3.01703 mu=1.1876 alpha=43.57 epsHOMO=-0.2595 epsLUMO=0.0352 epsgap=0.2948 =443.1687 zpve=0.102062 U0=-248.416462 U=-248.410358 H=-248.409414 G=-248.445651 Cv=20.256 pbc="F F F" +C -0.09160695 1.37540046 -0.00729426 +O -0.06964534 -0.04831581 -0.11148812 +C 0.68114625 -0.56142673 -1.10732458 +C 0.60405298 -2.06701550 -1.08766892 +N 1.32645533 0.19266343 -1.89967731 +H 0.91463916 1.76843655 0.16257460 +H -0.48562826 1.82624615 -0.92235216 +H -0.73971430 1.60007423 0.84110605 +H -0.43430065 -2.39242162 -1.20325205 +H 1.20627110 -2.50049977 -1.88745640 +H 0.95651337 -2.44976670 -0.12501838 +H 1.83504115 -0.36104361 -2.58486055 +11 +Properties=species:S:1:pos:R:3 A=10.20392 B=4.16834 C=3.07281 mu=1.7569 alpha=39.33 epsHOMO=-0.2685 epsLUMO=0.0174 epsgap=0.2859 =427.6606 zpve=0.089436 U0=-268.32127 U=-268.315051 H=-268.314106 G=-268.351214 Cv=19.501 pbc="F F F" +C 0.07295254 1.38840919 0.06442046 +O 0.21049114 -0.03740028 0.04612736 +C 0.50102805 -0.57224288 -1.15975346 +C 0.62218715 -2.07378323 -1.06030684 +O 0.63922154 0.08224418 -2.16157325 +H 1.00029720 1.87062570 -0.25532064 +H -0.73338957 1.70782089 -0.60080454 +H -0.15751280 1.65154060 1.09692954 +H -0.31330001 -2.50202851 -0.68941906 +H 0.85646588 -2.48430275 -2.04157352 +H 1.40651685 -2.34055496 -0.34634301 +10 +Properties=species:S:1:pos:R:3 A=10.68182 B=4.39532 C=3.17969 mu=2.329 alpha=35.5 epsHOMO=-0.2669 epsLUMO=0.0575 epsgap=0.3244 =398.5908 zpve=0.079271 U0=-284.385189 U=-284.379361 H=-284.378417 G=-284.414085 Cv=19.084 pbc="F F F" +C -0.02645771 1.38305753 0.02085660 +O 0.04139566 -0.04439503 -0.00277452 +C 0.66775338 -0.56001625 -1.09000344 +N 0.72605063 -1.92219983 -0.98822954 +O 1.12952381 0.09648143 -1.99322180 +H 0.97529779 1.82044307 0.02387306 +H -0.57085698 1.76214621 -0.84790425 +H -0.55410722 1.63835731 0.94043594 +H 0.12662696 -2.38401590 -0.32566929 +H 0.97230070 -2.41798421 -1.82732573 +10 +Properties=species:S:1:pos:R:3 A=10.20696 B=3.93135 C=2.9219 mu=4.5676 alpha=35.87 epsHOMO=-0.246 epsLUMO=0.0273 epsgap=0.2733 =418.5967 zpve=0.078222 U0=-284.360325 U=-284.354148 H=-284.353204 G=-284.390143 Cv=20.089 pbc="F F F" +N -0.08115607 1.31807026 0.01276742 +C 0.05442259 -0.04477733 0.08155388 +O 1.12163074 -0.59783950 0.20940553 +C -1.28687786 -0.79011642 0.06178435 +O -1.04605114 -2.13954195 -0.23957158 +H 0.76623366 1.85991128 -0.04483425 +H -0.93021346 1.74567447 -0.31565615 +H -1.74559696 -0.65521334 1.05526700 +H -1.96026061 -0.31149222 -0.67026256 +H -1.85637296 -2.63017646 -0.07625478 +10 +Properties=species:S:1:pos:R:3 A=10.14389 B=4.0714 C=3.02223 mu=5.3004 alpha=35.19 epsHOMO=-0.2527 epsLUMO=0.0208 epsgap=0.2735 =408.0279 zpve=0.080317 U0=-284.372483 U=-284.367172 H=-284.366228 G=-284.40095 Cv=17.931 pbc="F F F" +N 0.01838100 0.93057357 0.04943058 +H -0.70674506 0.56116798 -0.55550134 +H 0.68248307 0.18430002 0.21487099 +H 0.21928084 2.79858558 -0.32670585 +C -0.54586914 1.44688585 1.30274586 +C -0.80466975 2.96030569 1.21052835 +O -0.21468822 3.53648884 0.15531687 +O -1.45295676 3.56338618 2.02143528 +H -1.47662579 0.96512183 1.62072169 +H 0.17781209 1.30324286 2.11157176 +13 +Properties=species:S:1:pos:R:3 A=6.6508 B=4.19317 C=3.39441 mu=2.2854 alpha=42.01 epsHOMO=-0.2597 epsLUMO=0.0631 epsgap=0.3228 =438.3028 zpve=0.11345 U0=-269.479234 U=-269.472993 H=-269.472049 G=-269.508213 Cv=22.072 pbc="F F F" +C -0.02775167 1.53421433 0.00651151 +C 0.08343246 0.01218277 0.05258986 +O 0.95856392 -0.48200710 -0.96694113 +C 0.69767574 -0.49516451 1.35948812 +O 2.02247062 -0.02842530 1.52222186 +H 0.94333763 1.98841434 0.21841589 +H -0.37320516 1.87354041 -0.97716908 +H -0.74773817 1.89256394 0.74926853 +H -0.91671848 -0.43697036 -0.06040528 +H 0.76726710 -0.01412177 -1.78502043 +H 0.11650002 -0.13351914 2.21392431 +H 0.65814685 -1.59563445 1.36274474 +H 2.46378566 -0.22762476 0.68698431 +17 +Properties=species:S:1:pos:R:3 A=7.26511 B=3.32874 C=2.54491 mu=0.0618 alpha=56.26 epsHOMO=-0.3085 epsLUMO=0.085 epsgap=0.3934 =565.8412 zpve=0.159632 U0=-197.629387 U=-197.622325 H=-197.621381 G=-197.659365 Cv=25.169 pbc="F F F" +C 0.05879035 1.56174169 -0.01945240 +C -0.01544024 0.03182306 0.00308332 +C -1.44103237 -0.54862028 0.05028629 +C -1.40777007 -2.07231030 -0.13012010 +C -2.18400529 -0.17659759 1.34114303 +H 1.09435638 1.90233607 -0.11900072 +H -0.50689566 1.97217319 -0.86388098 +H -0.34407951 2.00628082 0.89579522 +H 0.48926758 -0.36011214 -0.88953902 +H 0.55254938 -0.35139446 0.86269887 +H -2.00031575 -0.12153682 -0.79559684 +H -0.85501131 -2.55065810 0.68775438 +H -2.41787513 -2.49543628 -0.13720127 +H -0.91928762 -2.35363802 -1.06914014 +H -3.19100197 -0.60699559 1.35137051 +H -1.65197294 -0.56010584 2.22063481 +H -2.28793759 0.90575132 1.46079572 +15 +Properties=species:S:1:pos:R:3 A=8.11598 B=3.43678 C=2.66869 mu=1.3894 alpha=49.45 epsHOMO=-0.2617 epsLUMO=0.0733 epsgap=0.335 =521.8605 zpve=0.136091 U0=-233.555951 U=-233.549143 H=-233.548199 G=-233.585602 Cv=23.99 pbc="F F F" +C -0.03841227 1.55621396 0.00935837 +C 0.01907135 0.02716079 -0.02074203 +C 0.73141506 -0.52709328 -1.25454422 +C 0.83975868 -2.05206973 -1.22872655 +O -0.01226470 -0.08514394 -2.38973758 +H 0.96845664 1.98863424 0.03526234 +H -0.54377006 1.93824134 -0.88074407 +H -0.57652955 1.91383386 0.89256698 +H 0.53166867 -0.34991486 0.87240999 +H -0.99727186 -0.38638654 -0.00185289 +H 1.74909346 -0.09943025 -1.28826184 +H 1.34143968 -2.42487137 -2.12957624 +H 1.41822012 -2.39229108 -0.36340871 +H -0.15616765 -2.50443859 -1.18308875 +H 0.43090825 -0.40901616 -3.17979642 +15 +Properties=species:S:1:pos:R:3 A=7.55335 B=3.78323 C=2.80566 mu=1.0758 alpha=49.8 epsHOMO=-0.2476 epsLUMO=0.086 epsgap=0.3336 =507.9614 zpve=0.135681 U0=-233.545899 U=-233.539034 H=-233.53809 G=-233.57582 Cv=23.573 pbc="F F F" +C -0.00857605 1.38097833 0.09454670 +O -0.02255106 -0.00739461 -0.13062576 +C -1.30243341 -0.61997805 -0.02088309 +C -1.18687286 -1.97885751 -0.70303918 +C -1.75587824 -0.74662760 1.43714508 +H 1.00321550 1.73258087 -0.12443496 +H -0.71511468 1.90838275 -0.56674280 +H -0.24909065 1.64967237 1.13384681 +H -2.03737978 -0.00432241 -0.56884198 +H -0.44792779 -2.59945605 -0.18596870 +H -2.14751041 -2.50225525 -0.69102581 +H -0.86524212 -1.85860679 -1.74077836 +H -2.73321482 -1.23674908 1.49205117 +H -1.03621350 -1.34483828 2.00544671 +H -1.84898410 0.22935483 1.92198087 +15 +Properties=species:S:1:pos:R:3 A=6.14183 B=5.17679 C=3.79297 mu=0.1068 alpha=52.92 epsHOMO=-0.2633 epsLUMO=0.0893 epsgap=0.3526 =439.1643 zpve=0.137025 U0=-196.409349 U=-196.403398 H=-196.402453 G=-196.437676 Cv=22.258 pbc="F F F" +C -0.03913204 1.50456305 0.04978913 +C 0.00985856 -0.01113880 0.01248619 +C -1.24167981 -0.66663044 -0.53989979 +C 1.33803387 -0.66895748 -0.27400430 +C 0.75126442 -0.72221586 1.11862571 +H 0.88633040 1.92490040 0.45579358 +H -0.18183878 1.92045743 -0.95489291 +H -0.86832076 1.85815718 0.67439648 +H -1.43570890 -0.34344387 -1.56976411 +H -1.15326757 -1.75757800 -0.54433832 +H -2.12221416 -0.40570820 0.05951361 +H 1.34398374 -1.58235208 -0.86005084 +H 2.20196897 -0.03329084 -0.43929326 +H 1.21949576 -0.12246018 1.89246580 +H 0.36153319 -1.67153717 1.47171932 +13 +Properties=species:S:1:pos:R:3 A=6.49534 B=5.52329 C=3.9108 mu=1.8235 alpha=45.7 epsHOMO=-0.2596 epsLUMO=0.091 epsgap=0.3505 =405.521 zpve=0.112851 U0=-232.335768 U=-232.329981 H=-232.329037 G=-232.364091 Cv=20.719 pbc="F F F" +C -0.03970330 1.50954998 -0.04112233 +C -0.03023556 -0.00335329 -0.06498361 +C -1.24613189 -0.68356648 0.52531190 +C 0.78937201 -0.71129193 -1.06027559 +O 1.22546798 -0.60059036 0.29712283 +H 0.88992772 1.90948478 -0.45296558 +H -0.87952723 1.90350108 -0.62376642 +H -0.14448576 1.87590604 0.98604429 +H -2.15218727 -0.41012616 -0.02629495 +H -1.13412584 -1.77005431 0.49747032 +H -1.38586631 -0.38055453 1.56882757 +H 0.50712690 -1.71386874 -1.38085018 +H 1.37088104 -0.14369322 -1.78640853 +13 +Properties=species:S:1:pos:R:3 A=6.57648 B=5.31603 C=3.98453 mu=1.3822 alpha=45.85 epsHOMO=-0.2494 epsLUMO=0.0776 epsgap=0.327 =401.4888 zpve=0.113237 U0=-232.33341 U=-232.327734 H=-232.32679 G=-232.36147 Cv=21.17 pbc="F F F" +C -0.03714052 1.53215920 0.01332310 +C 0.01344335 0.02117019 0.01922005 +O -1.17476104 -0.59735284 -0.40931260 +C 1.30744798 -0.69902106 -0.27389650 +C 0.70844749 -0.71373504 1.12226318 +H 0.89026861 1.96240279 0.40276135 +H -0.17846151 1.91561262 -1.00518522 +H -0.87011232 1.88955201 0.62730953 +H -1.28352265 -0.40225391 -1.34690641 +H 1.23973813 -1.61642115 -0.84783774 +H 2.20543993 -0.11523820 -0.44744474 +H 1.19539159 -0.13889834 1.90240284 +H 0.24735439 -1.63670241 1.45378480 +10 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1.08469664 2.25947795 0.18994331 +H -1.71642814 2.41772662 -0.68901078 +H -1.83392757 1.98415059 1.02444244 +H -0.05534741 4.17585264 -0.10670963 +H -0.02541477 3.61399048 1.57278917 +H 1.81074389 2.03288534 0.97578973 +H 1.63600801 2.46255380 -0.73376244 +10 +Properties=species:S:1:pos:R:3 A=12.23436 B=4.97895 C=3.70561 mu=3.6671 alpha=39.73 epsHOMO=-0.25 epsLUMO=0.038 epsgap=0.288 =355.0934 zpve=0.080442 U0=-247.225618 U=-247.220897 H=-247.219953 G=-247.253218 Cv=16.1 pbc="F F F" +O 0.03184134 -0.03461682 0.00849836 +C -0.02440334 1.16440533 0.02160811 +C -1.14541322 2.23712809 0.03012901 +C 0.00928805 3.27945878 0.04557758 +N 0.93843838 2.14652109 0.03554396 +H -1.77030529 2.23779730 -0.86428437 +H -1.77632809 2.21765386 0.92007934 +H 0.07170564 3.91863516 -0.84072193 +H 0.06622603 3.89826625 0.94657952 +H 1.94616840 2.09535713 0.03855945 +9 +Properties=species:S:1:pos:R:3 A=12.44643 B=5.23328 C=3.86599 mu=3.9339 alpha=35.4 epsHOMO=-0.2788 epsLUMO=0.0089 epsgap=0.2878 =336.0792 zpve=0.068574 U0=-267.106213 U=-267.101929 H=-267.100985 G=-267.13332 Cv=14.483 pbc="F F F" +O 0.04424004 0.03933243 -0.33071104 +C -0.04854479 1.17416230 0.00410076 +C -1.14211656 2.23611850 0.15338361 +C 0.02332538 3.12357010 0.61681625 +O 0.95663808 2.01190122 0.42603746 +H -1.62573755 2.51482212 -0.78401102 +H -1.90009213 1.99553386 0.90028663 +H 0.28368077 3.96172751 -0.03229287 +H 0.00782250 3.43942655 1.66162725 +10 +Properties=species:S:1:pos:R:3 A=11.54185 B=4.90368 C=3.63309 mu=2.5257 alpha=39.34 epsHOMO=-0.2437 epsLUMO=-0.0258 epsgap=0.2179 =359.152 zpve=0.07965 U0=-247.190194 U=-247.185543 H=-247.184599 G=-247.217688 Cv=16.083 pbc="F F F" +O 0.00053891 0.00665475 -0.09743049 +C -0.01371990 1.19213881 0.03139936 +C -1.10668681 2.26612284 0.17247913 +N -0.03543445 3.25123475 0.48533693 +C 1.05328615 2.29521506 0.14383904 +H -1.65047970 2.42796175 -0.76886054 +H -1.83606786 2.11370530 0.97567410 +H -0.05434600 4.07193609 -0.10727051 +H 1.80754206 2.16277968 0.92736250 +H 1.56737937 2.47130277 -0.81152688 +9 +Properties=species:S:1:pos:R:3 A=12.18264 B=4.94228 C=3.68162 mu=0.8477 alpha=35.94 epsHOMO=-0.2647 epsLUMO=-0.0352 epsgap=0.2295 =344.5473 zpve=0.066989 U0=-267.068488 U=-267.063976 H=-267.063032 G=-267.095887 Cv=15.115 pbc="F F F" +O -0.00031692 0.00676321 -0.17139774 +C -0.01417098 1.18672865 -0.00980279 +C -1.08283120 2.27653381 0.15574094 +O -0.03856115 3.26460423 0.27513986 +C 1.02857270 2.30515693 0.12774311 +H -1.72923855 2.43647507 -0.71671274 +H -1.70219898 2.19346725 1.05800754 +H 1.67366781 2.23923293 1.01324179 +H 1.64705068 2.48224548 -0.76148355 +15 +Properties=species:S:1:pos:R:3 A=9.63416 B=4.10254 C=3.28665 mu=0.095 alpha=52.94 epsHOMO=-0.2896 epsLUMO=0.0927 epsgap=0.3823 =455.0215 zpve=0.138424 U0=-196.407957 U=-196.40245 H=-196.401505 G=-196.436159 Cv=20.299 pbc="F F F" +C -0.47005934 1.44206547 -0.06684548 +C 0.14287306 0.05485144 0.03747984 +C 1.37254693 -0.30295273 -0.84679168 +C 1.95947994 -1.09364561 0.35326039 +C 0.99887433 -0.30768787 1.28563099 +H 0.29210839 2.21844829 0.06844100 +H -0.93614604 1.60094605 -1.04612799 +H -1.24148161 1.59707353 0.69627593 +H -0.63517341 -0.70560132 -0.10055095 +H 1.19338376 -0.84118177 -1.78129390 +H 1.96867777 0.58857708 -1.07004184 +H 1.69873597 -2.15500718 0.30521100 +H 3.03253510 -1.01077235 0.54148058 +H 1.48290386 0.58242428 1.70211026 +H 0.51304296 -0.84979686 2.10121062 +13 +Properties=species:S:1:pos:R:3 A=9.93129 B=4.56362 C=3.63704 mu=1.6826 alpha=45.72 epsHOMO=-0.241 epsLUMO=0.0915 epsgap=0.3325 =407.6909 zpve=0.11459 U0=-232.33123 U=-232.325877 H=-232.324933 G=-232.359589 Cv=18.582 pbc="F F F" +C -0.32308228 1.53751451 0.01947624 +C 0.11847214 0.08900172 -0.00523140 +C 0.87489284 -0.43763303 -1.24721738 +C 1.97841048 -0.80657483 -0.23740356 +O 1.27344926 -0.15419108 0.83794040 +H 0.49211985 2.19658795 -0.29527664 +H -1.17615835 1.68923490 -0.65134808 +H -0.62776963 1.82747912 1.03013205 +H -0.71410200 -0.56389187 0.29597305 +H 0.41033314 -1.26269706 -1.78910031 +H 1.15560811 0.34596269 -1.95459519 +H 2.09501012 -1.88349764 -0.05606083 +H 2.96970344 -0.36924147 -0.40408009 +13 +Properties=species:S:1:pos:R:3 A=10.13697 B=4.32512 C=3.50912 mu=1.8995 alpha=45.45 epsHOMO=-0.2419 epsLUMO=0.0814 epsgap=0.3233 =414.1575 zpve=0.114694 U0=-232.325947 U=-232.320548 H=-232.319604 G=-232.354576 Cv=18.443 pbc="F F F" +C -0.18371124 1.54668909 -0.01671129 +C 0.04513382 0.04386724 0.02270494 +C 1.20301104 -0.54034580 -0.81983464 +O 1.80061686 -1.15530615 0.33924991 +C 0.83391983 -0.54313754 1.21648003 +H 0.75102609 2.09256015 0.15344911 +H -0.58319248 1.86155707 -0.98694169 +H -0.89843724 1.85917126 0.75244153 +H -0.89506736 -0.48768372 -0.14843995 +H 0.93560472 -1.27857588 -1.58543917 +H 1.86392938 0.21379924 -1.27081649 +H 1.29414565 0.20948993 1.87273952 +H 0.31517042 -1.28326856 1.83756393 +13 +Properties=species:S:1:pos:R:3 A=10.09703 B=4.26865 C=3.40487 mu=1.4604 alpha=45.55 epsHOMO=-0.256 epsLUMO=0.0801 epsgap=0.3361 =413.9118 zpve=0.115063 U0=-232.333258 U=-232.328097 H=-232.327153 G=-232.361103 Cv=19.023 pbc="F F F" +O 0.54622847 1.03270424 -0.58398083 +C -0.00112269 -0.11036621 0.01898238 +C 0.13945950 -0.34310429 1.54777117 +C -1.26863784 -0.99876517 1.51632654 +C -1.53844651 -0.26418236 0.17423510 +H 0.20332379 1.80433874 -0.12071219 +H 0.41296647 -0.95857955 -0.53560402 +H 0.98890738 -0.92719626 1.90921867 +H 0.11786549 0.61323034 2.08356053 +H -1.20828813 -2.08191064 1.38036699 +H -1.94143614 -0.79119746 2.35014116 +H -2.00495682 0.71307933 0.34581493 +H -2.09160311 -0.78230202 -0.61249171 +11 +Properties=species:S:1:pos:R:3 A=10.85394 B=4.52867 C=3.62328 mu=2.4158 alpha=38.58 epsHOMO=-0.2465 epsLUMO=0.0604 epsgap=0.3069 =374.4087 zpve=0.091005 U0=-268.248371 U=-268.243234 H=-268.24229 G=-268.276572 Cv=17.344 pbc="F F F" +O 0.07721714 1.41738724 -0.14439016 +C 0.01675156 0.02484791 0.03233817 +C 0.11193823 -0.54392541 1.47000744 +O -1.16387804 -1.20093191 1.32441605 +C -1.41930178 -0.52040930 0.07750673 +H 1.00022214 1.67889479 -0.21697466 +H 0.66036495 -0.50380879 -0.68180327 +H 0.91806589 -1.25385265 1.68878628 +H 0.09146208 0.24427487 2.23499232 +H -2.16139683 0.28183960 0.18222436 +H -1.72867456 -1.20941157 -0.71550887 +15 +Properties=species:S:1:pos:R:3 A=7.14221 B=4.37083 C=3.30265 mu=0.1023 alpha=52.92 epsHOMO=-0.2632 epsLUMO=0.094 epsgap=0.3573 =465.3301 zpve=0.137341 U0=-196.406419 U=-196.40034 H=-196.399396 G=-196.435152 Cv=22.005 pbc="F F F" +C -0.05189850 1.55217435 -0.10318438 +C 0.01226648 0.04690675 0.03747520 +C 1.30115692 -0.70661553 -0.19725724 +C 0.70431939 -0.64893239 1.19003562 +C 1.39750324 0.09481280 2.31078011 +H 0.87404209 2.03543171 0.22162228 +H -0.21641357 1.83695278 -1.14852958 +H -0.87157646 1.97673684 0.48797046 +H -0.88508206 -0.45596941 -0.31580786 +H 1.27663658 -1.64149022 -0.74697967 +H 2.19424067 -0.11404121 -0.37569359 +H 0.21672461 -1.56377934 1.51902400 +H 1.91317356 0.99051175 1.95271747 +H 0.68738009 0.40901468 3.08446972 +H 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+ +.. only:: html + + .. image:: /examples/multi-gpu/images/thumb/sphx_glr_multi-gpu_thumb.png + :alt: + + :ref:`sphx_glr_examples_multi-gpu_multi-gpu.py` + +.. raw:: html + +
Multi-GPU training
+
+ + +.. thumbnail-parent-div-close + +.. raw:: html + + + + +.. toctree:: + :hidden: + + /examples/multi-gpu/multi-gpu + + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-gallery + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download all examples in Python source code: multi-gpu_python.zip ` + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download all examples in Jupyter notebooks: multi-gpu_jupyter.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/multi-gpu/multi-gpu.ipynb b/docs/src/examples/multi-gpu/multi-gpu.ipynb new file mode 100644 index 0000000000..6cc034d0f1 --- /dev/null +++ b/docs/src/examples/multi-gpu/multi-gpu.ipynb @@ -0,0 +1,32 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Multi-GPU training\n\n``metatrain`` supports training a model with several GPUs, which can accelerate the\ntraining, especially when the training dataset is large / there are many training\nepochs. This feature is enabled by the :py:mod:`torch.distributed` module, and thus can\ndo multiprocess parallelism across several nodes.\n\nIn multi-GPU training, every batch of samples is split into smaller mini-batches and the\ncomputation is run for each of the smaller mini-batches in parallel on different GPUs.\nThe different gradients obtained on each device are then summed. This approach allows\nthe user to reduce the time it takes to train models.\n\nTo know if the model supports multi-GPU training, please check [Available Architectures](../../docs/src/architectures/index.rst) and see if the default hyperparameters have\nthe ``distributed`` option.\n\n## Input file\nTo do this, you only need to switch on the ``distributed`` option in the ``.yaml`` file\nfor the training. Let's take [this tutorial](../beginner_tutorials/train-from-scratch.rst) as an example. Now, the\n``options.yaml`` is\n\n.. literalinclude:: ./options-distributed.yaml\n :language: yaml\n :linenos:\n\n## Slurm script\n\nBelow is an example Slurm script for submitting the job. Please be aware that the actual\nconfigurations vary from clusters to clusters, so you have to modify it. Different\nscheduler will require similar options. ``metatrain`` will automatically use all the\nGPUs that you have asked for. You should make a single GPU visible for each process\n(setting `--gpus-per-node` equal to the number of GPUs, or setting `--gpus-per-task=1`,\ndepending on your cluster configuration).\n\n```bash\n#!/bin/bash\n#SBATCH --nodes 1\n#SBATCH --ntasks 2 # must equal to the number of GPUs\n#SBATCH --ntasks-per-node 2\n#SBATCH --gpus-per-node 2 # use 2 GPUs\n#SBATCH --cpus-per-task 8\n#SBATCH --exclusive\n#SBATCH --partition=h100 # adapt this to your cluster\n#SBATCH --time=1:00:00\n\n# load modules and/or virtual environments and/or containers here\n\nsrun mtt train options-distributed.yaml\n```\n## Performance\n\nIf the multi-GPU training runs successfully, you should see this in the training log:\n\n```bash\n[2025-10-08 11:34:22][INFO] - Distributed environment set up with MASTER_ADDR=kh080,\nMASTER_PORT=39591, WORLD_SIZE=2, RANK=0, LOCAL_RANK=0\n[2025-10-08 11:34:23][INFO] - Training on 2 devices with dtype torch.float32\n```\nThis 100-epoch training takes 23 seconds.\n\n```bash\n[2025-10-08 11:34:22][INFO] - Starting training from scratch\n...\n[2025-10-08 11:34:45][INFO] - Training finished!\n```\nNow let's switch off the multi-GPU training by writing ``distributed: false``, and\nsubmit this job again. The training takes 69 seconds.\n\n```bash\n[2025-10-08 11:37:38][INFO] - Setting up model\n...\n[2025-10-08 11:38:47][INFO] - Training finished!\n```\n## Multi-GPU fine-tuning\nYou can use multi-GPU for fine-tuning too, by writing ``distributed: True`` in the\n``.yaml`` input. For information about fine-tuning, please refer to [this tutorial on\nfine-tuning](../../getting-started/finetuning-example.rst).\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/examples/multi-gpu/multi-gpu.py b/docs/src/examples/multi-gpu/multi-gpu.py similarity index 100% rename from examples/multi-gpu/multi-gpu.py rename to docs/src/examples/multi-gpu/multi-gpu.py diff --git a/docs/src/examples/multi-gpu/multi-gpu.py.md5 b/docs/src/examples/multi-gpu/multi-gpu.py.md5 new file mode 100644 index 0000000000..86e00a927d --- /dev/null +++ b/docs/src/examples/multi-gpu/multi-gpu.py.md5 @@ -0,0 +1 @@ +4666621a9eb6317d63ffb285c171dd9a \ No newline at end of file diff --git a/docs/src/examples/multi-gpu/multi-gpu.rst b/docs/src/examples/multi-gpu/multi-gpu.rst new file mode 100644 index 0000000000..e1d6b46bf7 --- /dev/null +++ b/docs/src/examples/multi-gpu/multi-gpu.rst @@ -0,0 +1,134 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "examples/multi-gpu/multi-gpu.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code. + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_examples_multi-gpu_multi-gpu.py: + + +================== +Multi-GPU training +================== + +``metatrain`` supports training a model with several GPUs, which can accelerate the +training, especially when the training dataset is large / there are many training +epochs. This feature is enabled by the :py:mod:`torch.distributed` module, and thus can +do multiprocess parallelism across several nodes. + +In multi-GPU training, every batch of samples is split into smaller mini-batches and the +computation is run for each of the smaller mini-batches in parallel on different GPUs. +The different gradients obtained on each device are then summed. This approach allows +the user to reduce the time it takes to train models. + +To know if the model supports multi-GPU training, please check `Available Architectures +<../../docs/src/architectures/index.rst>`_ and see if the default hyperparameters have +the ``distributed`` option. + +Input file +---------- +To do this, you only need to switch on the ``distributed`` option in the ``.yaml`` file +for the training. Let's take `this tutorial +<../beginner_tutorials/train-from-scratch.rst>`_ as an example. Now, the +``options.yaml`` is + +.. literalinclude:: ./options-distributed.yaml + :language: yaml + :linenos: + +Slurm script +------------ + +Below is an example Slurm script for submitting the job. Please be aware that the actual +configurations vary from clusters to clusters, so you have to modify it. Different +scheduler will require similar options. ``metatrain`` will automatically use all the +GPUs that you have asked for. You should make a single GPU visible for each process +(setting `--gpus-per-node` equal to the number of GPUs, or setting `--gpus-per-task=1`, +depending on your cluster configuration). + +.. code-block:: bash + + #!/bin/bash + #SBATCH --nodes 1 + #SBATCH --ntasks 2 # must equal to the number of GPUs + #SBATCH --ntasks-per-node 2 + #SBATCH --gpus-per-node 2 # use 2 GPUs + #SBATCH --cpus-per-task 8 + #SBATCH --exclusive + #SBATCH --partition=h100 # adapt this to your cluster + #SBATCH --time=1:00:00 + + # load modules and/or virtual environments and/or containers here + + srun mtt train options-distributed.yaml + +Performance +----------- + +If the multi-GPU training runs successfully, you should see this in the training log: + +.. code-block:: bash + + [2025-10-08 11:34:22][INFO] - Distributed environment set up with MASTER_ADDR=kh080, + MASTER_PORT=39591, WORLD_SIZE=2, RANK=0, LOCAL_RANK=0 + [2025-10-08 11:34:23][INFO] - Training on 2 devices with dtype torch.float32 + +This 100-epoch training takes 23 seconds. + +.. code-block:: bash + + [2025-10-08 11:34:22][INFO] - Starting training from scratch + ... + [2025-10-08 11:34:45][INFO] - Training finished! + +Now let's switch off the multi-GPU training by writing ``distributed: false``, and +submit this job again. The training takes 69 seconds. + +.. code-block:: bash + + [2025-10-08 11:37:38][INFO] - Setting up model + ... + [2025-10-08 11:38:47][INFO] - Training finished! + +Multi-GPU fine-tuning +--------------------- +You can use multi-GPU for fine-tuning too, by writing ``distributed: True`` in the +``.yaml`` input. For information about fine-tuning, please refer to `this tutorial on +fine-tuning <../../getting-started/finetuning-example.rst>`_. + + +.. _sphx_glr_download_examples_multi-gpu_multi-gpu.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: multi-gpu.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: multi-gpu.py ` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: multi-gpu.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/multi-gpu/multi-gpu.zip b/docs/src/examples/multi-gpu/multi-gpu.zip new file mode 100644 index 0000000000000000000000000000000000000000..b4c6adaa55d42c51bb1e2789ea2fc089a57de639 GIT binary patch literal 7233 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"module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "subprocess", + "module_short": "subprocess", + "name": "run" + } + ], + "system": [ + { + "is_class": false, + "is_explicit": false, + "module": "torch", + "module_short": "torch", + "name": "ScriptObject" + } + ], + "systems": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "list" + } + ], + "systems_to_torch": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "metatomic.torch", + "module_short": "metatomic.torch", + "name": "systems_to_torch" + } + ], + "torch.float64": [ + { + "is_class": false, + "is_explicit": false, + "module": "torch", + "module_short": "torch", + "name": "dtype" + }, + { + "is_class": false, + "is_explicit": false, + "module": "torch", + "module_short": "torch", + "name": "float64" + } + ], + "torch.tensor": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "builtin_function_or_method" + }, + { + "is_class": false, + "is_explicit": false, + "module": "torch", + "module_short": "torch", + "name": "tensor" + } + ], + "x_mm": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "memmap" + } + ], + "x_mm.flush": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "memmap.flush" + }, + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "ndarray.flush" + } + ], + "x_path": [ + { + "is_class": false, + "is_explicit": false, + "module": "pathlib._local", + "module_short": "pathlib", + "name": "PosixPath" + }, + { + "is_class": false, + "is_explicit": false, + "module": "pathlib", + "module_short": "pathlib", + "name": "PosixPath" + } + ] +} diff --git a/docs/src/examples/programmatic/data_preparation/data_preparation.ipynb b/docs/src/examples/programmatic/data_preparation/data_preparation.ipynb new file mode 100644 index 0000000000..bf87a8c055 --- /dev/null +++ b/docs/src/examples/programmatic/data_preparation/data_preparation.ipynb @@ -0,0 +1,169 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# How to prepare data for training\n\n.. attention::\n\n This tutorial is only relevant for users who need to prepare their data from scratch\n from several files or for big datasets. If you already have your data in a common\n file format (like XYZ or `ASE database`_), you can skip this tutorial and directly\n start training.\n\n\nXYZ, ASE databases, and also from metrain's\n:class:`metatrain.utils.data.dataset.DiskDataset ` file.\n\nFor the small datasets (<10k structures), you can simply provide an XYZ file or an ASE\ndatabase to ``metatrain``, and it will handle the data loading for you. Large datasets\n(>10k structures) may not fit into the GPU memory. In such cases, it is useful to\npre-process the dataset, save it to disk and load it on the fly during training.\n\nIn this tutorial, we will show how to prepare data for training using three different\nformats. You can choose the one that best fits your needs.\n\nWe start by importing the necessary packages.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import subprocess\nfrom pathlib import Path\n\nimport ase.io\nimport numpy as np\nimport torch\nfrom metatensor.torch import Labels, TensorBlock, TensorMap\nfrom metatomic.torch import NeighborListOptions, systems_to_torch\n\nfrom metatrain.utils.data.writers import DiskDatasetWriter\nfrom metatrain.utils.neighbor_lists import get_system_with_neighbor_lists" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create a XYZ training file (small datasets)\n\nFirst, we will show how to create a XYZ file with fields corresponding to the target\nproperties. On modern HPC systems, this format is suitable for datasets up to around\n1M structures. As an example, we will use 100 structures from a file read by ASE_.\nSince files from reference calculations may be located in different directories, we\nfirst create a list of all path that we want to read from. Here, for simplicity, we\nassume that all files are located in the same directory.\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "filelist = 100 * [\"qm9_reduced_100.xyz\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now read the structures using the ASE package. Check the ase documentation for\nmore details on how to read different file formats. Instead of creating the ``atoms``\nobject by reading from disk, you can also create an\n:class:`ase.Atoms` object containing the chemical ``symbols``, ``positions``, the\n``cell`` and the periodic boundary conditions (``pbc``) by hand using its constructor.\n\n.. hint::\n\n If a property is not read by the :func:`ase.io.read` function, you can add custom\n scalar properties to the ``info`` dictionary. Vector properties (e.g. forces) can be\n added to the ``arrays`` dictionary. Tensor properties (e.g. stress) must\n be flattened before adding them to the ``arrays`` dictionary.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "frames = []\nfor i, fname in enumerate(filelist):\n atoms = ase.io.read(fname, index=i)\n\n n_atoms = len(atoms)\n # scalar\n atoms.info[\"U0\"] = -100.0\n # vector\n atoms.arrays[\"forces\"] = np.zeros((n_atoms, 3))\n # tensor\n atoms.arrays[\"my_tensor\"] = np.zeros((n_atoms, 3, 3)).reshape(n_atoms, 9)\n\n frames.append(atoms)\n\nase.io.write(\"data.xyz\", frames)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Note

The names of the added properties (like, ``U0``, etc.) must be referenced correctly\n in the ``options.yaml`` file.

\n\n## Create a ``DiskDataset`` (large datasets)\n\nIn addition to the systems and targets (as above), we also save the neighbor\nlists that the model will use during training. We first create the writer object that\nwill write the data to a zip file.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "disk_dataset_writer = DiskDatasetWriter(\"qm9_reduced_100.zip\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we loop over all structures, convert them to the internal torch format using\n:func:`metatomic.torch.systems_to_torch`, compute the neighbor lists using\n:func:`metatrain.utils.neighbor_lists.get_system_with_neighbor_lists` and write\neverything to disk using the writer's ``write()`` method.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "for i, fname in enumerate(filelist):\n atoms = ase.io.read(fname, index=i)\n\n system = systems_to_torch(atoms, dtype=torch.float64)\n system = get_system_with_neighbor_lists(\n system,\n [NeighborListOptions(cutoff=5.0, full_list=True, strict=True)],\n )\n energy = TensorMap(\n keys=Labels.single(),\n blocks=[\n TensorBlock(\n values=torch.tensor([[atoms.info[\"U0\"]]], dtype=torch.float64),\n samples=Labels(\n names=[\"system\"],\n values=torch.tensor([[i]]),\n ),\n components=[],\n properties=Labels(\"energy\", torch.tensor([[0]])),\n )\n ],\n )\n disk_dataset_writer.write([system], {\"energy\": energy})\n\ndisk_dataset_writer.finish()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, you can also write the whole dataset at once, which might be more\nefficient (but also potentially run into memory issues). We use the same ``frames``\nthat we created above.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "disk_dataset_writer = DiskDatasetWriter(\"qm9_reduced_100_all_at_once.zip\")\n\nsystems = systems_to_torch(frames, dtype=torch.float64)\nsystems = [\n get_system_with_neighbor_lists(\n system,\n [NeighborListOptions(cutoff=5.0, full_list=True, strict=True)],\n )\n for system in systems\n]\nenergy = TensorMap(\n keys=Labels.single(),\n blocks=[\n TensorBlock(\n values=torch.tensor(\n [frame.info[\"U0\"] for frame in frames], dtype=torch.float64\n ).reshape(-1, 1),\n samples=Labels.range(\"system\", len(frames)),\n components=[],\n properties=Labels(\"energy\", torch.tensor([[0]])),\n )\n ],\n)\n\ndisk_dataset_writer.write(systems, {\"energy\": energy})\ndisk_dataset_writer.finish()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The dataset is saved to disk. You can now provide it to ``metatrain`` as a\ndataset to train from, simply by replacing your ``.xyz`` file with the newly created\nzip file (e.g. ``read_from: qm9_reduced_100.zip``).\n\n## Create a ``MemmapDataset`` (large datasets, parallel filesystems)\n\nIf your dataset is large and you are using a parallel filesystem (e.g. on an HPC\ncluster), it is recommended to use a ``MemmapDataset`` instead of a ``DiskDataset``.\nThe ``MemmapDataset`` stores the data inside memory-mapped numpy arrays instead of a\nzip file. Reading from this format avoids I/O bottlenecks, but it does not support\nspherical targets or storing neighbor lists.\n\nAs an example, we will use 100 structures from a dataset of carbon structures. The\nnumpy arrays must be saved inside a directory, using the following format.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "structures = ase.io.read(\"carbon_reduced_100.xyz\", index=\":\")\n\nroot = Path(\"carbon_reduced_100_memmap/\")\nroot.mkdir()\n\nns_path = root / \"ns.npy\"\nna_path = root / \"na.npy\"\na_path = root / \"a.bin\"\nx_path = root / \"x.bin\"\nc_path = root / \"c.bin\"\ne_path = root / \"e.bin\"\nf_path = root / \"f.bin\"\ns_path = root / \"s.bin\"\n\nns = len(structures)\nna = np.cumsum(np.array([0] + [len(s) for s in structures], dtype=np.int64))\nnp.save(ns_path, ns)\nnp.save(na_path, na)\n\na_mm = np.memmap(a_path, dtype=\"int32\", mode=\"w+\", shape=(na[-1],))\nx_mm = np.memmap(x_path, dtype=\"float32\", mode=\"w+\", shape=(na[-1], 3))\nc_mm = np.memmap(c_path, dtype=\"float32\", mode=\"w+\", shape=(ns, 3, 3))\ne_mm = np.memmap(e_path, dtype=\"float32\", mode=\"w+\", shape=(ns, 1))\nf_mm = np.memmap(f_path, dtype=\"float32\", mode=\"w+\", shape=(na[-1], 3))\ns_mm = np.memmap(s_path, dtype=\"float32\", mode=\"w+\", shape=(ns, 3, 3))\n\nfor i, s in enumerate(structures):\n a_mm[na[i] : na[i + 1]] = s.numbers\n x_mm[na[i] : na[i + 1]] = s.get_positions()\n c_mm[i] = s.get_cell()[:]\n e_mm[i] = s.get_potential_energy()\n f_mm[na[i] : na[i + 1]] = s.arrays[\"force\"]\n s_mm[i] = -s.info[\"virial\"] / s.get_volume()\n\na_mm.flush()\nx_mm.flush()\nc_mm.flush()\ne_mm.flush()\nf_mm.flush()\ns_mm.flush()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The dataset is saved to disk. You can now provide it to ``metatrain`` as a\ndataset to train from, simply by specifying the newly created\ndirectory as the path from which to read the systems\n(e.g. ``read_from: carbon_reduced_100_memmap/``).\n\nFor example, you can use the following options file:\n\n.. literalinclude:: options.yaml\n :language: yaml\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "subprocess.run([\"mtt\", \"train\", \"options.yaml\"])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/examples/programmatic/data_preparation/data_preparation.py b/docs/src/examples/programmatic/data_preparation/data_preparation.py similarity index 100% rename from examples/programmatic/data_preparation/data_preparation.py rename to docs/src/examples/programmatic/data_preparation/data_preparation.py diff --git a/docs/src/examples/programmatic/data_preparation/data_preparation.py.md5 b/docs/src/examples/programmatic/data_preparation/data_preparation.py.md5 new file mode 100644 index 0000000000..ec11fcf764 --- /dev/null +++ b/docs/src/examples/programmatic/data_preparation/data_preparation.py.md5 @@ -0,0 +1 @@ +54ca454338c67496640c3b734e586992 \ No newline at end of file diff --git a/docs/src/examples/programmatic/data_preparation/data_preparation.rst b/docs/src/examples/programmatic/data_preparation/data_preparation.rst new file mode 100644 index 0000000000..6aa58fb836 --- /dev/null +++ b/docs/src/examples/programmatic/data_preparation/data_preparation.rst @@ -0,0 +1,390 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "examples/programmatic/data_preparation/data_preparation.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code. + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_examples_programmatic_data_preparation_data_preparation.py: + + +How to prepare data for training +================================ + +.. attention:: + + This tutorial is only relevant for users who need to prepare their data from scratch + from several files or for big datasets. If you already have your data in a common + file format (like XYZ or `ASE database`_), you can skip this tutorial and directly + start training. + +.. _ASE database: https://ase-lib.org/ase/db/db.html + +XYZ, ASE databases, and also from metrain's +:class:`metatrain.utils.data.dataset.DiskDataset ` file. + +For the small datasets (<10k structures), you can simply provide an XYZ file or an ASE +database to ``metatrain``, and it will handle the data loading for you. Large datasets +(>10k structures) may not fit into the GPU memory. In such cases, it is useful to +pre-process the dataset, save it to disk and load it on the fly during training. + +In this tutorial, we will show how to prepare data for training using three different +formats. You can choose the one that best fits your needs. + +We start by importing the necessary packages. + +.. GENERATED FROM PYTHON SOURCE LINES 30-43 + +.. code-block:: Python + + import subprocess + from pathlib import Path + + import ase.io + import numpy as np + import torch + from metatensor.torch import Labels, TensorBlock, TensorMap + from metatomic.torch import NeighborListOptions, systems_to_torch + + from metatrain.utils.data.writers import DiskDatasetWriter + from metatrain.utils.neighbor_lists import get_system_with_neighbor_lists + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 44-55 + +Create a XYZ training file (small datasets) +------------------------------------------- + +First, we will show how to create a XYZ file with fields corresponding to the target +properties. On modern HPC systems, this format is suitable for datasets up to around +1M structures. As an example, we will use 100 structures from a file read by ASE_. +Since files from reference calculations may be located in different directories, we +first create a list of all path that we want to read from. Here, for simplicity, we +assume that all files are located in the same directory. + +.. _ASE: https://ase-lib.org/ + +.. GENERATED FROM PYTHON SOURCE LINES 55-58 + +.. code-block:: Python + + + filelist = 100 * ["qm9_reduced_100.xyz"] + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 59-71 + +We will now read the structures using the ASE package. Check the ase documentation for +more details on how to read different file formats. Instead of creating the ``atoms`` +object by reading from disk, you can also create an +:class:`ase.Atoms` object containing the chemical ``symbols``, ``positions``, the +``cell`` and the periodic boundary conditions (``pbc``) by hand using its constructor. + +.. hint:: + + If a property is not read by the :func:`ase.io.read` function, you can add custom + scalar properties to the ``info`` dictionary. Vector properties (e.g. forces) can be + added to the ``arrays`` dictionary. Tensor properties (e.g. stress) must + be flattened before adding them to the ``arrays`` dictionary. + +.. GENERATED FROM PYTHON SOURCE LINES 72-89 + +.. code-block:: Python + + + frames = [] + for i, fname in enumerate(filelist): + atoms = ase.io.read(fname, index=i) + + n_atoms = len(atoms) + # scalar + atoms.info["U0"] = -100.0 + # vector + atoms.arrays["forces"] = np.zeros((n_atoms, 3)) + # tensor + atoms.arrays["my_tensor"] = np.zeros((n_atoms, 3, 3)).reshape(n_atoms, 9) + + frames.append(atoms) + + ase.io.write("data.xyz", frames) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 90-101 + +.. note:: + + The names of the added properties (like, ``U0``, etc.) must be referenced correctly + in the ``options.yaml`` file. + +Create a ``DiskDataset`` (large datasets) +----------------------------------------- + +In addition to the systems and targets (as above), we also save the neighbor +lists that the model will use during training. We first create the writer object that +will write the data to a zip file. + +.. GENERATED FROM PYTHON SOURCE LINES 102-105 + +.. code-block:: Python + + + disk_dataset_writer = DiskDatasetWriter("qm9_reduced_100.zip") + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 106-110 + +Then we loop over all structures, convert them to the internal torch format using +:func:`metatomic.torch.systems_to_torch`, compute the neighbor lists using +:func:`metatrain.utils.neighbor_lists.get_system_with_neighbor_lists` and write +everything to disk using the writer's ``write()`` method. + +.. GENERATED FROM PYTHON SOURCE LINES 111-138 + +.. code-block:: Python + + + for i, fname in enumerate(filelist): + atoms = ase.io.read(fname, index=i) + + system = systems_to_torch(atoms, dtype=torch.float64) + system = get_system_with_neighbor_lists( + system, + [NeighborListOptions(cutoff=5.0, full_list=True, strict=True)], + ) + energy = TensorMap( + keys=Labels.single(), + blocks=[ + TensorBlock( + values=torch.tensor([[atoms.info["U0"]]], dtype=torch.float64), + samples=Labels( + names=["system"], + values=torch.tensor([[i]]), + ), + components=[], + properties=Labels("energy", torch.tensor([[0]])), + ) + ], + ) + disk_dataset_writer.write([system], {"energy": energy}) + + disk_dataset_writer.finish() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 139-142 + +Alternatively, you can also write the whole dataset at once, which might be more +efficient (but also potentially run into memory issues). We use the same ``frames`` +that we created above. + +.. GENERATED FROM PYTHON SOURCE LINES 143-171 + +.. code-block:: Python + + + disk_dataset_writer = DiskDatasetWriter("qm9_reduced_100_all_at_once.zip") + + systems = systems_to_torch(frames, dtype=torch.float64) + systems = [ + get_system_with_neighbor_lists( + system, + [NeighborListOptions(cutoff=5.0, full_list=True, strict=True)], + ) + for system in systems + ] + energy = TensorMap( + keys=Labels.single(), + blocks=[ + TensorBlock( + values=torch.tensor( + [frame.info["U0"] for frame in frames], dtype=torch.float64 + ).reshape(-1, 1), + samples=Labels.range("system", len(frames)), + components=[], + properties=Labels("energy", torch.tensor([[0]])), + ) + ], + ) + + disk_dataset_writer.write(systems, {"energy": energy}) + disk_dataset_writer.finish() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 172-187 + +The dataset is saved to disk. You can now provide it to ``metatrain`` as a +dataset to train from, simply by replacing your ``.xyz`` file with the newly created +zip file (e.g. ``read_from: qm9_reduced_100.zip``). + +Create a ``MemmapDataset`` (large datasets, parallel filesystems) +----------------------------------------------------------------- + +If your dataset is large and you are using a parallel filesystem (e.g. on an HPC +cluster), it is recommended to use a ``MemmapDataset`` instead of a ``DiskDataset``. +The ``MemmapDataset`` stores the data inside memory-mapped numpy arrays instead of a +zip file. Reading from this format avoids I/O bottlenecks, but it does not support +spherical targets or storing neighbor lists. + +As an example, we will use 100 structures from a dataset of carbon structures. The +numpy arrays must be saved inside a directory, using the following format. + +.. GENERATED FROM PYTHON SOURCE LINES 188-230 + +.. code-block:: Python + + + structures = ase.io.read("carbon_reduced_100.xyz", index=":") + + root = Path("carbon_reduced_100_memmap/") + root.mkdir() + + ns_path = root / "ns.npy" + na_path = root / "na.npy" + a_path = root / "a.bin" + x_path = root / "x.bin" + c_path = root / "c.bin" + e_path = root / "e.bin" + f_path = root / "f.bin" + s_path = root / "s.bin" + + ns = len(structures) + na = np.cumsum(np.array([0] + [len(s) for s in structures], dtype=np.int64)) + np.save(ns_path, ns) + np.save(na_path, na) + + a_mm = np.memmap(a_path, dtype="int32", mode="w+", shape=(na[-1],)) + x_mm = np.memmap(x_path, dtype="float32", mode="w+", shape=(na[-1], 3)) + c_mm = np.memmap(c_path, dtype="float32", mode="w+", shape=(ns, 3, 3)) + e_mm = np.memmap(e_path, dtype="float32", mode="w+", shape=(ns, 1)) + f_mm = np.memmap(f_path, dtype="float32", mode="w+", shape=(na[-1], 3)) + s_mm = np.memmap(s_path, dtype="float32", mode="w+", shape=(ns, 3, 3)) + + for i, s in enumerate(structures): + a_mm[na[i] : na[i + 1]] = s.numbers + x_mm[na[i] : na[i + 1]] = s.get_positions() + c_mm[i] = s.get_cell()[:] + e_mm[i] = s.get_potential_energy() + f_mm[na[i] : na[i + 1]] = s.arrays["force"] + s_mm[i] = -s.info["virial"] / s.get_volume() + + a_mm.flush() + x_mm.flush() + c_mm.flush() + e_mm.flush() + f_mm.flush() + s_mm.flush() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 231-240 + +The dataset is saved to disk. You can now provide it to ``metatrain`` as a +dataset to train from, simply by specifying the newly created +directory as the path from which to read the systems +(e.g. ``read_from: carbon_reduced_100_memmap/``). + +For example, you can use the following options file: + +.. literalinclude:: options.yaml + :language: yaml + +.. GENERATED FROM PYTHON SOURCE LINES 241-243 + +.. code-block:: Python + + + subprocess.run(["mtt", "train", "options.yaml"]) + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + + CompletedProcess(args=['mtt', 'train', 'options.yaml'], returncode=0) + + + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** (0 minutes 10.476 seconds) + + +.. _sphx_glr_download_examples_programmatic_data_preparation_data_preparation.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: data_preparation.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: data_preparation.py ` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: data_preparation.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by 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+ +.. only:: html + + .. image:: /examples/programmatic/data_preparation/images/thumb/sphx_glr_data_preparation_thumb.png + :alt: + + :ref:`sphx_glr_examples_programmatic_data_preparation_data_preparation.py` + +.. raw:: html + +
How to prepare data for training
+
+ + +.. thumbnail-parent-div-close + +.. raw:: html + + + + +.. toctree:: + :hidden: + + /examples/programmatic/data_preparation/data_preparation + + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-gallery + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download all examples in Python source code: data_preparation_python.zip ` + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download all examples in Jupyter notebooks: data_preparation_jupyter.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/examples/programmatic/data_preparation/options.yaml b/docs/src/examples/programmatic/data_preparation/options.yaml similarity index 100% rename from examples/programmatic/data_preparation/options.yaml rename to docs/src/examples/programmatic/data_preparation/options.yaml diff --git a/docs/src/examples/programmatic/data_preparation/qm9_reduced_100.xyz 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epsHOMO=-0.2543 epsLUMO=0.0302 epsgap=0.2845 =145.3078 zpve=0.045279 U0=-169.860788 U=-169.856903 H=-169.855958 G=-169.885594 Cv=10.89 pbc="F F F" +N -0.02589986 1.34614561 0.00889391 +C 0.04646728 -0.01174341 0.00120425 +O 1.07183520 -0.65258782 -0.01113313 +H 0.82535497 1.88504948 0.00373769 +H -0.90837680 1.82679638 0.01891992 +H -0.96144100 -0.47500376 0.00807391 +11 +Properties=species:S:1:pos:R:3 A=29.45018 B=8.37701 C=7.42076 mu=0.0597 alpha=34.75 epsHOMO=-0.323 epsLUMO=0.0949 epsgap=0.4179 =227.1361 zpve=0.103182 U0=-119.052475 U=-119.047927 H=-119.046983 G=-119.078157 Cv=14.84 pbc="F F F" +C -0.03113825 1.54081582 0.03192126 +C 0.01215347 0.01092235 -0.01603259 +C 0.72169129 -0.52583353 -1.26230570 +H 0.97955987 1.96459116 0.03098367 +H -0.55840223 1.94831192 -0.83816075 +H -0.54252252 1.90153531 0.93005671 +H 0.51522791 -0.36840234 0.88231134 +H -1.01070641 -0.38456999 0.02051783 +H 1.75851210 -0.17376585 -1.30871516 +H 0.74087192 -1.62024959 -1.27516511 +H 0.22023351 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1.79343530 -0.87251051 +H -0.53002895 1.72291969 0.91101696 +H 0.13993792 -0.25599333 -2.05098375 +H 1.69265275 -0.23868382 -1.17477743 +H 0.59959435 -1.64180161 -1.02407616 +9 +Properties=species:S:1:pos:R:3 A=20.15852 B=20.15302 C=12.5891 mu=0.0005 alpha=30.82 epsHOMO=-0.2888 epsLUMO=0.1042 epsgap=0.393 =155.8145 zpve=0.081231 U0=-117.824798 U=-117.821426 H=-117.820482 G=-117.849087 Cv=11.041 pbc="F F F" +C -0.01193280 1.51433198 0.01031700 +C 1.30299118 0.77886562 -0.00617842 +C 0.00867174 0.00767042 0.00201032 +H -0.30541464 2.01702117 0.92533239 +H -0.32275498 2.02680156 -0.89347839 +H 1.88493396 0.79145020 -0.92119393 +H 1.90226631 0.78165391 0.89766373 +H -0.27088043 -0.51289125 0.91137474 +H -0.28821983 -0.50310409 -0.90740824 +7 +Properties=species:S:1:pos:R:3 A=25.77385 B=22.12109 C=14.18066 mu=1.7675 alpha=24.04 epsHOMO=-0.2682 epsLUMO=0.1042 epsgap=0.3724 =129.891 zpve=0.057289 U0=-153.742562 U=-153.73941 H=-153.738466 G=-153.766642 Cv=9.176 pbc="F F F" +C 0.01537720 1.41764380 0.00956287 +C 1.26478161 0.64923984 -0.00655044 +O -0.00024018 -0.00771006 0.00204055 +H -0.31763326 1.88586598 0.93475751 +H -0.33527583 1.89576673 -0.90397578 +H 1.83246682 0.56256848 -0.93193290 +H 1.85008258 0.55267938 0.90680147 +10 +Properties=species:S:1:pos:R:3 A=10.12193 B=8.49011 C=4.89615 mu=2.7362 alpha=35.53 epsHOMO=-0.2431 epsLUMO=-0.0087 epsgap=0.2344 =292.4367 zpve=0.083382 U0=-193.08834 U=-193.082969 H=-193.082024 G=-193.116476 Cv=16.893 pbc="F F F" +C -0.00310189 1.48050328 -0.17259867 +C -0.04527202 -0.02887059 0.00139493 +C 1.29705800 -0.72194415 0.16889042 +O -1.08643172 -0.64273343 0.00630393 +H 0.59403529 1.74668579 -1.05225043 +H -1.01540407 1.87034023 -0.28382265 +H 0.48099600 1.95070605 0.69099376 +H 1.82512144 -0.32470731 1.04328658 +H 1.14892507 -1.79597689 0.28554393 +H 1.93682635 -0.52949692 -0.69995403 +9 +Properties=species:S:1:pos:R:3 A=10.78886 B=9.27509 C=5.14977 mu=3.6367 alpha=31.83 epsHOMO=-0.2436 epsLUMO=0.0347 epsgap=0.2783 =267.6148 zpve=0.07319 U0=-209.159302 U=-209.15402 H=-209.153076 G=-209.187468 Cv=16.561 pbc="F F F" +C 0.00679889 1.49969736 -0.02567030 +C -0.02740385 -0.01752051 -0.14894811 +N 1.17826667 -0.63324298 0.05691721 +O -1.03721965 -0.63980649 -0.40727572 +H 0.91390280 1.92923755 -0.46100067 +H -0.87080896 1.91124186 -0.52345176 +H -0.02826124 1.78517271 1.03149125 +H 1.98405139 -0.13175861 0.38674906 +H 1.19049421 -1.63919563 0.10006209 +8 +Properties=species:S:1:pos:R:3 A=11.1475 B=10.36388 C=5.42091 mu=3.4869 alpha=28.07 epsHOMO=-0.2495 epsLUMO=0.0556 epsgap=0.3051 =244.2308 zpve=0.063824 U0=-225.221461 U=-225.217075 H=-225.216131 G=-225.247724 Cv=15.292 pbc="F F F" +N 0.03605270 1.36077873 -0.12416403 +C -0.02591138 -0.02076560 0.00200645 +N 1.21968479 -0.62334220 0.11963222 +O -1.06822866 -0.64174597 0.00865573 +H 0.80749439 1.83455148 0.32144915 +H -0.85825775 1.80373500 0.02243770 +H 1.99927148 -0.17303054 -0.33602496 +H 1.18204327 -1.62147901 -0.02109234 +14 +Properties=species:S:1:pos:R:3 A=7.75166 B=7.74847 C=4.48668 mu=0.0897 alpha=45.46 epsHOMO=-0.3167 epsLUMO=0.0843 epsgap=0.401 =355.0621 zpve=0.131146 U0=-158.342346 U=-158.336603 H=-158.335658 G=-158.370016 Cv=20.273 pbc="F F F" +C -0.03215888 1.54021598 0.01074456 +C 0.03381741 0.00745852 0.00180716 +C 0.71375572 -0.50856404 -1.27296949 +C 0.73848999 -0.52220886 1.25750907 +H 0.97522736 1.97414341 0.00528362 +H -0.56196769 1.92232326 -0.86835418 +H -0.54817239 1.91268414 0.90208039 +H -0.99752525 -0.37310578 0.00977297 +H 1.75070021 -0.15586784 -1.33269631 +H 0.73446691 -1.60328288 -1.29939696 +H 0.19367011 -0.16085589 -2.17195508 +H 0.23611990 -0.18382017 2.17005260 +H 0.75964083 -1.61713153 1.27226101 +H 1.77635866 -0.16985318 1.30053019 +12 +Properties=species:S:1:pos:R:3 A=8.67553 B=8.00568 C=4.75542 mu=1.4259 alpha=38.58 epsHOMO=-0.2612 epsLUMO=0.074 epsgap=0.3351 =318.3721 zpve=0.107673 U0=-194.267232 U=-194.261748 H=-194.260804 G=-194.294663 Cv=19.052 pbc="F F F" +C -0.03315871 1.54782566 -0.00438815 +C -0.01108534 0.01859095 0.01676117 +C 0.70938384 -0.53596711 1.23978444 +O -1.33219841 -0.51712554 0.05236057 +H 0.98215692 1.95715900 -0.03182225 +H -0.56388100 1.92087623 -0.88845865 +H -0.54082548 1.93340945 0.88575489 +H 0.51042122 -0.33767241 -0.88819176 +H 0.22240492 -0.18785860 2.15650245 +H 0.68242024 -1.62887638 1.23305141 +H 1.75434301 -0.21246950 1.25524188 +H -1.81818681 -0.15577435 -0.69553962 +6 +Properties=species:S:1:pos:R:3 A=0 B=4.4259726 C=4.4259726 mu=0 alpha=38.52 epsHOMO=-0.2599 epsLUMO=-0.0214 epsgap=0.2386 =278.6264 zpve=0.037354 U0=-153.459846 U=-153.455442 H=-153.454498 G=-153.482621 Cv=15.312 pbc="F F F" +C 0.68098021 0.00000000 0.00000000 +C -0.68098021 0.00000000 0.00000000 +C -1.88766603 0.00000000 0.00000000 +C 1.88766603 0.00000000 0.00000000 +H -2.94960000 0.00000000 0.00000000 +H 2.94960000 0.00000000 0.00000000 +5 +Properties=species:S:1:pos:R:3 A=0 B=4.5793222 C=4.5793222 mu=3.792 alpha=32.66 epsHOMO=-0.3102 epsLUMO=-0.0543 epsgap=0.2559 =260.1896 zpve=0.027259 U0=-169.557758 U=-169.553764 H=-169.55282 G=-169.581024 Cv=12.93 pbc="F F F" +C 0.01510390 0.00000000 1.00000000 +C 1.38233149 0.00000000 1.00000000 +C -1.18862525 0.00000000 1.00000000 +H -2.25177201 0.00000000 1.00000000 +N 2.54296187 0.00000000 1.00000000 +4 +Properties=species:S:1:pos:R:3 A=0 B=4.73269 C=4.73269 mu=0.0023 alpha=27.7 epsHOMO=-0.3696 epsLUMO=-0.0926 epsgap=0.277 =242.9308 zpve=0.015951 U0=-185.648533 U=-185.644825 H=-185.64388 G=-185.667652 Cv=10.398 pbc="F F F" +N 0.01745734 -1.16134217 -0.00415342 +C 0.00253212 -0.00344278 0.00179939 +C -0.01611419 1.37220987 0.00934873 +N -0.03264302 2.53008276 0.01609090 +6 +Properties=species:S:1:pos:R:3 A=68.5171 B=4.8345 C=4.51586 mu=2.7824 alpha=31.14 epsHOMO=-0.2777 epsLUMO=-0.0735 epsgap=0.2042 =268.3921 zpve=0.037208 U0=-190.624631 U=-190.620363 H=-190.619419 G=-190.650543 Cv=13.049 pbc="F F F" +O -0.06776120 -0.04233838 0.00282489 +C -0.00562945 1.16382879 0.00743862 +C 1.22985272 1.91339997 0.00028781 +C 2.23625322 2.57430970 -0.00529277 +H -0.91670701 1.79467546 0.01781352 +H 3.13429662 3.14370267 -0.01037656 +5 +Properties=species:S:1:pos:R:3 A=67.88408 B=5.00823 C=4.66413 mu=2.3112 alpha=26.25 epsHOMO=-0.3166 epsLUMO=-0.11 epsgap=0.2066 =251.0007 zpve=0.02654 U0=-206.721858 U=-206.717875 H=-206.716931 G=-206.747625 Cv=11.329 pbc="F F F" +O -0.04055978 -0.03799144 0.00266513 +C -0.01768870 1.16193251 0.00746008 +C 1.23315534 1.93381336 0.00043371 +N 2.19857378 2.57055097 -0.00483320 +H -0.92627534 1.78959196 0.01764859 +6 +Properties=species:S:1:pos:R:3 A=56.25048 B=4.77441 C=4.40088 mu=0.002 alpha=26.12 epsHOMO=-0.2668 epsLUMO=-0.1113 epsgap=0.1555 =266.8164 zpve=0.036943 U0=-227.798785 U=-227.79457 H=-227.793626 G=-227.825074 Cv=12.147 pbc="F F F" +O 0.00337122 -0.03146586 0.00157206 +C -0.01713174 1.17081409 0.00676444 +C 1.26555783 1.99719410 -0.00007703 +O 1.24507069 3.19946589 0.00681275 +H -0.94690483 1.77678142 0.01852668 +H 2.19534410 1.39121374 -0.01006739 +10 +Properties=species:S:1:pos:R:3 A=80.28766 B=3.3688 C=3.3688 mu=0 alpha=42.32 epsHOMO=-0.2412 epsLUMO=0.0684 epsgap=0.3096 =400.2236 zpve=0.083896 U0=-155.908941 U=-155.90318 H=-155.902236 G=-155.937641 Cv=17.447 pbc="F F F" +C -0.01787959 1.46676893 0.01011180 +C 0.00186644 0.01072913 0.00211109 +C 0.01819704 -1.19272861 -0.00450723 +C 0.03795170 -2.64876793 -0.01251193 +H 0.86460312 1.87589979 0.51479719 +H -0.03217624 1.87210581 -1.00791002 +H -0.90233115 1.85185524 0.53003953 +H -0.83477011 -3.06339007 0.50451436 +H 0.03276422 -3.04315564 -1.03491223 +H 0.93213486 -3.03931642 0.48626606 +10 +Properties=species:S:1:pos:R:3 A=27.39459 B=4.53005 C=4.08342 mu=0.7067 alpha=40.09 epsHOMO=-0.2592 epsLUMO=0.0566 epsgap=0.3157 =333.9589 zpve=0.084338 U0=-155.897345 U=-155.892291 H=-155.891347 G=-155.924226 Cv=17.13 pbc="F F F" +C -0.03095840 1.54775025 0.03167945 +C 0.01485375 0.00962471 -0.02082154 +C 0.69099073 -0.49954575 -1.20857624 +C 1.25222275 -0.89514467 -2.19527879 +H 0.97899300 1.96621609 0.03373834 +H -0.56082354 1.95002902 -0.83580745 +H -0.54576708 1.88225519 0.93709062 +H 0.51920135 -0.37425398 0.87496224 +H -1.00644802 -0.39030838 0.01340632 +H 1.74594541 -1.25604187 -3.06299415 +9 +Properties=species:S:1:pos:R:3 A=28.0016 B=4.67752 C=4.21765 mu=3.9233 alpha=35.38 epsHOMO=-0.3213 epsLUMO=0.034 epsgap=0.3553 =314.5335 zpve=0.07419 U0=-172.006141 U=-172.001467 H=-172.000523 G=-172.032826 Cv=14.988 pbc="F F F" +C -0.02522005 1.54773054 0.02133370 +C 0.01183939 0.00999278 -0.01556702 +C 0.68915114 -0.50836846 -1.20331488 +N 1.22565733 -0.90360846 -2.14737482 +H 0.98571620 1.96212990 0.02370153 +H -0.55522192 1.94599662 -0.84711771 +H -0.53962556 1.88294354 0.92536604 +H 0.52481910 -0.38226048 0.86933355 +H -1.00453542 -0.39832109 0.00530071 +8 +Properties=species:S:1:pos:R:3 A=32.19893 B=4.76299 C=4.30696 mu=4.4361 alpha=31.81 epsHOMO=-0.2683 epsLUMO=0.0173 epsgap=0.2855 =295.6635 zpve=0.063305 U0=-188.042067 U=-188.037478 H=-188.036534 G=-188.06863 Cv=14.488 pbc="F F F" +N -0.03608099 1.48762533 0.03678072 +C -0.01039471 0.02517605 -0.01506608 +C -1.37180956 -0.52108920 0.02739272 +N -2.44026695 -0.95956444 0.03370519 +H 0.91729752 1.83547547 0.05202682 +H -0.47162178 1.85677205 -0.80275569 +H 0.48946161 -0.40880401 -0.89715168 +H 0.52827150 -0.34533338 0.86469547 +8 +Properties=species:S:1:pos:R:3 A=35.11615 B=4.66117 C=4.22354 mu=1.7211 alpha=33.1 epsHOMO=-0.2595 epsLUMO=0.0277 epsgap=0.2872 =300.0993 zpve=0.060632 U0=-191.810916 U=-191.806025 H=-191.805081 G=-191.837634 Cv=15.855 pbc="F F F" +O -0.02013197 1.45738137 0.01010195 +C 0.00168478 0.03579980 0.00224736 +C -1.36366691 -0.47481101 0.01255056 +C -2.47902835 -0.91970202 0.02082451 +H 0.89446911 1.75736553 0.00327363 +H 0.51979028 -0.35175413 -0.88980302 +H 0.53670458 -0.36127661 0.88000894 +H -3.46773502 -1.30737009 0.02818120 +7 +Properties=species:S:1:pos:R:3 A=35.93172 B=4.83443 C=4.37827 mu=4.6788 alpha=28.56 epsHOMO=-0.3018 epsLUMO=0.0022 epsgap=0.3039 =280.6659 zpve=0.050262 U0=-207.916786 U=-207.912215 H=-207.911271 G=-207.943384 Cv=13.845 pbc="F F F" +O -0.03738221 1.44547841 0.01010769 +C 0.01292906 0.03441661 0.00209165 +C -1.35747452 -0.48874523 0.01239738 +N -2.42135277 -0.93685184 0.02016053 +H 0.86898057 1.76972949 0.00417577 +H 0.51692713 -0.36329790 -0.89215814 +H 0.53387616 -0.37278749 0.88223921 +10 +Properties=species:S:1:pos:R:3 A=26.14564 B=4.2893 C=4.12773 mu=2.6741 alpha=35.83 epsHOMO=-0.25 epsLUMO=-0.0205 epsgap=0.2295 =333.3276 zpve=0.084175 U0=-193.075202 U=-193.070116 H=-193.069171 G=-193.102798 Cv=15.954 pbc="F F F" +C 0.16879340 1.52513042 -0.15747056 +C -0.18737625 0.06194763 0.14679374 +C 0.50917645 -0.43996215 1.39125850 +O 1.18191079 -1.43565585 1.45816384 +H 1.23126511 1.63137254 -0.39632693 +H -0.40744668 1.89431681 -1.00969246 +H -0.04931034 2.17266260 0.69857432 +H -1.26905160 -0.01668067 0.32854433 +H 0.06279062 -0.60250479 -0.68474036 +H 0.35384841 0.20663370 2.28871052 +9 +Properties=species:S:1:pos:R:3 A=19.98749 B=6.11589 C=4.82584 mu=3.7071 alpha=32.78 epsHOMO=-0.2516 epsLUMO=0.0335 epsgap=0.2851 =279.7863 zpve=0.074166 U0=-209.144909 U=-209.139976 H=-209.139032 G=-209.172305 Cv=15.058 pbc="F F F" +C 0.01474916 1.47888256 -0.03509326 +N 0.18708077 0.03831317 -0.02906851 +C 0.45703661 -0.66858895 1.10069001 +O 0.58457996 -0.19876202 2.21119705 +H 0.76025289 1.96736104 -0.67198126 +H -0.98462423 1.75984483 -0.38485103 +H 0.14269849 1.82387718 0.99149397 +H 0.10386349 -0.46937726 -0.89461379 +H 0.55047568 -1.75253452 0.88868242 +8 +Properties=species:S:1:pos:R:3 A=48.62351 B=4.67298 C=4.38187 mu=3.92 alpha=29.47 epsHOMO=-0.2814 epsLUMO=0.0074 epsgap=0.2888 =293.917 zpve=0.061327 U0=-229.013797 U=-229.009003 H=-229.008059 G=-229.041086 Cv=13.885 pbc="F F F" +C 0.10653781 1.43512049 0.03336100 +O 0.42686564 0.04716929 0.09309945 +C 0.50714862 -0.63652665 -1.06318297 +O 0.76927014 -1.79955017 -1.11010475 +H 0.92349251 2.00385139 0.48465728 +H -0.03757815 1.76927183 -1.00057425 +H -0.81269569 1.60801916 0.59873287 +H 0.30458460 -0.00231902 -1.94965553 +8 +Properties=species:S:1:pos:R:3 A=36.49282 B=4.31175 C=4.04493 mu=1.7341 alpha=28.53 epsHOMO=-0.2537 epsLUMO=-0.0341 epsgap=0.2196 =303.8129 zpve=0.060508 U0=-228.992613 U=-228.987769 H=-228.986825 G=-229.019918 Cv=14.78 pbc="F F F" +O 0.19375856 1.37170995 0.31398297 +C 0.04992637 0.01405481 -0.04526239 +C -1.40888025 -0.39635680 -0.20601574 +O -1.77729462 -1.37233860 -0.80361689 +H -0.01723186 1.91269132 -0.45337758 +H 0.60440478 -0.26519766 -0.95205725 +H 0.46740406 -0.57521679 0.78176270 +H -2.11835055 0.30075962 0.29870515 +14 +Properties=species:S:1:pos:R:3 A=23.44662 B=3.60588 C=3.38683 mu=0 alpha=45.71 epsHOMO=-0.317 epsLUMO=0.0937 epsgap=0.4107 =426.2996 zpve=0.131708 U0=-158.340943 U=-158.33517 H=-158.334226 G=-158.36894 Cv=19.668 pbc="F F F" +C -0.03007624 1.55804912 0.02980774 +C 0.00707277 0.02834877 -0.00712529 +C 0.71320792 -0.52989340 -1.24765581 +C 0.75036895 -2.05959366 -1.28458957 +H 0.98195330 1.97845113 0.02800797 +H -0.55582433 1.96212343 -0.84270157 +H -0.54083762 1.92692755 0.92493697 +H 0.50928672 -0.34909049 0.89361319 +H -1.01733481 -0.36542299 0.03207393 +H 0.21098948 -0.15245770 -2.14839302 +H 1.73761331 -0.13611625 -1.28685672 +H 1.27616854 -2.46366410 -0.41210980 +H -0.26165699 -2.48000447 -1.28273552 +H 1.26108491 -2.42846444 -2.17974803 +12 +Properties=species:S:1:pos:R:3 A=26.59789 B=3.79186 C=3.53779 mu=1.3402 alpha=38.61 epsHOMO=-0.2619 epsLUMO=0.081 epsgap=0.3429 =382.8628 zpve=0.108241 U0=-194.261089 U=-194.255495 H=-194.254551 G=-194.28893 Cv=18.431 pbc="F F F" +C -0.00379012 1.52196790 0.01027996 +C -0.03589372 -0.00778254 0.00225671 +C -1.45219016 -0.56653711 0.01284386 +O -1.36799785 -1.98394604 0.00453606 +H 1.02477576 1.89411231 0.00239793 +H -0.51286674 1.93764614 -0.86653888 +H -0.49584080 1.92801344 0.90121721 +H 0.48140784 -0.39530552 -0.88316064 +H 0.49828589 -0.40486256 0.87330348 +H -1.98734220 -0.20399494 0.90666062 +H -2.00431485 -0.19459473 -0.86666460 +H -2.26171819 -2.33824298 0.01034748 +12 +Properties=species:S:1:pos:R:3 A=28.29567 B=4.14901 C=3.88349 mu=1.0363 alpha=39.34 epsHOMO=-0.2503 epsLUMO=0.0925 epsgap=0.3428 =368.9331 zpve=0.107895 U0=-194.254127 U=-194.248585 H=-194.247641 G=-194.281899 Cv=17.888 pbc="F F F" +C -0.00519551 1.50868224 0.03308760 +C 0.01316580 -0.01037819 0.01988643 +O 0.69758944 -0.46344140 1.16868979 +C 0.76688491 -1.86508183 1.24602669 +H 1.01445476 1.90423799 0.02996018 +H -0.52990866 1.89184059 -0.84726911 +H -0.51263765 1.87805843 0.92875424 +H -1.01599507 -0.40998729 0.00714282 +H 0.51093143 -0.38369435 -0.89219395 +H -0.23415841 -2.32451489 1.28977780 +H 1.30129276 -2.29853663 0.38486095 +H 1.30990287 -2.11811998 2.16013206 +10 +Properties=species:S:1:pos:R:3 A=14.79671 B=5.6956 C=4.58846 mu=0.0075 alpha=31.42 epsHOMO=-0.2594 epsLUMO=0.0584 epsgap=0.3179 =297.8398 zpve=0.085172 U0=-230.183076 U=-230.177723 H=-230.176779 G=-230.211195 Cv=16.837 pbc="F F F" +O -0.01418735 1.42642242 -0.05423651 +C -0.00391839 0.00717714 0.03496062 +C -1.42317019 -0.55560600 -0.01256639 +O -2.11229413 -0.10563875 -1.17228313 +H -0.18987882 1.64154705 -0.97555148 +H 0.59659504 -0.43315430 -0.77409095 +H 0.47311116 -0.24903733 0.98747216 +H -1.39494859 -1.64975279 -0.06283188 +H -1.96497417 -0.27216915 0.90131972 +H -2.35925991 0.80944396 -1.00614597 +12 +Properties=species:S:1:pos:R:3 A=15.61472 B=6.31363 C=5.54514 mu=0.1136 alpha=41.96 epsHOMO=-0.2727 epsLUMO=0.1012 epsgap=0.3738 =298.6061 zpve=0.109284 U0=-157.116735 U=-157.11209 H=-157.111146 G=-157.143262 Cv=16.49 pbc="F F F" +C -0.03602245 1.52092949 0.01569161 +C 0.01938487 0.01004403 0.04814953 +C 1.30891671 -0.71251995 -0.24920416 +C 0.75347423 -0.70267714 1.15564676 +H 0.90105691 1.95389291 0.38283338 +H -0.19640365 1.89437369 -1.00208569 +H -0.84685273 1.90588930 0.64451322 +H -0.88416809 -0.48001889 -0.30566335 +H 1.26900901 -1.63903068 -0.81150371 +H 2.19010738 -0.11131209 -0.44927513 +H 1.26248862 -0.09487455 1.89690028 +H 0.33540836 -1.62248918 1.54980695 +10 +Properties=species:S:1:pos:R:3 A=18.21931 B=6.63877 C=5.92459 mu=1.812 alpha=35.01 epsHOMO=-0.2633 epsLUMO=0.1052 epsgap=0.3685 =267.2979 zpve=0.085275 U0=-193.039603 U=-193.035186 H=-193.034242 G=-193.065979 Cv=14.764 pbc="F F F" +C -0.01646343 1.51830705 0.00771445 +C -0.02743227 0.01657694 -0.10889741 +C 0.77296553 -0.70131886 -1.10863115 +O 1.17212293 -0.66702969 0.26278829 +H 0.91865980 1.92914217 -0.38285880 +H -0.85158014 1.95456506 -0.55109460 +H -0.11487702 1.82596470 1.05397777 +H -0.93582598 -0.47350600 0.24682793 +H 0.44443922 -1.67201495 -1.47810622 +H 1.40538284 -0.13600339 -1.79271491 +11 +Properties=species:S:1:pos:R:3 A=16.58914 B=7.18798 C=6.11415 mu=1.1353 alpha=39.02 epsHOMO=-0.2304 epsLUMO=0.0968 epsgap=0.3271 =270.5508 zpve=0.097671 U0=-173.147782 U=-173.143343 H=-173.142399 G=-173.174073 Cv=15.298 pbc="F F F" +C -0.05275541 1.47419574 0.00999108 +N 0.01940516 0.02189947 0.06334866 +C 1.32511048 -0.56446850 -0.21420725 +C 0.78051759 -0.54057993 1.17242760 +H 0.86784202 1.97176076 0.36297285 +H -0.23845155 1.79095005 -1.02142866 +H -0.88683448 1.81938959 0.62950398 +H 1.31992909 -1.48186782 -0.79602038 +H 2.15202820 0.11020204 -0.43285248 +H 1.24563570 0.14996098 1.87499245 +H 0.37013358 -1.44020447 1.62233365 +10 +Properties=species:S:1:pos:R:3 A=16.79477 B=6.71015 C=5.86781 mu=1.3894 alpha=34.64 epsHOMO=-0.239 epsLUMO=0.0775 epsgap=0.3166 =263.966 zpve=0.085106 U0=-193.034988 U=-193.030356 H=-193.029411 G=-193.061689 Cv=15.679 pbc="F F F" +O 0.21577583 1.35386859 -0.10695107 +C 0.01628099 -0.02832718 -0.02915694 +C -0.39081465 -0.71497009 1.24315669 +C -1.35118138 -0.64280380 0.06273589 +H -0.43642338 1.79062849 0.45006761 +H 0.74981167 -0.52404050 -0.65624767 +H 0.05959375 -1.66840483 1.49729237 +H -0.61369353 -0.08848006 2.10170640 +H -2.19821932 0.03058430 0.15411451 +H -1.56395912 -1.54640426 -0.49826515 +12 +Properties=species:S:1:pos:R:3 A=10.66107 B=10.66107 C=6.30452 mu=0 alpha=41.83 epsHOMO=-0.2982 epsLUMO=0.0956 epsgap=0.3938 =268.4432 zpve=0.110511 U0=-157.115484 U=-157.111322 H=-157.110378 G=-157.141657 Cv=14.696 pbc="F F F" +C -0.02542957 1.54032578 -0.04215944 +C 1.52564399 1.50419989 0.04121276 +C 1.42146727 0.06146790 0.60844885 +C -0.02638833 -0.01070956 0.04935088 +H -0.46591372 1.99639894 0.84962894 +H -0.47900239 2.00028207 -0.92365389 +H 1.98248278 1.51375334 -0.95303183 +H 2.02191365 2.25986579 0.65522129 +H 2.14291679 -0.67956358 0.25531480 +H 1.43105853 0.05751479 1.70261879 +H -0.79053440 -0.48530296 0.66996573 +H -0.05233245 -0.47238446 -0.94236394 +10 +Properties=species:S:1:pos:R:3 A=12.06545 B=11.77731 C=6.71997 mu=1.7978 alpha=34.56 epsHOMO=-0.2424 epsLUMO=0.0859 epsgap=0.3283 =236.9759 zpve=0.086675 U0=-193.034094 U=-193.029968 H=-193.029024 G=-193.060777 Cv=12.915 pbc="F F F" +C -0.03489789 1.55259455 -0.08507691 +C 1.49349183 1.45459700 0.10114677 +O 1.42725884 0.02348990 0.27254886 +C -0.00634758 0.02236494 0.10944630 +H -0.56579210 2.11301721 0.68591527 +H -0.36628352 1.89393380 -1.06692327 +H 2.10031724 1.73525720 -0.76898349 +H 1.90023090 1.95506611 0.98896748 +H -0.51987849 -0.35595179 1.00235948 +H -0.31970062 -0.57567421 -0.75559275 +12 +Properties=species:S:1:pos:R:3 A=8.63483 B=4.11977 C=2.88825 mu=0.6875 alpha=45.37 epsHOMO=-0.2392 epsLUMO=0.0192 epsgap=0.2584 =452.5112 zpve=0.100501 U0=-248.375248 U=-248.368823 H=-248.367879 G=-248.405354 Cv=21.616 pbc="F F F" +C -0.10255370 1.49907127 -0.00133642 +C -0.00519774 -0.00343114 -0.02723470 +C -0.00688536 -0.76890740 1.26434744 +N 0.08186799 -0.69202791 -1.10087306 +O 0.07439067 0.13215394 -2.24195258 +H 0.78342093 1.94690862 -0.46354050 +H -0.96150430 1.83614082 -0.59021965 +H -0.19854173 1.87462989 1.01931607 +H -0.92620012 -0.57297946 1.82982479 +H 0.07256154 -1.84064782 1.07516863 +H 0.82937178 -0.45553340 1.90133089 +H 0.13975924 -0.51254299 -2.95389260 +10 +Properties=species:S:1:pos:R:3 A=9.17136 B=9.04195 C=4.5531 mu=1.8689 alpha=43.14 epsHOMO=-0.2029 epsLUMO=0.0499 epsgap=0.2528 =303.9808 zpve=0.082433 U0=-210.101789 U=-210.097816 H=-210.096872 G=-210.12818 Cv=14.821 pbc="F F F" +N -0.00829694 1.35362838 0.00995971 +C 1.28033254 1.82457421 -0.00022459 +C 2.12216639 0.73698898 -0.01445600 +C 1.30849904 -0.43109580 -0.01284154 +C -0.00342782 -0.01836884 0.00235736 +H -0.83243234 1.92772668 0.02122995 +H 1.48471820 2.88312630 0.00354313 +H 3.20066918 0.77343077 -0.02488416 +H 1.64810599 -1.45539683 -0.02180394 +H -0.92551647 -0.57697054 0.00839402 +9 +Properties=species:S:1:pos:R:3 A=9.77329 B=9.41192 C=4.7946 mu=3.6193 alpha=39.13 epsHOMO=-0.2253 epsLUMO=0.0332 epsgap=0.2585 =283.6817 zpve=0.071145 U0=-226.160842 U=-226.157088 H=-226.156144 G=-226.187104 Cv=13.371 pbc="F F F" +N -0.00996090 1.35636172 0.01000855 +C 1.29535422 1.79575578 -0.00047017 +C 2.05550519 0.65637438 -0.01422927 +N 1.25385745 -0.46275132 -0.01260894 +C 0.02301170 -0.00706382 0.00200146 +H -0.83195718 1.93618436 0.02132575 +H 1.54553961 2.84344998 0.00289054 +H 3.13177434 0.57569766 -0.02524155 +H -0.87667864 -0.60414531 0.00750794 +9 +Properties=species:S:1:pos:R:3 A=9.50228 B=9.29497 C=4.69874 mu=0.5571 alpha=39.2 epsHOMO=-0.2246 epsLUMO=0.0199 epsgap=0.2445 =289.005 zpve=0.069883 U0=-229.969129 U=-229.965414 H=-229.96447 G=-229.995393 Cv=13.358 pbc="F F F" +O -0.03255819 1.35065130 0.00967376 +C 1.25319237 1.79167710 -0.00030351 +C 2.11664681 0.74375623 -0.01441494 +C 1.30397796 -0.43852387 -0.01283316 +C 0.01629568 -0.00777185 0.00191294 +H 1.37951089 2.86161135 0.00427433 +H 3.19376406 0.79865501 -0.02458604 +H 1.63858893 -1.46383271 -0.02156949 +H -0.93736295 -0.50899925 0.00843891 +8 +Properties=species:S:1:pos:R:3 A=10.10926 B=9.69372 C=4.94857 mu=1.5081 alpha=35.17 epsHOMO=-0.2509 epsLUMO=0.001 epsgap=0.2519 =269.2444 zpve=0.058593 U0=-246.02915 U=-246.025614 H=-246.024669 G=-246.055309 Cv=12.04 pbc="F F F" +O -0.03808298 1.35643965 0.00975193 +C 1.26496982 1.76855319 -0.00061744 +C 2.04955750 0.66630033 -0.01406047 +N 1.24227075 -0.46732066 -0.01237541 +C 0.04019820 0.00561577 0.00183569 +H 1.43513907 2.83095265 0.00340823 +H 3.12532898 0.59783015 -0.02480757 +H -0.89387707 -0.53342154 0.00796935 +17 +Properties=species:S:1:pos:R:3 A=4.4178 B=4.41753 C=4.41717 mu=0.0003 alpha=56.01 epsHOMO=-0.3145 epsLUMO=0.0737 epsgap=0.3882 =486.2719 zpve=0.158836 U0=-197.632222 U=-197.625241 H=-197.624297 G=-197.661411 Cv=26.084 pbc="F F F" +C -0.01859029 1.53486266 0.01039904 +C 0.00210320 -0.00413672 0.00196980 +C 0.74643954 -0.51390760 1.24887638 +C -1.44190166 -0.53673615 0.01311457 +C 0.72234971 -0.50049606 -1.26445632 +H 0.99776928 1.94420529 0.00435474 +H -0.54181883 1.92841415 -0.86819911 +H -0.52758402 1.91888376 0.90148530 +H 0.25549576 -0.17602728 2.16833067 +H 0.77815389 -1.60892372 1.27088904 +H 1.77964786 -0.14975834 1.26944035 +H -1.99871895 -0.18888794 -0.86408066 +H -1.45956909 -1.63227235 0.00649916 +H -1.98088827 -0.19979639 0.90561580 +H 1.75482473 -0.13554018 -1.30118083 +H 0.75396989 -1.59524036 -1.29822280 +H 0.21358221 -0.15343900 -2.17067181 +15 +Properties=species:S:1:pos:R:3 A=4.69474 B=4.66938 C=4.47909 mu=1.384 alpha=49.04 epsHOMO=-0.2601 epsLUMO=0.0664 epsgap=0.3265 =449.0573 zpve=0.134977 U0=-233.560626 U=-233.553779 H=-233.552834 G=-233.589759 Cv=25.128 pbc="F F F" +C -0.00855999 1.54270147 0.00152717 +C 0.00506780 0.00702635 0.01856160 +C 0.76137731 -0.51839196 1.24766443 +C -1.42012774 -0.54933183 -0.02116008 +O 0.62590477 -0.48363745 -1.17657057 +H 1.01361406 1.94020776 0.00281128 +H -0.51459669 1.90702466 -0.89705781 +H -0.52214815 1.94882514 0.87918344 +H 0.28130613 -0.20165548 2.17943598 +H 0.79965506 -1.61131746 1.23022549 +H 1.79096916 -0.14072935 1.26083003 +H -1.93854829 -0.19910724 -0.91828281 +H -1.39937632 -1.64259062 -0.04563833 +H -1.98850725 -0.23059656 0.85726520 +H 1.53111188 -0.15488046 -1.19210222 +9 +Properties=species:S:1:pos:R:3 A=10.21876 B=4.04324 C=2.94978 mu=2.8579 alpha=42.02 epsHOMO=-0.2654 epsLUMO=-0.0575 epsgap=0.2079 =416.7799 zpve=0.065175 U0=-229.927277 U=-229.921622 H=-229.920677 G=-229.955843 Cv=18.723 pbc="F F F" +C -0.00693851 1.49559567 -0.01063358 +C -0.02986170 -0.01594623 0.06248673 +O -0.08815239 -0.63215062 1.10334812 +C 0.02336981 -0.70256747 -1.22062379 +C 0.06738132 -1.26058341 -2.28623129 +H 0.90429806 1.83281887 -0.51655384 +H -0.85338524 1.85556926 -0.60552819 +H -0.05237764 1.91099807 0.99695949 +H 0.10466868 -1.77036047 -3.21832617 +8 +Properties=species:S:1:pos:R:3 A=10.16063 B=4.15847 C=3.0056 mu=3.3351 alpha=37.08 epsHOMO=-0.3007 epsLUMO=-0.0889 epsgap=0.2118 =397.7757 zpve=0.05451 U0=-246.027383 U=-246.022024 H=-246.02108 G=-246.055769 Cv=16.963 pbc="F F F" +C -0.01171097 1.49973776 -0.01213268 +C -0.02919740 -0.00338745 0.07151218 +O -0.09164028 -0.64592927 1.08732241 +C 0.03831104 -0.69690258 -1.23748557 +N 0.09136286 -1.22982650 -2.26228068 +H 0.90093132 1.83370670 -0.51814928 +H -0.85663933 1.84864400 -0.61618135 +H -0.06416540 1.92131542 0.99203818 +8 +Properties=species:S:1:pos:R:3 A=10.68423 B=4.3198 C=3.07993 mu=5.1815 alpha=38.31 epsHOMO=-0.2739 epsLUMO=-0.0438 epsgap=0.2301 =384.5574 zpve=0.056619 U0=-242.19573 U=-242.190591 H=-242.189646 G=-242.223513 Cv=17.465 pbc="F F F" +N -0.05232108 1.34991769 0.15535538 +C 0.08549378 -0.01476951 0.02668890 +N 1.14555215 -0.71993906 -0.03966037 +C -1.19426556 -0.71152088 0.01802151 +N -2.22747610 -1.22790543 0.00132148 +H 0.75474469 1.91998737 -0.04411334 +H -0.94458527 1.76600473 -0.05932293 +H 1.98312429 -0.13415500 -0.02878551 +9 +Properties=species:S:1:pos:R:3 A=9.08854 B=4.42607 C=3.03169 mu=0.9461 alpha=36.51 epsHOMO=-0.2538 epsLUMO=-0.0964 epsgap=0.1574 =399.222 zpve=0.064992 U0=-267.10335 U=-267.097658 H=-267.096714 G=-267.132534 Cv=17.806 pbc="F F F" +C -0.43126066 1.30586664 -0.12758585 +C 0.25285715 -0.01026988 0.13138867 +O 0.69872099 -0.36890333 1.19604690 +C 0.38978943 -0.96773680 -1.06226208 +O -0.01611268 -0.71564293 -2.16583718 +H 0.10006565 1.85324490 -0.91332597 +H -1.44270433 1.13069361 -0.50923371 +H -0.46757554 1.89548158 0.78836479 +H 0.90329886 -1.91558845 -0.79531972 +9 +Properties=species:S:1:pos:R:3 A=10.08927 B=4.54115 C=3.13487 mu=2.7707 alpha=38.09 epsHOMO=-0.2561 epsLUMO=-0.064 epsgap=0.192 =384.4827 zpve=0.067668 U0=-263.278851 U=-263.273589 H=-263.272645 G=-263.306835 Cv=17.972 pbc="F F F" +N -0.16032929 1.08528962 -0.45398652 +C 0.18649127 -0.08762327 0.14758021 +N 1.28979627 -0.46427423 0.67927030 +C -0.94204384 -1.08440264 0.20423621 +O -2.01802205 -0.89007687 -0.30583149 +H 0.54574437 1.73837930 -0.74689246 +H -1.04684920 1.09570857 -0.93607796 +H 2.01738195 0.24991828 0.59881811 +H -0.67740148 -2.01003995 0.74746441 +13 +Properties=species:S:1:pos:R:3 A=7.92874 B=3.8272 C=2.82581 mu=0.6578 alpha=51.2 epsHOMO=-0.2589 epsLUMO=0.0571 epsgap=0.316 =481.9854 zpve=0.112471 U0=-195.186772 U=-195.180446 H=-195.179502 G=-195.215658 Cv=22.569 pbc="F F F" +C -0.01763434 1.54859468 -0.00253584 +C -0.01719180 0.00735789 0.03269604 +C 0.75818463 -0.52820155 1.25278862 +C -1.38213303 -0.51768438 0.00775516 +C -2.50796392 -0.94005629 0.00478552 +H 1.00944396 1.92639613 -0.02632830 +H -0.54514614 1.92217052 -0.88393459 +H -0.51243000 1.95339892 0.88560642 +H 0.49366179 -0.34904155 -0.87265890 +H 0.28968458 -0.19345372 2.18334679 +H 0.77833392 -1.62102506 1.25783727 +H 1.78952492 -0.16181727 1.23574954 +H -3.49998244 -1.31810165 -0.00723572 +12 +Properties=species:S:1:pos:R:3 A=7.90009 B=3.95466 C=2.88643 mu=3.9512 alpha=46.23 epsHOMO=-0.318 epsLUMO=0.0365 epsgap=0.3545 =460.694 zpve=0.102281 U0=-211.295796 U=-211.289821 H=-211.288877 G=-211.324525 Cv=20.467 pbc="F F F" +C -0.00242350 1.55035837 0.01056187 +C -0.01856600 0.01004352 -0.02261770 +C -1.45045318 -0.55766178 0.01293347 +C 0.70125749 -0.48583168 -1.20172764 +N 1.26190946 -0.87126766 -2.13619740 +H 1.01959496 1.93673215 0.00349668 +H -0.52972844 1.96141639 -0.85498386 +H -0.50134251 1.90346839 0.91755862 +H 0.52481877 -0.36222456 0.85525462 +H -2.02427903 -0.21428716 -0.85251869 +H -1.44438296 -1.65026927 0.00754107 +H -1.95805494 -0.21725882 0.91996760 +11 +Properties=species:S:1:pos:R:3 A=8.30095 B=3.97615 C=2.93764 mu=2.7429 alpha=42.87 epsHOMO=-0.2704 epsLUMO=0.0278 epsgap=0.2983 =440.0738 zpve=0.091554 U0=-227.338075 U=-227.332253 H=-227.331309 G=-227.366638 Cv=19.946 pbc="F F F" +C -0.01064295 1.54128691 0.00722087 +C -0.00203811 0.00756015 0.03863463 +N 0.76268677 -0.47101822 1.19250856 +C -1.38541954 -0.52099256 -0.00194097 +N -2.45680847 -0.95636072 0.00694969 +H 1.02043523 1.90106589 0.00392365 +H -0.52873161 1.91193452 -0.88031480 +H -0.51849536 1.94150431 0.89013197 +H 0.50418828 -0.35805500 -0.86276072 +H 0.30070681 -0.18364934 2.05155305 +H 0.78148992 -1.48670299 1.20428183 +11 +Properties=species:S:1:pos:R:3 A=8.55919 B=3.89952 C=2.91869 mu=1.3582 alpha=44.03 epsHOMO=-0.2665 epsLUMO=0.0336 epsgap=0.3001 =444.6452 zpve=0.088908 U0=-231.108368 U=-231.102292 H=-231.101348 G=-231.137061 Cv=21.306 pbc="F F F" +C -0.00754278 1.55566144 0.08650633 +C 0.01207541 0.02096553 0.04935916 +O -1.29150251 -0.52785128 0.22126386 +C 0.65093362 -0.47718651 -1.17430507 +C 1.13902673 -0.86577343 -2.20205518 +H 1.00824243 1.95969458 0.04929733 +H -0.56272245 1.95192010 -0.76978085 +H -0.49328161 1.88929438 1.00704123 +H 0.57811967 -0.34464907 0.91458864 +H -1.78516532 -0.35903262 -0.58909400 +H 1.58078877 -1.22365060 -3.09919447 +10 +Properties=species:S:1:pos:R:3 A=8.55908 B=4.01671 C=2.97882 mu=3.269 alpha=39.28 epsHOMO=-0.3051 epsLUMO=0.0115 epsgap=0.3166 =424.3395 zpve=0.078602 U0=-247.214861 U=-247.209162 H=-247.208218 G=-247.243338 Cv=19.252 pbc="F F F" +C -0.00992462 1.54951908 0.00732200 +C 0.00740485 0.01722020 0.05031270 +O 0.72085722 -0.49630479 1.15864583 +C -1.36995095 -0.52355929 0.00894432 +N -2.45081572 -0.93233138 0.00716912 +H 1.01892314 1.91630447 0.02724149 +H -0.50261490 1.90969362 -0.89997951 +H -0.54800426 1.95377611 0.87053664 +H 0.53602846 -0.36494195 -0.82970945 +H 0.27702643 -0.20644534 1.96421773 +13 +Properties=species:S:1:pos:R:3 A=7.66289 B=3.70925 C=2.79937 mu=2.6921 alpha=46.58 epsHOMO=-0.2469 epsLUMO=-0.0188 epsgap=0.2281 =482.0475 zpve=0.112328 U0=-232.364952 U=-232.358577 H=-232.357633 G=-232.394589 Cv=21.434 pbc="F F F" +C -0.02629647 1.53877116 -0.00783747 +C 0.01468904 0.00095310 0.00166037 +C 0.74737862 -0.53388859 1.24420471 +C -1.40211264 -0.53859257 -0.01557820 +O -1.87620455 -1.23582351 -0.87399559 +H 0.98823696 1.94625080 0.03705522 +H -0.50725090 1.92153422 -0.91285565 +H -0.57756514 1.92360883 0.85796386 +H 0.50584680 -0.36421091 -0.90619124 +H 0.22958380 -0.23909403 2.16431052 +H 0.81692736 -1.62559499 1.22973438 +H 1.76267153 -0.12846566 1.29050755 +H -2.00861510 -0.22792742 0.87121666 +11 +Properties=species:S:1:pos:R:3 A=8.31096 B=4.07963 C=2.92918 mu=2.8354 alpha=39.16 epsHOMO=-0.255 epsLUMO=-0.0279 epsgap=0.2271 =432.1489 zpve=0.088443 U0=-268.287661 U=-268.281505 H=-268.280561 G=-268.316982 Cv=20.344 pbc="F F F" +C -0.05483118 1.46032843 -0.17068647 +C 0.07105714 -0.03524485 0.11225841 +O 0.58204486 -0.33316656 1.40176256 +C -1.27639279 -0.74383027 0.03051185 +O -2.11855775 -0.49643175 -0.79163412 +H 0.93158072 1.93091398 -0.23353204 +H -0.57713995 1.61290939 -1.11835562 +H -0.62444315 1.94776119 0.62573670 +H 0.69687521 -0.50071620 -0.67159471 +H 1.45603316 0.06066659 1.47858584 +H -1.40723217 -1.53388627 0.80211181 +12 +Properties=species:S:1:pos:R:3 A=8.9331 B=4.19405 C=2.96046 mu=3.7163 alpha=44.42 epsHOMO=-0.2424 epsLUMO=0.0327 epsgap=0.2751 =441.85 zpve=0.10227 U0=-248.430371 U=-248.424309 H=-248.423365 G=-248.459383 Cv=19.918 pbc="F F F" +C -0.09776499 1.44917268 0.04561984 +N 0.01043053 0.00373818 -0.00070600 +C 0.05323028 -0.71432112 1.25264891 +C 0.06728716 -0.64067225 -1.20060778 +O 0.03636432 -0.11006515 -2.29109228 +H 0.75650660 1.88747109 0.57647421 +H -0.11648577 1.81632103 -0.98049172 +H -1.01641106 1.75521548 0.56151415 +H -0.85453556 -0.53657675 1.84390168 +H 0.13297722 -1.78696336 1.05851351 +H 0.91637348 -0.40737846 1.85761345 +H 0.14762240 -1.73801339 -1.06805139 +11 +Properties=species:S:1:pos:R:3 A=10.13982 B=3.81346 C=2.86965 mu=2.9514 alpha=39.33 epsHOMO=-0.2699 epsLUMO=-0.0262 epsgap=0.2437 =440.1727 zpve=0.088924 U0=-268.301176 U=-268.295084 H=-268.29414 G=-268.331307 Cv=19.824 pbc="F F F" +C -0.01708808 1.44281136 0.03225501 +C -0.05544542 -0.06560572 -0.02375999 +O -0.17379913 -0.76196233 0.96223129 +C 0.06068107 -0.73615179 -1.38825252 +O 0.00877771 -2.12574390 -1.27749486 +H 0.92062950 1.81463913 -0.39522215 +H -0.83050523 1.86542281 -0.56780350 +H -0.10911975 1.78049045 1.06493675 +H -0.75015067 -0.35235060 -2.03056074 +H 1.00312898 -0.40389029 -1.85592942 +H -0.09063255 -2.29450960 -0.32608019 +13 +Properties=species:S:1:pos:R:3 A=9.55216 B=3.57844 C=2.7341 mu=2.6168 alpha=46.19 epsHOMO=-0.2423 epsLUMO=-0.0072 epsgap=0.2351 =489.8518 zpve=0.112006 U0=-232.377706 U=-232.371073 H=-232.370129 G=-232.408256 Cv=21.716 pbc="F F F" +C -0.04384102 1.54099793 -0.03913814 +C 0.02765132 0.01885817 0.04778773 +C 0.76814644 -0.48842218 1.28000847 +C 0.87850208 -1.99746312 1.42393323 +O 1.24179142 0.26651340 2.09788702 +H 0.95796454 1.97690198 -0.06816012 +H -0.58447717 1.85356031 -0.93690369 +H -0.55173626 1.95737759 0.83440529 +H -0.97692824 -0.42709877 0.05353178 +H 0.51853486 -0.40806507 -0.83809409 +H 1.39924980 -2.42678188 0.56053177 +H 1.42001187 -2.24449367 2.33776426 +H -0.11750602 -2.45360651 1.45141965 +12 +Properties=species:S:1:pos:R:3 A=9.78802 B=3.78441 C=2.84416 mu=3.499 alpha=42.54 epsHOMO=-0.2438 epsLUMO=0.0355 epsgap=0.2793 =457.447 zpve=0.101847 U0=-248.448467 U=-248.441988 H=-248.441044 G=-248.478935 Cv=21.374 pbc="F F F" +C -0.00220144 1.53344329 -0.05960369 +C -0.03235837 0.01206167 0.06840265 +C 0.69666086 -0.48196462 1.31828322 +N 0.98104007 -1.82034162 1.31984698 +O 1.00392124 0.24606232 2.24136749 +H 1.02111190 1.89525169 -0.19182672 +H -0.59707204 1.86206427 -0.91645604 +H -0.39728839 1.99920712 0.84545201 +H -1.06936083 -0.34552935 0.13040806 +H 0.39947165 -0.46906818 -0.81777020 +H 0.63281951 -2.43917647 0.60874785 +H 1.35708014 -2.22217793 2.16303522 +12 +Properties=species:S:1:pos:R:3 A=10.01185 B=3.84195 C=2.87635 mu=3.5402 alpha=43.61 epsHOMO=-0.2418 epsLUMO=0.0387 epsgap=0.2805 =458.9803 zpve=0.101735 U0=-248.443503 U=-248.436899 H=-248.435955 G=-248.474272 Cv=20.967 pbc="F F F" +C 0.00619941 1.45933202 -0.03893859 +N -0.06411616 0.01422963 -0.12046901 +C -1.20595394 -0.68196796 0.16117120 +C -1.08982704 -2.19284899 0.02095775 +O -2.24482911 -0.14443077 0.49994418 +H 0.75053989 1.78372520 0.69715573 +H 0.25692001 1.90395263 -1.00876215 +H -0.97856026 1.81121438 0.27082194 +H 0.75820982 -0.49543660 -0.39464024 +H -0.09909703 -2.53543104 -0.28970003 +H -1.34097671 -2.65395851 0.97972885 +H -1.82903100 -2.53483935 -0.70813076 +11 +Properties=species:S:1:pos:R:3 A=10.44045 B=4.04692 C=2.98547 mu=3.5648 alpha=39.51 epsHOMO=-0.2436 epsLUMO=0.0599 epsgap=0.3034 =428.404 zpve=0.091713 U0=-264.504487 U=-264.498452 H=-264.497508 G=-264.533633 Cv=20.274 pbc="F F F" +C 0.00922630 1.45021814 -0.03194543 +N -0.01429488 -0.00054646 -0.01782667 +C -1.21576579 -0.67252403 0.04421896 +N -1.09733352 -2.04644898 -0.15997741 +O -2.28599299 -0.12893711 0.24196027 +H 0.89659038 1.81885022 0.49120949 +H 0.00233950 1.86388504 -1.04914155 +H -0.88562684 1.79943346 0.48353298 +H 0.75257305 -0.47506631 -0.46719265 +H -0.24882333 -2.48772194 0.16375654 +H -1.93419610 -2.53898393 0.11482880 +12 +Properties=species:S:1:pos:R:3 A=9.69683 B=4.1533 C=3.01703 mu=1.1876 alpha=43.57 epsHOMO=-0.2595 epsLUMO=0.0352 epsgap=0.2948 =443.1687 zpve=0.102062 U0=-248.416462 U=-248.410358 H=-248.409414 G=-248.445651 Cv=20.256 pbc="F F F" +C -0.09160695 1.37540046 -0.00729426 +O -0.06964534 -0.04831581 -0.11148812 +C 0.68114625 -0.56142673 -1.10732458 +C 0.60405298 -2.06701550 -1.08766892 +N 1.32645533 0.19266343 -1.89967731 +H 0.91463916 1.76843655 0.16257460 +H -0.48562826 1.82624615 -0.92235216 +H -0.73971430 1.60007423 0.84110605 +H -0.43430065 -2.39242162 -1.20325205 +H 1.20627110 -2.50049977 -1.88745640 +H 0.95651337 -2.44976670 -0.12501838 +H 1.83504115 -0.36104361 -2.58486055 +11 +Properties=species:S:1:pos:R:3 A=10.20392 B=4.16834 C=3.07281 mu=1.7569 alpha=39.33 epsHOMO=-0.2685 epsLUMO=0.0174 epsgap=0.2859 =427.6606 zpve=0.089436 U0=-268.32127 U=-268.315051 H=-268.314106 G=-268.351214 Cv=19.501 pbc="F F F" +C 0.07295254 1.38840919 0.06442046 +O 0.21049114 -0.03740028 0.04612736 +C 0.50102805 -0.57224288 -1.15975346 +C 0.62218715 -2.07378323 -1.06030684 +O 0.63922154 0.08224418 -2.16157325 +H 1.00029720 1.87062570 -0.25532064 +H -0.73338957 1.70782089 -0.60080454 +H -0.15751280 1.65154060 1.09692954 +H -0.31330001 -2.50202851 -0.68941906 +H 0.85646588 -2.48430275 -2.04157352 +H 1.40651685 -2.34055496 -0.34634301 +10 +Properties=species:S:1:pos:R:3 A=10.68182 B=4.39532 C=3.17969 mu=2.329 alpha=35.5 epsHOMO=-0.2669 epsLUMO=0.0575 epsgap=0.3244 =398.5908 zpve=0.079271 U0=-284.385189 U=-284.379361 H=-284.378417 G=-284.414085 Cv=19.084 pbc="F F F" +C -0.02645771 1.38305753 0.02085660 +O 0.04139566 -0.04439503 -0.00277452 +C 0.66775338 -0.56001625 -1.09000344 +N 0.72605063 -1.92219983 -0.98822954 +O 1.12952381 0.09648143 -1.99322180 +H 0.97529779 1.82044307 0.02387306 +H -0.57085698 1.76214621 -0.84790425 +H -0.55410722 1.63835731 0.94043594 +H 0.12662696 -2.38401590 -0.32566929 +H 0.97230070 -2.41798421 -1.82732573 +10 +Properties=species:S:1:pos:R:3 A=10.20696 B=3.93135 C=2.9219 mu=4.5676 alpha=35.87 epsHOMO=-0.246 epsLUMO=0.0273 epsgap=0.2733 =418.5967 zpve=0.078222 U0=-284.360325 U=-284.354148 H=-284.353204 G=-284.390143 Cv=20.089 pbc="F F F" +N -0.08115607 1.31807026 0.01276742 +C 0.05442259 -0.04477733 0.08155388 +O 1.12163074 -0.59783950 0.20940553 +C -1.28687786 -0.79011642 0.06178435 +O -1.04605114 -2.13954195 -0.23957158 +H 0.76623366 1.85991128 -0.04483425 +H -0.93021346 1.74567447 -0.31565615 +H -1.74559696 -0.65521334 1.05526700 +H -1.96026061 -0.31149222 -0.67026256 +H -1.85637296 -2.63017646 -0.07625478 +10 +Properties=species:S:1:pos:R:3 A=10.14389 B=4.0714 C=3.02223 mu=5.3004 alpha=35.19 epsHOMO=-0.2527 epsLUMO=0.0208 epsgap=0.2735 =408.0279 zpve=0.080317 U0=-284.372483 U=-284.367172 H=-284.366228 G=-284.40095 Cv=17.931 pbc="F F F" +N 0.01838100 0.93057357 0.04943058 +H -0.70674506 0.56116798 -0.55550134 +H 0.68248307 0.18430002 0.21487099 +H 0.21928084 2.79858558 -0.32670585 +C -0.54586914 1.44688585 1.30274586 +C -0.80466975 2.96030569 1.21052835 +O -0.21468822 3.53648884 0.15531687 +O -1.45295676 3.56338618 2.02143528 +H -1.47662579 0.96512183 1.62072169 +H 0.17781209 1.30324286 2.11157176 +13 +Properties=species:S:1:pos:R:3 A=6.6508 B=4.19317 C=3.39441 mu=2.2854 alpha=42.01 epsHOMO=-0.2597 epsLUMO=0.0631 epsgap=0.3228 =438.3028 zpve=0.11345 U0=-269.479234 U=-269.472993 H=-269.472049 G=-269.508213 Cv=22.072 pbc="F F F" +C -0.02775167 1.53421433 0.00651151 +C 0.08343246 0.01218277 0.05258986 +O 0.95856392 -0.48200710 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epsgap=0.3526 =439.1643 zpve=0.137025 U0=-196.409349 U=-196.403398 H=-196.402453 G=-196.437676 Cv=22.258 pbc="F F F" +C -0.03913204 1.50456305 0.04978913 +C 0.00985856 -0.01113880 0.01248619 +C -1.24167981 -0.66663044 -0.53989979 +C 1.33803387 -0.66895748 -0.27400430 +C 0.75126442 -0.72221586 1.11862571 +H 0.88633040 1.92490040 0.45579358 +H -0.18183878 1.92045743 -0.95489291 +H -0.86832076 1.85815718 0.67439648 +H -1.43570890 -0.34344387 -1.56976411 +H -1.15326757 -1.75757800 -0.54433832 +H -2.12221416 -0.40570820 0.05951361 +H 1.34398374 -1.58235208 -0.86005084 +H 2.20196897 -0.03329084 -0.43929326 +H 1.21949576 -0.12246018 1.89246580 +H 0.36153319 -1.67153717 1.47171932 +13 +Properties=species:S:1:pos:R:3 A=6.49534 B=5.52329 C=3.9108 mu=1.8235 alpha=45.7 epsHOMO=-0.2596 epsLUMO=0.091 epsgap=0.3505 =405.521 zpve=0.112851 U0=-232.335768 U=-232.329981 H=-232.329037 G=-232.364091 Cv=20.719 pbc="F F F" +C -0.03970330 1.50954998 -0.04112233 +C -0.03023556 -0.00335329 -0.06498361 +C -1.24613189 -0.68356648 0.52531190 +C 0.78937201 -0.71129193 -1.06027559 +O 1.22546798 -0.60059036 0.29712283 +H 0.88992772 1.90948478 -0.45296558 +H -0.87952723 1.90350108 -0.62376642 +H -0.14448576 1.87590604 0.98604429 +H -2.15218727 -0.41012616 -0.02629495 +H -1.13412584 -1.77005431 0.49747032 +H -1.38586631 -0.38055453 1.56882757 +H 0.50712690 -1.71386874 -1.38085018 +H 1.37088104 -0.14369322 -1.78640853 +13 +Properties=species:S:1:pos:R:3 A=6.57648 B=5.31603 C=3.98453 mu=1.3822 alpha=45.85 epsHOMO=-0.2494 epsLUMO=0.0776 epsgap=0.327 =401.4888 zpve=0.113237 U0=-232.33341 U=-232.327734 H=-232.32679 G=-232.36147 Cv=21.17 pbc="F F F" +C -0.03714052 1.53215920 0.01332310 +C 0.01344335 0.02117019 0.01922005 +O -1.17476104 -0.59735284 -0.40931260 +C 1.30744798 -0.69902106 -0.27389650 +C 0.70844749 -0.71373504 1.12226318 +H 0.89026861 1.96240279 0.40276135 +H -0.17846151 1.91561262 -1.00518522 +H -0.87011232 1.88955201 0.62730953 +H -1.28352265 -0.40225391 -1.34690641 +H 1.23973813 -1.61642115 -0.84783774 +H 2.20543993 -0.11523820 -0.44744474 +H 1.19539159 -0.13889834 1.90240284 +H 0.24735439 -1.63670241 1.45378480 +10 +Properties=species:S:1:pos:R:3 A=12.18399 B=5.1384 C=3.78868 mu=2.5732 alpha=40.19 epsHOMO=-0.263 epsLUMO=0.0277 epsgap=0.2907 =352.0378 zpve=0.080558 U0=-247.201165 U=-247.196699 H=-247.195755 G=-247.22841 Cv=15.51 pbc="F F F" +N -0.04445152 1.25623533 0.31681915 +C -0.03914559 0.04913710 -0.01670558 +C -1.08749619 -1.04164446 -0.11488033 +C 0.09869688 -1.90754844 -0.58225894 +O 0.99647508 -0.76306629 -0.44028010 +H 0.88935980 1.66481350 0.24916893 +H -1.54324114 -1.32235812 0.83572172 +H -1.86910036 -0.85713839 -0.85325910 +H 0.39739753 -2.72227350 0.08158721 +H 0.06945080 -2.25418345 -1.61796084 +11 +Properties=species:S:1:pos:R:3 A=10.79571 B=4.78732 C=3.54292 mu=2.7119 alpha=42.77 epsHOMO=-0.2415 epsLUMO=-0.0194 epsgap=0.2222 =379.6371 zpve=0.090544 U0=-231.15578 U=-231.150804 H=-231.149859 G=-231.184401 Cv=17.076 pbc="F F F" +O -0.00101746 0.01846628 -0.20114742 +C -0.01405811 1.19658059 0.00205306 +C -1.13542911 2.22979148 0.21957980 +C -0.03500884 3.29097637 0.53052223 +C 1.08469664 2.25947795 0.18994331 +H -1.71642814 2.41772662 -0.68901078 +H -1.83392757 1.98415059 1.02444244 +H -0.05534741 4.17585264 -0.10670963 +H -0.02541477 3.61399048 1.57278917 +H 1.81074389 2.03288534 0.97578973 +H 1.63600801 2.46255380 -0.73376244 +10 +Properties=species:S:1:pos:R:3 A=12.23436 B=4.97895 C=3.70561 mu=3.6671 alpha=39.73 epsHOMO=-0.25 epsLUMO=0.038 epsgap=0.288 =355.0934 zpve=0.080442 U0=-247.225618 U=-247.220897 H=-247.219953 G=-247.253218 Cv=16.1 pbc="F F F" +O 0.03184134 -0.03461682 0.00849836 +C -0.02440334 1.16440533 0.02160811 +C -1.14541322 2.23712809 0.03012901 +C 0.00928805 3.27945878 0.04557758 +N 0.93843838 2.14652109 0.03554396 +H -1.77030529 2.23779730 -0.86428437 +H -1.77632809 2.21765386 0.92007934 +H 0.07170564 3.91863516 -0.84072193 +H 0.06622603 3.89826625 0.94657952 +H 1.94616840 2.09535713 0.03855945 +9 +Properties=species:S:1:pos:R:3 A=12.44643 B=5.23328 C=3.86599 mu=3.9339 alpha=35.4 epsHOMO=-0.2788 epsLUMO=0.0089 epsgap=0.2878 =336.0792 zpve=0.068574 U0=-267.106213 U=-267.101929 H=-267.100985 G=-267.13332 Cv=14.483 pbc="F F F" +O 0.04424004 0.03933243 -0.33071104 +C -0.04854479 1.17416230 0.00410076 +C -1.14211656 2.23611850 0.15338361 +C 0.02332538 3.12357010 0.61681625 +O 0.95663808 2.01190122 0.42603746 +H -1.62573755 2.51482212 -0.78401102 +H -1.90009213 1.99553386 0.90028663 +H 0.28368077 3.96172751 -0.03229287 +H 0.00782250 3.43942655 1.66162725 +10 +Properties=species:S:1:pos:R:3 A=11.54185 B=4.90368 C=3.63309 mu=2.5257 alpha=39.34 epsHOMO=-0.2437 epsLUMO=-0.0258 epsgap=0.2179 =359.152 zpve=0.07965 U0=-247.190194 U=-247.185543 H=-247.184599 G=-247.217688 Cv=16.083 pbc="F F F" +O 0.00053891 0.00665475 -0.09743049 +C -0.01371990 1.19213881 0.03139936 +C -1.10668681 2.26612284 0.17247913 +N -0.03543445 3.25123475 0.48533693 +C 1.05328615 2.29521506 0.14383904 +H -1.65047970 2.42796175 -0.76886054 +H -1.83606786 2.11370530 0.97567410 +H -0.05434600 4.07193609 -0.10727051 +H 1.80754206 2.16277968 0.92736250 +H 1.56737937 2.47130277 -0.81152688 +9 +Properties=species:S:1:pos:R:3 A=12.18264 B=4.94228 C=3.68162 mu=0.8477 alpha=35.94 epsHOMO=-0.2647 epsLUMO=-0.0352 epsgap=0.2295 =344.5473 zpve=0.066989 U0=-267.068488 U=-267.063976 H=-267.063032 G=-267.095887 Cv=15.115 pbc="F F F" +O -0.00031692 0.00676321 -0.17139774 +C -0.01417098 1.18672865 -0.00980279 +C -1.08283120 2.27653381 0.15574094 +O -0.03856115 3.26460423 0.27513986 +C 1.02857270 2.30515693 0.12774311 +H -1.72923855 2.43647507 -0.71671274 +H -1.70219898 2.19346725 1.05800754 +H 1.67366781 2.23923293 1.01324179 +H 1.64705068 2.48224548 -0.76148355 +15 +Properties=species:S:1:pos:R:3 A=9.63416 B=4.10254 C=3.28665 mu=0.095 alpha=52.94 epsHOMO=-0.2896 epsLUMO=0.0927 epsgap=0.3823 =455.0215 zpve=0.138424 U0=-196.407957 U=-196.40245 H=-196.401505 G=-196.436159 Cv=20.299 pbc="F F F" +C -0.47005934 1.44206547 -0.06684548 +C 0.14287306 0.05485144 0.03747984 +C 1.37254693 -0.30295273 -0.84679168 +C 1.95947994 -1.09364561 0.35326039 +C 0.99887433 -0.30768787 1.28563099 +H 0.29210839 2.21844829 0.06844100 +H -0.93614604 1.60094605 -1.04612799 +H -1.24148161 1.59707353 0.69627593 +H -0.63517341 -0.70560132 -0.10055095 +H 1.19338376 -0.84118177 -1.78129390 +H 1.96867777 0.58857708 -1.07004184 +H 1.69873597 -2.15500718 0.30521100 +H 3.03253510 -1.01077235 0.54148058 +H 1.48290386 0.58242428 1.70211026 +H 0.51304296 -0.84979686 2.10121062 +13 +Properties=species:S:1:pos:R:3 A=9.93129 B=4.56362 C=3.63704 mu=1.6826 alpha=45.72 epsHOMO=-0.241 epsLUMO=0.0915 epsgap=0.3325 =407.6909 zpve=0.11459 U0=-232.33123 U=-232.325877 H=-232.324933 G=-232.359589 Cv=18.582 pbc="F F F" +C -0.32308228 1.53751451 0.01947624 +C 0.11847214 0.08900172 -0.00523140 +C 0.87489284 -0.43763303 -1.24721738 +C 1.97841048 -0.80657483 -0.23740356 +O 1.27344926 -0.15419108 0.83794040 +H 0.49211985 2.19658795 -0.29527664 +H -1.17615835 1.68923490 -0.65134808 +H -0.62776963 1.82747912 1.03013205 +H -0.71410200 -0.56389187 0.29597305 +H 0.41033314 -1.26269706 -1.78910031 +H 1.15560811 0.34596269 -1.95459519 +H 2.09501012 -1.88349764 -0.05606083 +H 2.96970344 -0.36924147 -0.40408009 +13 +Properties=species:S:1:pos:R:3 A=10.13697 B=4.32512 C=3.50912 mu=1.8995 alpha=45.45 epsHOMO=-0.2419 epsLUMO=0.0814 epsgap=0.3233 =414.1575 zpve=0.114694 U0=-232.325947 U=-232.320548 H=-232.319604 G=-232.354576 Cv=18.443 pbc="F F F" +C -0.18371124 1.54668909 -0.01671129 +C 0.04513382 0.04386724 0.02270494 +C 1.20301104 -0.54034580 -0.81983464 +O 1.80061686 -1.15530615 0.33924991 +C 0.83391983 -0.54313754 1.21648003 +H 0.75102609 2.09256015 0.15344911 +H -0.58319248 1.86155707 -0.98694169 +H -0.89843724 1.85917126 0.75244153 +H -0.89506736 -0.48768372 -0.14843995 +H 0.93560472 -1.27857588 -1.58543917 +H 1.86392938 0.21379924 -1.27081649 +H 1.29414565 0.20948993 1.87273952 +H 0.31517042 -1.28326856 1.83756393 +13 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0.01675156 0.02484791 0.03233817 +C 0.11193823 -0.54392541 1.47000744 +O -1.16387804 -1.20093191 1.32441605 +C -1.41930178 -0.52040930 0.07750673 +H 1.00022214 1.67889479 -0.21697466 +H 0.66036495 -0.50380879 -0.68180327 +H 0.91806589 -1.25385265 1.68878628 +H 0.09146208 0.24427487 2.23499232 +H -2.16139683 0.28183960 0.18222436 +H -1.72867456 -1.20941157 -0.71550887 +15 +Properties=species:S:1:pos:R:3 A=7.14221 B=4.37083 C=3.30265 mu=0.1023 alpha=52.92 epsHOMO=-0.2632 epsLUMO=0.094 epsgap=0.3573 =465.3301 zpve=0.137341 U0=-196.406419 U=-196.40034 H=-196.399396 G=-196.435152 Cv=22.005 pbc="F F F" +C -0.05189850 1.55217435 -0.10318438 +C 0.01226648 0.04690675 0.03747520 +C 1.30115692 -0.70661553 -0.19725724 +C 0.70431939 -0.64893239 1.19003562 +C 1.39750324 0.09481280 2.31078011 +H 0.87404209 2.03543171 0.22162228 +H -0.21641357 1.83695278 -1.14852958 +H -0.87157646 1.97673684 0.48797046 +H -0.88508206 -0.45596941 -0.31580786 +H 1.27663658 -1.64149022 -0.74697967 +H 2.19424067 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epsLUMO=0.0931 epsgap=0.313 =432.5081 zpve=0.125556 U0=-212.438188 U=-212.432286 H=-212.431342 G=-212.466725 Cv=20.993 pbc="F F F" +C -0.17034561 1.55765735 -0.10065470 +C 0.01075256 0.05574638 -0.05225833 +C 0.71794239 -0.67802425 -1.14204150 +N 1.29811574 -0.59777508 0.19386866 +C 2.50047290 0.20256923 0.36428818 +H 0.60876703 2.05717913 -0.68119301 +H -1.13238698 1.79957181 -0.56489127 +H -0.17350949 1.98946323 0.90659028 +H -0.81010441 -0.46148051 0.44427396 +H 0.39136106 -1.66274618 -1.46771171 +H 1.16668484 -0.06908735 -1.92776126 +H 2.58340476 1.06745445 -0.31262255 +H 2.55143652 0.56892747 1.39502638 +H 3.37185012 -0.43769461 0.19201862 +13 +Properties=species:S:1:pos:R:3 A=8.15202 B=4.38313 C=3.43318 mu=1.8159 alpha=46.02 epsHOMO=-0.2573 epsLUMO=0.0984 epsgap=0.3557 =432.2224 zpve=0.113178 U0=-232.334436 U=-232.32857 H=-232.327626 G=-232.363064 Cv=20.451 pbc="F F F" +C -0.03345961 1.54674842 -0.13373520 +C -0.04966080 0.05914273 0.10861437 +O 1.17261123 -0.65888877 -0.09925231 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"module_short": "ase", + "name": "Atoms" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Atoms" + } + ], + "s.arrays": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "dict" + } + ], + "spacing": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "int" + } + ], + "store_trajectory": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + } + ], + "structures_for_dataset": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "list" + } + ], + "structures_for_dataset.append": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "builtin_function_or_method" + } + ], + "subprocess.run": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "subprocess", + "module_short": "subprocess", + "name": "run" + } + ], + "time_lag": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "int" + } + ], + "trajectory": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "list" + } + ], + "trajectory.append": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "builtin_function_or_method" + } + ] +} diff --git a/docs/src/examples/programmatic/flashmd/flashmd.ipynb b/docs/src/examples/programmatic/flashmd/flashmd.ipynb new file mode 100644 index 0000000000..6333cb2726 --- /dev/null +++ b/docs/src/examples/programmatic/flashmd/flashmd.ipynb @@ -0,0 +1,97 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Training a FlashMD model\n\nThis tutorial demonstrates how to train a FlashMD model for the direct prediction\nof molecular dynamics. This type of model affords faster MD simulations compared to\nMLIPs by a factor between 10 and 30 (https://arxiv.org/abs/2505.19350).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import copy\nimport subprocess\n\nimport ase\nimport ase.build\nimport ase.io\nimport ase.units\nfrom ase.calculators.emt import EMT\nfrom ase.md import VelocityVerlet\nfrom ase.md.langevin import Langevin\nfrom ase.md.velocitydistribution import MaxwellBoltzmannDistribution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data generation\n\nFlashMD models train on molecular dynamics trajectories in the NVE ensemble (i.e.,\nmost often with the velocity Verlet integrator). These trajectories can be generated\nwith almost any MD code (i-PI, LAMMPS, etc.). Here, for simplicity, we will use ASE\nand its built-in EMT potential. In reality, you might want to use a more accurate\nbaseline such as ab initio MD or a machine-learned interatomic potential (MLIP).\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# We start by creating a simple system (a small box of aluminum).\natoms = ase.build.bulk(\"Al\", \"fcc\", cubic=True) * (2, 2, 2)\n\n# We first equilibrate the system at 300K using a Langevin thermostat.\nMaxwellBoltzmannDistribution(atoms, temperature_K=300)\natoms.calc = EMT()\ndyn = Langevin(\n atoms, 2 * ase.units.fs, temperature_K=300, friction=1 / (100 * ase.units.fs)\n)\ndyn.run(1000) # 2 ps equilibration (around 10 ps is better in practice)\n\n# Then, we run a production simulation in the NVE ensemble.\ntrajectory = []\n\n\ndef store_trajectory():\n trajectory.append(copy.deepcopy(atoms))\n\n\ndyn = VelocityVerlet(atoms, 1 * ase.units.fs)\ndyn.attach(store_trajectory, interval=1)\ndyn.run(2000) # 2 ps NVE run" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data preparation\n\nNow, we need to generate the training data from the trajectory. FlashMD models\nrequire future positions and momenta as targets. We will save them in an `.xyz`\nfile under the `future_positions` and `future_momenta` keys.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# The FlashMD model will be trained to predict 30 steps into the future, i.e., 30 fs\n# since we ran the reference simulation with a time step of 1 fs. For this type\n# of system, FlashMD is expected to perform well up to around 60-80 fs.\ntime_lag = 30\n\n# We pick starting structures that are 200 steps apart. To avoid wasting training\n# structures, this should be set to be around the expected velocity-velocity\n# autocorrelation time for the system. This is the time scale that quantifies how long\n# it takes for the system to forget its original velocities.\nspacing = 200\n\n\ndef get_structure_for_dataset(frame_now, frame_ahead):\n s = copy.deepcopy(frame_now)\n s.arrays[\"future_positions\"] = frame_ahead.get_positions()\n s.arrays[\"future_momenta\"] = frame_ahead.get_momenta()\n return s\n\n\nstructures_for_dataset = []\nfor i in range(0, len(trajectory) - time_lag, spacing):\n frame_now = trajectory[i]\n frame_ahead = trajectory[i + time_lag]\n s = get_structure_for_dataset(frame_now, frame_ahead)\n structures_for_dataset.append(s)\n\n # Here, we also add the time-reversed pair (optional)\n frame_now_trev = copy.deepcopy(frame_now)\n frame_ahead_trev = copy.deepcopy(frame_ahead)\n frame_now_trev.set_momenta(-frame_now_trev.get_momenta())\n frame_ahead_trev.set_momenta(-frame_ahead_trev.get_momenta())\n s = get_structure_for_dataset(frame_ahead_trev, frame_now_trev)\n structures_for_dataset.append(s)\n\n# Write the structures to an xyz file\nase.io.write(\"flashmd.xyz\", structures_for_dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training the model\n\nThe dataset is now ready for training. You can now provide it to ``metatrain`` and\ntrain your FlashMD model!\n\nFor example, you can use the following options file:\n\n.. literalinclude:: options.yaml\n :language: yaml\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "subprocess.run([\"mtt\", \"train\", \"options.yaml\"])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/examples/programmatic/flashmd/flashmd.py b/docs/src/examples/programmatic/flashmd/flashmd.py similarity index 100% rename from examples/programmatic/flashmd/flashmd.py rename to docs/src/examples/programmatic/flashmd/flashmd.py diff --git a/docs/src/examples/programmatic/flashmd/flashmd.py.md5 b/docs/src/examples/programmatic/flashmd/flashmd.py.md5 new file mode 100644 index 0000000000..09379633c1 --- /dev/null +++ b/docs/src/examples/programmatic/flashmd/flashmd.py.md5 @@ -0,0 +1 @@ +dc5c04be3dfe900edcf26c73a0ad6880 \ No newline at end of file diff --git a/docs/src/examples/programmatic/flashmd/flashmd.rst b/docs/src/examples/programmatic/flashmd/flashmd.rst new file mode 100644 index 0000000000..1e80ddd319 --- /dev/null +++ b/docs/src/examples/programmatic/flashmd/flashmd.rst @@ -0,0 +1,225 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "examples/programmatic/flashmd/flashmd.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code. + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_examples_programmatic_flashmd_flashmd.py: + + +Training a FlashMD model +======================== + +This tutorial demonstrates how to train a FlashMD model for the direct prediction +of molecular dynamics. This type of model affords faster MD simulations compared to +MLIPs by a factor between 10 and 30 (https://arxiv.org/abs/2505.19350). + +.. GENERATED FROM PYTHON SOURCE LINES 12-26 + +.. code-block:: Python + + + import copy + import subprocess + + import ase + import ase.build + import ase.io + import ase.units + from ase.calculators.emt import EMT + from ase.md import VelocityVerlet + from ase.md.langevin import Langevin + from ase.md.velocitydistribution import MaxwellBoltzmannDistribution + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 27-35 + +Data generation +--------------- + +FlashMD models train on molecular dynamics trajectories in the NVE ensemble (i.e., +most often with the velocity Verlet integrator). These trajectories can be generated +with almost any MD code (i-PI, LAMMPS, etc.). Here, for simplicity, we will use ASE +and its built-in EMT potential. In reality, you might want to use a more accurate +baseline such as ab initio MD or a machine-learned interatomic potential (MLIP). + +.. GENERATED FROM PYTHON SOURCE LINES 36-61 + +.. code-block:: Python + + + # We start by creating a simple system (a small box of aluminum). + atoms = ase.build.bulk("Al", "fcc", cubic=True) * (2, 2, 2) + + # We first equilibrate the system at 300K using a Langevin thermostat. + MaxwellBoltzmannDistribution(atoms, temperature_K=300) + atoms.calc = EMT() + dyn = Langevin( + atoms, 2 * ase.units.fs, temperature_K=300, friction=1 / (100 * ase.units.fs) + ) + dyn.run(1000) # 2 ps equilibration (around 10 ps is better in practice) + + # Then, we run a production simulation in the NVE ensemble. + trajectory = [] + + + def store_trajectory(): + trajectory.append(copy.deepcopy(atoms)) + + + dyn = VelocityVerlet(atoms, 1 * ase.units.fs) + dyn.attach(store_trajectory, interval=1) + dyn.run(2000) # 2 ps NVE run + + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + + True + + + +.. GENERATED FROM PYTHON SOURCE LINES 62-68 + +Data preparation +---------------- + +Now, we need to generate the training data from the trajectory. FlashMD models +require future positions and momenta as targets. We will save them in an `.xyz` +file under the `future_positions` and `future_momenta` keys. + +.. GENERATED FROM PYTHON SOURCE LINES 69-107 + +.. code-block:: Python + + + # The FlashMD model will be trained to predict 30 steps into the future, i.e., 30 fs + # since we ran the reference simulation with a time step of 1 fs. For this type + # of system, FlashMD is expected to perform well up to around 60-80 fs. + time_lag = 30 + + # We pick starting structures that are 200 steps apart. To avoid wasting training + # structures, this should be set to be around the expected velocity-velocity + # autocorrelation time for the system. This is the time scale that quantifies how long + # it takes for the system to forget its original velocities. + spacing = 200 + + + def get_structure_for_dataset(frame_now, frame_ahead): + s = copy.deepcopy(frame_now) + s.arrays["future_positions"] = frame_ahead.get_positions() + s.arrays["future_momenta"] = frame_ahead.get_momenta() + return s + + + structures_for_dataset = [] + for i in range(0, len(trajectory) - time_lag, spacing): + frame_now = trajectory[i] + frame_ahead = trajectory[i + time_lag] + s = get_structure_for_dataset(frame_now, frame_ahead) + structures_for_dataset.append(s) + + # Here, we also add the time-reversed pair (optional) + frame_now_trev = copy.deepcopy(frame_now) + frame_ahead_trev = copy.deepcopy(frame_ahead) + frame_now_trev.set_momenta(-frame_now_trev.get_momenta()) + frame_ahead_trev.set_momenta(-frame_ahead_trev.get_momenta()) + s = get_structure_for_dataset(frame_ahead_trev, frame_now_trev) + structures_for_dataset.append(s) + + # Write the structures to an xyz file + ase.io.write("flashmd.xyz", structures_for_dataset) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 108-118 + +Training the model +------------------ + +The dataset is now ready for training. You can now provide it to ``metatrain`` and +train your FlashMD model! + +For example, you can use the following options file: + +.. literalinclude:: options.yaml + :language: yaml + +.. GENERATED FROM PYTHON SOURCE LINES 119-121 + +.. code-block:: Python + + + subprocess.run(["mtt", "train", "options.yaml"]) + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + + CompletedProcess(args=['mtt', 'train', 'options.yaml'], returncode=0) + + + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** (0 minutes 16.440 seconds) + + +.. _sphx_glr_download_examples_programmatic_flashmd_flashmd.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: flashmd.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: flashmd.py ` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: flashmd.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/programmatic/flashmd/flashmd.xyz b/docs/src/examples/programmatic/flashmd/flashmd.xyz new file mode 100644 index 0000000000..7a9bb6e0ee --- /dev/null +++ b/docs/src/examples/programmatic/flashmd/flashmd.xyz @@ -0,0 +1,680 @@ +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=0.8948308788199846 free_energy=0.8948308788199846 stress="0.00582921114852916 0.000442369304973728 0.0011759981285591327 0.000442369304973728 0.004847050505392154 1.8183467986477127e-06 0.0011759981285591327 1.8183467986477127e-06 0.007675243103858247" pbc="T T T" +Al 0.16464029 0.00846807 0.08508833 -0.51435652 -1.29123021 -0.40577065 0.06123463 -0.13595049 0.07731856 -1.12368820 -1.14173022 0.32159375 0.02624888 -0.43303143 -0.16939770 0.17687361 +Al 0.04333576 2.12405991 2.14636843 0.32093878 -0.17299006 1.71382084 0.06562479 2.10857776 2.28688450 -0.03325533 -0.03348663 0.72445134 -0.00221246 0.00546396 -0.03608654 -0.18260135 +Al 2.21922479 0.20052938 2.21911130 -0.44642369 0.68326702 -0.86115870 2.10411598 0.19875347 2.10572830 -1.45828161 -0.77836419 -1.04256479 0.03444733 -0.51126629 -0.40563433 -0.22658428 +Al 1.96155048 2.02748026 -0.04177685 0.96270926 -0.14683668 2.36933351 2.07343613 2.03409454 0.21257465 0.87860336 0.18665112 1.78096908 0.03471748 0.21428015 0.19684797 0.35198798 +Al 0.05568839 0.05574736 4.18671791 0.06245496 0.81230955 -0.20747444 0.06765827 0.10043852 4.11442519 -0.00327411 -0.12717917 -1.13769447 0.03523763 0.14215879 -0.15572925 -0.23576948 +Al -0.13186583 1.98855492 6.20781636 0.22280189 -1.60667901 -1.21899781 -0.09891252 1.85903274 6.09773601 0.49242377 -0.48907979 -0.62171015 0.02370026 -0.01704901 0.05339132 -0.03220987 +Al 2.03621737 0.09348554 6.20878227 -0.11797775 -0.45796433 -0.25441550 2.04174920 -0.00219866 6.16899661 0.14285863 -0.95547093 -0.35848765 0.03794931 0.12529531 -0.50020256 -0.16476891 +Al 1.94284613 1.98171088 4.32022368 -0.11779476 0.99420017 0.01357815 1.98680439 2.11439821 4.26497106 0.92095190 1.32594662 -0.91399551 0.05025639 0.32354246 0.19859126 -0.39892430 +Al -0.03643397 4.06958069 -0.08827052 -0.87775289 -0.23551193 -1.08274508 -0.12164270 4.03738893 -0.14312537 -0.49788673 -0.37859298 0.01252870 0.01644881 -0.05787561 0.00822122 0.41571657 +Al -0.00276283 6.07523118 2.26506718 -0.08348851 -1.25362582 0.10228474 0.01270856 5.94288617 2.18829289 0.26251117 -0.99075759 -1.46067169 0.02998422 0.20385822 -0.10906399 -0.51252453 +Al 2.03395119 4.06067541 1.99971494 0.10277670 0.14604391 -0.61824753 2.02947851 4.06920737 1.96448282 -0.03548154 -0.07999657 0.19416636 0.02826364 -0.21588698 0.05715279 0.03450527 +Al 1.98925195 6.07039489 0.13152820 -0.70107353 -0.86590020 0.12070077 1.94248161 5.99823209 0.12004608 -0.12069101 -0.36317825 -0.36952856 0.00976045 0.14688968 0.06222498 -0.11347800 +Al 0.09824214 3.96907265 4.23344143 -1.10674903 1.42633014 0.74990889 -0.02406983 4.10656556 4.26687676 -1.00051381 0.85934586 -0.07518529 0.02755296 -0.10461418 0.07870897 -0.31515772 +Al 0.12009291 5.93594422 6.01046922 -1.18594847 0.30481321 -0.29021400 0.01409687 6.01670929 6.01936247 -0.62232620 1.02709759 0.47965827 0.02709685 0.02595310 0.35991925 0.20568057 +Al 2.17590269 3.96699525 6.18607872 1.82666693 0.88078720 0.01094781 2.31621025 4.10654665 6.18066021 0.51549710 1.49197521 -0.08331562 0.03066521 -0.16477845 0.36717608 -0.07313241 +Al 2.11661542 6.14055183 4.02959445 -1.21684111 0.18480678 0.01285619 1.99885366 6.16084043 4.05270339 -0.78737459 0.07326348 0.35179917 0.01482222 -0.05129393 0.08802409 0.16252990 +Al 4.08197493 0.10490606 0.17085796 1.18346222 0.39760916 -0.36612355 4.19092003 0.13248906 0.08093809 0.61699589 0.09336852 -1.26574518 0.01022865 0.01600799 -0.07344046 -0.29331826 +Al 3.93645088 2.18854557 2.09631276 1.82853584 -1.42156010 0.59584715 4.14907873 2.00771831 2.17584454 1.73625133 -1.65709058 0.77212338 0.05895968 0.28880840 -0.31079094 0.11048325 +Al 6.22340708 0.06013780 2.05279540 -1.24895018 0.55835249 -0.97816458 6.08747412 0.10501728 1.95480608 -0.94397278 0.14753550 -0.52447199 0.02477423 -0.20011305 -0.03687653 -0.14529374 +Al 6.02132034 2.07641943 0.14100884 0.65846298 0.04825727 0.02423917 6.14091227 2.06706350 0.11684310 1.45056452 -0.18007649 -0.52704098 0.02479758 0.29892593 -0.11730661 -0.11169449 +Al 4.18660823 -0.00242056 4.12764259 -0.01325988 -0.40875091 -0.61885377 4.14992585 -0.03770428 4.06126252 -0.56901563 -0.18660433 -0.46420485 0.02748321 -0.28348925 0.01573260 -0.08948531 +Al 4.09333604 1.99813612 6.08092681 0.40479253 1.09617456 -0.45695243 4.16603783 2.07017504 6.04884325 0.74864192 0.03050378 -0.02498149 0.00968249 0.27257342 -0.11621141 0.05646164 +Al 6.23948472 -0.22255063 6.05928214 1.59323678 -0.41493906 1.09744752 6.33990711 -0.19936221 6.21256097 -0.07209436 0.78147382 1.34706809 0.04886645 -0.21504796 0.45884785 0.45930897 +Al 6.09879426 1.89696639 4.07909200 -0.70948079 1.25639048 0.10789133 6.02790682 2.05765944 4.11081737 -0.39377453 1.39654577 0.50055116 0.02507895 -0.05684998 0.34628181 0.09833994 +Al 4.15353887 3.98323517 -0.00693228 0.07234339 -0.91889267 0.42736080 4.13333436 3.92650157 0.06146214 -0.32182670 -0.09386815 0.87097930 0.03166976 -0.23582388 0.21337205 0.08628749 +Al 4.13836479 6.07840665 1.93039247 -0.65108121 0.29673392 0.30647594 4.03241715 6.12659693 1.99383823 -1.21564110 0.47432752 0.76998567 0.02564584 -0.23677724 0.16515317 0.23204498 +Al 5.91356098 4.05332772 2.30407210 -0.11207184 -0.57732050 -0.43473224 6.00009513 3.99166004 2.18735879 1.52404557 -0.43452393 -1.67790947 0.04651277 0.65974572 -0.07548183 -0.39155185 +Al 6.04952230 6.03040117 0.00447251 -0.69510041 0.14983012 0.70220847 6.03184281 6.04602950 0.10751122 0.23359967 0.03041771 1.11755336 0.02607780 0.41601265 0.07073372 0.19255014 +Al 3.90492926 4.15165660 4.00534018 1.40576279 -0.78672061 0.03995367 4.05583797 4.06899126 4.06876345 1.18275263 -0.50712983 0.81502571 0.05153768 0.15003627 -0.14429056 0.51414408 +Al 4.07173410 6.18254887 6.19658769 -0.49226230 1.23361909 0.07352391 4.00532367 6.28118217 6.18870997 -0.47037602 0.49815600 -0.18710539 0.00176712 -0.22482859 -0.16464236 -0.11230117 +Al 6.02614131 4.07344579 5.98266586 -0.63584207 0.56407956 0.72323350 5.95129296 4.14512639 6.05337693 -0.64565669 0.77230350 0.62910140 -0.01167201 -0.08023362 0.05346028 -0.05969545 +Al 6.11856558 6.19884837 3.97257857 0.28150990 -0.47468254 -1.39776206 6.11209593 6.11582640 3.89617986 -0.39056651 -0.79178237 0.04705835 0.06848149 -0.20059260 -0.37868432 0.36157672 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=0.9775593220180188 free_energy=0.9775593220180188 stress="0.00561896524110683 0.00010955929300027058 0.0005917793502733804 0.00010955929300027058 0.004925564335948191 0.0001104654741423019 0.0005917793502733804 0.0001104654741423019 0.00593663920646172" pbc="T T T" +Al 0.06123463 -0.13595049 0.07731856 1.12368820 1.14173022 -0.32159375 0.16464029 0.00846807 0.08508833 0.51435652 1.29123021 0.40577065 0.02598423 0.06720061 0.25551433 0.28941604 +Al 0.06562479 2.10857776 2.28688450 0.03325533 0.03348663 -0.72445134 0.04333576 2.12405991 2.14636843 -0.32093878 0.17299006 -1.71382084 0.02256491 -0.22565110 0.11432577 -0.45364058 +Al 2.10411598 0.19875347 2.10572830 1.45828161 0.77836419 1.04256479 2.21922479 0.20052938 2.21911130 0.44642369 -0.68326702 0.86115870 0.03250304 -0.12232050 -0.53465264 0.10968291 +Al 2.07343613 2.03409454 0.21257465 -0.87860336 -0.18665112 -1.78096908 1.96155048 2.02748026 -0.04177685 -0.96270926 0.14683668 -2.36933351 0.03670059 -0.20199496 0.03991207 -0.65029019 +Al 0.06765827 0.10043852 4.11442519 0.00327411 0.12717917 1.13769447 0.05568839 0.05574736 4.18671791 -0.06245496 -0.81230955 0.20747444 0.03628151 -0.17114302 -0.41222499 -0.27086310 +Al -0.09891252 1.85903274 6.09773601 -0.49242377 0.48907979 0.62171015 -0.13186583 1.98855492 6.20781636 -0.22280189 1.60667901 1.21899781 0.05391302 0.20809381 0.59019410 0.31097807 +Al 2.04174920 -0.00219866 6.16899661 -0.14285863 0.95547093 0.35848765 2.03621737 0.09348554 6.20878227 0.11797775 0.45796433 0.25441550 0.01213505 -0.02215628 0.16967770 0.06344988 +Al 1.98680439 2.11439821 4.26497106 -0.92095190 -1.32594662 0.91399551 1.94284613 1.98171088 4.32022368 0.11779476 -0.99420017 -0.01357815 0.01865606 0.31303686 -0.02240909 -0.18621893 +Al -0.12164270 4.03738893 -0.14312537 0.49788673 0.37859298 -0.01252870 -0.03643397 4.06958069 -0.08827052 0.87775289 0.23551193 1.08274508 0.01028514 0.31481548 -0.04290457 0.28606025 +Al 0.01270856 5.94288617 2.18829289 -0.26251117 0.99075759 1.46067169 -0.00276283 6.07523118 2.26506718 0.08348851 1.25362582 -0.10228474 0.03642072 0.00160227 0.24928412 -0.40906695 +Al 2.02947851 4.06920737 1.96448282 0.03548154 0.07999657 -0.19416636 2.03395119 4.06067541 1.99971494 -0.10277670 -0.14604391 0.61824753 0.03527415 0.07754863 -0.12299194 0.46552486 +Al 1.94248161 5.99823209 0.12004608 0.12069101 0.36317825 0.36952856 1.98925195 6.07039489 0.13152820 0.70107353 0.86590020 -0.12070077 0.03370322 0.22262050 0.24402805 -0.18565889 +Al -0.02406983 4.10656556 4.26687676 1.00051381 -0.85934586 0.07518529 0.09824214 3.96907265 4.23344143 1.10674903 -1.42633014 -0.74990889 0.03694843 0.16340137 -0.37375743 -0.19319468 +Al 0.01409687 6.01670929 6.01936247 0.62232620 -1.02709759 -0.47965827 0.12009291 5.93594422 6.01046922 1.18594847 -0.30481321 0.29021400 0.06765257 0.28591903 0.07279401 0.25629675 +Al 2.31621025 4.10654665 6.18066021 -0.51549710 -1.49197521 0.08331562 2.17590269 3.96699525 6.18607872 -1.82666693 -0.88078720 -0.01094781 0.03913992 -0.61868554 -0.00577420 -0.01350206 +Al 1.99885366 6.16084043 4.05270339 0.78737459 -0.07326348 -0.35179917 2.11661542 6.14055183 4.02959445 1.21684111 -0.18480678 -0.01285619 0.00425755 0.25499548 -0.14281365 0.04460780 +Al 4.19092003 0.13248906 0.08093809 -0.61699589 -0.09336852 1.26574518 4.08197493 0.10490606 0.17085796 -1.18346222 -0.39760916 0.36612355 0.02757163 -0.36567294 -0.10165634 -0.24845931 +Al 4.14907873 2.00771831 2.17584454 -1.73625133 1.65709058 -0.77212338 3.93645088 2.18854557 2.09631276 -1.82853584 1.42156010 -0.59584715 0.05028353 -0.37036750 0.16837018 -0.06963989 +Al 6.08747412 0.10501728 1.95480608 0.94397278 -0.14753550 0.52447199 6.22340708 0.06013780 2.05279540 1.24895018 -0.55835249 0.97816458 0.03575680 0.38855109 -0.26863586 0.41829455 +Al 6.14091227 2.06706350 0.11684310 -1.45056452 0.18007649 0.52704098 6.02132034 2.07641943 0.14100884 -0.65846298 -0.04825727 -0.02423917 0.03669586 0.13526855 -0.05052286 -0.22414578 +Al 4.14992585 -0.03770428 4.06126252 0.56901563 0.18660433 0.46420485 4.18660823 -0.00242056 4.12764259 0.01325988 0.40875091 0.61885377 0.01061884 -0.10696225 0.11600464 0.18260704 +Al 4.16603783 2.07017504 6.04884325 -0.74864192 -0.03050378 0.02498149 4.09333604 1.99813612 6.08092681 -0.40479253 -1.09617456 0.45695243 0.02857475 -0.07814720 -0.49425034 0.26260027 +Al 6.33990711 -0.19936221 6.21256097 0.07209436 -0.78147382 -1.34706809 6.23948472 -0.22255063 6.05928214 -1.59323678 0.41493906 -1.09744752 0.07280774 -0.82921050 0.33606149 -0.26767690 +Al 6.02790682 2.05765944 4.11081737 0.39377453 -1.39654577 -0.50055116 6.09879426 1.89696639 4.07909200 0.70948079 -1.25639048 -0.10789133 0.04101935 0.32693833 -0.22536169 0.15011873 +Al 4.13333436 3.92650157 0.06146214 0.32182670 0.09386815 -0.87097930 4.15353887 3.98323517 -0.00693228 -0.07234339 0.91889267 -0.42736080 0.02816260 -0.00405970 0.27124079 0.16760924 +Al 4.03241715 6.12659693 1.99383823 1.21564110 -0.47432752 -0.76998567 4.13836479 6.07840665 1.93039247 0.65108121 -0.29673392 -0.30647594 0.00215586 -0.09098616 -0.05117162 0.05839465 +Al 6.00009513 3.99166004 2.18735879 -1.52404557 0.43452393 1.67790947 5.91356098 4.05332772 2.30407210 0.11207184 0.57732050 0.43473224 0.03399237 0.33102920 0.15723171 -0.33119674 +Al 6.03184281 6.04602950 0.10751122 -0.23359967 -0.03041771 -1.11755336 6.04952230 6.03040117 0.00447251 0.69510041 -0.14983012 -0.70220847 0.00997002 0.14757047 -0.13376238 0.05685800 +Al 4.05583797 4.06899126 4.06876345 -1.18275263 0.50712983 -0.81502571 3.90492926 4.15165660 4.00534018 -1.40576279 0.78672061 -0.03995367 0.02750048 -0.21361793 0.30143009 -0.08016059 +Al 4.00532367 6.28118217 6.18870997 0.47037602 -0.49815600 0.18710539 4.07173410 6.18254887 6.19658769 0.49226230 -1.23361909 -0.07352391 0.02729147 0.27109491 -0.31810102 -0.05656443 +Al 5.95129296 4.14512639 6.05337693 0.64565669 -0.77230350 -0.62910140 6.02614131 4.07344579 5.98266586 0.63584207 -0.56407956 -0.72323350 0.01327066 0.09816123 0.09290604 0.05317744 +Al 6.11209593 6.11582640 3.89617986 0.39056651 0.79178237 -0.04705835 6.11856558 6.19884837 3.97257857 -0.28150990 0.47468254 1.39776206 0.02946727 -0.18687223 0.12201556 0.46460257 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=0.8431874507022643 free_energy=0.8431874507022643 stress="0.006244031443369388 0.0007165788141183207 -0.0001627289267088679 0.0007165788141183207 0.005467715397826384 0.00047222160456977303 -0.0001627289267088679 0.00047222160456977303 0.0065578157468693605" pbc="T T T" +Al 0.00264000 0.07844353 -0.12467518 -0.24833940 -0.96183864 0.49485711 -0.00613836 -0.01402459 -0.02917609 0.11464281 -0.61415357 1.04937503 0.02354378 0.07510104 -0.00840324 0.37378042 +Al 0.01973625 1.94453284 2.07781540 -1.14858181 0.43868461 0.26323310 -0.06034934 1.99671851 2.07559403 -0.06503116 0.39052762 -0.24704382 0.01148113 0.05369455 0.09880419 -0.20381966 +Al 2.12606659 -0.00311886 2.19972208 0.41302172 -0.56223045 -0.78333704 2.16448074 -0.04161635 2.08799427 0.27800815 0.11297422 -1.05153068 0.01817628 -0.04086683 -0.06843728 -0.30666921 +Al 2.08365382 1.99498282 0.09681029 0.06882021 0.63561182 0.23612025 2.06579896 2.08443873 0.08890775 -0.32866700 0.89063479 -0.32704702 0.00011097 -0.18451322 0.18586768 -0.22949960 +Al 0.12624551 0.03061316 4.28957870 -0.65159606 0.30279168 -0.14250778 0.01688464 0.02731380 4.25777021 -1.11169736 -0.18773383 -0.34981465 0.02738317 -0.41340684 -0.31632109 -0.16491883 +Al -0.12317083 1.98438986 6.08615311 -0.68033828 -1.02831320 0.81904355 -0.13183977 1.92300711 6.19473968 0.38176425 0.04409683 1.08051663 0.07402757 0.51180957 0.15316222 0.16612647 +Al 2.21175225 -0.05970562 6.17702698 1.71592151 -0.01437753 0.71618332 2.33255833 -0.03585943 6.20959721 0.16274206 0.29935664 -0.22485442 0.01697825 -0.15226446 0.25002542 -0.16014115 +Al 1.89525038 2.00666748 4.05656774 -0.19076047 1.70309258 0.77182852 1.93014534 2.15094689 4.16742316 0.67201897 0.56694128 1.13024239 0.03364249 0.44639722 0.06190913 0.26114939 +Al 0.06324146 4.13002584 0.21584986 -2.29720152 -0.73838022 0.28813726 -0.15219333 4.02591028 0.20765365 -1.23671285 -1.01266081 -0.62514988 0.01526475 -0.04390983 -0.24796852 -0.14860606 +Al -0.03568176 6.33257459 2.11146462 -0.42398127 1.76229272 0.33760784 -0.07002781 6.45271076 2.14810086 -0.14533080 0.17006376 0.39452288 -0.00237100 0.05110291 -0.25026777 -0.04388833 +Al 2.10471123 4.07299382 2.11509901 0.47122512 0.43019454 0.05486540 2.11829867 4.14627541 2.10388427 -0.29998206 0.77682088 -0.38295811 0.01438037 -0.14629987 0.20813554 0.00577124 +Al 1.97633693 6.14574144 0.04358988 -1.05480691 0.28916459 1.55234288 1.90811728 6.14999876 0.22260918 -0.17152022 -0.35306556 1.41560132 0.01932936 0.25472308 -0.06090382 0.23382698 +Al 0.05576764 4.07998668 4.01400335 0.57940655 -0.69956036 -0.77452503 0.08668458 4.01869212 3.92835072 -0.17503553 -0.25442022 -0.69464186 0.01526375 -0.04964464 -0.03080400 -0.05355566 +Al 0.02355291 5.97583815 6.21389775 1.29498290 -1.18774745 -1.11923094 0.15227294 5.91293447 6.07765261 0.83971435 0.19996515 -1.17915732 0.03685764 0.07105344 0.27335028 -0.22054127 +Al 2.03002194 4.09589495 5.96428476 -1.40614172 0.07190351 0.68383253 1.89204490 4.05702037 6.10583172 -0.78217195 -0.76552063 1.83558429 0.02500748 -0.18326424 -0.28465803 0.43550242 +Al 2.10294141 6.13517588 4.16052368 0.37528798 0.06546951 -1.32171616 2.12907650 6.12170770 4.01699769 0.07558778 -0.25067742 -1.03910889 0.02080381 -0.08017251 -0.15558951 -0.18798561 +Al 4.05154633 0.04180795 0.09587695 0.65312298 -0.02520858 -1.44851649 4.11018101 -0.00892605 -0.00370261 0.47925477 -0.82387382 -0.06336589 0.03791563 -0.11142811 -0.33346680 0.10579317 +Al 3.99463169 2.11178591 2.08298854 -0.76972578 0.43303785 1.45166045 3.92031111 2.12118979 2.21012431 -0.46042768 -0.30497609 0.66557543 0.04547036 -0.02623818 -0.15264150 0.00397004 +Al 6.06752037 0.16677762 2.13683865 0.78676613 0.52003149 -0.55703345 6.12683194 0.12611308 2.05526672 0.04974244 -1.11123483 -0.86262203 0.05797723 0.02567701 -0.60535123 -0.15552347 +Al 5.86666543 1.98359002 0.13390663 0.14656181 0.34355113 1.00219990 5.94749354 2.06926988 0.20344323 1.06654883 1.03497871 0.17235924 0.03670554 0.54268980 0.34223721 -0.17161289 +Al 4.16577409 0.00121745 4.10194437 1.27315080 0.00074655 -0.12617867 4.26403075 -0.00106685 4.08963455 0.44919701 -0.14322398 -0.23043618 0.01149277 -0.17588287 0.06046008 0.09255009 +Al 4.07582143 2.12841289 6.08245812 1.38235068 -2.27850567 -0.84858267 4.19026486 1.88563097 6.03156973 0.57677380 -1.58734033 -0.07971653 0.02925887 -0.11131245 -0.33460233 0.22692870 +Al 5.98796219 0.06005076 6.14967933 -0.30947349 0.38719007 1.15895734 5.98875373 0.07194356 6.22854943 0.59269769 -0.40128101 0.30465130 0.02411334 0.04497396 -0.01868090 -0.27046677 +Al 6.06100384 2.05139366 3.94813839 -0.49745219 -0.04888184 -0.80333862 6.06592325 2.06154953 3.94251448 0.55104434 0.29164259 0.63815221 0.05209771 0.33987961 0.02698787 0.48412612 +Al 3.98653190 4.16133100 -0.02674004 0.73984761 0.38361114 -0.54089747 4.07643916 4.17447757 -0.05548456 0.74131189 -0.16140478 -0.03102748 0.01030857 0.16089545 -0.14489250 0.22158812 +Al 4.20417461 5.98448795 2.23233022 -0.20346446 -0.31251847 0.04086883 4.17099734 5.99067130 2.19698932 -0.23762055 0.42943188 -0.68889399 0.00680334 -0.18787453 0.23188721 -0.23712622 +Al 6.10462315 4.08037294 2.06163275 0.38894279 0.29950384 -1.10535613 6.12924789 4.11675117 1.96135721 -0.17956907 0.39845727 -0.28799727 0.01783917 0.05689690 -0.00826779 -0.15578421 +Al 6.09358588 6.26490740 -0.03882978 0.91781049 -1.07153163 0.44265129 6.17717319 6.10261802 0.04548933 0.55468604 -1.63616333 0.85314067 0.02017304 -0.05987567 -0.42047706 0.36560357 +Al 4.10680770 3.82744588 4.14546261 -0.81027804 -0.44715914 0.36296012 4.02050430 3.89795647 4.17643776 -0.67074170 1.63304057 0.32819991 0.06887453 -0.05253364 0.72242265 -0.15967413 +Al 4.11682929 5.93532890 6.08322235 0.81699115 0.44743862 -0.34601489 4.19368248 6.01081999 6.04143619 0.35039176 0.86499219 -0.32412669 0.02209869 0.09430284 0.22566179 -0.06844949 +Al 6.30009733 3.91190767 6.18755949 -1.39862320 0.96217439 -1.09305327 6.09853461 4.03300185 6.08106214 -1.90717822 0.93747738 -0.80260399 0.04093503 -0.55378817 0.34079773 0.04585371 +Al 6.19758956 5.96463840 4.22287004 0.06655417 -0.10023746 0.33293893 6.18804712 5.99231814 4.22843248 -0.16444078 0.56632849 -0.37582460 0.01124382 -0.15592133 0.26002436 -0.08430789 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.199462930854991 free_energy=1.199462930854991 stress="0.004767208885128757 0.0018557943876572078 0.0006883010676196985 0.0018557943876572078 0.0037451877241799847 0.00010112084390507357 0.0006883010676196985 0.00010112084390507357 0.005853968447125383" pbc="T T T" +Al -0.00613836 -0.01402459 -0.02917609 -0.11464281 0.61415357 -1.04937503 0.00264000 0.07844353 -0.12467518 0.24833940 0.96183864 -0.49485711 0.03349547 0.13631418 0.22371231 -0.02665473 +Al -0.06034934 1.99671851 2.07559403 0.06503116 -0.39052762 0.24704382 0.01973625 1.94453284 2.07781540 1.14858181 -0.43868461 -0.26323310 0.03069566 0.54953215 -0.15485292 -0.09515438 +Al 2.16448074 -0.04161635 2.08799427 -0.27800815 -0.11297422 1.05153068 2.12606659 -0.00311886 2.19972208 -0.41302172 0.56223045 0.78333704 0.02903386 -0.07183026 0.43753749 0.11822710 +Al 2.06579896 2.08443873 0.08890775 0.32866700 -0.89063479 0.32704702 2.08365382 1.99498282 0.09681029 -0.06882021 -0.63561182 -0.23612025 -0.01261383 -0.05041681 -0.04377495 -0.12021375 +Al 0.01688464 0.02731380 4.25777021 1.11169736 0.18773383 0.34981465 0.12624551 0.03061316 4.28957870 0.65159606 -0.30279168 0.14250778 0.00959253 0.06685513 0.02610194 0.01406195 +Al -0.13183977 1.92300711 6.19473968 -0.38176425 -0.04409683 -1.08051663 -0.12317083 1.98438986 6.08615311 0.68033828 1.02831320 -0.81904355 0.02594094 0.22847308 0.43171417 -0.01594792 +Al 2.33255833 -0.03585943 6.20959721 -0.16274206 -0.29935664 0.22485442 2.21175225 -0.05970562 6.17702698 -1.71592151 0.01437753 -0.71618332 0.04715044 -0.80048541 -0.04283806 -0.36059252 +Al 1.93014534 2.15094689 4.16742316 -0.67201897 -0.56694128 -1.13024239 1.89525038 2.00666748 4.05656774 0.19076047 -1.70309258 -0.77182852 0.04547979 0.13494897 -0.66677570 -0.00006346 +Al -0.15219333 4.02591028 0.20765365 1.23671285 1.01266081 0.62514988 0.06324146 4.13002584 0.21584986 2.29720152 0.73838022 -0.28813726 0.05785902 0.75702091 0.06974274 -0.48877325 +Al -0.07002781 6.45271076 2.14810086 0.14533080 -0.17006376 -0.39452288 -0.03568176 6.33257459 2.11146462 0.42398127 -1.76229272 -0.33760784 0.06474253 0.17077224 -0.77802528 0.07261592 +Al 2.11829867 4.14627541 2.10388427 0.29998206 -0.77682088 0.38295811 2.10471123 4.07299382 2.11509901 -0.47122512 -0.43019454 -0.05486540 0.04325769 -0.29954835 -0.06494235 -0.23070870 +Al 1.90811728 6.14999876 0.22260918 0.17152022 0.35306556 -1.41560132 1.97633693 6.14574144 0.04358988 1.05480691 -0.28916459 -1.55234288 0.03400526 0.30380031 -0.33563679 -0.38137659 +Al 0.08668458 4.01869212 3.92835072 0.17503553 0.25442022 0.69464186 0.05576764 4.07998668 4.01400335 -0.57940655 0.69956036 0.77452503 0.03395830 -0.35545700 0.29317011 0.14656180 +Al 0.15227294 5.91293447 6.07765261 -0.83971435 -0.19996515 1.17915732 0.02355291 5.97583815 6.21389775 -1.29498290 1.18774745 1.11923094 0.05380499 -0.35515339 0.58618643 0.16363494 +Al 1.89204490 4.05702037 6.10583172 0.78217195 0.76552063 -1.83558429 2.03002194 4.09589495 5.96428476 1.40614172 -0.07190351 -0.68383253 0.05655014 0.48087682 -0.23330014 0.28346975 +Al 2.12907650 6.12170770 4.01699769 -0.07558778 0.25067742 1.03910889 2.10294141 6.13517588 4.16052368 -0.37528798 -0.06546951 1.32171616 0.01383164 -0.13423586 -0.02847313 0.33897934 +Al 4.11018101 -0.00892605 -0.00370261 -0.47925477 0.82387382 0.06336589 4.05154633 0.04180795 0.09587695 -0.65312298 0.02520858 1.44851649 0.05512610 -0.01100678 -0.17680588 0.69708319 +Al 3.92031111 2.12118979 2.21012431 0.46042768 0.30497609 -0.66557543 3.99463169 2.11178591 2.08298854 0.76972578 -0.43303785 -1.45166045 0.03966986 0.23246647 -0.23272805 -0.41295966 +Al 6.12683194 0.12611308 2.05526672 -0.04974244 1.11123483 0.86262203 6.06752037 0.16677762 2.13683865 -0.78676613 -0.52003149 0.55703345 0.06854007 -0.45842272 -0.30669064 -0.01343308 +Al 5.94749354 2.06926988 0.20344323 -1.06654883 -1.03497871 -0.17235924 5.86666543 1.98359002 0.13390663 -0.14656181 -0.34355113 -1.00219990 0.02575345 0.02044889 -0.02948428 -0.36560040 +Al 4.26403075 -0.00106685 4.08963455 -0.44919701 0.14322398 0.23043618 4.16577409 0.00121745 4.10194437 -1.27315080 -0.00074655 0.12617867 0.02971316 -0.33221050 -0.12872405 -0.15561704 +Al 4.19026486 1.88563097 6.03156973 -0.57677380 1.58734033 0.07971653 4.07582143 2.12841289 6.08245812 -1.38235068 2.27850567 0.84858267 0.07937888 -0.38169238 0.78551687 0.25347125 +Al 5.98875373 0.07194356 6.22854943 -0.59269769 0.40128101 -0.30465130 5.98796219 0.06005076 6.14967933 0.30947349 -0.38719007 -1.15895734 0.08191988 0.55016627 -0.46022891 -0.23925496 +Al 6.06592325 2.06154953 3.94251448 -0.55104434 -0.29164259 -0.63815221 6.06100384 2.05139366 3.94813839 0.49745219 0.04888184 0.80333862 0.03121993 0.27105175 0.13738104 0.36978353 +Al 4.07643916 4.17447757 -0.05548456 -0.74131189 0.16140478 0.03102748 3.98653190 4.16133100 -0.02674004 -0.73984761 -0.38361114 0.54089747 0.00022805 -0.16847597 -0.18531003 0.13206767 +Al 4.17099734 5.99067130 2.19698932 0.23762055 -0.42943188 0.68889399 4.20417461 5.98448795 2.23233022 0.20346446 0.31251847 -0.04086883 0.01073897 0.13320509 0.24022310 -0.23658737 +Al 6.12924789 4.11675117 1.96135721 0.17956907 -0.39845727 0.28799727 6.10462315 4.08037294 2.06163275 -0.38894279 -0.29950384 1.10535613 0.07379302 -0.39731176 0.04396371 0.70195297 +Al 6.17717319 6.10261802 0.04548933 -0.55468604 1.63616333 -0.85314067 6.09358588 6.26490740 -0.03882978 -0.91781049 1.07153163 -0.44265129 0.01294625 -0.17323240 0.10112039 -0.12388794 +Al 4.02050430 3.89795647 4.17643776 0.67074170 -1.63304057 -0.32819991 4.10680770 3.82744588 4.14546261 0.81027804 0.44715914 -0.36296012 0.04311384 0.13335415 0.51719125 0.05994098 +Al 4.19368248 6.01081999 6.04143619 -0.35039176 -0.86499219 0.32412669 4.11682929 5.93532890 6.08322235 -0.81699115 -0.44743862 0.34601489 0.04040675 -0.38230370 0.08651462 0.12154124 +Al 6.09853461 4.03300185 6.08106214 1.90717822 -0.93747738 0.80260399 6.30009733 3.91190767 6.18755949 1.39862320 -0.96217439 1.09305327 0.01528697 0.21304517 -0.28664949 0.15362757 +Al 6.18804712 5.99231814 4.22843248 0.16444078 -0.56632849 0.37582460 6.19758956 5.96463840 4.22287004 -0.06655417 0.10023746 -0.33293893 0.02484329 -0.01054830 0.17516447 -0.36019346 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=0.9065094691624092 free_energy=0.9065094691624092 stress="0.006507009423339605 0.0024200002914994603 -0.00022075970102456615 0.0024200002914994603 0.003678820038033757 0.0003131131401096385 -0.00022075970102456615 0.0003131131401096385 0.007312699442367703" pbc="T T T" +Al -0.05687315 -0.03388876 0.16287718 -0.62415401 0.48575283 -0.55836291 -0.09751901 0.03599860 0.09321066 -0.12394847 0.73523479 -0.62019847 0.01112616 0.16295799 0.13968044 -0.12882471 +Al -0.12234110 2.19231320 2.15387766 1.00861367 -1.57941151 0.66276345 0.01139616 1.96556019 2.19882218 1.19138721 -2.21416778 -0.02033057 0.03917191 0.35088781 -0.57624461 -0.01860402 +Al 2.09633081 -0.05920609 2.03506729 -0.21253948 0.87178322 -1.02489539 2.03574680 0.05555292 1.96041217 -0.75263694 0.89100467 -0.28676913 0.02203356 -0.28844433 0.33445779 0.17295760 +Al 1.92195757 2.08566514 0.09981783 -0.92722952 1.07135520 0.46979408 1.92061339 2.10189245 0.14542741 0.70217813 -0.55943463 0.26313512 0.05592186 0.63441651 -0.62812231 0.01798893 +Al -0.00741120 -0.03077141 4.04497130 -0.44031493 -0.32289174 1.47291583 -0.03049963 -0.05056105 4.19982037 0.11424348 -0.12324487 1.23577439 -0.00389129 0.08429529 0.14616256 0.05357573 +Al 0.06992523 2.04324862 6.11008585 -0.26161740 -0.57231641 -0.29198561 0.03932700 1.98467390 6.03866854 -0.21793746 -0.35217998 -0.75598771 0.03158290 -0.06447048 -0.05904760 -0.35260513 +Al 1.96694875 0.08177180 6.08827943 1.53776900 -0.23958866 -0.09184192 2.11906023 0.03117454 6.13345218 1.10025815 -0.70347641 0.86064749 0.01614244 0.01636473 -0.13935295 0.32015827 +Al 2.08259697 2.05876291 3.99972874 -0.71842131 -0.82714834 0.00275744 2.00973480 1.97622512 4.02010251 -0.48526775 -0.46694493 0.35788291 0.01335781 -0.05040502 -0.13893131 0.16019511 +Al 0.20340214 3.89848819 -0.04075709 -0.25118538 0.02070701 -0.38765410 0.09294391 4.03739570 -0.04416142 -1.63262323 2.16328731 0.41349061 0.09654046 -0.50035612 0.94298176 0.16323541 +Al 0.07689771 5.95830737 2.05970779 0.11743480 1.13969252 1.04743038 0.10625812 6.11052427 2.14867226 0.36136407 1.64129812 0.42440703 0.01154623 0.12392107 0.15510911 -0.02683190 +Al 2.19147068 3.95328790 2.10796172 -0.00514515 1.84588262 -0.70971788 2.16131747 4.15904964 2.03922631 -0.50961952 1.63928809 -0.41946845 0.02905612 -0.22188394 0.22307029 -0.06689831 +Al 2.03339315 6.16149670 0.04281063 0.52403967 -0.58476155 0.40192600 2.11644421 6.07746229 0.05635386 0.90003300 -0.72050319 -0.08069261 0.00581559 0.21754649 -0.25349589 -0.22580373 +Al 0.10294054 4.02601996 4.12311921 -1.36287959 0.33238141 -1.97616984 -0.06872219 4.07655930 3.97710345 -1.49994028 0.48060013 -0.63148491 0.02093027 -0.31100270 0.15198661 0.28935017 +Al -0.10075637 6.07907040 6.25758904 -0.71819774 1.03251371 0.50502251 -0.16586567 6.17207637 6.29087578 -0.33258390 0.53377023 0.07704100 -0.01041834 0.00855072 -0.01225639 -0.11767073 +Al 2.18238812 4.10976764 6.06159169 -0.71634565 -0.34096018 1.71826176 2.08821832 4.07619989 6.22921551 -1.02299598 -0.27678490 1.08482611 0.01440522 -0.08110527 0.00289577 0.09799612 +Al 1.95471271 6.07099717 4.20016064 0.75539272 -1.48482873 0.13063489 2.05265912 5.92594709 4.20224038 0.91757975 -1.07261277 0.00018278 0.02004944 0.17276229 0.02533458 -0.12964673 +Al 4.14520964 0.09029539 0.03096883 0.29927440 -0.41128145 0.24919694 4.12053850 0.05927389 0.02285378 -0.65678862 -0.16807727 -0.34884643 0.02387441 -0.36354715 0.05905165 -0.19197670 +Al 4.19095976 1.82325787 2.09487272 -0.44549341 -0.59107529 -0.26642180 4.11967879 1.85218666 2.05326459 -0.79192471 1.11950872 -0.41421409 0.03170229 -0.16143128 0.52091574 -0.12674294 +Al 6.10398953 -0.00041724 2.11202646 -1.21671586 -0.70773067 0.45664155 6.00074920 -0.06527471 2.19013045 -0.49063111 -0.32781240 0.85265293 0.04838621 0.02988905 -0.02665872 0.19671820 +Al 6.28235358 2.07527407 -0.03202213 -0.36282641 1.18302989 0.09069705 6.17846980 2.17374566 0.02863224 -1.34417167 0.41372027 1.02766412 0.03038438 -0.49310962 -0.03787292 0.26705480 +Al 3.95149447 0.02423752 4.27168672 0.02698226 -0.88889052 -0.35920435 3.99165084 -0.06423077 4.17176729 0.70901358 -0.55830115 -1.24128662 0.03865897 0.17595498 -0.06817318 -0.46785640 +Al 4.02852893 2.12907013 6.09892033 -0.58939947 0.32605456 -0.05051467 3.96240436 2.12229634 6.11590360 -0.46660715 -0.46612718 0.21238287 0.02675293 -0.11126647 -0.19710196 0.24401900 +Al 6.04747231 0.11921411 6.12828967 0.11896527 -0.01256561 0.14624619 6.07684964 0.09564256 6.14706748 0.15923475 -0.26946916 0.11189882 0.02629113 0.26802832 -0.25192606 0.08901300 +Al 5.98145593 1.97409250 4.18293057 1.06899451 1.18135986 -1.09935286 6.15374823 2.07830632 4.06222184 1.79928918 0.49655379 -0.99841919 0.04114691 0.49166094 0.06508637 -0.11671418 +Al 4.12069553 4.01088427 0.09583877 -0.10679640 -0.08411900 -0.57593156 4.08884167 4.01005571 0.06664839 -0.45825209 0.11176405 0.05765539 0.00141192 -0.12361624 0.01410260 0.14955568 +Al 3.91385716 6.07753167 2.15237661 1.47169633 0.34783071 1.20608420 4.08951615 6.12905060 2.27741939 1.40640378 0.54343716 0.89411612 0.02683878 0.31858513 0.07829785 0.05659930 +Al 6.23662182 4.14496687 2.09678581 -0.61185756 1.69440233 -1.32144143 6.13771079 4.27103656 1.92537324 -0.91341194 0.34631586 -1.61980826 0.05937909 -0.43106324 -0.11741197 -0.21663190 +Al 5.90523809 6.19847867 0.25338484 -0.45080405 0.48458824 0.63436761 5.92213432 6.19010676 0.23478642 0.72199407 -0.46862759 -1.06309747 0.05178327 0.38838352 -0.40836912 -0.42988073 +Al 4.29741270 4.00473024 3.96935420 1.84337850 -0.79392536 -0.34554586 4.38331856 3.95080738 3.95984944 -0.37118994 -0.18458946 0.17716336 0.04375586 -0.55020238 0.17184611 0.17495914 +Al 4.20614660 6.12307351 6.04186950 0.67167299 -0.92593602 -0.32961301 4.21571980 6.01739599 6.05861261 -0.37489047 -0.93957132 0.52387035 0.03886869 -0.44187052 -0.07436257 0.36902865 +Al 5.97036097 4.09193150 6.18669339 -0.33576846 -0.97460702 0.34933105 6.02210565 3.99831477 6.20417538 1.21472514 -0.62595645 -0.09808774 0.02905907 0.51335127 -0.02178934 -0.07992779 +Al 6.06685097 6.13854115 4.10618547 0.91347767 -0.67529603 -0.15541771 6.18968122 6.06604804 4.08890238 1.14771696 -0.61790175 0.02390026 0.01484524 0.23621866 -0.01986234 -0.12578925 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=0.8936910426021347 free_energy=0.8936910426021347 stress="0.006456601417350244 0.001245362542856234 0.000314848374501027 0.001245362542856234 0.004203273121418416 0.0004897180596170072 0.000314848374501027 0.0004897180596170072 0.006303993845666421" pbc="T T T" +Al -0.09751901 0.03599860 0.09321066 0.12394847 -0.73523479 0.62019847 -0.05687315 -0.03388876 0.16287718 0.62415401 -0.48575283 0.55836291 0.00654396 0.15740654 0.01938202 0.05850025 +Al 0.01139616 1.96556019 2.19882218 -1.19138721 2.21416778 0.02033057 -0.12234110 2.19231320 2.15387766 -1.00861367 1.57941151 -0.66276345 0.03104177 -0.16760058 0.15216819 -0.37220376 +Al 2.03574680 0.05555292 1.96041217 0.75263694 -0.89100467 0.28676913 2.09633081 -0.05920609 2.03506729 0.21253948 -0.87178322 1.02489539 0.02657263 0.00907289 -0.33967957 0.29360387 +Al 1.92061339 2.10189245 0.14542741 -0.70217813 0.55943463 -0.26313512 1.92195757 2.08566514 0.09981783 0.92722952 -1.07135520 -0.46979408 0.02608954 0.27583605 -0.24057319 -0.18386389 +Al -0.03049963 -0.05056105 4.19982037 -0.11424348 0.12324487 -1.23577439 -0.00741120 -0.03077141 4.04497130 0.44031493 0.32289174 -1.47291583 0.02126788 0.27900379 -0.02032248 -0.20400825 +Al 0.03932700 1.98467390 6.03866854 0.21793746 0.35217998 0.75598771 0.06992523 2.04324862 6.11008585 0.26161740 0.57231641 0.29198561 0.00833124 0.09559553 0.22599691 0.14420947 +Al 2.11906023 0.03117454 6.13345218 -1.10025815 0.70347641 -0.86064749 1.96694875 0.08177180 6.08827943 -1.53776900 0.23958866 0.09184192 0.01002900 -0.28461997 -0.16785895 0.20314519 +Al 2.00973480 1.97622512 4.02010251 0.48526775 0.46694493 -0.35788291 2.08259697 2.05876291 3.99972874 0.71842131 0.82714834 -0.00275744 0.01197306 0.20906580 0.29939441 0.12684890 +Al 0.09294391 4.03739570 -0.04416142 1.63262323 -2.16328731 -0.41349061 0.20340214 3.89848819 -0.04075709 0.25118538 -0.02070701 0.38765410 0.04140552 -0.24556934 0.27879586 0.34258475 +Al 0.10625812 6.11052427 2.14867226 -0.36136407 -1.64129812 -0.42440703 0.07689771 5.95830737 2.05970779 -0.11743480 -1.13969252 -1.04743038 0.01396109 0.00777312 0.13408010 -0.33941082 +Al 2.16131747 4.15904964 2.03922631 0.50961952 -1.63928809 0.41946845 2.19147068 3.95328790 2.10796172 0.00514515 -1.84588262 0.70971788 0.01707042 -0.14382861 -0.34434364 0.20024871 +Al 2.11644421 6.07746229 0.05635386 -0.90003300 0.72050319 0.08069261 2.03339315 6.16149670 0.04281063 -0.52403967 0.58476155 -0.40192600 0.00206081 0.02033281 0.20432512 -0.08595024 +Al -0.06872219 4.07655930 3.97710345 1.49994028 -0.48060013 0.63148491 0.10294054 4.02601996 4.12311921 1.36287959 -0.33238141 1.97616984 0.02819301 0.26157916 -0.06778076 0.43655768 +Al -0.16586567 6.17207637 6.29087578 0.33258390 -0.53377023 -0.07704100 -0.10075637 6.07907040 6.25758904 0.71819774 -1.03251371 -0.50502251 0.01598050 0.28840170 -0.28415746 -0.17346984 +Al 2.08821832 4.07619989 6.22921551 1.02299598 0.27678490 -1.08482611 2.18238812 4.10976764 6.06159169 0.71634565 0.34096018 -1.71826176 0.04921525 -0.11593535 -0.00429203 -0.43390088 +Al 2.05265912 5.92594709 4.20224038 -0.91757975 1.07261277 -0.00018278 1.95471271 6.07099717 4.20016064 -0.75539272 1.48482873 -0.13063489 0.01529379 -0.06989421 0.23106615 0.05048688 +Al 4.12053850 0.05927389 0.02285378 0.65678862 0.16807727 0.34884643 4.14520964 0.09029539 0.03096883 -0.29927440 0.41128145 -0.24919694 0.02351633 -0.17773931 0.04432774 -0.09262356 +Al 4.11967879 1.85218666 2.05326459 0.79192471 -1.11950872 0.41421409 4.19095976 1.82325787 2.09487272 0.44549341 0.59107529 0.26642180 0.02433009 -0.03320538 0.51507295 0.03712893 +Al 6.00074920 -0.06527471 2.19013045 0.49063111 0.32781240 -0.85265293 6.10398953 -0.00041724 2.11202646 1.21671586 0.70773067 -0.45664155 0.04017997 0.40328961 0.27513973 -0.04573773 +Al 6.17846980 2.17374566 0.02863224 1.34417167 -0.41372027 -1.02766412 6.28235358 2.07527407 -0.03202213 0.36282641 -1.18302989 -0.09069705 0.02467001 -0.11429253 -0.44737685 0.27739501 +Al 3.99165084 -0.06423077 4.17176729 -0.70901358 0.55830115 1.24128662 3.95149447 0.02423752 4.27168672 -0.02698226 0.88889052 0.35920435 0.02679854 0.17328928 0.28407592 -0.00929847 +Al 3.96240436 2.12229634 6.11590360 0.46660715 0.46612718 -0.21238287 4.02852893 2.12907013 6.09892033 0.58939947 -0.32605456 0.05051467 0.01460498 0.19503240 -0.22878397 -0.04784979 +Al 6.07684964 0.09564256 6.14706748 -0.15923475 0.26946916 -0.11189882 6.04747231 0.11921411 6.12828967 -0.11896527 0.01256561 -0.14624619 0.01093029 -0.24023966 0.04731775 -0.08321038 +Al 6.15374823 2.07830632 4.06222184 -1.79928918 -0.49655379 0.99841919 5.98145593 1.97409250 4.18293057 -1.06899451 -1.18135986 1.09935286 0.05435442 -0.08874101 -0.39336106 0.11115771 +Al 4.08884167 4.01005571 0.06664839 0.45825209 -0.11176405 -0.05765539 4.12069553 4.01088427 0.09583877 0.10679640 0.08411900 0.57593156 0.02565535 -0.08767682 0.09731989 0.18006274 +Al 4.08951615 6.12905060 2.27741939 -1.40640378 -0.54343716 -0.89411612 3.91385716 6.07753167 2.15237661 -1.47169633 -0.34783071 -1.20608420 0.04858553 -0.34151032 -0.03621993 -0.32699512 +Al 6.13771079 4.27103656 1.92537324 0.91341194 -0.34631586 1.61980826 6.23662182 4.14496687 2.09678581 0.61185756 -1.69440233 1.32144143 0.06559281 0.14105015 -0.65322859 0.17886841 +Al 5.92213432 6.19010676 0.23478642 -0.72199407 0.46862759 1.06309747 5.90523809 6.19847867 0.25338484 0.45080405 -0.48458824 -0.63436761 0.06961904 0.31176115 -0.08418942 -0.59340663 +Al 4.38331856 3.95080738 3.95984944 0.37118994 0.18458946 -0.17716336 4.29741270 4.00473024 3.96935420 -1.84337850 0.79392536 0.34554586 0.05705253 -0.75167478 0.18459187 0.18259713 +Al 4.21571980 6.01739599 6.05861261 0.37489047 0.93957132 -0.52387035 4.20614660 6.12307351 6.04186950 -0.67167299 0.92593602 0.32961301 0.00309027 -0.20696170 0.06265517 0.15023070 +Al 6.02210565 3.99831477 6.20417538 -1.21472514 0.62595645 0.09808774 5.97036097 4.09193150 6.18669339 0.33576846 0.97460702 -0.34933105 0.03698178 0.38061707 0.19963221 -0.21003717 +Al 6.18968122 6.06604804 4.08890238 -1.14771696 0.61790175 -0.02390026 6.06685097 6.13854115 4.10618547 -0.91347767 0.67529603 0.15541771 0.04269961 -0.13961749 0.05682592 0.22834020 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.0496213996973287 free_energy=1.0496213996973287 stress="0.0050412540679878135 0.0006477853945826963 -0.0001668343085574835 0.0006477853945826963 0.004431297660261729 -8.201800653474678e-05 -0.0001668343085574835 -8.201800653474678e-05 0.006065264190204926" pbc="T T T" +Al 0.10122258 -0.10966878 0.11583238 -0.65183463 0.62379972 1.47616753 -0.02789025 -0.03552281 0.22769330 -1.61899170 0.61514262 0.44175987 0.01627154 -0.33604521 0.15207386 -0.15394549 +Al 0.04298388 1.98852636 2.08224377 -0.65436195 -0.13546458 0.57742725 -0.04970584 2.06319289 2.12731842 -0.90325891 1.27448603 0.38532747 0.04248188 -0.16366281 0.59843150 -0.19576280 +Al 1.99206376 0.14503676 1.98033833 -1.79781972 0.55562620 -1.56759129 1.88811699 0.11538020 1.91465887 -0.00780193 -1.00060361 0.43777414 0.06728315 0.40458477 -0.52792677 0.49227841 +Al 2.10797560 1.99011134 0.11372571 -0.44475192 0.44312265 0.92397889 2.05613089 2.09040771 0.15826877 -0.41214089 1.17508447 -0.20829008 0.03413359 -0.10775604 0.37858706 -0.21800418 +Al -0.15936233 0.17057456 3.91544150 -1.29978743 -0.97098952 0.00587215 -0.21763631 0.00103324 3.96971247 0.20235474 -1.72504926 0.78135607 0.06272531 0.49123211 -0.60877838 0.43075244 +Al -0.01152506 2.00306105 6.12939722 0.94784953 0.11075907 -1.37244704 0.08819301 1.99481137 6.01982464 0.68219599 -0.23021869 -0.46865056 0.03775713 0.12358988 -0.11514859 0.09023538 +Al 2.14323951 0.20075913 6.10010622 -1.73812154 -0.17182853 0.29106492 1.90025233 0.11955926 6.13488825 -2.24051188 -1.22601727 0.33663713 0.06683578 -0.58901543 -0.43573075 0.04714049 +Al 1.97865080 2.04835628 4.18844391 0.45187924 0.63826775 -0.80148921 2.07250112 2.11931772 4.06044556 1.20307312 0.43090982 -1.37451993 0.01568506 0.28505701 0.16048250 -0.32379161 +Al -0.04370556 3.90728441 -0.10008633 0.43830490 0.25321690 -1.23832876 0.03923277 3.99019439 -0.18228154 0.83114663 1.06259081 -0.33475925 0.04327799 0.35962944 0.46916804 0.36505477 +Al -0.09389839 5.86400230 2.19930966 0.51305389 -1.33457864 0.55029550 -0.00117448 5.78115198 2.20513526 1.07547071 -0.23326776 -0.41136707 0.03528062 0.27999378 0.39776872 -0.32603095 +Al 2.22516603 4.07412600 2.04313126 0.21175610 -0.54153479 -0.14682908 2.18346539 3.97293698 2.04838010 -0.79429155 -1.02113711 0.29409029 0.03528168 -0.52255050 -0.40714140 0.08135634 +Al 1.96041189 5.99331499 0.12839923 0.58861329 -0.89940228 -0.19320009 2.01824176 5.94296639 0.11195992 0.43202325 0.09997826 -0.04108933 0.02267279 0.02547308 0.17486406 -0.02555815 +Al 0.01622504 4.03305362 4.24629539 -0.20897970 -0.43290867 0.94771227 0.02499006 3.99292013 4.31635930 0.28724972 -0.11688449 0.25301718 0.03019158 0.24590168 -0.08028449 -0.13735206 +Al -0.04717474 6.10258323 5.98345334 -0.19924822 1.18923225 -0.23774787 0.02741756 6.21451803 5.99028232 1.35933240 0.67652992 0.45769964 0.03764803 0.63139411 0.02387530 0.14450647 +Al 2.19535093 4.07950163 6.27092103 0.49669069 0.42004002 0.21328905 2.20978472 4.11524578 6.23947720 -0.05515350 0.15201378 -0.84314754 0.02159794 -0.36514127 0.00680721 -0.28237673 +Al 2.05921180 6.16495709 3.90030620 -0.69666048 -0.11172841 0.05873806 1.98920727 6.16778992 3.96357739 -0.43021369 0.08401848 1.03229593 0.00713044 -0.06691243 0.11796941 0.38115234 +Al 4.05202003 0.01070115 0.03065185 0.31142016 0.04184735 -0.76860160 4.10296519 0.03301753 0.00685599 0.37715428 0.37813873 0.31260723 0.03359944 0.23804609 0.06016911 0.32284858 +Al 4.16048556 2.01738399 2.11378911 1.71041171 -0.56244486 -0.40338931 4.28023528 1.98793431 2.08875939 0.24151183 0.00146091 -0.01257181 0.00854906 -0.20256318 0.18174341 0.08334349 +Al 5.97946595 0.04290261 2.07413626 -0.63565391 0.16744851 -0.20527034 5.96914732 0.03056657 2.00681229 0.59948520 -0.41408151 -0.81513480 0.03257702 0.23362178 -0.13418913 -0.35317077 +Al 6.14869392 2.13655303 0.04954117 0.02697863 0.07880540 0.72690466 6.14798601 2.07663662 0.09817659 0.10826231 -0.98203882 -0.12943480 0.02941124 -0.14807618 -0.51257652 0.02897729 +Al 4.13968993 0.01500317 4.13107062 0.36686831 -0.52119845 -0.50714151 4.12849918 -0.01625099 4.07786007 -0.54926543 -0.02627973 -0.33540446 0.02332059 -0.28133810 0.13256547 -0.08767421 +Al 4.12198454 2.06695547 6.18224982 0.23483739 -0.77006126 -0.42204804 4.14691602 1.97643788 6.13939562 0.04527294 -0.78914839 -0.26333537 0.02368015 0.11217059 -0.10695550 -0.06611671 +Al 6.25566481 -0.10128551 6.15264624 0.07767739 -0.66391012 1.34869573 6.19542255 -0.10985806 6.31740764 -1.02804911 0.47648802 1.45681029 0.02828435 -0.47408145 0.33607811 0.20598649 +Al 6.06172674 2.04814788 4.26981803 0.10951947 0.65028507 0.52808798 6.06452125 2.11571020 4.29040165 0.02683886 0.42280976 -0.19900004 0.03186952 -0.09483808 0.08096578 -0.18784736 +Al 4.21267789 4.01513856 0.03586771 0.53490136 0.11884854 1.02157862 4.23023799 4.03581066 0.14419383 -0.29683180 0.22521400 0.78710524 0.04025142 -0.18199172 0.08392670 0.10520734 +Al 4.17385429 5.90062313 2.02517111 0.17128695 1.71280142 0.48869317 4.15723009 6.14546482 2.06602523 -0.61260436 2.36001851 0.26488937 0.04729958 -0.11317749 0.60326078 -0.07991139 +Al 6.13144139 3.99610944 2.30979861 0.23302038 -0.55368936 -1.89995381 6.14445941 3.92170496 2.08885168 0.04995955 -0.54317511 -1.76785371 0.03477328 -0.09796674 -0.23400137 -0.33491051 +Al 6.16283748 5.95400700 0.06897947 0.23148447 -1.03266710 1.40056725 6.19340006 5.90029562 0.21703943 0.34282833 0.12252987 1.11075021 0.01300785 -0.00206709 0.29220819 0.09141552 +Al 4.01950371 4.14201927 4.18919487 0.92682378 -0.14722422 -0.61737093 4.10340797 4.11578306 4.11517003 0.47645258 -0.23647008 -0.85203587 0.01185028 -0.00287716 -0.12312126 0.02622525 +Al 3.92146091 6.31423008 6.24958566 0.24791905 0.66040367 0.55511546 3.98540207 6.29439016 6.25071365 0.76382995 -0.87797052 -0.53694638 0.04517418 0.31196126 -0.60089521 -0.30642207 +Al 6.03248216 4.21619905 6.06675612 -1.07580833 1.32665565 -0.63066453 5.92275141 4.32792490 6.02127062 -0.73867048 0.61925818 0.06170182 0.02368320 -0.06660190 -0.14018440 -0.04115811 +Al 6.00340549 6.19022467 4.05053520 0.57173114 -0.14152942 -0.10211507 6.07052178 6.13902154 4.06241672 0.58334284 -0.75432982 0.17971912 0.04603572 0.07400719 -0.22401144 0.22355249 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.0598670279781714 free_energy=1.0598670279781714 stress="0.005052295521701254 0.0003623609712633322 -0.00036006216669003474 0.0003623609712633322 0.005464424852972196 2.0063803643432364e-05 -0.00036006216669003474 2.0063803643432364e-05 0.0069520428514399865" pbc="T T T" +Al -0.02789025 -0.03552281 0.22769330 1.61899170 -0.61514262 -0.44175987 0.10122258 -0.10966878 0.11583238 0.65183463 -0.62379972 -1.47616753 0.04677184 -0.11585444 -0.08582853 -0.40591217 +Al -0.04970584 2.06319289 2.12731842 0.90325891 -1.27448603 -0.38532747 0.04298388 1.98852636 2.08224377 0.65436195 0.13546458 -0.57742725 0.01754470 0.11219607 0.16306876 0.05525110 +Al 1.88811699 0.11538020 1.91465887 0.00780193 1.00060361 -0.43777414 1.99206376 0.14503676 1.98033833 1.79781972 -0.55562620 1.56759129 0.06239616 0.59048502 -0.34073568 0.63061900 +Al 2.05613089 2.09040771 0.15826877 0.41214089 -1.17508447 0.20829008 2.10797560 1.99011134 0.11372571 0.44475192 -0.44312265 -0.92397889 0.03123103 0.07552269 -0.04896383 -0.42003529 +Al -0.21763631 0.00103324 3.96971247 -0.20235474 1.72504926 -0.78135607 -0.15936233 0.17057456 3.91544150 1.29978743 0.97098952 -0.00587215 0.02442416 0.43939881 0.18014228 0.03613343 +Al 0.08819301 1.99481137 6.01982464 -0.68219599 0.23021869 0.46865056 -0.01152506 2.00306105 6.12939722 -0.94784953 -0.11075907 1.37244704 0.07245426 -0.26654639 -0.05319396 0.41796345 +Al 1.90025233 0.11955926 6.13488825 2.24051188 1.22601727 -0.33663713 2.14323951 0.20075913 6.10010622 1.73812154 0.17182853 -0.29106492 0.03011933 0.34609724 -0.24095767 0.02867786 +Al 2.07250112 2.11931772 4.06044556 -1.20307312 -0.43090982 1.37451993 1.97865080 2.04835628 4.18844391 -0.45187924 -0.63826775 0.80148921 0.00901304 0.14949738 -0.29855848 0.01534920 +Al 0.03923277 3.99019439 -0.18228154 -0.83114663 -1.06259081 0.33475925 -0.04370556 3.90728441 -0.10008633 -0.43830490 -0.25321690 1.23832876 0.01462821 -0.12137574 0.07376550 0.23510663 +Al -0.00117448 5.78115198 2.20513526 -1.07547071 0.23326776 0.41136707 -0.09389839 5.86400230 2.19930966 -0.51305389 1.33457864 -0.55029550 0.03359128 0.07004072 0.31193338 -0.26943981 +Al 2.18346539 3.97293698 2.04838010 0.79429155 1.02113711 -0.29409029 2.22516603 4.07412600 2.04313126 -0.21175610 0.54153479 0.14682908 0.03338994 -0.16830166 0.16270713 0.17446616 +Al 2.01824176 5.94296639 0.11195992 -0.43202325 -0.09997826 0.04108933 1.96041189 5.99331499 0.12839923 -0.58861329 0.89940228 0.19320009 0.01583102 -0.05773059 0.41657068 0.09985010 +Al 0.02499006 3.99292013 4.31635930 -0.28724972 0.11688449 -0.25301718 0.01622504 4.03305362 4.24629539 0.20897970 0.43290867 -0.94771227 0.03385578 0.08442802 0.27496233 -0.29517719 +Al 0.02741756 6.21451803 5.99028232 -1.35933240 -0.67652992 -0.45769964 -0.04717474 6.10258323 5.98345334 0.19924822 -1.18923225 0.23774787 0.03586727 0.21394511 -0.35792691 0.33497121 +Al 2.20978472 4.11524578 6.23947720 0.05515350 -0.15201378 0.84314754 2.19535093 4.07950163 6.27092103 -0.49669069 -0.42004002 -0.21328905 0.01842292 -0.01942167 -0.15205282 -0.38517509 +Al 1.98920727 6.16778992 3.96357739 0.43021369 -0.08401848 -1.03229593 2.05921180 6.16495709 3.90030620 0.69666048 0.11172841 -0.05873806 0.00685140 0.24241490 -0.03414395 0.24087567 +Al 4.10296519 0.03301753 0.00685599 -0.37715428 -0.37813873 -0.31260723 4.05202003 0.01070115 0.03065185 -0.31142016 -0.04184735 0.76860160 0.04250339 -0.24205869 0.08233928 0.29312272 +Al 4.28023528 1.98793431 2.08875939 -0.24151183 -0.00146091 0.01257181 4.16048556 2.01738399 2.11378911 -1.71041171 0.56244486 0.40338931 0.04993652 -0.66461868 0.13158732 0.15889151 +Al 5.96914732 0.03056657 2.00681229 -0.59948520 0.41408151 0.81513480 5.97946595 0.04290261 2.07413626 0.63565391 -0.16744851 0.20527034 0.07777627 0.51622871 -0.16776067 0.06714457 +Al 6.14798601 2.07663662 0.09817659 -0.10826231 0.98203882 0.12943480 6.14869392 2.13655303 0.04954117 -0.02697863 -0.07880540 -0.72690466 0.02947094 0.13317446 -0.12164259 -0.53733238 +Al 4.12849918 -0.01625099 4.07786007 0.54926543 0.02627973 0.33540446 4.13968993 0.01500317 4.13107062 -0.36686831 0.52119845 0.50714151 0.01151456 -0.24044548 0.18451663 0.17698936 +Al 4.14691602 1.97643788 6.13939562 -0.04527294 0.78914839 0.26333537 4.12198454 2.06695547 6.18224982 -0.23483739 0.77006126 0.42204804 0.00773501 -0.23033708 0.09615680 0.12908107 +Al 6.19542255 -0.10985806 6.31740764 1.02804911 -0.47648802 -1.45681029 6.25566481 -0.10128551 6.15264624 -0.07767739 0.66391012 -1.34869573 0.01888789 -0.16980109 0.27931138 -0.22127630 +Al 6.06452125 2.11571020 4.29040165 -0.02683886 -0.42280976 0.19900004 6.06172674 2.04814788 4.26981803 -0.10951947 -0.65028507 -0.52808798 0.03039213 0.06230484 -0.23591972 -0.27487674 +Al 4.23023799 4.03581066 0.14419383 0.29683180 -0.22521400 -0.78710524 4.21267789 4.01513856 0.03586771 -0.53490136 -0.11884854 -1.02157862 0.02983579 -0.34345818 0.01392646 -0.23961528 +Al 4.15723009 6.14546482 2.06602523 0.61260436 -2.36001851 -0.26488937 4.17385429 5.90062313 2.02517111 -0.17128695 -1.71280142 -0.48869317 0.02107550 -0.37514881 -0.20604012 -0.07170910 +Al 6.14445941 3.92170496 2.08885168 -0.04995955 0.54317511 1.76785371 6.13144139 3.99610944 2.30979861 -0.23302038 0.55368936 1.89995381 0.06509629 -0.02235608 0.28176745 0.40562405 +Al 6.19340006 5.90029562 0.21703943 -0.34282833 -0.12252987 -1.11075021 6.16283748 5.95400700 0.06897947 -0.23148447 1.03266710 -1.40056725 0.03240190 0.01905578 0.43541944 -0.30060952 +Al 4.10340797 4.11578306 4.11517003 -0.47645258 0.23647008 0.85203587 4.01950371 4.14201927 4.18919487 -0.92682378 0.14722422 0.61737093 0.02111889 -0.25861542 0.06941220 -0.18311221 +Al 3.98540207 6.29439016 6.25071365 -0.76382995 0.87797052 0.53694638 3.92146091 6.31423008 6.24958566 -0.24791905 -0.66040367 -0.55511546 0.03988236 -0.00157099 -0.31690941 -0.31468515 +Al 5.92275141 4.32792490 6.02127062 0.73867048 -0.61925818 -0.06170182 6.03248216 4.21619905 6.06675612 1.07580833 -1.32665565 0.63066453 0.04791296 0.31466609 -0.35458918 0.46986949 +Al 6.07052178 6.13902154 4.06241672 -0.58334284 0.75432982 -0.17971912 6.00340549 6.19022467 4.05053520 -0.57173114 0.14152942 0.10211507 0.04793427 -0.07181483 -0.14236349 -0.05103034 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.1701265082332784 free_energy=1.1701265082332784 stress="0.004240275766441019 0.0004635646804701818 -0.00019295021039451258 0.0004635646804701818 0.004553247416723432 -7.657078983153338e-05 -0.00019295021039451258 -7.657078983153338e-05 0.007360803371220558" pbc="T T T" +Al 0.02344403 0.01551248 -0.19761095 -1.16323505 -0.26122577 -0.29488629 -0.02535890 -0.01666860 -0.15289106 0.28826462 -0.30466071 1.07928487 0.03848448 0.38635724 -0.01650272 0.45105419 +Al 0.03869236 1.83830745 2.13618222 -0.19190768 -0.80007077 0.53464521 0.08203055 1.76798180 2.14508712 0.89406061 -0.34609683 -0.51131534 0.01523279 0.38861915 0.03657230 -0.17717062 +Al 2.21696033 -0.00369555 2.08103589 0.49172392 -0.98175087 -1.06769837 2.19078758 -0.10086233 1.96301195 -0.84339155 -0.62867711 -0.85437542 0.03245089 -0.51758763 -0.04627487 -0.13487795 +Al 2.16865957 2.03296493 0.08130823 0.73865548 -0.13990468 -0.19515489 2.19124544 2.02189874 0.05478480 -0.28379326 -0.00199163 -0.20331497 0.00608690 -0.34940230 -0.03453984 -0.09684311 +Al 0.11565556 -0.08703035 4.32584055 -0.60495818 0.49127841 -1.39549901 0.04339555 0.01238779 4.16767894 -0.66737671 1.12465011 -1.24941081 0.01355355 -0.06584591 0.38837878 -0.23459667 +Al -0.09739854 2.11083217 6.25209748 -0.29516044 0.35653077 1.04581886 -0.08667627 2.14215956 6.30564995 0.50258723 0.07103308 -0.19853601 0.04747541 0.23084929 0.03537826 -0.24619136 +Al 2.35267551 0.00857491 6.05157987 -0.46205813 0.04785286 0.21264578 2.23590422 0.01790017 6.07751606 -1.72194435 0.13108337 0.20121051 0.02223262 -0.36276836 0.02793866 0.09346508 +Al 1.97634963 2.18784736 4.17164038 0.77580769 1.02425092 -0.82884860 2.02584481 2.21163753 4.13023792 0.08198376 -0.57431653 0.12525941 0.02439692 -0.11994031 -0.47622501 0.20545082 +Al 0.01137334 4.01510313 0.24021383 -0.93415439 0.83732410 -0.98372568 -0.08986066 4.09259345 0.16573580 -0.67317829 0.43937864 -0.21180730 0.01860832 -0.09398153 0.00980540 0.02322964 +Al -0.10751780 6.36589664 2.03998191 0.11822679 -0.54233461 0.26748073 -0.03820982 6.26443372 2.07001341 1.02046144 -1.22516014 0.30771802 0.00482065 0.40180929 -0.31689531 -0.00398226 +Al 2.09552761 4.14451928 2.18418504 0.28280458 -0.14393301 0.00424007 2.09234396 4.13172013 2.10658804 -0.31946865 -0.16413754 -1.41682083 0.03780661 -0.19702601 0.03480980 -0.40777609 +Al 1.99087608 6.03041168 -0.04696864 -0.14353749 0.00033405 0.83188883 2.01064771 6.07441477 0.09136432 0.41685005 0.58729020 1.39480062 0.02136328 0.24767567 0.35521372 0.42009104 +Al -0.06811528 4.04554951 3.88373122 0.19556418 -0.00422045 -0.49531416 -0.02250295 4.08105928 3.87934750 0.51574885 0.51481387 0.42592389 0.03209531 0.21748865 0.27722318 0.27892949 +Al -0.06436346 5.98382832 6.16365794 0.54787878 -0.04163019 -0.40358502 -0.01197730 6.03704335 6.09441764 0.50293963 1.09095144 -0.90914127 0.07055460 -0.07208947 0.22177421 -0.08190182 +Al 1.75291701 4.11946475 6.27009913 -0.75441855 0.62759591 0.88376122 1.78535119 4.11234955 6.29094723 1.31931167 -0.62635373 -0.37150565 0.07954511 0.64657652 -0.45730187 -0.48359978 +Al 2.00672533 6.04974713 4.10309527 -0.18108971 -0.70710692 -0.49781637 1.99809219 5.98659917 4.06941863 -0.01399102 -0.24475885 -0.13255745 0.00993975 0.09944542 -0.06148956 0.11360764 +Al 4.11539389 0.03520825 0.06083098 -0.37689914 -0.24152312 -0.46323548 4.07469952 -0.03498634 0.02684264 -0.19923830 -0.99129311 -0.08715584 0.04132444 -0.15383560 -0.24851788 0.03160766 +Al 3.96405453 2.17752042 2.18998955 0.19189540 0.58832274 1.30730034 4.01802925 2.19751151 2.27659620 0.70331049 -0.25955173 0.13583508 0.04210457 0.22735563 -0.22016853 -0.19501995 +Al 6.02419456 0.06907121 2.15899948 -0.86179089 -0.52327712 -0.55594996 5.95351650 -0.02605980 2.10710666 -0.35401754 -1.01180705 -0.56216711 0.10819101 0.09030648 -0.34026152 0.16686401 +Al 6.06682015 1.83842144 0.35078828 0.90247749 0.13493551 -0.43509367 6.15717909 1.94699849 0.23962681 0.61351328 1.60199540 -1.27106336 0.06957469 0.07509888 0.68263391 -0.61665581 +Al 4.09559994 -0.01213247 3.92601624 -0.26958396 0.72276936 -0.29079217 4.06700132 0.08203661 4.00185596 -0.31957091 0.83035284 1.49004461 0.06181064 0.05031530 0.19264484 0.70677658 +Al 4.29055238 2.04739453 6.18569088 0.58759882 -1.28668758 0.36965449 4.27448707 1.96363150 6.20005144 -0.96264502 0.09207187 -0.21098962 0.05947481 -0.40519337 0.03382496 -0.04825640 +Al 6.12561752 0.17978212 6.10323681 -1.04249764 0.97887128 1.72843701 6.00728087 0.17225198 6.22503583 -0.77944975 -1.27841362 0.36626718 0.04775849 -0.28276633 -0.50715965 -0.23480464 +Al 6.15462339 2.15209799 3.95509619 0.62962767 -0.16602841 -0.68791306 6.19023259 2.09200001 3.94830015 0.00623352 -0.80143549 0.68591550 0.02957361 -0.13902931 -0.34116219 0.27727649 +Al 4.07354781 4.00321974 0.02550575 1.77424394 -0.37405529 0.38363590 4.25264567 3.99322319 0.05213031 1.25414992 0.19130653 -0.01109703 0.00777593 0.07227276 0.18198188 0.00223878 +Al 4.14674599 6.05104520 2.10483322 0.03280139 0.08676754 -0.19676071 4.12920284 6.03270448 2.07159733 -0.32141806 -0.39517706 -0.36038710 0.01713185 -0.15750992 -0.16402530 -0.08583804 +Al 6.08991039 4.12647927 1.95742699 -0.62354249 -0.68569643 0.37027988 6.01740939 4.05582249 2.03816183 -0.50607760 -0.53380887 1.15359371 0.03803245 -0.15271479 -0.02539625 0.22190570 +Al 6.06431252 6.22291666 0.03871599 2.18534921 -1.54573041 0.31498948 6.25920008 6.04374176 0.05037981 1.05099556 -1.43098646 -0.05659016 0.00767646 -0.00159240 -0.23646063 -0.13268791 +Al 4.01942104 3.87517101 4.25704919 -0.88586858 0.93575525 0.82201085 3.96334456 4.04024212 4.31720590 0.00146938 1.77670670 0.27764780 0.07506267 0.09139631 0.56879153 -0.22611728 +Al 4.13837525 5.87134393 6.04631195 -1.03231625 0.54871167 -0.61389713 4.01524921 6.00221715 6.02907801 -1.03591986 1.75098043 0.43472914 0.02877591 -0.19312635 0.47347531 0.17717706 +Al 6.15013780 4.04861391 6.05201872 0.69980634 1.03198283 0.66840442 6.21496319 4.13313340 6.15742268 0.27083775 0.42638431 0.94623041 0.05145999 0.10110606 -0.08241841 0.38824867 +Al 6.11246211 6.07650590 4.14447109 -0.33144313 0.03189243 -0.33902251 6.06873209 6.08937632 4.09675090 -0.44123689 0.18962766 -0.40622548 0.00975180 -0.06226305 0.05435279 -0.15160318 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.070238329593166 free_energy=1.070238329593166 stress="0.005001714831958823 0.0014787882206239893 -8.696280675829449e-05 0.0014787882206239893 0.003844643493447092 -0.0001922692050899891 -8.696280675829449e-05 -0.0001922692050899891 0.0070443138583341485" pbc="T T T" +Al -0.02535890 -0.01666860 -0.15289106 -0.28826462 0.30466071 -1.07928487 0.02344403 0.01551248 -0.19761095 1.16323505 0.26122577 0.29488629 0.03722304 0.42582734 0.02702365 0.38695244 +Al 0.08203055 1.76798180 2.14508712 -0.89406061 0.34609683 0.51131534 0.03869236 1.83830745 2.13618222 0.19190768 0.80007077 -0.53464521 0.02672090 0.21143978 0.32090730 -0.44958622 +Al 2.19078758 -0.10086233 1.96301195 0.84339155 0.62867711 0.85437542 2.21696033 -0.00369555 2.08103589 -0.49172392 0.98175087 1.06769837 0.03911533 -0.27001058 0.29666503 0.33427476 +Al 2.19124544 2.02189874 0.05478480 0.28379326 0.00199163 0.20331497 2.16865957 2.03296493 0.08130823 -0.73865548 0.13990468 0.19515489 -0.00314954 -0.27327489 0.08254668 0.07618516 +Al 0.04339555 0.01238779 4.16767894 0.66737671 -1.12465011 1.24941081 0.11565556 -0.08703035 4.32584055 0.60495818 -0.49127841 1.39549901 0.00963083 0.03281695 -0.01581709 0.26729276 +Al -0.08667627 2.14215956 6.30564995 -0.50258723 -0.07103308 0.19853601 -0.09739854 2.11083217 6.25209748 0.29516044 -0.35653077 -1.04581886 0.04710413 0.25331477 -0.23971365 -0.50879010 +Al 2.23590422 0.01790017 6.07751606 1.72194435 -0.13108337 -0.20121051 2.35267551 0.00857491 6.05157987 0.46205813 -0.04785286 -0.21264578 0.01508188 -0.44633611 0.04535953 -0.02872732 +Al 2.02584481 2.21163753 4.13023792 -0.08198376 0.57431653 -0.12525941 1.97634963 2.18784736 4.17164038 -0.77580769 -1.02425092 0.82884860 0.03426526 -0.22181921 -0.44990337 0.31539808 +Al -0.08986066 4.09259345 0.16573580 0.67317829 -0.43937864 0.21180730 0.01137334 4.01510313 0.24021383 0.93415439 -0.83732410 0.98372568 0.04397739 0.39526212 -0.28067905 0.36081251 +Al -0.03820982 6.26443372 2.07001341 -1.02046144 1.22516014 -0.30771802 -0.10751780 6.36589664 2.03998191 -0.11822679 0.54233461 -0.26748073 0.00806430 0.13589729 -0.13224951 0.04267469 +Al 2.09234396 4.13172013 2.10658804 0.31946865 0.16413754 1.41682083 2.09552761 4.14451928 2.18418504 -0.28280458 0.14393301 -0.00424007 0.03078722 -0.15619484 -0.10972569 -0.38485851 +Al 2.01064771 6.07441477 0.09136432 -0.41685005 -0.58729020 -1.39480062 1.99087608 6.03041168 -0.04696864 0.14353749 -0.00033405 -0.83188883 0.01603801 0.07817414 -0.06739502 -0.18467174 +Al -0.02250295 4.08105928 3.87934750 -0.51574885 -0.51481387 -0.42592389 -0.06811528 4.04554951 3.88373122 -0.19556418 0.00422045 0.49531416 0.02086440 -0.01636202 0.00576295 0.30074486 +Al -0.01197730 6.03704335 6.09441764 -0.50293963 -1.09095144 0.90914127 -0.06436346 5.98382832 6.16365794 -0.54787878 0.04163019 0.40358502 0.05282273 0.08804989 0.36704810 -0.16508080 +Al 1.78535119 4.11234955 6.29094723 -1.31931167 0.62635373 0.37150565 1.75291701 4.11946475 6.27009913 0.75441855 -0.62759591 -0.88376122 0.05243553 0.59585008 -0.22017073 -0.24897694 +Al 1.99809219 5.98659917 4.06941863 0.01399102 0.24475885 0.13255745 2.00672533 6.04974713 4.10309527 0.18108971 0.70710692 0.49781637 0.01532469 0.03231636 0.33995106 0.08668348 +Al 4.07469952 -0.03498634 0.02684264 0.19923830 0.99129311 0.08715584 4.11539389 0.03520825 0.06083098 0.37689914 0.24152312 0.46323548 0.04074881 0.18382248 -0.14097405 0.16679055 +Al 4.01802925 2.19751151 2.27659620 -0.70331049 0.25955173 -0.13583508 3.96405453 2.17752042 2.18998955 -0.19189540 -0.58832274 -1.30730034 0.04682243 0.04302862 -0.29901108 -0.48121469 +Al 5.95351650 -0.02605980 2.10710666 0.35401754 1.01180705 0.56216711 6.02419456 0.06907121 2.15899948 0.86179089 0.52327712 0.55594996 0.03783924 0.24606853 0.05579295 -0.14373582 +Al 6.15717909 1.94699849 0.23962681 -0.61351328 -1.60199540 1.27106336 6.06682015 1.83842144 0.35078828 -0.90247749 -0.13493551 0.43509367 0.03135380 -0.20568383 0.18195181 0.01840027 +Al 4.06700132 0.08203661 4.00185596 0.31957091 -0.83035284 -1.49004461 4.09559994 -0.01213247 3.92601624 0.26958396 -0.72276936 0.29079217 0.02636238 -0.07034558 -0.14278764 0.34231774 +Al 4.27448707 1.96363150 6.20005144 0.96264502 -0.09207187 0.21098962 4.29055238 2.04739453 6.18569088 -0.58759882 1.28668758 -0.36965449 0.07827745 -0.53037369 0.67598726 -0.24720494 +Al 6.00728087 0.17225198 6.22503583 0.77944975 1.27841362 -0.36626718 6.12561752 0.17978212 6.10323681 1.04249764 -0.97887128 -1.72843701 0.08523737 0.37537492 -0.79728642 -0.50933463 +Al 6.19023259 2.09200001 3.94830015 -0.00623352 0.80143549 -0.68591550 6.15462339 2.15209799 3.95509619 -0.62962767 0.16602841 0.68791306 0.02275020 -0.18558137 -0.07633978 0.52265087 +Al 4.25264567 3.99322319 0.05213031 -1.25414992 -0.19130653 0.01109703 4.07354781 4.00321974 0.02550575 -1.77424394 0.37405529 -0.38363590 0.01350932 -0.42846414 0.18846228 -0.22125765 +Al 4.12920284 6.03270448 2.07159733 0.32141806 0.39517706 0.36038710 4.14674599 6.05104520 2.10483322 -0.03280139 -0.08676754 0.19676071 0.01800742 -0.09019933 -0.11484602 0.01389448 +Al 6.01740939 4.05582249 2.03816183 0.50607760 0.53380887 -1.15359371 6.08991039 4.12647927 1.95742699 0.62354249 0.68569643 -0.37027988 0.06125176 0.23028801 0.12380966 0.30482279 +Al 6.25920008 6.04374176 0.05037981 -1.05099556 1.43098646 0.05659016 6.06431252 6.22291666 0.03871599 -2.18534921 1.54573041 -0.31498948 0.04723209 -0.66841658 0.37808134 -0.04875717 +Al 3.96334456 4.04024212 4.31720590 -0.00146938 -1.77670670 -0.27764780 4.01942104 3.87517101 4.25704919 0.88586858 -0.93575525 -0.82201085 0.05314517 0.38205832 -0.04498728 -0.22280964 +Al 4.01524921 6.00221715 6.02907801 1.03591986 -1.75098043 -0.43472914 4.13837525 5.87134393 6.04631195 1.03231625 -0.54871167 0.61389713 0.02835011 0.17479331 0.27056336 0.44538917 +Al 6.21496319 4.13313340 6.15742268 -0.27083775 -0.42638431 -0.94623041 6.15013780 4.04861391 6.05201872 -0.69980634 -1.03198283 -0.66840442 0.03431130 -0.31711057 -0.26362013 -0.24207206 +Al 6.06873209 6.08937632 4.09675090 0.44123689 -0.18962766 0.40622548 6.11246211 6.07650590 4.14447109 0.33144313 -0.03189243 0.33902251 -0.00126660 -0.00421014 0.03559352 0.10179363 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=0.9510780384805617 free_energy=0.9510780384805617 stress="0.006018694982681886 0.000707532545302181 -0.00027031016091648506 0.000707532545302181 0.004672717481672366 -0.00033102340476843346 -0.00027031016091648506 -0.00033102340476843346 0.005535259343197416" pbc="T T T" +Al -0.01275222 0.05726802 0.18005727 -0.19927745 0.57237382 -0.71890947 -0.02605177 0.10230075 0.07012401 0.05724316 0.26450821 -1.29509040 0.01715142 -0.00947614 -0.09650966 -0.17147379 +Al -0.04999668 2.09243871 2.18464611 1.16756593 -1.12969892 -0.21790407 0.07843493 1.98449425 2.15335321 1.05132111 -0.77155548 -0.19687641 0.01395655 0.09345415 0.04660318 -0.17104505 +Al 2.03841892 0.08400047 1.95797645 -0.21404749 0.13441634 -0.50957714 2.03483420 0.10028453 1.98389770 0.16796648 -0.10216174 0.79790502 0.03647746 0.09405088 0.15662974 0.57829811 +Al 1.88149100 2.16983810 0.17118949 -0.37348322 0.82040314 0.79865440 1.90338778 2.19789253 0.20698188 0.73824318 -0.36892834 -0.24218248 0.03894211 0.37671562 -0.31266842 -0.22837753 +Al 0.14528137 0.00609861 4.16828852 0.51651896 0.04198044 1.30642404 0.13990167 -0.00336235 4.22699801 -0.60545575 -0.26572591 -0.30847152 0.01662716 -0.34466581 -0.04509918 -0.38810039 +Al 0.04182469 1.96808443 6.11673011 0.95765160 -0.31122890 -0.76605778 0.13966297 1.96766674 6.06165521 0.68463553 0.33608300 -0.02917049 0.01239413 0.07942294 0.15698910 -0.01756646 +Al 2.14379409 -0.12620265 6.05723504 0.29418097 -0.70748394 -0.21503271 2.15377844 -0.18480510 6.10440512 -0.05892927 -0.28482844 1.00274337 0.03794733 -0.19517314 0.08730603 0.42342000 +Al 2.15660798 1.93854607 4.19392680 -0.18126174 -0.96469831 0.84297807 2.11891387 1.88693950 4.21250084 -0.39286002 0.15025753 -0.44609361 0.01900670 -0.19472662 0.19664517 -0.42360793 +Al -0.06455924 3.99585417 -0.07151346 -0.69408874 -0.10696503 2.18650854 -0.10422580 4.04038397 0.15494813 0.02422449 0.85208347 1.72868698 0.02889958 0.16905584 0.34485614 0.12291162 +Al 0.10204672 5.90301118 2.08022324 -0.36262648 0.97833494 -0.55983239 0.01170307 6.08867921 2.02963497 -1.02291003 2.29986604 -0.33505130 0.05030673 -0.42298381 0.50281089 0.03691161 +Al 2.05870312 4.15893716 2.17240187 -1.16319169 1.06499491 -1.14366143 1.97633583 4.19644025 2.07173789 -0.38942544 -0.34491464 -0.51929877 0.03741481 0.25165522 -0.43786106 -0.00881342 +Al 2.03093237 6.16616343 0.22636209 1.10853852 -0.60495543 0.06030238 2.13028979 6.05720479 0.12437434 0.56192064 -1.10937502 -1.72467316 0.05134746 -0.02221241 -0.42220860 -0.72697251 +Al -0.03248695 4.00614451 4.07624522 -2.09309665 0.34674387 -0.80620611 -0.19927375 4.05760085 4.03833007 -0.53714714 0.63756118 0.00335450 0.01991890 0.07730724 0.05282723 0.36410710 +Al -0.09799495 6.12242326 6.12879692 -0.47834541 1.61534925 -0.08451546 -0.09511775 6.22080798 6.14893328 0.50769178 0.03447663 0.43884577 0.01447713 0.32358306 -0.30293348 0.14506716 +Al 2.18056963 4.02380800 6.07306091 0.40203843 -0.98284014 1.18021244 2.16192487 3.96734595 6.20660576 -0.81339916 0.00838017 1.00797297 0.00791789 -0.31024128 0.23116548 0.18355736 +Al 2.01765645 6.04958972 4.15201148 -0.01861823 -0.58504763 -0.46188455 2.04342734 5.99328302 4.09422797 0.34837466 -0.40386889 -0.49054059 0.02178846 0.24261821 0.03990691 -0.11911530 +Al 4.05950027 -0.06948805 -0.04315484 0.58650611 -0.92751197 -0.32352926 4.12873504 -0.09385544 -0.04070397 0.53032298 0.48212852 0.47057884 0.03338857 0.12557091 0.41969896 0.17389399 +Al 4.19843764 2.12498899 2.10447206 -1.23468406 0.15096150 0.05290245 4.00730035 2.08501061 2.10790045 -1.94036692 -0.69849121 0.07967466 0.04405164 -0.53520078 -0.43452428 -0.10314871 +Al 6.05319828 0.12654426 2.12190414 -0.08523618 -0.98627825 -0.69445973 6.09741148 0.00653072 2.03616757 0.75210300 -1.00224225 -0.63806698 0.03369725 0.38187535 -0.23784589 -0.21805822 +Al 6.08315617 2.13848904 0.03278364 0.34836385 0.78684029 1.80041807 6.11034052 2.14825483 0.21182502 0.08442768 -0.68640145 1.37842183 0.01696111 -0.01858395 -0.34007695 0.00341296 +Al 4.20171060 -0.00534617 4.08410867 0.05322740 0.18446773 -0.22596081 4.18230453 0.01957983 4.08632220 -0.32423008 0.24543437 0.13977754 0.02462109 -0.23015003 0.03204022 0.22542410 +Al 4.02856458 1.93877892 6.15737613 0.25549493 0.15341887 -0.05673810 4.07999080 1.95201866 6.17330241 0.55505176 0.05601350 0.30160711 0.01943686 0.18372586 0.04424184 0.13678912 +Al 6.21393996 0.00944117 6.24427663 -1.40626442 -0.40271162 -0.73926252 6.04080561 0.00917481 6.13038896 -1.45023888 0.50548269 -1.28920177 0.02606800 -0.29677378 0.11749940 -0.24596496 +Al 6.09409643 2.06035727 4.10435555 2.00132806 1.00508627 -0.45776236 6.27316329 2.12537318 4.07608664 0.87434549 -0.05772887 -0.25061144 0.02706454 0.06834237 -0.08794993 0.27737462 +Al 3.97908084 3.96675084 0.12495806 -0.25445326 -0.00480584 -0.31471881 3.95079123 4.02823658 0.05441541 -0.04630141 1.00228514 -0.79129964 0.03631462 -0.09855059 0.42483074 -0.30497350 +Al 4.09072487 6.31549215 2.04941714 0.27041855 -0.22481211 0.29090850 4.08969105 6.21746492 2.11108375 -0.31599317 -1.37128517 0.71912391 0.04687197 -0.15187606 -0.54152674 0.22824196 +Al 5.91610022 4.17335776 1.94905240 -1.06862616 0.85962761 -0.97767055 5.87327491 4.22164516 1.92540329 0.18556879 -0.01720883 0.49616957 0.06920451 0.49634203 -0.23608660 0.48410634 +Al 6.11551220 6.04983000 0.17353957 0.57098905 0.83736373 -0.40771347 6.16862108 6.16298195 0.07892599 0.26759031 0.99142965 -1.25033343 0.03249291 0.01854674 0.26723564 -0.32819190 +Al 4.13322622 3.97642393 4.10132135 0.45184882 1.00528488 0.89563720 4.14516662 4.11240863 4.16214297 -0.21441665 1.18576487 0.17789512 0.03625823 -0.19862484 0.35325575 -0.13373786 +Al 4.26643379 6.07914675 6.09461896 0.28178320 0.06450424 -0.18289899 4.23721508 6.08072290 6.08307472 -0.68653876 -0.21080882 -0.04560572 0.03582803 -0.44375770 0.10709073 0.07250139 +Al 5.91104231 4.07169010 6.11094541 -0.13442338 -1.78041860 0.70994746 5.97789206 3.87813881 6.20956249 1.29526782 -1.44949375 1.04090804 0.04048538 0.50676704 -0.22659101 0.13008584 +Al 6.15996987 6.04803280 4.01943776 0.69527018 -0.90269514 -0.26059786 6.21360121 5.99364994 4.00244441 0.11191382 0.09326383 0.06890248 0.00375953 -0.01603652 0.14024865 0.00304423 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.0918324809063336 free_energy=1.0918324809063336 stress="0.004911883159152096 0.00013220167420827304 0.0002369478789135862 0.00013220167420827304 0.003970602953314662 -0.0003438719444869235 0.0002369478789135862 -0.0003438719444869235 0.0055649138761324265" pbc="T T T" +Al -0.02605177 0.10230075 0.07012401 -0.05724316 -0.26450821 1.29509040 -0.01275222 0.05726802 0.18005727 0.19927745 -0.57237382 0.71890947 0.01089759 0.18593413 -0.07582203 -0.16820964 +Al 0.07843493 1.98449425 2.15335321 -1.05132111 0.77155548 0.19687641 -0.04999668 2.09243871 2.18464611 -1.16756593 1.12969892 0.21790407 0.04567581 -0.17172117 0.19717195 0.13080363 +Al 2.03483420 0.10028453 1.98389770 -0.16796648 0.10216174 -0.79790502 2.03841892 0.08400047 1.95797645 0.21404749 -0.13441634 0.50957714 0.01691608 0.13022594 -0.35664845 0.22282597 +Al 1.90338778 2.19789253 0.20698188 -0.73824318 0.36892834 0.24218248 1.88149100 2.16983810 0.17118949 0.37348322 -0.82040314 -0.79865440 0.06815795 0.31039774 -0.43757469 -0.42454519 +Al 0.13990167 -0.00336235 4.22699801 0.60545575 0.26572591 0.30847152 0.14528137 0.00609861 4.16828852 -0.51651896 -0.04198044 -1.30642404 0.04009873 -0.32360977 -0.13723595 -0.53365538 +Al 0.13966297 1.96766674 6.06165521 -0.68463553 -0.33608300 0.02917049 0.04182469 1.96808443 6.11673011 -0.95765160 0.31122890 0.76605778 0.02510051 -0.22070553 0.21686273 0.39568814 +Al 2.15377844 -0.18480510 6.10440512 0.05892927 0.28482844 -1.00274337 2.14379409 -0.12620265 6.05723504 -0.29418097 0.70748394 0.21503271 0.03250977 -0.08766551 0.24981086 0.27030230 +Al 2.11891387 1.88693950 4.21250084 0.39286002 -0.15025753 0.44609361 2.15660798 1.93854607 4.19392680 0.18126174 0.96469831 -0.84297807 0.04096731 0.02458988 0.46219317 -0.31579068 +Al -0.10422580 4.04038397 0.15494813 -0.02422449 -0.85208347 -1.72868698 -0.06455924 3.99585417 -0.07151346 0.69408874 0.10696503 -2.18650854 0.03328949 0.27590974 0.20745221 -0.34762655 +Al 0.01170307 6.08867921 2.02963497 1.02291003 -2.29986604 0.33505130 0.10204672 5.90301118 2.08022324 0.36262648 -0.97833494 0.55983239 0.02244153 0.09457648 0.25475448 0.09846705 +Al 1.97633583 4.19644025 2.07173789 0.38942544 0.34491464 0.51929877 2.05870312 4.15893716 2.17240187 1.16319169 -1.06499491 1.14366143 0.04186118 0.18711983 -0.38195184 0.35018185 +Al 2.13028979 6.05720479 0.12437434 -0.56192064 1.10937502 1.72467316 2.03093237 6.16616343 0.22636209 -1.10853852 0.60495543 -0.06030238 0.02477497 -0.32445294 0.12500193 -0.32464125 +Al -0.19927375 4.05760085 4.03833007 0.53714714 -0.63756118 -0.00335450 -0.03248695 4.00614451 4.07624522 2.09309665 -0.34674387 0.80620611 0.09022963 0.89309837 0.11954330 0.16250083 +Al -0.09511775 6.22080798 6.14893328 -0.50769178 -0.03447663 -0.43884577 -0.09799495 6.12242326 6.12879692 0.47834541 -1.61534925 0.08451546 0.03257992 0.28207578 -0.61847458 0.11533067 +Al 2.16192487 3.96734595 6.20660576 0.81339916 -0.00838017 -1.00797297 2.18056963 4.02380800 6.07306091 -0.40203843 0.98284014 -1.18021244 0.03489733 -0.44904038 0.35302437 -0.32493968 +Al 2.04342734 5.99328302 4.09422797 -0.34837466 0.40386889 0.49054059 2.01765645 6.04958972 4.15201148 0.01861823 0.58504763 0.46188455 0.00966126 -0.03890277 0.12400499 0.09699627 +Al 4.12873504 -0.09385544 -0.04070397 -0.53032298 -0.48212852 -0.47057884 4.05950027 -0.06948805 -0.04315484 -0.58650611 0.92751197 0.32352926 0.03556891 -0.16783018 0.42389581 0.36210099 +Al 4.00730035 2.08501061 2.10790045 1.94036692 0.69849121 -0.07967466 4.19843764 2.12498899 2.10447206 1.23468406 -0.15096150 -0.05290245 -0.00293970 0.11086241 -0.07470684 0.04269246 +Al 6.09741148 0.00653072 2.03616757 -0.75210300 1.00224225 0.63806698 6.05319828 0.12654426 2.12190414 0.08523618 0.98627825 0.69445973 0.01104747 0.09768037 0.18464649 0.24915921 +Al 6.11034052 2.14825483 0.21182502 -0.08442768 0.68640145 -1.37842183 6.08315617 2.13848904 0.03278364 -0.34836385 -0.78684029 -1.80041807 0.03573598 -0.14190429 -0.49806651 -0.21100660 +Al 4.18230453 0.01957983 4.08632220 0.32423008 -0.24543437 -0.13977754 4.20171060 -0.00534617 4.08410867 -0.05322740 -0.18446773 0.22596081 0.01371445 -0.06840396 -0.02096407 -0.02025451 +Al 4.07999080 1.95201866 6.17330241 -0.55505176 -0.05601350 -0.30160711 4.02856458 1.93877892 6.15737613 -0.25549493 -0.15341887 0.05673810 0.02841402 -0.07095965 -0.01594452 0.05239176 +Al 6.04080561 0.00917481 6.13038896 1.45023888 -0.50548269 1.28920177 6.21393996 0.00944117 6.24427663 1.40626442 0.40271162 0.73926252 0.03629277 0.32068290 0.33369400 -0.11607776 +Al 6.27316329 2.12537318 4.07608664 -0.87434549 0.05772887 0.25061144 6.09409643 2.06035727 4.10435555 -2.00132806 -1.00508627 0.45776236 0.05742928 -0.71649904 -0.54630815 -0.09325273 +Al 3.95079123 4.02823658 0.05441541 0.04630141 -1.00228514 0.79129964 3.97908084 3.96675084 0.12495806 0.25445326 0.00480584 0.31471881 0.05548307 0.31981913 0.16868008 0.05237449 +Al 4.08969105 6.21746492 2.11108375 0.31599317 1.37128517 -0.71912391 4.09072487 6.31549215 2.04941714 -0.27041855 0.22481211 -0.29090850 0.02844169 -0.20167795 -0.15027434 -0.00927075 +Al 5.87327491 4.22164516 1.92540329 -0.18556879 0.01720883 -0.49616957 5.91610022 4.17335776 1.94905240 1.06862616 -0.85962761 0.97767055 0.06559015 0.31393846 -0.32464927 0.39609546 +Al 6.16862108 6.16298195 0.07892599 -0.26759031 -0.99142965 1.25033343 6.11551220 6.04983000 0.17353957 -0.57098905 -0.83736373 0.40771347 0.04572929 -0.24487065 -0.20481584 -0.18512544 +Al 4.14516662 4.11240863 4.16214297 0.21441665 -1.18576487 -0.17789512 4.13322622 3.97642393 4.10132135 -0.45184882 -1.00528488 -0.89563720 0.00884758 -0.17041663 -0.24813674 -0.22303960 +Al 4.23721508 6.08072290 6.08307472 0.68653876 0.21080882 0.04560572 4.26643379 6.07914675 6.09461896 -0.28178320 -0.06450424 0.18289899 0.02204928 -0.18264197 -0.22128409 0.03875775 +Al 5.97789206 3.87813881 6.20956249 -1.29526782 1.44949375 -1.04090804 5.91104231 4.07169010 6.11094541 0.13442338 1.78041860 -0.70994746 0.02835153 0.38848951 0.39279792 0.02366620 +Al 6.21360121 5.99364994 4.00244441 -0.11191382 -0.09326383 -0.06890248 6.15996987 6.04803280 4.01943776 -0.69527018 0.90269514 0.26059786 0.05201764 -0.35409825 0.49932362 0.23710075 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.0130284021902485 free_energy=1.0130284021902485 stress="0.005128678845193628 8.697537281318916e-07 -0.00029998580829961133 8.697537281318916e-07 0.0044229703237791355 6.279173330563964e-05 -0.00029998580829961133 6.279173330563964e-05 0.005507960838444401" pbc="T T T" +Al 0.02213502 -0.08451190 0.25018385 -0.26435718 0.55167561 0.67885138 -0.00601513 -0.01338803 0.26538651 -0.12007549 0.66756936 -0.40063079 0.02408400 -0.06484044 0.12179066 -0.34869014 +Al -0.11538440 1.96031939 2.04291776 -1.43214907 1.27051127 0.75093292 -0.20701340 2.14730757 2.12562758 0.04706424 1.92342147 0.65133519 0.03202621 0.15205486 0.42208068 0.08403587 +Al 2.03849331 0.18810670 2.08207549 0.59865585 -0.11796993 0.07979404 2.11775516 0.08872301 2.09423589 0.61551726 -1.41529797 0.18435427 0.04260248 0.23273160 -0.66690936 -0.00670461 +Al 1.99624644 2.10310818 0.14973596 -0.11425410 -0.48290690 0.22861129 1.99121706 2.05443881 0.16027995 -0.06084396 -0.25244343 0.01634178 0.02138092 0.12577098 -0.11886664 -0.12707024 +Al -0.05837425 0.06203386 4.04433271 -0.79962697 -0.75040507 -0.85240723 -0.07338641 -0.02033140 3.99608904 0.62142255 -0.46085540 0.02651968 0.02673161 0.31441920 -0.18786780 0.19621972 +Al -0.00770457 2.06909293 6.10104693 0.70817611 -0.29592739 -0.03445779 0.09317355 1.97944054 6.12719561 0.95505116 -1.21265131 0.50688098 0.06705886 0.23824199 -0.38420829 0.13357847 +Al 1.97896566 0.12917286 6.23590763 -0.80309374 -0.52241192 1.76837785 1.92181187 0.05904736 6.36191356 -0.07616280 -0.80955649 0.46944109 0.01683225 0.07847420 -0.05961487 -0.31959155 +Al 2.05369233 2.06438910 3.96006411 1.16961714 0.61354318 -0.65646365 2.17885121 2.12222930 3.95703070 0.92327454 0.22511332 0.53560529 0.01971213 0.11191042 0.09528210 0.42521272 +Al 0.00698362 3.98154618 -0.17370970 -0.69730309 1.47085102 -0.97100251 -0.05014748 4.17053588 -0.21631364 -0.16757894 1.42047130 0.17073287 0.03988046 0.00149831 0.52149023 0.40411433 +Al -0.05288955 5.95606644 2.22626273 0.52308388 0.54286053 0.91214082 0.02455706 6.00411830 2.26357548 0.76346422 0.18179056 -0.39144355 0.02830709 0.22229435 0.07992085 -0.25793799 +Al 2.11289958 4.12089312 2.01835908 -1.00165741 -0.25236311 -0.09011107 1.98148873 4.04688871 2.00932370 -1.26842272 -0.98754574 -0.02296711 0.03429157 -0.21815781 -0.31953105 -0.00845476 +Al 2.07385933 6.02691843 0.11851837 -0.51781348 -0.76700998 0.31669745 2.00474070 5.99913554 0.16090479 -0.65142880 0.30661728 0.36585202 0.01347043 -0.14407845 0.26818065 0.10918844 +Al 0.07379181 3.95074811 4.16066022 0.28274843 -0.63740086 1.66182124 0.09728314 3.95784625 4.32313585 0.09514929 0.79912115 1.20054960 0.01885460 -0.01987759 0.37385512 -0.01594232 +Al 0.01970170 6.31508873 6.13616062 0.66174879 -0.13277108 0.33549058 0.09583308 6.22386576 6.15136584 0.67824314 -1.40931939 -0.13831786 0.01521367 0.06967147 -0.52011188 -0.07661197 +Al 2.20594642 4.04358694 6.26889780 0.08089119 -0.05692082 -0.31614389 2.17778868 4.03407371 6.16573066 -0.53269470 -0.06858254 -1.36773904 0.02939705 -0.26239430 -0.04485116 -0.51278746 +Al 2.04939216 6.02783801 4.00101857 -0.07919856 -0.15244839 -0.85845379 2.03830579 6.07919793 3.98217142 -0.00570732 0.94869788 0.57250429 0.02402397 -0.07222048 0.45815395 0.38046490 +Al 4.04865600 0.03497252 -0.07876810 0.10580979 1.34718334 0.49769567 4.06059397 0.14738452 0.07982591 0.20250051 0.45891526 1.92089753 0.05650232 -0.06961689 0.00056470 0.86917019 +Al 4.22026774 1.99102600 2.05061721 0.60510986 -0.84381460 0.06119475 4.25729372 1.92260931 2.06541232 -0.05954731 -0.32524965 0.24966477 -0.00166435 -0.09625805 0.08753587 0.04403594 +Al 6.07549009 -0.08385214 2.14347932 1.30988168 0.84804195 -1.06791337 6.18056567 0.02814837 1.99756354 0.46774409 0.99668802 -1.38919434 0.03859889 -0.07418075 0.24944452 -0.26409109 +Al 6.19186074 2.09740161 -0.02857643 -0.06702953 -0.24934370 0.00668792 6.15579182 2.02780824 -0.04953139 -0.50020516 -0.84999854 -0.51819493 0.02756169 -0.22344633 -0.33945515 -0.00589771 +Al 4.14299385 0.08645619 4.14261476 1.17104305 -0.15778604 0.15932594 4.21210948 0.04321468 4.15160924 0.09843822 -0.56420467 -0.03427003 0.00692053 -0.32967189 -0.20223724 -0.00630615 +Al 4.16366296 1.99290003 6.16679844 -0.66254727 -1.02372937 0.09626487 4.05934754 1.93621067 6.14446934 -1.09792379 -0.05101719 -0.44714627 0.03185297 -0.29924423 0.30695412 -0.20001791 +Al 5.94480474 -0.10034035 6.23393033 -0.04907645 -1.75398260 -0.61710366 6.04243363 -0.23671976 6.16832261 1.67840732 -0.52946212 -0.36547481 0.05717584 0.67699772 0.16576331 -0.15697046 +Al 6.21648228 2.01870097 4.25738983 0.90066960 0.19860041 0.07877886 6.21560078 2.05080104 4.22636022 -0.88568653 0.33096460 -0.42276020 0.04531873 -0.54938602 0.09238392 -0.38424506 +Al 4.14912259 4.01367788 0.05212177 -0.92324580 1.05232549 -0.45720373 4.04592202 4.11349092 0.03147643 -0.97211388 0.62728330 0.13266298 0.02480920 -0.01688396 0.04148863 0.11571892 +Al 4.21417820 6.02712474 2.15191252 -0.70303143 1.65229380 -0.07107904 4.09333020 6.24255705 2.08300373 -1.25278141 1.96456036 -1.02883463 0.05230762 -0.42375731 0.38880200 -0.44569508 +Al 6.08982307 4.00502052 2.00375293 -1.44048406 -1.11928088 -1.31327079 5.96676282 3.90728881 1.94460099 -0.82399861 -0.33750090 0.25827774 0.03384097 0.18680633 -0.09610198 0.42573264 +Al 6.17182647 5.99789598 0.17494200 0.73652825 -1.35023526 0.41123487 6.22025715 5.90599463 0.17281582 -0.00098980 -0.05324400 -0.29314965 0.01796102 -0.11751999 0.13384365 -0.36771480 +Al 4.01232337 4.19970871 4.04024162 -0.39263944 0.14090196 -0.38674701 4.00711671 4.13838430 4.02226635 0.38052503 -0.96741127 -0.06566993 0.05085593 0.13910101 -0.62654015 0.21938361 +Al 4.01156082 6.12397827 6.26155146 0.96586937 0.07250967 -0.16429524 4.07155007 6.13058473 6.19820112 -0.03834417 0.12849413 -0.84500948 0.04665229 -0.10336532 -0.06773895 -0.38038120 +Al 5.93181598 4.19017322 5.97824763 -0.93957766 0.01456575 0.82293726 5.89401831 4.16565552 6.09467449 0.23795735 -0.48981186 1.05748443 0.02535221 0.37888875 -0.14028154 0.34871659 +Al 6.06160705 6.11125177 4.12436328 1.06725222 0.89084394 -1.01018489 6.17529306 6.16395072 4.03832704 0.74974647 -0.19555552 -0.58830190 0.04511526 0.15603860 -0.03321890 0.12953815 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.1047037076242896 free_energy=1.1047037076242896 stress="0.004138521127263458 0.0009391501441625187 0.00011793231796938067 0.0009391501441625187 0.004119625289886451 -0.00018275874172973586 0.00011793231796938067 -0.00018275874172973586 0.007073958518883559" pbc="T T T" +Al -0.00601513 -0.01338803 0.26538651 0.12007549 -0.66756936 0.40063079 0.02213502 -0.08451190 0.25018385 0.26435718 -0.55167561 -0.67885138 0.03076914 0.19096507 -0.04593207 -0.34803027 +Al -0.20701340 2.14730757 2.12562758 -0.04706424 -1.92342147 -0.65133519 -0.11538440 1.96031939 2.04291776 1.43214907 -1.27051127 -0.75093292 0.04393056 0.71520172 -0.03680347 -0.13705089 +Al 2.11775516 0.08872301 2.09423589 -0.61551726 1.41529797 -0.18435427 2.03849331 0.18810670 2.08207549 -0.59865585 0.11796993 -0.07979404 0.02049922 -0.25179988 -0.09134542 0.06993599 +Al 1.99121706 2.05443881 0.16027995 0.06084396 0.25244343 -0.01634178 1.99624644 2.10310818 0.14973596 0.11425410 0.48290690 -0.22861129 0.01246022 -0.03595561 0.18666288 -0.02643292 +Al -0.07338641 -0.02033140 3.99608904 -0.62142255 0.46085540 -0.02651968 -0.05837425 0.06203386 4.04433271 0.79962697 0.75040507 0.85240723 0.03892371 0.50396552 0.38058985 0.30839175 +Al 0.09317355 1.97944054 6.12719561 -0.95505116 1.21265131 -0.50688098 -0.00770457 2.06909293 6.10104693 -0.70817611 0.29592739 0.03445779 0.04634591 -0.12009872 -0.12086814 0.11597975 +Al 1.92181187 0.05904736 6.36191356 0.07616280 0.80955649 -0.46944109 1.97896566 0.12917286 6.23590763 0.80309374 0.52241192 -1.76837785 0.05695456 0.40878577 -0.14799086 -0.47405682 +Al 2.17885121 2.12222930 3.95703070 -0.92327454 -0.22511332 -0.53560529 2.05369233 2.06438910 3.96006411 -1.16961714 -0.61354318 0.65646365 0.02718331 -0.28795978 -0.34411634 0.30539513 +Al -0.05014748 4.17053588 -0.21631364 0.16757894 -1.42047130 -0.17073287 0.00698362 3.98154618 -0.17370970 0.69730309 -1.47085102 0.97100251 0.05741352 0.35071467 -0.58712393 0.37469906 +Al 0.02455706 6.00411830 2.26357548 -0.76346422 -0.18179056 0.39144355 -0.05288955 5.95606644 2.22626273 -0.52308388 -0.54286053 -0.91214082 0.05477183 -0.04443676 -0.23061949 -0.55987018 +Al 1.98148873 4.04688871 2.00932370 1.26842272 0.98754574 0.02296711 2.11289958 4.12089312 2.01835908 1.00165741 0.25236311 0.09011107 0.03509882 0.06153255 -0.08999473 0.09253752 +Al 2.00474070 5.99913554 0.16090479 0.65142880 -0.30661728 -0.36585202 2.07385933 6.02691843 0.11851837 0.51781348 0.76700998 -0.31669745 0.01364835 0.05071369 0.36745917 -0.07874840 +Al 0.09728314 3.95784625 4.32313585 -0.09514929 -0.79912115 -1.20054960 0.07379181 3.95074811 4.16066022 -0.28274843 0.63740086 -1.66182124 0.02306465 -0.13254854 0.43200105 -0.25592987 +Al 0.09583308 6.22386576 6.15136584 -0.67824314 1.40931939 0.13831786 0.01970170 6.31508873 6.13616062 -0.66174879 0.13277108 -0.33549058 0.03722723 -0.04317109 -0.25700677 -0.23399412 +Al 2.17778868 4.03407371 6.16573066 0.53269470 0.06858254 1.36773904 2.20594642 4.04358694 6.26889780 -0.08089119 0.05692082 0.31614389 0.01701042 -0.12802143 0.04953544 -0.10675663 +Al 2.03830579 6.07919793 3.98217142 0.00570732 -0.94869788 -0.57250429 2.04939216 6.02783801 4.00101857 0.07919856 0.15244839 0.85845379 0.02706303 0.15199849 0.17959259 0.49733150 +Al 4.06059397 0.14738452 0.07982591 -0.20250051 -0.45891526 -1.92089753 4.04865600 0.03497252 -0.07876810 -0.10580979 -1.34718334 -0.49769567 0.03940503 0.09237554 -0.49490403 -0.08433439 +Al 4.25729372 1.92260931 2.06541232 0.05954731 0.32524965 -0.24966477 4.22026774 1.99102600 2.05061721 -0.60510986 0.84381460 -0.06119475 0.05047209 -0.35149253 0.26052588 0.11741839 +Al 6.18056567 0.02814837 1.99756354 -0.46774409 -0.99668802 1.38919434 6.07549009 -0.08385214 2.14347932 -1.30988168 -0.84804195 1.06791337 0.06588470 -0.35899921 -0.15016671 0.16723139 +Al 6.15579182 2.02780824 -0.04953139 0.50020516 0.84999854 0.51819493 6.19186074 2.09740161 -0.02857643 0.06702953 0.24934370 -0.00668792 0.00663271 -0.04669239 0.00974961 -0.25286891 +Al 4.21210948 0.04321468 4.15160924 -0.09843822 0.56420467 0.03427003 4.14299385 0.08645619 4.14261476 -1.17104305 0.15778604 -0.15932594 0.02100275 -0.32405759 -0.06506621 -0.08030997 +Al 4.05934754 1.93621067 6.14446934 1.09792379 0.05101719 0.44714627 4.16366296 1.99290003 6.16679844 0.66254727 1.02372937 -0.09626487 0.01455896 0.00164852 0.24458914 -0.08242188 +Al 6.04243363 -0.23671976 6.16832261 -1.67840732 0.52946212 0.36547481 5.94480474 -0.10034035 6.23393033 0.04907645 1.75398260 0.61710366 0.04211444 0.37058568 0.57817759 0.26001291 +Al 6.21560078 2.05080104 4.22636022 0.88568653 -0.33096460 0.42276020 6.21648228 2.01870097 4.25738983 -0.90066960 -0.19860041 -0.07877886 0.02772826 -0.49207051 -0.02178290 0.04977811 +Al 4.04592202 4.11349092 0.03147643 0.97211388 -0.62728330 -0.13266298 4.14912259 4.01367788 0.05212177 0.92324580 -1.05232549 0.45720373 0.02983322 -0.02166875 -0.25777803 0.21986734 +Al 4.09333020 6.24255705 2.08300373 1.25278141 -1.96456036 1.02883463 4.21417820 6.02712474 2.15191252 0.70303143 -1.65229380 0.07107904 0.00366273 0.09250872 -0.27189183 -0.12916503 +Al 5.96676282 3.90728881 1.94460099 0.82399861 0.33750090 -0.25827774 6.08982307 4.00502052 2.00375293 1.44048406 1.11928088 1.31327079 0.08337120 0.18539275 0.55661727 0.48285839 +Al 6.22025715 5.90599463 0.17281582 0.00098980 0.05324400 0.29314965 6.17182647 5.99789598 0.17494200 -0.73652825 1.35023526 -0.41123487 0.05662362 -0.41202555 0.67932921 -0.07057314 +Al 4.00711671 4.13838430 4.02226635 -0.38052503 0.96741127 0.06566993 4.01232337 4.19970871 4.04024162 0.39263944 -0.14090196 0.38674701 0.01472974 0.30459472 -0.05071633 -0.01069060 +Al 4.07155007 6.13058473 6.19820112 0.03834417 -0.12849413 0.84500948 4.01156082 6.12397827 6.26155146 -0.96586937 -0.07250967 0.16429524 0.03660563 -0.43194861 0.08406947 -0.08097677 +Al 5.89401831 4.16565552 6.09467449 -0.23795735 0.48981186 -1.05748443 5.93181598 4.19017322 5.97824763 0.93957766 -0.01456575 -0.82293726 0.02227255 0.35322095 -0.18358794 -0.14640537 +Al 6.17529306 6.16395072 4.03832704 -0.74974647 0.19555552 0.58830190 6.06160705 6.11125177 4.12436328 -1.06725222 -0.89084394 1.01018489 0.04744160 -0.35125741 -0.56120394 0.09717892 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.1705957239373603 free_energy=1.1705957239373603 stress="0.003845133113172732 0.00044194282378009364 -0.0003317322932143324 0.00044194282378009364 0.004823164759565864 -0.00029437895860849356 -0.0003317322932143324 -0.00029437895860849356 0.00632812513627653" pbc="T T T" +Al -0.07821786 0.12108144 -0.16766018 -0.31105836 0.08388703 -0.63141108 -0.05270062 0.06097786 -0.17412374 0.70497751 -1.21496841 0.70935559 0.05109044 0.36299030 -0.33364679 0.23888644 +Al 0.09085967 1.71555159 2.07646922 0.95086557 -0.57698993 -0.08295244 0.19853509 1.69386181 2.06649659 0.90014887 0.23915207 -0.25077749 0.00038390 0.09802415 0.22986456 0.09326055 +Al 2.19426707 -0.15809549 2.02373705 1.03647976 -1.03793313 -0.04976920 2.23658087 -0.19380258 2.03850145 -0.21319336 0.49623588 0.43911276 0.05712845 -0.40657940 0.36796046 0.04493915 +Al 2.09224055 1.98196121 -0.05771524 -0.13416708 0.33750053 -0.29578459 2.07887852 2.01921285 -0.06688840 -0.14288731 0.29808352 0.12765216 -0.00460580 0.01594345 0.04017677 0.13878125 +Al -0.08478475 -0.12847821 4.22978432 0.04816760 1.42413180 0.02858846 -0.08829754 0.07314862 4.22138691 -0.00453537 1.72391754 -0.16935684 0.04496107 -0.06362365 0.61444069 -0.08989657 +Al -0.11142129 2.19243089 6.10770098 -0.29508494 -1.74031306 0.04224444 -0.09678244 1.99392413 6.09074772 0.45594717 -1.43132773 -0.32804129 0.03419664 0.35177307 -0.37446681 -0.10103132 +Al 1.96624328 0.03494261 6.02228034 -0.64379813 0.26059213 1.01041857 1.94546271 0.07343064 6.15728247 0.28186001 0.37447223 1.24972324 0.02227456 0.27337636 0.10202422 0.27831770 +Al 2.06870533 2.22704702 4.07403457 -0.06714251 0.61567618 -0.46811984 2.03518431 2.23346279 4.05781615 -0.47171450 -0.43154372 0.14868740 0.02538211 -0.19260893 -0.40686164 0.21489186 +Al 0.18967762 4.08336422 0.21743947 -0.56962473 1.38747015 -0.09640643 0.03782485 4.17525923 0.16782586 -1.89001058 0.15668653 -0.68728980 0.05252502 -0.64896938 -0.24446278 -0.26072600 +Al 0.01222183 6.25978028 2.03920652 0.80500617 0.40527830 0.35732957 0.09526192 6.28916164 2.07937186 0.56667804 0.12459322 0.38512026 0.01442015 0.07565116 -0.11384181 0.00532914 +Al 2.00164369 3.99107963 1.99727438 -0.12767300 -1.11536353 -2.17565712 2.06495718 3.91150630 1.83616264 1.05294354 -0.25973113 -0.66956097 0.06160533 0.55238040 0.21839811 0.33217773 +Al 2.15614552 6.00964837 0.04723443 -0.36000976 0.40697371 0.78991870 2.10781449 6.08554736 0.11366897 -0.51514776 0.77564353 0.28362216 0.02468155 -0.08517049 0.31151973 -0.00470665 +Al -0.02563888 4.10402757 3.89647265 0.47653601 1.32818343 0.76603495 0.03241275 4.21604793 4.00576873 0.58030786 0.46142675 1.11029174 0.02265039 0.03234221 0.00109147 0.23913714 +Al -0.01618421 6.05639081 6.24826744 0.72018435 -0.07301222 -0.08632860 0.05203349 6.06872737 6.22529035 0.43790944 0.33572790 -0.40137470 0.04648780 0.03031433 0.07432401 -0.05158156 +Al 1.94771696 4.13587640 6.16413404 -0.03963949 0.07061201 -0.48028331 1.97983812 4.10452836 6.11160637 0.57463206 -0.61689164 -0.28871923 0.02109376 0.24079029 -0.22729036 -0.13619823 +Al 1.86084557 6.09813012 4.16931790 0.03314649 -0.25956277 0.59576932 1.94573769 6.05014443 4.19139282 1.41026781 -0.37083548 -0.24236961 0.03404173 0.53960505 -0.28615455 -0.21038931 +Al 4.07882182 0.00677076 0.10893728 0.18806050 -0.25676481 0.97901444 4.07076995 -0.02019098 0.15472006 -0.27146475 -0.15993975 -0.19035691 0.04430474 -0.16355851 -0.05056738 -0.27788435 +Al 4.09698915 2.04241337 2.13967434 0.67562370 -0.07071069 0.19576034 4.11409461 2.04203469 2.13012541 -0.31081755 0.02257674 -0.39023380 0.02657714 -0.33227946 0.06828139 -0.14414582 +Al 5.93247690 -0.07858327 2.09892254 -0.89349890 -0.46046371 -0.84058752 5.93331369 -0.08370514 2.07968937 0.83191229 0.22731745 0.45711485 0.08222741 0.54708726 0.31663741 0.38228639 +Al 6.10748533 1.87416468 0.29330146 0.59206701 -0.84707729 -0.79441788 6.16150442 1.89159061 0.16804059 0.33829256 1.17602453 -1.23748826 0.06435485 -0.00997542 0.54848301 -0.43998100 +Al 3.92045497 0.06163327 4.07252328 -0.91223561 0.47767476 0.79626074 3.87722111 0.10313340 4.18873592 0.15217493 0.27151528 1.14257570 0.02482290 0.29862544 -0.04882112 0.27752877 +Al 4.21848770 2.13036532 6.14141824 -0.41114079 0.10240664 0.23353929 4.11356789 2.15132489 6.16104690 -1.24434655 0.21610760 -0.00815013 0.07155155 -0.51189100 0.06379645 0.07858018 +Al 5.83816546 0.19180441 6.18106669 -0.41228672 -0.38450265 1.11629972 5.88112726 0.07078982 6.23781975 1.00302767 -1.67088185 -0.14582247 0.06566150 0.64779487 -0.54878397 -0.29055007 +Al 6.13965224 2.17354209 3.95807566 0.59199442 -0.20549863 -1.31688692 6.20761334 2.07960042 3.88674503 0.51467342 -1.28628043 0.13875943 0.03296307 0.06960998 -0.52944362 0.28892537 +Al 4.21364971 4.02582056 0.04027400 0.63015129 0.22185216 0.01522223 4.23271569 4.05833408 0.04915904 -0.29191187 0.35947221 0.13961150 0.00859065 -0.28835755 0.05580523 0.04202125 +Al 4.22484888 6.13119549 2.12010265 0.09500864 -0.04043648 -0.08435994 4.15892190 6.01897958 2.13387411 -1.21149386 -1.69843909 0.27506941 0.05828532 -0.47979900 -0.73308901 0.13153562 +Al 6.09355870 4.00713122 1.99412437 -0.42926307 0.18877520 1.56403723 6.08490501 4.06392335 2.14781591 0.36585391 0.69472622 0.98088513 0.04450934 0.16143462 0.29058982 0.11129000 +Al 6.22427957 6.11304095 0.15661615 0.13062756 -1.01140828 0.52108241 6.18973320 6.01762233 0.15892707 -0.73797405 -0.52137499 -0.37754255 0.02995663 -0.33164617 -0.02589610 -0.36225408 +Al 4.17757328 4.00448317 4.26864029 0.21320757 0.73021792 -1.51694048 4.15864148 4.12972052 4.07699427 -0.64123928 1.20563015 -1.63272963 0.04009722 -0.21789368 0.47546617 -0.39690543 +Al 4.12732324 6.06330384 6.15537448 -1.55587482 1.46599085 -0.20539925 3.95865176 6.21074962 6.13660947 -1.37174879 0.99341933 -0.10773843 0.00159240 -0.09847691 0.08233686 0.01224113 +Al 6.25680380 4.06583361 6.25279967 0.05038495 0.04702882 0.57699152 6.22342951 4.07849676 6.29671196 -0.58779115 0.04362041 0.00625911 0.04900440 -0.29861961 0.14390298 0.01326880 +Al 6.12933970 6.08283506 4.22722171 -0.07501366 -1.47421443 -0.46320732 6.10527835 5.95295029 4.16772913 -0.26533035 -0.53413487 -0.46628833 0.01777950 -0.16829377 -0.08177340 -0.15714810 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.1319334923518758 free_energy=1.1319334923518758 stress="0.004724185153377603 7.628423560682366e-05 -0.0002545923994040662 7.628423560682366e-05 0.005218669922821519 -0.0007487346003771602 -0.0002545923994040662 -0.0007487346003771602 0.005293740127255423" pbc="T T T" +Al -0.05270062 0.06097786 -0.17412374 -0.70497751 1.21496841 -0.70935559 -0.07821786 0.12108144 -0.16766018 0.31105836 -0.08388703 0.63141108 0.07696546 0.21245686 -0.37979575 0.61031702 +Al 0.19853509 1.69386181 2.06649659 -0.90014887 -0.23915207 0.25077749 0.09085967 1.71555159 2.07646922 -0.95086557 0.57698993 0.08295244 0.02353866 -0.15155912 0.34707640 -0.19713105 +Al 2.23658087 -0.19380258 2.03850145 0.21319336 -0.49623588 -0.43911276 2.19426707 -0.15809549 2.02373705 -1.03647976 1.03793313 0.04976920 0.09072832 -0.31324147 0.58453916 0.26290110 +Al 2.07887852 2.01921285 -0.06688840 0.14288731 -0.29808352 -0.12765216 2.09224055 1.98196121 -0.05771524 0.13416708 -0.33750053 0.29578459 0.02375263 -0.04589477 -0.05241661 0.14080337 +Al -0.08829754 0.07314862 4.22138691 0.00453537 -1.72391754 0.16935684 -0.08478475 -0.12847821 4.22978432 -0.04816760 -1.42413180 -0.02858846 0.03241941 0.14300888 -0.46633462 -0.07246492 +Al -0.09678244 1.99392413 6.09074772 -0.45594717 1.43132773 0.32804129 -0.11142129 2.19243089 6.10770098 0.29508494 1.74031306 -0.04224444 0.04259439 0.14716773 0.52984534 -0.03711024 +Al 1.94546271 0.07343064 6.15728247 -0.28186001 -0.37447223 -1.24972324 1.96624328 0.03494261 6.02228034 0.64379813 -0.26059213 -1.01041857 0.03894151 0.30674188 -0.02846450 -0.13871674 +Al 2.03518431 2.23346279 4.05781615 0.47171450 0.43154372 -0.14868740 2.06870533 2.22704702 4.07403457 0.06714251 -0.61567618 0.46811984 0.01766624 -0.04441021 -0.27918251 0.17114627 +Al 0.03782485 4.17525923 0.16782586 1.89001058 -0.15668653 0.68728980 0.18967762 4.08336422 0.21743947 0.56962473 -1.38747015 0.09640643 0.04639039 -0.03141727 -0.52073599 -0.03208457 +Al 0.09526192 6.28916164 2.07937186 -0.56667804 -0.12459322 -0.38512026 0.01222183 6.25978028 2.03920652 -0.80500617 -0.40527830 -0.35732957 0.00748749 -0.21630445 -0.13170005 0.02047973 +Al 2.06495718 3.91150630 1.83616264 -1.05294354 0.25973113 0.66956097 2.00164369 3.99107963 1.99727438 0.12767300 1.11536353 2.17565712 0.05023971 0.09226620 0.38100607 0.56053859 +Al 2.10781449 6.08554736 0.11366897 0.51514776 -0.77564353 -0.28362216 2.15614552 6.00964837 0.04723443 0.36000976 -0.40697371 -0.78991870 0.02690310 -0.05944763 -0.09206261 -0.28167760 +Al 0.03241275 4.21604793 4.00576873 -0.58030786 -0.46142675 -1.11029174 -0.02563888 4.10402757 3.89647265 -0.47653601 -1.32818343 -0.76603495 0.04407131 0.01865042 -0.50807453 -0.00717840 +Al 0.05203349 6.06872737 6.22529035 -0.43790944 -0.33572790 0.40137470 -0.01618421 6.05639081 6.24826744 -0.72018435 0.07301222 0.08632860 0.03391349 -0.15246141 0.14326701 -0.17910228 +Al 1.97983812 4.10452836 6.11160637 -0.57463206 0.61689164 0.28871923 1.94771696 4.13587640 6.16413404 0.03963949 -0.07061201 0.48028331 0.01173459 0.13290160 -0.16687558 0.24062884 +Al 1.94573769 6.05014443 4.19139282 -1.41026781 0.37083548 0.24236961 1.86084557 6.09813012 4.16931790 -0.03314649 0.25956277 -0.59576932 0.03391715 0.31065990 0.18796839 -0.31160774 +Al 4.07076995 -0.02019098 0.15472006 0.27146475 0.15993975 0.19035691 4.07882182 0.00677076 0.10893728 -0.18806050 0.25676481 -0.97901444 0.05183028 -0.04269523 0.10126469 -0.37168808 +Al 4.11409461 2.04203469 2.13012541 0.31081755 -0.02257674 0.39023380 4.09698915 2.04241337 2.13967434 -0.67562370 0.07071069 -0.19576034 0.01849459 -0.23017099 -0.01318411 -0.18292420 +Al 5.93331369 -0.08370514 2.07968937 -0.83191229 -0.22731745 -0.45711485 5.93247690 -0.07858327 2.09892254 0.89349890 0.46046371 0.84058752 0.03547313 0.41018630 0.06012462 0.32619196 +Al 6.16150442 1.89159061 0.16804059 -0.33829256 -1.17602453 1.23748826 6.10748533 1.87416468 0.29330146 -0.59206701 0.84707729 0.79441788 0.02401281 -0.12497142 0.56777704 0.07678397 +Al 3.87722111 0.10313340 4.18873592 -0.15217493 -0.27151528 -1.14257570 3.92045497 0.06163327 4.07252328 0.91223561 -0.47767476 -0.79626074 0.02068391 0.36984144 -0.07058954 -0.09454685 +Al 4.11356789 2.15132489 6.16104690 1.24434655 -0.21610760 0.00815013 4.21848770 2.13036532 6.14141824 0.41114079 -0.10240664 -0.23353929 0.01320282 0.00686839 -0.04902407 -0.18646135 +Al 5.88112726 0.07078982 6.23781975 -1.00302767 1.67088185 0.14582247 5.83816546 0.19180441 6.18106669 0.41228672 0.38450265 -1.11629972 0.03781755 0.27659027 -0.23868751 -0.42541641 +Al 6.20761334 2.07960042 3.88674503 -0.51467342 1.28628043 -0.13875943 6.13965224 2.17354209 3.95807566 -0.59199442 0.20549863 1.31688692 0.04506028 -0.20682894 -0.07030927 0.54440327 +Al 4.23271569 4.05833408 0.04915904 0.29191187 -0.35947221 -0.13961150 4.21364971 4.02582056 0.04027400 -0.63015129 -0.22185216 -0.01522223 0.01553884 -0.30541546 0.02468783 0.01859276 +Al 4.15892190 6.01897958 2.13387411 1.21149386 1.69843909 -0.27506941 4.22484888 6.13119549 2.12010265 -0.09500864 0.04043648 0.08435994 0.04838400 -0.30999667 -0.11359591 0.00080967 +Al 6.08490501 4.06392335 2.14781591 -0.36585391 -0.69472622 -0.98088513 6.09355870 4.00713122 1.99412437 0.42926307 -0.18877520 -1.56403723 0.06658670 0.35172883 -0.02878338 -0.42193113 +Al 6.18973320 6.01762233 0.15892707 0.73797405 0.52137499 0.37754255 6.22427957 6.11304095 0.15661615 -0.13062756 1.01140828 -0.52108241 0.03404906 -0.27780295 0.39168594 -0.17430482 +Al 4.15864148 4.12972052 4.07699427 0.64123928 -1.20563015 1.63272963 4.17757328 4.00448317 4.26864029 -0.21320757 -0.73021792 1.51694048 0.03561012 -0.36476290 -0.23650006 0.32937773 +Al 3.95865176 6.21074962 6.13660947 1.37174879 -0.99341933 0.10773843 4.12732324 6.06330384 6.15537448 1.55587482 -1.46599085 0.20539925 0.01435656 0.22762121 -0.39890259 0.07316909 +Al 6.22342951 4.07849676 6.29671196 0.58779115 -0.04362041 -0.00625911 6.25680380 4.06583361 6.25279967 -0.05038495 -0.04702882 -0.57699152 0.03400607 -0.15967021 -0.12221934 -0.42208732 +Al 6.10527835 5.95295029 4.16772913 0.26533035 0.53413487 0.46628833 6.12933970 6.08283506 4.22722171 0.07501366 1.47421443 0.46320732 0.03556289 0.03036121 0.64819608 0.16029033 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=0.8951962562931346 free_energy=0.8951962562931346 stress="0.006762199627886309 0.0004169919220461864 -0.0008031643384668323 0.0004169919220461864 0.005747662582846801 -0.000428147773379447 -0.0008031643384668323 -0.000428147773379447 0.004994554919625807" pbc="T T T" +Al 0.04030615 0.09171277 0.07398912 0.38636985 -0.26785991 -1.18800449 0.06581519 0.04879040 -0.01555996 0.01961857 -0.34441577 -0.41055198 0.02629904 -0.04086984 -0.20727226 0.19639264 +Al -0.12171355 2.17383175 2.12351050 1.74966288 -0.20615385 -0.60071966 0.08038219 2.12948842 2.07436373 1.67722418 -0.80460017 -0.11102183 0.02141505 0.26516093 0.02322641 -0.07555855 +Al 2.03765560 -0.01002666 1.97078838 -0.62320780 -0.94937909 0.88574523 1.95879126 -0.08023619 2.10366340 -0.68772421 -0.33659151 1.23440452 0.02093106 -0.13340948 0.20230105 0.42010620 +Al 2.00171901 2.14455269 0.15523686 0.00321539 0.51057209 -0.48895757 2.04587205 2.12931995 0.08137624 0.87260899 -0.88441054 -0.86187432 0.02624709 0.19260231 -0.31137041 -0.12425624 +Al -0.09804956 0.06403908 3.98361968 -0.68929423 0.25759542 0.04306192 -0.12152908 0.05559522 4.00783576 0.10720760 -0.27312751 0.40484185 0.02128379 0.40046616 -0.30232589 0.12058408 +Al 0.15539836 1.97759483 6.24745943 0.87733745 0.17902510 -0.97164565 0.19943780 2.01780899 6.10701190 -0.20382960 0.54310498 -1.41613739 0.02505161 -0.18748584 0.14341215 -0.30518330 +Al 1.98624033 -0.15814989 6.19826227 -0.60773897 0.07461010 0.32927300 1.96694088 -0.10344309 6.19559125 0.40128717 0.84846884 -0.41404884 0.02540262 0.14631467 0.32192273 -0.18883617 +Al 2.04642751 1.89297675 4.15788683 -0.62869663 -0.13074701 -0.76423065 1.96736042 1.91065810 4.06338249 -0.63139454 0.52782311 -0.79878893 0.01199395 -0.15239640 0.13096791 -0.16119262 +Al -0.01747146 4.10007167 0.08539584 0.90325506 -1.61264617 1.64213071 0.12587224 3.97546132 0.23412357 1.39754803 -0.36322689 0.82907806 0.04763147 0.47137546 0.01176526 -0.01225831 +Al -0.10141215 5.99272522 2.07955022 0.61617945 1.31755784 -0.56483227 0.00340532 6.12824527 2.05016579 1.05375268 0.99523202 -0.05685841 0.01357684 0.37229650 0.05506781 0.22206913 +Al 2.12914650 3.94779452 2.14985644 0.86537330 -0.37586189 -0.99826326 2.20524110 3.96277242 2.08137541 0.63507760 0.61405245 -0.14791065 0.00930486 -0.18502388 0.32459501 0.12354929 +Al 2.10070277 6.13114012 0.16554730 0.10690922 -0.45122988 -0.00453352 2.06587039 6.02516082 0.11880134 -0.65364368 -1.17912109 -0.79129445 0.03197627 -0.32541784 -0.50132716 -0.30043325 +Al -0.00868798 3.98356535 4.22366706 -0.05772660 1.19852707 -0.83847878 -0.00235878 4.11774520 4.10616391 0.07879199 1.08551819 -1.21673047 0.02067019 0.14274806 0.13289078 -0.23181123 +Al -0.00632027 6.12440168 6.00449636 -0.28596208 1.22293318 0.12824325 -0.00767530 6.20129015 6.04712119 0.22000838 0.02427421 0.69871558 0.02763927 0.20998319 -0.17951572 0.13515265 +Al 2.16945475 3.98020268 6.04987816 0.57012647 0.10567414 -0.00825937 2.17804046 4.05003176 6.08039845 -0.50270234 1.13340743 0.36505972 0.02098777 -0.26108484 0.34398379 0.31495971 +Al 2.16814667 6.02303491 4.08164440 -1.14321789 -0.40270175 -0.79903788 2.01239751 6.01421621 3.99371947 -1.47880632 0.19508605 -0.58124010 0.00548880 -0.33213446 0.23488200 -0.14431356 +Al 4.08252758 -0.04479429 0.01262739 0.31174153 -0.51720945 0.16647834 4.11405658 -0.02918545 0.03730931 0.03406929 0.72011542 0.32169577 0.02708811 0.13870641 0.44236477 -0.00466326 +Al 4.11235350 1.97171785 2.18742279 -1.32958999 -1.52447359 0.18497931 3.96856818 1.83732052 2.16108047 -1.08780885 -0.76436510 -0.62167924 0.02836949 -0.14933564 0.02861238 -0.28262655 +Al 6.08381770 0.12544549 1.99268556 -0.44145584 -1.12110738 -0.60654371 6.05551529 -0.01294167 1.94669499 -0.02640194 -1.16919255 -0.05117513 0.02299619 0.06752537 -0.27914362 0.01791927 +Al 6.16610854 2.17394690 0.06744149 0.51495135 1.00235602 0.82543738 6.16218986 2.19148373 0.16019636 -0.53621336 -0.89440095 0.86565878 0.02532385 -0.35542614 -0.33718829 -0.00116998 +Al 4.07349123 0.06111168 4.08523602 -0.52503638 0.25400097 0.79263136 4.03559140 0.03489630 4.18004288 -0.24119216 -0.71501090 0.79505778 0.01948209 0.15015112 -0.28683814 0.13270393 +Al 4.04105891 1.96200610 6.13931792 -0.23991478 0.83969869 -0.56219122 4.03459946 2.03956322 6.10684676 0.01169608 0.46076989 0.00278711 -0.00287159 0.16588633 0.01577100 0.12521895 +Al 6.19833383 0.00456750 6.17281595 -1.73641053 -1.22728472 -0.44627987 6.02227943 -0.05614444 6.11849488 -1.14910064 0.42125459 -0.47054757 0.01187952 -0.15250100 0.17639050 -0.07632223 +Al 6.17546528 2.00658843 4.16939598 -0.89303301 0.41757900 0.13320522 6.04807583 2.08194629 4.18477719 -1.15524930 0.79987472 -0.03870507 0.02492388 -0.37832236 0.26108141 0.13908683 +Al 4.02300101 4.05460594 0.10407229 -0.08466468 0.88580504 -1.05547603 4.01649545 4.15694469 -0.05446419 0.09875659 0.87810922 -1.46360084 0.03982559 -0.07081709 0.10336648 -0.47897970 +Al 4.13349863 6.23926263 2.00253602 -0.32231191 -1.31722110 0.64706809 4.06992697 6.11066867 2.09916276 -0.71747734 -0.99336096 1.03186059 0.06579938 -0.22099494 0.01163989 0.19237468 +Al 5.67385897 4.18246137 2.07058895 0.51978493 -0.13990880 -0.51579553 5.87648240 4.10437225 2.02638211 2.79126132 -1.08325026 -0.21472952 0.12375417 1.11265999 -0.44058548 0.01588787 +Al 6.27963843 6.07074260 0.02249205 -0.14107743 0.95290867 0.00736737 6.20537212 6.16858980 0.02929378 -1.00616679 0.50341561 0.12020389 0.03831953 -0.51261400 0.21842855 0.03203579 +Al 4.07926200 4.08278615 4.13191192 1.64531526 1.38040234 1.10297444 4.24425510 4.18878201 4.27058686 1.15037549 0.54752722 1.18204432 0.05376507 0.06859666 -0.23184758 0.27856857 +Al 4.18633985 6.20421370 6.11489840 0.55329629 1.07419882 0.39805994 4.20374882 6.23617210 6.15914449 -0.48551820 -0.60066098 0.37044394 0.00953607 -0.07119094 -0.39193268 0.05899364 +Al 6.17803387 4.05672476 6.25146659 -0.38203238 -1.59931754 1.76792776 6.11338408 3.91725158 6.36368277 -0.45984604 -0.68749219 0.23439178 0.02796470 -0.36416804 -0.01186680 -0.40726781 +Al 6.07589854 6.00963866 4.02135256 0.50785270 0.16965766 1.35866614 6.12982596 6.06786846 4.17828535 0.47379136 0.79519342 1.21065105 0.02314055 -0.01128042 0.29854411 0.26926950 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=0.9510229816315583 free_energy=0.9510229816315583 stress="0.005355766511518642 -0.00015898575467537403 0.0002944926131876724 -0.00015898575467537403 0.005196658301976934 0.0003584193039226458 0.0002944926131876724 0.0003584193039226458 0.005599291602127005" pbc="T T T" +Al 0.06581519 0.04879040 -0.01555996 -0.01961857 0.34441577 0.41055198 0.04030615 0.09171277 0.07398912 -0.38636985 0.26785991 1.18800449 0.01576916 -0.15968619 0.13283827 0.27661730 +Al 0.08038219 2.12948842 2.07436373 -1.67722418 0.80460017 0.11102183 -0.12171355 2.17383175 2.12351050 -1.74966288 0.20615385 0.60071966 0.04460707 -0.28255525 -0.35259785 0.28011178 +Al 1.95879126 -0.08023619 2.10366340 0.68772421 0.33659151 -1.23440452 2.03765560 -0.01002666 1.97078838 0.62320780 0.94937909 -0.88574523 0.02359368 0.13992990 0.21201345 -0.19557014 +Al 2.04587205 2.12931995 0.08137624 -0.87260899 0.88441054 0.86187432 2.00171901 2.14455269 0.15523686 -0.00321539 -0.51057209 0.48895757 0.03279676 0.30898664 -0.48741908 -0.11215886 +Al -0.12152908 0.05559522 4.00783576 -0.10720760 0.27312751 -0.40484185 -0.09804956 0.06403908 3.98361968 0.68929423 -0.25759542 -0.04306192 0.01347739 0.10973139 -0.03677510 0.13421175 +Al 0.19943780 2.01780899 6.10701190 0.20382960 -0.54310498 1.41613739 0.15539836 1.97759483 6.24745943 -0.87733745 -0.17902510 0.97164565 0.01909485 -0.45547710 0.11054971 0.05347960 +Al 1.96694088 -0.10344309 6.19559125 -0.40128717 -0.84846884 0.41404884 1.98624033 -0.15814989 6.19826227 0.60773897 -0.07461010 -0.32927300 0.02848132 0.42244280 0.16448257 -0.25993558 +Al 1.96736042 1.91065810 4.06338249 0.63139454 -0.52782311 0.79878893 2.04642751 1.89297675 4.15788683 0.62869663 0.13074701 0.76423065 0.03727213 0.21379297 0.27802780 0.17032791 +Al 0.12587224 3.97546132 0.23412357 -1.39754803 0.36322689 -0.82907806 -0.01747146 4.10007167 0.08539584 -0.90325506 1.61264617 -1.64213071 0.06199020 -0.16357235 0.60377604 -0.50190637 +Al 0.00340532 6.12824527 2.05016579 -1.05375268 -0.99523202 0.05685841 -0.10141215 5.99272522 2.07955022 -0.61617945 -1.31755784 0.56483227 0.02054842 -0.12371944 -0.27899698 0.05954010 +Al 2.20524110 3.96277242 2.08137541 -0.63507760 -0.61405245 0.14791065 2.12914650 3.94779452 2.14985644 -0.86537330 0.37586189 0.99826326 0.02316608 0.01740835 0.24796754 0.33990223 +Al 2.06587039 6.02516082 0.11880134 0.65364368 1.17912109 0.79129445 2.10070277 6.13114012 0.16554730 -0.10690922 0.45122988 0.00453352 0.00840819 -0.14770860 0.10625320 -0.18062693 +Al -0.00235878 4.11774520 4.10616391 -0.07879199 -1.08551819 1.21673047 -0.00868798 3.98356535 4.22366706 0.05772660 -1.19852707 0.83847878 0.00081534 -0.04712535 -0.21330453 -0.03399417 +Al -0.00767530 6.20129015 6.04712119 -0.22000838 -0.02427421 -0.69871558 -0.00632027 6.12440168 6.00449636 0.28596208 -1.22293318 -0.12824325 0.03753049 0.12671702 -0.50446786 0.22636876 +Al 2.17804046 4.05003176 6.08039845 0.50270234 -1.13340743 -0.36505972 2.16945475 3.98020268 6.04987816 -0.57012647 -0.10567414 0.00825937 0.02336355 -0.43951671 0.25837907 -0.08081847 +Al 2.01239751 6.01421621 3.99371947 1.47880632 -0.19508605 0.58124010 2.16814667 6.02303491 4.08164440 1.14321789 0.40270175 0.79903788 0.01082547 0.12696809 0.14945724 0.31764973 +Al 4.11405658 -0.02918545 0.03730931 -0.03406929 -0.72011542 -0.32169577 4.08252758 -0.04479429 0.01262739 -0.31174153 0.51720945 -0.16647834 0.01906301 -0.30466960 0.27693186 0.05877250 +Al 3.96856818 1.83732052 2.16108047 1.08780885 0.76436510 0.62167924 4.11235350 1.97171785 2.18742279 1.32958999 1.52447359 -0.18497931 0.03263914 0.27954480 0.38528529 -0.18614564 +Al 6.05551529 -0.01294167 1.94669499 0.02640194 1.16919255 0.05117513 6.08381770 0.12544549 1.99268556 0.44145584 1.12110738 0.60654371 0.02332039 0.16683356 0.20183902 0.37917529 +Al 6.16218986 2.19148373 0.16019636 0.53621336 0.89440095 -0.86565878 6.16610854 2.17394690 0.06744149 -0.51495135 -1.00235602 -0.82543738 0.06315257 -0.25916765 -0.74877786 -0.03497495 +Al 4.03559140 0.03489630 4.18004288 0.24119216 0.71501090 -0.79505778 4.07349123 0.06111168 4.08523602 0.52503638 -0.25400097 -0.79263136 0.02223663 0.01150428 -0.25588228 -0.16690466 +Al 4.03459946 2.03956322 6.10684676 -0.01169608 -0.46076989 -0.00278711 4.04105891 1.96200610 6.13931792 0.23991478 -0.83969869 0.56219122 0.01165805 -0.04428177 -0.21964557 0.19817130 +Al 6.02227943 -0.05614444 6.11849488 1.14910064 -0.42125459 0.47054757 6.19833383 0.00456750 6.17281595 1.73641053 1.22728472 0.44627987 0.06512900 0.49711281 0.76615985 0.09028909 +Al 6.04807583 2.08194629 4.18477719 1.15524930 -0.79987472 0.03870507 6.17546528 2.00658843 4.16939598 0.89303301 -0.41757900 -0.13320522 0.00438215 0.18170041 -0.05711852 -0.23361038 +Al 4.01649545 4.15694469 -0.05446419 -0.09875659 -0.87810922 1.46360084 4.02300101 4.05460594 0.10407229 0.08466468 -0.88580504 1.05547603 0.04556192 0.18355018 -0.12843955 0.27279422 +Al 4.06992697 6.11066867 2.09916276 0.71747734 0.99336096 -1.03186059 4.13349863 6.23926263 2.00253602 0.32231191 1.31722110 -0.64706809 0.01208245 0.02307459 0.12557888 0.01444798 +Al 5.87648240 4.10437225 2.02638211 -2.79126132 1.08325026 0.21472952 5.67385897 4.18246137 2.07058895 -0.51978493 0.13990880 0.51579553 0.01720789 0.34241871 -0.05139472 0.17649261 +Al 6.20537212 6.16858980 0.02929378 1.00616679 -0.50341561 -0.12020389 6.27963843 6.07074260 0.02249205 0.14107743 -0.95290867 -0.00736737 0.02524995 -0.09127068 -0.43879120 0.03037808 +Al 4.24425510 4.18878201 4.27058686 -1.15037549 -0.54752722 -1.18204432 4.07926200 4.08278615 4.13191192 -1.64531526 -1.38040234 -1.10297444 0.04393131 -0.38433958 -0.26739960 -0.22946120 +Al 4.20374882 6.23617210 6.15914449 0.48551820 0.60066098 -0.37044394 4.18633985 6.20421370 6.11489840 -0.55329629 -1.07419882 -0.39805994 0.07028986 -0.53670625 -0.58233609 -0.02857253 +Al 6.11338408 3.91725158 6.36368277 0.45984604 0.68749219 -0.23439178 6.17803387 4.05672476 6.25146659 0.38203238 1.59931754 -1.76792776 0.05020167 0.31411432 0.50908613 -0.50588602 +Al 6.12982596 6.06786846 4.17828535 -0.47379136 -0.79519342 -1.21065105 6.07589854 6.00963866 4.02135256 -0.50785270 -0.16965766 -1.35866614 0.04317689 -0.02603431 0.09472086 -0.32816435 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.1675676068749237 free_energy=1.1675676068749237 stress="0.004908787662223326 -0.00033893401826187074 -0.00032429419235869704 -0.00033893401826187074 0.003913777206277226 -0.0007783761478527335 -0.00032429419235869704 -0.0007783761478527335 0.005075587681380582" pbc="T T T" +Al -0.00185302 0.02426664 0.16434278 0.12720369 -0.18378306 0.97776962 0.04317956 -0.00732802 0.21327814 0.53704185 -0.35326694 -0.12768044 0.02478353 0.23577860 -0.08203303 -0.27762373 +Al 0.01515044 2.07047049 2.12761419 -0.54305909 1.08927945 -0.02476894 -0.07414190 2.16810837 2.11370144 -0.92590854 0.60312353 -0.16969529 0.02646097 -0.24940757 -0.06215032 -0.10220150 +Al 2.12911798 0.06413381 2.11670377 -0.63193583 -0.88833950 -0.73418424 2.05568077 -0.06543782 2.01649404 -0.71430213 -1.06142925 -0.85744623 0.03271342 -0.03776546 -0.44659353 -0.28475965 +Al 2.04692238 2.04879653 0.23296122 0.82191423 -0.39734617 0.89358241 2.10467880 2.02652171 0.26734181 0.14311315 0.07710744 -0.32285886 0.03616315 -0.10735262 0.04534988 -0.31003431 +Al 0.07292698 0.00331094 4.01254402 -0.59531092 -0.61308493 -1.01977183 -0.00335281 -0.04100425 3.96620129 -0.66947288 -0.06458096 0.13750223 0.03282747 -0.14161936 0.01479325 0.37205277 +Al -0.05651734 1.90310706 6.22378422 0.46654326 -0.63291996 0.22779528 0.04412518 1.85090503 6.24204019 1.27846986 -0.29405123 0.12375454 0.01548789 0.32372064 0.09764366 -0.05639423 +Al 2.18769487 0.12011645 6.37437933 1.15993144 -0.78009484 0.96318082 2.25691278 0.00348286 6.41013064 -0.11834806 -1.25356898 -0.34525565 0.01688474 -0.17966655 -0.22714995 -0.40023312 +Al 2.00415971 1.90910936 4.04193885 -1.17122376 0.07582396 0.31190970 1.91023702 1.97021005 4.12805772 -0.49516514 0.73356827 1.00317123 0.03434012 0.14255335 0.51860156 0.46947175 +Al -0.25239450 4.10335980 0.02242564 -0.69683066 1.62570703 -0.32339798 -0.18710041 4.23784600 -0.01968754 1.85059825 0.64667348 -0.33199462 0.05784604 0.77381162 -0.08468665 -0.13149140 +Al -0.01215844 6.16217913 2.15758673 0.11013689 0.10018702 0.73447951 0.02853716 6.11675336 2.17225547 0.55742211 -0.97767944 -0.48468735 0.04879425 0.17788003 -0.27367699 -0.31312362 +Al 1.77529919 4.10550461 1.97620131 0.41382126 -0.67263535 0.28007888 1.88149467 4.03509934 2.04652599 1.43232492 -0.49658820 0.90008700 0.04690850 0.43369480 -0.07990822 0.31127974 +Al 2.02788973 5.98343987 0.07453357 -0.26863157 -0.55294163 -0.51026941 1.96741618 6.01044886 0.03400322 -0.73176476 0.76760756 -0.19601635 0.04568437 -0.18122048 0.63224725 0.04079831 +Al 0.03581419 4.00324193 4.22211570 0.27420213 0.71951715 1.26050839 0.08433936 4.08900385 4.35250833 0.42766594 0.60688305 0.84216730 0.00970041 0.19683984 0.18607845 0.09661851 +Al 0.10862960 6.18459276 6.09922979 1.58546667 -0.98017218 -1.38608442 0.26764130 6.08208888 5.96728535 1.12358099 -0.59757810 -0.83462249 -0.00100301 0.04895198 -0.15932505 -0.00060835 +Al 2.17990164 4.02680262 6.31606666 0.44723311 0.08849840 -0.26741732 2.13417363 3.98587652 6.18938034 -0.98552785 -0.77306952 -1.60734689 0.06492956 -0.68840023 -0.28622209 -0.78400965 +Al 2.07995963 6.02822313 4.03364142 0.57025551 -0.22930274 -0.65458184 2.13760259 6.04368598 4.00481849 0.47447187 0.48658019 0.09740398 0.01668477 -0.01513304 0.24503464 0.25468046 +Al 4.27054899 0.22143854 0.01660427 0.06118528 1.70818804 0.13362958 4.24464168 0.28650627 0.05614728 -0.40179527 -0.61986983 0.47823966 0.05659592 -0.30523991 -0.59143380 0.19369338 +Al 4.07664153 1.98663560 2.14441130 0.06417520 -0.76922333 -0.53474677 4.11325620 1.96361281 2.10187921 0.46184229 0.36275948 -0.04651572 0.02175900 0.25101132 0.33421525 -0.07072516 +Al 6.17171796 -0.01228142 1.97264767 -0.38881147 0.59572477 -0.81452230 6.08468826 0.06627537 1.96578331 -0.97797324 0.72849580 0.64438343 0.03532097 -0.35863836 0.14988671 0.50180839 +Al 6.15807277 2.09675177 0.05468414 0.40063793 0.73764000 -0.44812065 6.16710095 2.16376533 0.02441337 -0.18280135 0.46448263 -0.16731328 0.05807746 -0.23274447 -0.07558895 0.13259220 +Al 4.16097172 0.13497395 4.19020531 -0.02362303 -0.48826234 0.37275462 4.12378863 0.02963705 4.16655131 -0.61938066 -1.25530625 -0.76329818 0.04012127 -0.20597200 -0.41513788 -0.36259576 +Al 4.21794216 2.04681036 6.13715320 -1.16793728 -1.46010669 0.07390457 4.05627445 1.94029903 6.12659085 -1.50241383 -0.26633184 -0.27493638 0.06002539 -0.41023425 0.07874366 -0.09253041 +Al 5.99547231 -0.24515960 6.00903860 0.56026203 -0.68877577 -0.27697202 6.13481543 -0.24111003 6.02398672 1.78596149 0.69732803 0.53149961 0.07150722 0.55520675 0.52240333 0.25974533 +Al 6.23570967 2.11009799 4.24484450 -0.19907115 0.19874853 0.94092219 6.16936911 2.08792428 4.27794876 -0.88830542 -0.54928839 -0.21758544 0.03992958 -0.34185769 -0.29049410 -0.46468112 +Al 3.93007466 4.10046340 0.03181401 0.03098557 1.49654007 -0.50491144 4.04497018 4.19949967 0.07129031 1.72663821 0.25895382 0.99190441 0.07235608 0.79722560 -0.29198894 0.64980201 +Al 4.06922429 6.09154755 2.01019928 -1.54673877 1.27925131 -0.40728082 3.94069600 6.24228568 1.96361284 -0.55756108 1.18992474 -0.39526222 0.00296661 0.05598775 0.25053795 -0.04749189 +Al 6.06034779 4.09099328 2.11504827 0.20450501 -0.15112673 1.10530144 6.08149030 4.05429204 2.20193983 0.09331740 -0.40100700 0.42272420 0.01420419 0.07474584 -0.18139013 -0.13994017 +Al 6.17281603 6.07611340 -0.01198680 0.45516323 -0.25690963 -0.20399511 6.14803655 6.09298262 -0.00285753 -0.87385972 0.65027837 0.40420258 0.03562887 -0.40138833 0.13992465 0.16507872 +Al 4.02008785 4.12273212 3.94214357 -0.67106962 -0.77308791 0.43758277 3.94465049 3.99777711 4.02421544 -0.55242949 -1.33540912 0.96216734 0.02024145 -0.11776071 -0.34871473 0.28078714 +Al 4.08970161 6.00429343 6.11167176 -0.43365585 0.45554041 0.02111044 4.01401383 6.08616142 6.10372337 -0.73265454 0.85275676 -0.13980305 0.04281139 -0.31083308 0.28390454 -0.04738626 +Al 5.90646120 4.04704461 6.11929009 0.40417930 -0.09248174 0.71659416 5.99282082 4.07623325 6.16536055 0.94135989 0.57743653 -0.02512404 0.05472461 0.45436064 0.27419845 -0.06612105 +Al 6.16789697 6.00738288 4.01321235 0.18009723 0.43994835 -1.34007928 6.13219381 6.06809034 3.92213019 -0.90414425 0.59506537 -0.23176503 0.03209141 -0.23653464 0.12293115 0.22354268 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.0779681848667755 free_energy=1.0779681848667755 stress="0.005570476181737316 0.0008721194749470843 -0.0006003171921574077 0.0008721194749470843 0.004591245131958984 -0.0016003314761296812 -0.0006003171921574077 -0.0016003314761296812 0.006566874217938637" pbc="T T T" +Al 0.04317956 -0.00732802 0.21327814 -0.53704185 0.35326694 0.12768044 -0.00185302 0.02426664 0.16434278 -0.12720369 0.18378306 -0.97776962 0.02239671 -0.07553828 0.00070951 -0.37807752 +Al -0.07414190 2.16810837 2.11370144 0.92590854 -0.60312353 0.16969529 0.01515044 2.07047049 2.12761419 0.54305909 -1.08927945 0.02476894 0.02301890 0.07672844 -0.25089499 0.01228203 +Al 2.05568077 -0.06543782 2.01649404 0.71430213 1.06142925 0.85744623 2.12911798 0.06413381 2.11670377 0.63193583 0.88833950 0.73418424 0.05008443 -0.03642079 0.37924914 0.19624098 +Al 2.10467880 2.02652171 0.26734181 -0.14311315 -0.07710744 0.32285886 2.04692238 2.04879653 0.23296122 -0.82191423 0.39734617 -0.89358241 0.03592085 -0.28639518 0.21829333 -0.42589864 +Al -0.00335281 -0.04100425 3.96620129 0.66947288 0.06458096 -0.13750223 0.07292698 0.00331094 4.01254402 0.59531092 0.61308493 1.01977183 0.03551378 0.11675540 0.26821401 0.30347234 +Al 0.04412518 1.85090503 6.24204019 -1.27846986 0.29405123 -0.12375454 -0.05651734 1.90310706 6.22378422 -0.46654326 0.63291996 -0.22779528 0.00589069 0.13162838 0.15419406 -0.01786246 +Al 2.25691278 0.00348286 6.41013064 0.11834806 1.25356898 0.34525565 2.18769487 0.12011645 6.37437933 -1.15993144 0.78009484 -0.96318082 0.06410727 -0.61097444 -0.00536474 -0.46713461 +Al 1.91023702 1.97021005 4.12805772 0.49516514 -0.73356827 -1.00317123 2.00415971 1.90910936 4.04193885 1.17122376 -0.07582396 -0.31190970 -0.00334055 0.25471921 -0.09553707 -0.05018883 +Al -0.18710041 4.23784600 -0.01968754 -1.85059825 -0.64667348 0.33199462 -0.25239450 4.10335980 0.02242564 0.69683066 -1.62570703 0.32339798 0.05740507 0.68343079 -0.45841369 0.08957556 +Al 0.02853716 6.11675336 2.17225547 -0.55742211 0.97767944 0.48468735 -0.01215844 6.16217913 2.15758673 -0.11013689 -0.10018702 -0.73447951 0.04668176 0.02407138 -0.35997873 -0.34209595 +Al 1.88149467 4.03509934 2.04652599 -1.43232492 0.49658820 -0.90008700 1.77529919 4.10550461 1.97620131 -0.41382126 0.67263535 -0.28007888 0.02303169 0.23621631 0.15463526 0.09872497 +Al 1.96741618 6.01044886 0.03400322 0.73176476 -0.76760756 0.19601635 2.02788973 5.98343987 0.07453357 0.26863157 0.55294163 0.51026941 0.04230172 0.02850741 0.10221535 0.12050694 +Al 0.08433936 4.08900385 4.35250833 -0.42766594 -0.60688305 -0.84216730 0.03581419 4.00324193 4.22211570 -0.27420213 -0.71951715 -1.26050839 0.03931527 -0.17361967 -0.28984702 -0.47332818 +Al 0.26764130 6.08208888 5.96728535 -1.12358099 0.59757810 0.83462249 0.10862960 6.18459276 6.09922979 -1.58546667 0.98017218 1.38608442 0.05385198 -0.35716948 0.43455739 0.38657695 +Al 2.13417363 3.98587652 6.18938034 0.98552785 0.77306952 1.60734689 2.17990164 4.02680262 6.31606666 -0.44723311 -0.08849840 0.26741732 0.01678674 -0.12570206 -0.15944670 0.06259454 +Al 2.13760259 6.04368598 4.00481849 -0.47447187 -0.48658019 -0.09740398 2.07995963 6.02822313 4.03364142 -0.57025551 0.22930274 0.65458184 0.03949359 -0.04210938 0.19021848 0.19446536 +Al 4.24464168 0.28650627 0.05614728 0.40179527 0.61986983 -0.47823966 4.27054899 0.22143854 0.01660427 -0.06118528 -1.70818804 -0.13362958 0.10752855 -0.05256253 -0.79250132 -0.03239122 +Al 4.11325620 1.96361281 2.10187921 -0.46184229 -0.36275948 0.04651572 4.07664153 1.98663560 2.14441130 -0.06417520 0.76922333 0.53474677 0.03412090 -0.03277463 0.36315719 0.30675432 +Al 6.08468826 0.06627537 1.96578331 0.97797324 -0.72849580 -0.64438343 6.17171796 -0.01228142 1.97264767 0.38881147 -0.59572477 0.81452230 0.03498198 0.07580074 -0.07192954 0.41289466 +Al 6.16710095 2.16376533 0.02441337 0.18280135 -0.46448263 0.16731328 6.15807277 2.09675177 0.05468414 -0.40063793 -0.73764000 0.44812065 0.04139852 -0.13291746 -0.12764716 0.01418213 +Al 4.12378863 0.02963705 4.16655131 0.61938066 1.25530625 0.76329818 4.16097172 0.13497395 4.19020531 0.02362303 0.48826234 -0.37275462 0.00878155 -0.13880643 -0.03606089 -0.28214339 +Al 4.05627445 1.94029903 6.12659085 1.50241383 0.26633184 0.27493638 4.21794216 2.04681036 6.13715320 1.16793728 1.46010669 -0.07390457 0.05392076 0.16209448 0.51354399 -0.09288679 +Al 6.13481543 -0.24111003 6.02398672 -1.78596149 -0.69732803 -0.53149961 5.99547231 -0.24515960 6.00903860 -0.56026203 0.68877577 0.27697202 0.02346474 0.15307512 0.38606855 0.22935264 +Al 6.16936911 2.08792428 4.27794876 0.88830542 0.54928839 0.21758544 6.23570967 2.11009799 4.24484450 0.19907115 -0.19874853 -0.94092219 0.00696993 -0.09571687 -0.18340230 -0.24252026 +Al 4.04497018 4.19949967 0.07129031 -1.72663821 -0.25895382 -0.99190441 3.93007466 4.10046340 0.03181401 -0.03098557 -1.49654007 0.50491144 0.01144029 0.15355988 -0.42200494 0.22050861 +Al 3.94069600 6.24228568 1.96361284 0.55756108 -1.18992474 0.39526222 4.06922429 6.09154755 2.01019928 1.54673877 -1.27925131 0.40728082 0.03283755 0.54823779 -0.33280892 0.05473488 +Al 6.08149030 4.05429204 2.20193983 -0.09331740 0.40100700 -0.42272420 6.06034779 4.09099328 2.11504827 -0.20450501 0.15112673 -1.10530144 0.02501352 -0.10734021 0.06167712 -0.26580084 +Al 6.14803655 6.09298262 -0.00285753 0.87385972 -0.65027837 -0.40420258 6.17281603 6.07611340 -0.01198680 -0.45516323 0.25690963 0.20399511 0.04460258 -0.32200083 0.29799099 0.22714580 +Al 3.94465049 3.99777711 4.02421544 0.55242949 1.33540912 -0.96216734 4.02008785 4.12273212 3.94214357 0.67106962 0.77308791 -0.43758277 0.00567062 0.20095701 0.01404996 0.07651259 +Al 4.01401383 6.08616142 6.10372337 0.73265454 -0.85275676 0.13980305 4.08970161 6.00429343 6.11167176 0.43365585 -0.45554041 -0.02111044 0.02286111 0.12880412 -0.09543934 0.00432424 +Al 5.99282082 4.07623325 6.16536055 -0.94135989 -0.57743653 0.02512404 5.90646120 4.04704461 6.11929009 -0.40417930 0.09248174 -0.71659416 0.03070519 -0.01419711 0.17706290 -0.35908659 +Al 6.13219381 6.06809034 3.92213019 0.90414425 -0.59506537 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+ +.. only:: html + + .. image:: /examples/programmatic/flashmd/images/thumb/sphx_glr_flashmd_thumb.png + :alt: + + :ref:`sphx_glr_examples_programmatic_flashmd_flashmd.py` + +.. raw:: html + +
Training a FlashMD model
+
+ + +.. thumbnail-parent-div-close + +.. raw:: html + + + + +.. toctree:: + :hidden: + + /examples/programmatic/flashmd/flashmd + + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-gallery + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download all examples in Python source code: flashmd_python.zip ` + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download all examples in Jupyter notebooks: flashmd_jupyter.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/examples/programmatic/flashmd/options.yaml b/docs/src/examples/programmatic/flashmd/options.yaml similarity index 100% rename from examples/programmatic/flashmd/options.yaml rename to docs/src/examples/programmatic/flashmd/options.yaml diff --git a/docs/src/examples/programmatic/use_architectures_outside/images/thumb/sphx_glr_use_outside_thumb.png 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+ +.. only:: html + + .. image:: /examples/programmatic/use_architectures_outside/images/thumb/sphx_glr_use_outside_thumb.png + :alt: + + :ref:`sphx_glr_examples_programmatic_use_architectures_outside_use_outside.py` + +.. raw:: html + +
Using metatrain architectures outside of metatrain
+
+ + +.. thumbnail-parent-div-close + +.. raw:: html + + + + +.. toctree:: + :hidden: + + /examples/programmatic/use_architectures_outside/use_outside + + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-gallery + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download all examples in Python source code: use_architectures_outside_python.zip ` + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download all examples in Jupyter notebooks: use_architectures_outside_jupyter.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/programmatic/use_architectures_outside/qm9_reduced_100.xyz b/docs/src/examples/programmatic/use_architectures_outside/qm9_reduced_100.xyz new file mode 100644 index 0000000000..14b2a0416e --- /dev/null +++ b/docs/src/examples/programmatic/use_architectures_outside/qm9_reduced_100.xyz @@ -0,0 +1,1201 @@ +5 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B=20.15302 C=12.5891 mu=0.0005 alpha=30.82 epsHOMO=-0.2888 epsLUMO=0.1042 epsgap=0.393 =155.8145 zpve=0.081231 U0=-117.824798 U=-117.821426 H=-117.820482 G=-117.849087 Cv=11.041 pbc="F F F" +C -0.01193280 1.51433198 0.01031700 +C 1.30299118 0.77886562 -0.00617842 +C 0.00867174 0.00767042 0.00201032 +H -0.30541464 2.01702117 0.92533239 +H -0.32275498 2.02680156 -0.89347839 +H 1.88493396 0.79145020 -0.92119393 +H 1.90226631 0.78165391 0.89766373 +H -0.27088043 -0.51289125 0.91137474 +H -0.28821983 -0.50310409 -0.90740824 +7 +Properties=species:S:1:pos:R:3 A=25.77385 B=22.12109 C=14.18066 mu=1.7675 alpha=24.04 epsHOMO=-0.2682 epsLUMO=0.1042 epsgap=0.3724 =129.891 zpve=0.057289 U0=-153.742562 U=-153.73941 H=-153.738466 G=-153.766642 Cv=9.176 pbc="F F F" +C 0.01537720 1.41764380 0.00956287 +C 1.26478161 0.64923984 -0.00655044 +O -0.00024018 -0.00771006 0.00204055 +H -0.31763326 1.88586598 0.93475751 +H -0.33527583 1.89576673 -0.90397578 +H 1.83246682 0.56256848 -0.93193290 +H 1.85008258 0.55267938 0.90680147 +10 +Properties=species:S:1:pos:R:3 A=10.12193 B=8.49011 C=4.89615 mu=2.7362 alpha=35.53 epsHOMO=-0.2431 epsLUMO=-0.0087 epsgap=0.2344 =292.4367 zpve=0.083382 U0=-193.08834 U=-193.082969 H=-193.082024 G=-193.116476 Cv=16.893 pbc="F F F" +C -0.00310189 1.48050328 -0.17259867 +C -0.04527202 -0.02887059 0.00139493 +C 1.29705800 -0.72194415 0.16889042 +O -1.08643172 -0.64273343 0.00630393 +H 0.59403529 1.74668579 -1.05225043 +H -1.01540407 1.87034023 -0.28382265 +H 0.48099600 1.95070605 0.69099376 +H 1.82512144 -0.32470731 1.04328658 +H 1.14892507 -1.79597689 0.28554393 +H 1.93682635 -0.52949692 -0.69995403 +9 +Properties=species:S:1:pos:R:3 A=10.78886 B=9.27509 C=5.14977 mu=3.6367 alpha=31.83 epsHOMO=-0.2436 epsLUMO=0.0347 epsgap=0.2783 =267.6148 zpve=0.07319 U0=-209.159302 U=-209.15402 H=-209.153076 G=-209.187468 Cv=16.561 pbc="F F F" +C 0.00679889 1.49969736 -0.02567030 +C -0.02740385 -0.01752051 -0.14894811 +N 1.17826667 -0.63324298 0.05691721 +O -1.03721965 -0.63980649 -0.40727572 +H 0.91390280 1.92923755 -0.46100067 +H -0.87080896 1.91124186 -0.52345176 +H -0.02826124 1.78517271 1.03149125 +H 1.98405139 -0.13175861 0.38674906 +H 1.19049421 -1.63919563 0.10006209 +8 +Properties=species:S:1:pos:R:3 A=11.1475 B=10.36388 C=5.42091 mu=3.4869 alpha=28.07 epsHOMO=-0.2495 epsLUMO=0.0556 epsgap=0.3051 =244.2308 zpve=0.063824 U0=-225.221461 U=-225.217075 H=-225.216131 G=-225.247724 Cv=15.292 pbc="F F F" +N 0.03605270 1.36077873 -0.12416403 +C -0.02591138 -0.02076560 0.00200645 +N 1.21968479 -0.62334220 0.11963222 +O -1.06822866 -0.64174597 0.00865573 +H 0.80749439 1.83455148 0.32144915 +H -0.85825775 1.80373500 0.02243770 +H 1.99927148 -0.17303054 -0.33602496 +H 1.18204327 -1.62147901 -0.02109234 +14 +Properties=species:S:1:pos:R:3 A=7.75166 B=7.74847 C=4.48668 mu=0.0897 alpha=45.46 epsHOMO=-0.3167 epsLUMO=0.0843 epsgap=0.401 =355.0621 zpve=0.131146 U0=-158.342346 U=-158.336603 H=-158.335658 G=-158.370016 Cv=20.273 pbc="F F F" +C -0.03215888 1.54021598 0.01074456 +C 0.03381741 0.00745852 0.00180716 +C 0.71375572 -0.50856404 -1.27296949 +C 0.73848999 -0.52220886 1.25750907 +H 0.97522736 1.97414341 0.00528362 +H -0.56196769 1.92232326 -0.86835418 +H -0.54817239 1.91268414 0.90208039 +H -0.99752525 -0.37310578 0.00977297 +H 1.75070021 -0.15586784 -1.33269631 +H 0.73446691 -1.60328288 -1.29939696 +H 0.19367011 -0.16085589 -2.17195508 +H 0.23611990 -0.18382017 2.17005260 +H 0.75964083 -1.61713153 1.27226101 +H 1.77635866 -0.16985318 1.30053019 +12 +Properties=species:S:1:pos:R:3 A=8.67553 B=8.00568 C=4.75542 mu=1.4259 alpha=38.58 epsHOMO=-0.2612 epsLUMO=0.074 epsgap=0.3351 =318.3721 zpve=0.107673 U0=-194.267232 U=-194.261748 H=-194.260804 G=-194.294663 Cv=19.052 pbc="F F F" +C -0.03315871 1.54782566 -0.00438815 +C -0.01108534 0.01859095 0.01676117 +C 0.70938384 -0.53596711 1.23978444 +O -1.33219841 -0.51712554 0.05236057 +H 0.98215692 1.95715900 -0.03182225 +H -0.56388100 1.92087623 -0.88845865 +H -0.54082548 1.93340945 0.88575489 +H 0.51042122 -0.33767241 -0.88819176 +H 0.22240492 -0.18785860 2.15650245 +H 0.68242024 -1.62887638 1.23305141 +H 1.75434301 -0.21246950 1.25524188 +H -1.81818681 -0.15577435 -0.69553962 +6 +Properties=species:S:1:pos:R:3 A=0 B=4.4259726 C=4.4259726 mu=0 alpha=38.52 epsHOMO=-0.2599 epsLUMO=-0.0214 epsgap=0.2386 =278.6264 zpve=0.037354 U0=-153.459846 U=-153.455442 H=-153.454498 G=-153.482621 Cv=15.312 pbc="F F F" +C 0.68098021 0.00000000 0.00000000 +C -0.68098021 0.00000000 0.00000000 +C -1.88766603 0.00000000 0.00000000 +C 1.88766603 0.00000000 0.00000000 +H -2.94960000 0.00000000 0.00000000 +H 2.94960000 0.00000000 0.00000000 +5 +Properties=species:S:1:pos:R:3 A=0 B=4.5793222 C=4.5793222 mu=3.792 alpha=32.66 epsHOMO=-0.3102 epsLUMO=-0.0543 epsgap=0.2559 =260.1896 zpve=0.027259 U0=-169.557758 U=-169.553764 H=-169.55282 G=-169.581024 Cv=12.93 pbc="F F F" +C 0.01510390 0.00000000 1.00000000 +C 1.38233149 0.00000000 1.00000000 +C -1.18862525 0.00000000 1.00000000 +H -2.25177201 0.00000000 1.00000000 +N 2.54296187 0.00000000 1.00000000 +4 +Properties=species:S:1:pos:R:3 A=0 B=4.73269 C=4.73269 mu=0.0023 alpha=27.7 epsHOMO=-0.3696 epsLUMO=-0.0926 epsgap=0.277 =242.9308 zpve=0.015951 U0=-185.648533 U=-185.644825 H=-185.64388 G=-185.667652 Cv=10.398 pbc="F F F" +N 0.01745734 -1.16134217 -0.00415342 +C 0.00253212 -0.00344278 0.00179939 +C -0.01611419 1.37220987 0.00934873 +N -0.03264302 2.53008276 0.01609090 +6 +Properties=species:S:1:pos:R:3 A=68.5171 B=4.8345 C=4.51586 mu=2.7824 alpha=31.14 epsHOMO=-0.2777 epsLUMO=-0.0735 epsgap=0.2042 =268.3921 zpve=0.037208 U0=-190.624631 U=-190.620363 H=-190.619419 G=-190.650543 Cv=13.049 pbc="F F F" +O -0.06776120 -0.04233838 0.00282489 +C -0.00562945 1.16382879 0.00743862 +C 1.22985272 1.91339997 0.00028781 +C 2.23625322 2.57430970 -0.00529277 +H -0.91670701 1.79467546 0.01781352 +H 3.13429662 3.14370267 -0.01037656 +5 +Properties=species:S:1:pos:R:3 A=67.88408 B=5.00823 C=4.66413 mu=2.3112 alpha=26.25 epsHOMO=-0.3166 epsLUMO=-0.11 epsgap=0.2066 =251.0007 zpve=0.02654 U0=-206.721858 U=-206.717875 H=-206.716931 G=-206.747625 Cv=11.329 pbc="F F F" +O -0.04055978 -0.03799144 0.00266513 +C -0.01768870 1.16193251 0.00746008 +C 1.23315534 1.93381336 0.00043371 +N 2.19857378 2.57055097 -0.00483320 +H -0.92627534 1.78959196 0.01764859 +6 +Properties=species:S:1:pos:R:3 A=56.25048 B=4.77441 C=4.40088 mu=0.002 alpha=26.12 epsHOMO=-0.2668 epsLUMO=-0.1113 epsgap=0.1555 =266.8164 zpve=0.036943 U0=-227.798785 U=-227.79457 H=-227.793626 G=-227.825074 Cv=12.147 pbc="F F F" +O 0.00337122 -0.03146586 0.00157206 +C -0.01713174 1.17081409 0.00676444 +C 1.26555783 1.99719410 -0.00007703 +O 1.24507069 3.19946589 0.00681275 +H -0.94690483 1.77678142 0.01852668 +H 2.19534410 1.39121374 -0.01006739 +10 +Properties=species:S:1:pos:R:3 A=80.28766 B=3.3688 C=3.3688 mu=0 alpha=42.32 epsHOMO=-0.2412 epsLUMO=0.0684 epsgap=0.3096 =400.2236 zpve=0.083896 U0=-155.908941 U=-155.90318 H=-155.902236 G=-155.937641 Cv=17.447 pbc="F F F" +C -0.01787959 1.46676893 0.01011180 +C 0.00186644 0.01072913 0.00211109 +C 0.01819704 -1.19272861 -0.00450723 +C 0.03795170 -2.64876793 -0.01251193 +H 0.86460312 1.87589979 0.51479719 +H -0.03217624 1.87210581 -1.00791002 +H -0.90233115 1.85185524 0.53003953 +H -0.83477011 -3.06339007 0.50451436 +H 0.03276422 -3.04315564 -1.03491223 +H 0.93213486 -3.03931642 0.48626606 +10 +Properties=species:S:1:pos:R:3 A=27.39459 B=4.53005 C=4.08342 mu=0.7067 alpha=40.09 epsHOMO=-0.2592 epsLUMO=0.0566 epsgap=0.3157 =333.9589 zpve=0.084338 U0=-155.897345 U=-155.892291 H=-155.891347 G=-155.924226 Cv=17.13 pbc="F F F" +C -0.03095840 1.54775025 0.03167945 +C 0.01485375 0.00962471 -0.02082154 +C 0.69099073 -0.49954575 -1.20857624 +C 1.25222275 -0.89514467 -2.19527879 +H 0.97899300 1.96621609 0.03373834 +H -0.56082354 1.95002902 -0.83580745 +H -0.54576708 1.88225519 0.93709062 +H 0.51920135 -0.37425398 0.87496224 +H -1.00644802 -0.39030838 0.01340632 +H 1.74594541 -1.25604187 -3.06299415 +9 +Properties=species:S:1:pos:R:3 A=28.0016 B=4.67752 C=4.21765 mu=3.9233 alpha=35.38 epsHOMO=-0.3213 epsLUMO=0.034 epsgap=0.3553 =314.5335 zpve=0.07419 U0=-172.006141 U=-172.001467 H=-172.000523 G=-172.032826 Cv=14.988 pbc="F F F" +C -0.02522005 1.54773054 0.02133370 +C 0.01183939 0.00999278 -0.01556702 +C 0.68915114 -0.50836846 -1.20331488 +N 1.22565733 -0.90360846 -2.14737482 +H 0.98571620 1.96212990 0.02370153 +H -0.55522192 1.94599662 -0.84711771 +H -0.53962556 1.88294354 0.92536604 +H 0.52481910 -0.38226048 0.86933355 +H -1.00453542 -0.39832109 0.00530071 +8 +Properties=species:S:1:pos:R:3 A=32.19893 B=4.76299 C=4.30696 mu=4.4361 alpha=31.81 epsHOMO=-0.2683 epsLUMO=0.0173 epsgap=0.2855 =295.6635 zpve=0.063305 U0=-188.042067 U=-188.037478 H=-188.036534 G=-188.06863 Cv=14.488 pbc="F F F" +N -0.03608099 1.48762533 0.03678072 +C -0.01039471 0.02517605 -0.01506608 +C -1.37180956 -0.52108920 0.02739272 +N -2.44026695 -0.95956444 0.03370519 +H 0.91729752 1.83547547 0.05202682 +H -0.47162178 1.85677205 -0.80275569 +H 0.48946161 -0.40880401 -0.89715168 +H 0.52827150 -0.34533338 0.86469547 +8 +Properties=species:S:1:pos:R:3 A=35.11615 B=4.66117 C=4.22354 mu=1.7211 alpha=33.1 epsHOMO=-0.2595 epsLUMO=0.0277 epsgap=0.2872 =300.0993 zpve=0.060632 U0=-191.810916 U=-191.806025 H=-191.805081 G=-191.837634 Cv=15.855 pbc="F F F" +O -0.02013197 1.45738137 0.01010195 +C 0.00168478 0.03579980 0.00224736 +C -1.36366691 -0.47481101 0.01255056 +C -2.47902835 -0.91970202 0.02082451 +H 0.89446911 1.75736553 0.00327363 +H 0.51979028 -0.35175413 -0.88980302 +H 0.53670458 -0.36127661 0.88000894 +H -3.46773502 -1.30737009 0.02818120 +7 +Properties=species:S:1:pos:R:3 A=35.93172 B=4.83443 C=4.37827 mu=4.6788 alpha=28.56 epsHOMO=-0.3018 epsLUMO=0.0022 epsgap=0.3039 =280.6659 zpve=0.050262 U0=-207.916786 U=-207.912215 H=-207.911271 G=-207.943384 Cv=13.845 pbc="F F F" +O -0.03738221 1.44547841 0.01010769 +C 0.01292906 0.03441661 0.00209165 +C -1.35747452 -0.48874523 0.01239738 +N -2.42135277 -0.93685184 0.02016053 +H 0.86898057 1.76972949 0.00417577 +H 0.51692713 -0.36329790 -0.89215814 +H 0.53387616 -0.37278749 0.88223921 +10 +Properties=species:S:1:pos:R:3 A=26.14564 B=4.2893 C=4.12773 mu=2.6741 alpha=35.83 epsHOMO=-0.25 epsLUMO=-0.0205 epsgap=0.2295 =333.3276 zpve=0.084175 U0=-193.075202 U=-193.070116 H=-193.069171 G=-193.102798 Cv=15.954 pbc="F F F" +C 0.16879340 1.52513042 -0.15747056 +C -0.18737625 0.06194763 0.14679374 +C 0.50917645 -0.43996215 1.39125850 +O 1.18191079 -1.43565585 1.45816384 +H 1.23126511 1.63137254 -0.39632693 +H -0.40744668 1.89431681 -1.00969246 +H -0.04931034 2.17266260 0.69857432 +H -1.26905160 -0.01668067 0.32854433 +H 0.06279062 -0.60250479 -0.68474036 +H 0.35384841 0.20663370 2.28871052 +9 +Properties=species:S:1:pos:R:3 A=19.98749 B=6.11589 C=4.82584 mu=3.7071 alpha=32.78 epsHOMO=-0.2516 epsLUMO=0.0335 epsgap=0.2851 =279.7863 zpve=0.074166 U0=-209.144909 U=-209.139976 H=-209.139032 G=-209.172305 Cv=15.058 pbc="F F F" +C 0.01474916 1.47888256 -0.03509326 +N 0.18708077 0.03831317 -0.02906851 +C 0.45703661 -0.66858895 1.10069001 +O 0.58457996 -0.19876202 2.21119705 +H 0.76025289 1.96736104 -0.67198126 +H -0.98462423 1.75984483 -0.38485103 +H 0.14269849 1.82387718 0.99149397 +H 0.10386349 -0.46937726 -0.89461379 +H 0.55047568 -1.75253452 0.88868242 +8 +Properties=species:S:1:pos:R:3 A=48.62351 B=4.67298 C=4.38187 mu=3.92 alpha=29.47 epsHOMO=-0.2814 epsLUMO=0.0074 epsgap=0.2888 =293.917 zpve=0.061327 U0=-229.013797 U=-229.009003 H=-229.008059 G=-229.041086 Cv=13.885 pbc="F F F" +C 0.10653781 1.43512049 0.03336100 +O 0.42686564 0.04716929 0.09309945 +C 0.50714862 -0.63652665 -1.06318297 +O 0.76927014 -1.79955017 -1.11010475 +H 0.92349251 2.00385139 0.48465728 +H -0.03757815 1.76927183 -1.00057425 +H -0.81269569 1.60801916 0.59873287 +H 0.30458460 -0.00231902 -1.94965553 +8 +Properties=species:S:1:pos:R:3 A=36.49282 B=4.31175 C=4.04493 mu=1.7341 alpha=28.53 epsHOMO=-0.2537 epsLUMO=-0.0341 epsgap=0.2196 =303.8129 zpve=0.060508 U0=-228.992613 U=-228.987769 H=-228.986825 G=-229.019918 Cv=14.78 pbc="F F F" +O 0.19375856 1.37170995 0.31398297 +C 0.04992637 0.01405481 -0.04526239 +C -1.40888025 -0.39635680 -0.20601574 +O -1.77729462 -1.37233860 -0.80361689 +H -0.01723186 1.91269132 -0.45337758 +H 0.60440478 -0.26519766 -0.95205725 +H 0.46740406 -0.57521679 0.78176270 +H -2.11835055 0.30075962 0.29870515 +14 +Properties=species:S:1:pos:R:3 A=23.44662 B=3.60588 C=3.38683 mu=0 alpha=45.71 epsHOMO=-0.317 epsLUMO=0.0937 epsgap=0.4107 =426.2996 zpve=0.131708 U0=-158.340943 U=-158.33517 H=-158.334226 G=-158.36894 Cv=19.668 pbc="F F F" +C -0.03007624 1.55804912 0.02980774 +C 0.00707277 0.02834877 -0.00712529 +C 0.71320792 -0.52989340 -1.24765581 +C 0.75036895 -2.05959366 -1.28458957 +H 0.98195330 1.97845113 0.02800797 +H -0.55582433 1.96212343 -0.84270157 +H -0.54083762 1.92692755 0.92493697 +H 0.50928672 -0.34909049 0.89361319 +H -1.01733481 -0.36542299 0.03207393 +H 0.21098948 -0.15245770 -2.14839302 +H 1.73761331 -0.13611625 -1.28685672 +H 1.27616854 -2.46366410 -0.41210980 +H -0.26165699 -2.48000447 -1.28273552 +H 1.26108491 -2.42846444 -2.17974803 +12 +Properties=species:S:1:pos:R:3 A=26.59789 B=3.79186 C=3.53779 mu=1.3402 alpha=38.61 epsHOMO=-0.2619 epsLUMO=0.081 epsgap=0.3429 =382.8628 zpve=0.108241 U0=-194.261089 U=-194.255495 H=-194.254551 G=-194.28893 Cv=18.431 pbc="F F F" +C -0.00379012 1.52196790 0.01027996 +C -0.03589372 -0.00778254 0.00225671 +C -1.45219016 -0.56653711 0.01284386 +O -1.36799785 -1.98394604 0.00453606 +H 1.02477576 1.89411231 0.00239793 +H -0.51286674 1.93764614 -0.86653888 +H -0.49584080 1.92801344 0.90121721 +H 0.48140784 -0.39530552 -0.88316064 +H 0.49828589 -0.40486256 0.87330348 +H -1.98734220 -0.20399494 0.90666062 +H -2.00431485 -0.19459473 -0.86666460 +H -2.26171819 -2.33824298 0.01034748 +12 +Properties=species:S:1:pos:R:3 A=28.29567 B=4.14901 C=3.88349 mu=1.0363 alpha=39.34 epsHOMO=-0.2503 epsLUMO=0.0925 epsgap=0.3428 =368.9331 zpve=0.107895 U0=-194.254127 U=-194.248585 H=-194.247641 G=-194.281899 Cv=17.888 pbc="F F F" +C -0.00519551 1.50868224 0.03308760 +C 0.01316580 -0.01037819 0.01988643 +O 0.69758944 -0.46344140 1.16868979 +C 0.76688491 -1.86508183 1.24602669 +H 1.01445476 1.90423799 0.02996018 +H -0.52990866 1.89184059 -0.84726911 +H -0.51263765 1.87805843 0.92875424 +H -1.01599507 -0.40998729 0.00714282 +H 0.51093143 -0.38369435 -0.89219395 +H -0.23415841 -2.32451489 1.28977780 +H 1.30129276 -2.29853663 0.38486095 +H 1.30990287 -2.11811998 2.16013206 +10 +Properties=species:S:1:pos:R:3 A=14.79671 B=5.6956 C=4.58846 mu=0.0075 alpha=31.42 epsHOMO=-0.2594 epsLUMO=0.0584 epsgap=0.3179 =297.8398 zpve=0.085172 U0=-230.183076 U=-230.177723 H=-230.176779 G=-230.211195 Cv=16.837 pbc="F F F" +O -0.01418735 1.42642242 -0.05423651 +C -0.00391839 0.00717714 0.03496062 +C -1.42317019 -0.55560600 -0.01256639 +O -2.11229413 -0.10563875 -1.17228313 +H -0.18987882 1.64154705 -0.97555148 +H 0.59659504 -0.43315430 -0.77409095 +H 0.47311116 -0.24903733 0.98747216 +H -1.39494859 -1.64975279 -0.06283188 +H -1.96497417 -0.27216915 0.90131972 +H -2.35925991 0.80944396 -1.00614597 +12 +Properties=species:S:1:pos:R:3 A=15.61472 B=6.31363 C=5.54514 mu=0.1136 alpha=41.96 epsHOMO=-0.2727 epsLUMO=0.1012 epsgap=0.3738 =298.6061 zpve=0.109284 U0=-157.116735 U=-157.11209 H=-157.111146 G=-157.143262 Cv=16.49 pbc="F F F" +C -0.03602245 1.52092949 0.01569161 +C 0.01938487 0.01004403 0.04814953 +C 1.30891671 -0.71251995 -0.24920416 +C 0.75347423 -0.70267714 1.15564676 +H 0.90105691 1.95389291 0.38283338 +H -0.19640365 1.89437369 -1.00208569 +H -0.84685273 1.90588930 0.64451322 +H -0.88416809 -0.48001889 -0.30566335 +H 1.26900901 -1.63903068 -0.81150371 +H 2.19010738 -0.11131209 -0.44927513 +H 1.26248862 -0.09487455 1.89690028 +H 0.33540836 -1.62248918 1.54980695 +10 +Properties=species:S:1:pos:R:3 A=18.21931 B=6.63877 C=5.92459 mu=1.812 alpha=35.01 epsHOMO=-0.2633 epsLUMO=0.1052 epsgap=0.3685 =267.2979 zpve=0.085275 U0=-193.039603 U=-193.035186 H=-193.034242 G=-193.065979 Cv=14.764 pbc="F F F" +C -0.01646343 1.51830705 0.00771445 +C -0.02743227 0.01657694 -0.10889741 +C 0.77296553 -0.70131886 -1.10863115 +O 1.17212293 -0.66702969 0.26278829 +H 0.91865980 1.92914217 -0.38285880 +H -0.85158014 1.95456506 -0.55109460 +H -0.11487702 1.82596470 1.05397777 +H -0.93582598 -0.47350600 0.24682793 +H 0.44443922 -1.67201495 -1.47810622 +H 1.40538284 -0.13600339 -1.79271491 +11 +Properties=species:S:1:pos:R:3 A=16.58914 B=7.18798 C=6.11415 mu=1.1353 alpha=39.02 epsHOMO=-0.2304 epsLUMO=0.0968 epsgap=0.3271 =270.5508 zpve=0.097671 U0=-173.147782 U=-173.143343 H=-173.142399 G=-173.174073 Cv=15.298 pbc="F F F" +C -0.05275541 1.47419574 0.00999108 +N 0.01940516 0.02189947 0.06334866 +C 1.32511048 -0.56446850 -0.21420725 +C 0.78051759 -0.54057993 1.17242760 +H 0.86784202 1.97176076 0.36297285 +H -0.23845155 1.79095005 -1.02142866 +H -0.88683448 1.81938959 0.62950398 +H 1.31992909 -1.48186782 -0.79602038 +H 2.15202820 0.11020204 -0.43285248 +H 1.24563570 0.14996098 1.87499245 +H 0.37013358 -1.44020447 1.62233365 +10 +Properties=species:S:1:pos:R:3 A=16.79477 B=6.71015 C=5.86781 mu=1.3894 alpha=34.64 epsHOMO=-0.239 epsLUMO=0.0775 epsgap=0.3166 =263.966 zpve=0.085106 U0=-193.034988 U=-193.030356 H=-193.029411 G=-193.061689 Cv=15.679 pbc="F F F" +O 0.21577583 1.35386859 -0.10695107 +C 0.01628099 -0.02832718 -0.02915694 +C -0.39081465 -0.71497009 1.24315669 +C -1.35118138 -0.64280380 0.06273589 +H -0.43642338 1.79062849 0.45006761 +H 0.74981167 -0.52404050 -0.65624767 +H 0.05959375 -1.66840483 1.49729237 +H -0.61369353 -0.08848006 2.10170640 +H -2.19821932 0.03058430 0.15411451 +H -1.56395912 -1.54640426 -0.49826515 +12 +Properties=species:S:1:pos:R:3 A=10.66107 B=10.66107 C=6.30452 mu=0 alpha=41.83 epsHOMO=-0.2982 epsLUMO=0.0956 epsgap=0.3938 =268.4432 zpve=0.110511 U0=-157.115484 U=-157.111322 H=-157.110378 G=-157.141657 Cv=14.696 pbc="F F F" +C -0.02542957 1.54032578 -0.04215944 +C 1.52564399 1.50419989 0.04121276 +C 1.42146727 0.06146790 0.60844885 +C -0.02638833 -0.01070956 0.04935088 +H -0.46591372 1.99639894 0.84962894 +H -0.47900239 2.00028207 -0.92365389 +H 1.98248278 1.51375334 -0.95303183 +H 2.02191365 2.25986579 0.65522129 +H 2.14291679 -0.67956358 0.25531480 +H 1.43105853 0.05751479 1.70261879 +H -0.79053440 -0.48530296 0.66996573 +H -0.05233245 -0.47238446 -0.94236394 +10 +Properties=species:S:1:pos:R:3 A=12.06545 B=11.77731 C=6.71997 mu=1.7978 alpha=34.56 epsHOMO=-0.2424 epsLUMO=0.0859 epsgap=0.3283 =236.9759 zpve=0.086675 U0=-193.034094 U=-193.029968 H=-193.029024 G=-193.060777 Cv=12.915 pbc="F F F" +C -0.03489789 1.55259455 -0.08507691 +C 1.49349183 1.45459700 0.10114677 +O 1.42725884 0.02348990 0.27254886 +C -0.00634758 0.02236494 0.10944630 +H -0.56579210 2.11301721 0.68591527 +H -0.36628352 1.89393380 -1.06692327 +H 2.10031724 1.73525720 -0.76898349 +H 1.90023090 1.95506611 0.98896748 +H -0.51987849 -0.35595179 1.00235948 +H -0.31970062 -0.57567421 -0.75559275 +12 +Properties=species:S:1:pos:R:3 A=8.63483 B=4.11977 C=2.88825 mu=0.6875 alpha=45.37 epsHOMO=-0.2392 epsLUMO=0.0192 epsgap=0.2584 =452.5112 zpve=0.100501 U0=-248.375248 U=-248.368823 H=-248.367879 G=-248.405354 Cv=21.616 pbc="F F F" +C -0.10255370 1.49907127 -0.00133642 +C -0.00519774 -0.00343114 -0.02723470 +C -0.00688536 -0.76890740 1.26434744 +N 0.08186799 -0.69202791 -1.10087306 +O 0.07439067 0.13215394 -2.24195258 +H 0.78342093 1.94690862 -0.46354050 +H -0.96150430 1.83614082 -0.59021965 +H -0.19854173 1.87462989 1.01931607 +H -0.92620012 -0.57297946 1.82982479 +H 0.07256154 -1.84064782 1.07516863 +H 0.82937178 -0.45553340 1.90133089 +H 0.13975924 -0.51254299 -2.95389260 +10 +Properties=species:S:1:pos:R:3 A=9.17136 B=9.04195 C=4.5531 mu=1.8689 alpha=43.14 epsHOMO=-0.2029 epsLUMO=0.0499 epsgap=0.2528 =303.9808 zpve=0.082433 U0=-210.101789 U=-210.097816 H=-210.096872 G=-210.12818 Cv=14.821 pbc="F F F" +N -0.00829694 1.35362838 0.00995971 +C 1.28033254 1.82457421 -0.00022459 +C 2.12216639 0.73698898 -0.01445600 +C 1.30849904 -0.43109580 -0.01284154 +C -0.00342782 -0.01836884 0.00235736 +H -0.83243234 1.92772668 0.02122995 +H 1.48471820 2.88312630 0.00354313 +H 3.20066918 0.77343077 -0.02488416 +H 1.64810599 -1.45539683 -0.02180394 +H -0.92551647 -0.57697054 0.00839402 +9 +Properties=species:S:1:pos:R:3 A=9.77329 B=9.41192 C=4.7946 mu=3.6193 alpha=39.13 epsHOMO=-0.2253 epsLUMO=0.0332 epsgap=0.2585 =283.6817 zpve=0.071145 U0=-226.160842 U=-226.157088 H=-226.156144 G=-226.187104 Cv=13.371 pbc="F F F" +N -0.00996090 1.35636172 0.01000855 +C 1.29535422 1.79575578 -0.00047017 +C 2.05550519 0.65637438 -0.01422927 +N 1.25385745 -0.46275132 -0.01260894 +C 0.02301170 -0.00706382 0.00200146 +H -0.83195718 1.93618436 0.02132575 +H 1.54553961 2.84344998 0.00289054 +H 3.13177434 0.57569766 -0.02524155 +H -0.87667864 -0.60414531 0.00750794 +9 +Properties=species:S:1:pos:R:3 A=9.50228 B=9.29497 C=4.69874 mu=0.5571 alpha=39.2 epsHOMO=-0.2246 epsLUMO=0.0199 epsgap=0.2445 =289.005 zpve=0.069883 U0=-229.969129 U=-229.965414 H=-229.96447 G=-229.995393 Cv=13.358 pbc="F F F" +O -0.03255819 1.35065130 0.00967376 +C 1.25319237 1.79167710 -0.00030351 +C 2.11664681 0.74375623 -0.01441494 +C 1.30397796 -0.43852387 -0.01283316 +C 0.01629568 -0.00777185 0.00191294 +H 1.37951089 2.86161135 0.00427433 +H 3.19376406 0.79865501 -0.02458604 +H 1.63858893 -1.46383271 -0.02156949 +H -0.93736295 -0.50899925 0.00843891 +8 +Properties=species:S:1:pos:R:3 A=10.10926 B=9.69372 C=4.94857 mu=1.5081 alpha=35.17 epsHOMO=-0.2509 epsLUMO=0.001 epsgap=0.2519 =269.2444 zpve=0.058593 U0=-246.02915 U=-246.025614 H=-246.024669 G=-246.055309 Cv=12.04 pbc="F F F" +O -0.03808298 1.35643965 0.00975193 +C 1.26496982 1.76855319 -0.00061744 +C 2.04955750 0.66630033 -0.01406047 +N 1.24227075 -0.46732066 -0.01237541 +C 0.04019820 0.00561577 0.00183569 +H 1.43513907 2.83095265 0.00340823 +H 3.12532898 0.59783015 -0.02480757 +H -0.89387707 -0.53342154 0.00796935 +17 +Properties=species:S:1:pos:R:3 A=4.4178 B=4.41753 C=4.41717 mu=0.0003 alpha=56.01 epsHOMO=-0.3145 epsLUMO=0.0737 epsgap=0.3882 =486.2719 zpve=0.158836 U0=-197.632222 U=-197.625241 H=-197.624297 G=-197.661411 Cv=26.084 pbc="F F F" +C -0.01859029 1.53486266 0.01039904 +C 0.00210320 -0.00413672 0.00196980 +C 0.74643954 -0.51390760 1.24887638 +C -1.44190166 -0.53673615 0.01311457 +C 0.72234971 -0.50049606 -1.26445632 +H 0.99776928 1.94420529 0.00435474 +H -0.54181883 1.92841415 -0.86819911 +H -0.52758402 1.91888376 0.90148530 +H 0.25549576 -0.17602728 2.16833067 +H 0.77815389 -1.60892372 1.27088904 +H 1.77964786 -0.14975834 1.26944035 +H -1.99871895 -0.18888794 -0.86408066 +H -1.45956909 -1.63227235 0.00649916 +H -1.98088827 -0.19979639 0.90561580 +H 1.75482473 -0.13554018 -1.30118083 +H 0.75396989 -1.59524036 -1.29822280 +H 0.21358221 -0.15343900 -2.17067181 +15 +Properties=species:S:1:pos:R:3 A=4.69474 B=4.66938 C=4.47909 mu=1.384 alpha=49.04 epsHOMO=-0.2601 epsLUMO=0.0664 epsgap=0.3265 =449.0573 zpve=0.134977 U0=-233.560626 U=-233.553779 H=-233.552834 G=-233.589759 Cv=25.128 pbc="F F F" +C -0.00855999 1.54270147 0.00152717 +C 0.00506780 0.00702635 0.01856160 +C 0.76137731 -0.51839196 1.24766443 +C -1.42012774 -0.54933183 -0.02116008 +O 0.62590477 -0.48363745 -1.17657057 +H 1.01361406 1.94020776 0.00281128 +H -0.51459669 1.90702466 -0.89705781 +H -0.52214815 1.94882514 0.87918344 +H 0.28130613 -0.20165548 2.17943598 +H 0.79965506 -1.61131746 1.23022549 +H 1.79096916 -0.14072935 1.26083003 +H -1.93854829 -0.19910724 -0.91828281 +H -1.39937632 -1.64259062 -0.04563833 +H -1.98850725 -0.23059656 0.85726520 +H 1.53111188 -0.15488046 -1.19210222 +9 +Properties=species:S:1:pos:R:3 A=10.21876 B=4.04324 C=2.94978 mu=2.8579 alpha=42.02 epsHOMO=-0.2654 epsLUMO=-0.0575 epsgap=0.2079 =416.7799 zpve=0.065175 U0=-229.927277 U=-229.921622 H=-229.920677 G=-229.955843 Cv=18.723 pbc="F F F" +C -0.00693851 1.49559567 -0.01063358 +C -0.02986170 -0.01594623 0.06248673 +O -0.08815239 -0.63215062 1.10334812 +C 0.02336981 -0.70256747 -1.22062379 +C 0.06738132 -1.26058341 -2.28623129 +H 0.90429806 1.83281887 -0.51655384 +H -0.85338524 1.85556926 -0.60552819 +H -0.05237764 1.91099807 0.99695949 +H 0.10466868 -1.77036047 -3.21832617 +8 +Properties=species:S:1:pos:R:3 A=10.16063 B=4.15847 C=3.0056 mu=3.3351 alpha=37.08 epsHOMO=-0.3007 epsLUMO=-0.0889 epsgap=0.2118 =397.7757 zpve=0.05451 U0=-246.027383 U=-246.022024 H=-246.02108 G=-246.055769 Cv=16.963 pbc="F F F" +C -0.01171097 1.49973776 -0.01213268 +C -0.02919740 -0.00338745 0.07151218 +O -0.09164028 -0.64592927 1.08732241 +C 0.03831104 -0.69690258 -1.23748557 +N 0.09136286 -1.22982650 -2.26228068 +H 0.90093132 1.83370670 -0.51814928 +H -0.85663933 1.84864400 -0.61618135 +H -0.06416540 1.92131542 0.99203818 +8 +Properties=species:S:1:pos:R:3 A=10.68423 B=4.3198 C=3.07993 mu=5.1815 alpha=38.31 epsHOMO=-0.2739 epsLUMO=-0.0438 epsgap=0.2301 =384.5574 zpve=0.056619 U0=-242.19573 U=-242.190591 H=-242.189646 G=-242.223513 Cv=17.465 pbc="F F F" +N -0.05232108 1.34991769 0.15535538 +C 0.08549378 -0.01476951 0.02668890 +N 1.14555215 -0.71993906 -0.03966037 +C -1.19426556 -0.71152088 0.01802151 +N -2.22747610 -1.22790543 0.00132148 +H 0.75474469 1.91998737 -0.04411334 +H -0.94458527 1.76600473 -0.05932293 +H 1.98312429 -0.13415500 -0.02878551 +9 +Properties=species:S:1:pos:R:3 A=9.08854 B=4.42607 C=3.03169 mu=0.9461 alpha=36.51 epsHOMO=-0.2538 epsLUMO=-0.0964 epsgap=0.1574 =399.222 zpve=0.064992 U0=-267.10335 U=-267.097658 H=-267.096714 G=-267.132534 Cv=17.806 pbc="F F F" +C -0.43126066 1.30586664 -0.12758585 +C 0.25285715 -0.01026988 0.13138867 +O 0.69872099 -0.36890333 1.19604690 +C 0.38978943 -0.96773680 -1.06226208 +O -0.01611268 -0.71564293 -2.16583718 +H 0.10006565 1.85324490 -0.91332597 +H -1.44270433 1.13069361 -0.50923371 +H -0.46757554 1.89548158 0.78836479 +H 0.90329886 -1.91558845 -0.79531972 +9 +Properties=species:S:1:pos:R:3 A=10.08927 B=4.54115 C=3.13487 mu=2.7707 alpha=38.09 epsHOMO=-0.2561 epsLUMO=-0.064 epsgap=0.192 =384.4827 zpve=0.067668 U0=-263.278851 U=-263.273589 H=-263.272645 G=-263.306835 Cv=17.972 pbc="F F F" +N -0.16032929 1.08528962 -0.45398652 +C 0.18649127 -0.08762327 0.14758021 +N 1.28979627 -0.46427423 0.67927030 +C -0.94204384 -1.08440264 0.20423621 +O -2.01802205 -0.89007687 -0.30583149 +H 0.54574437 1.73837930 -0.74689246 +H -1.04684920 1.09570857 -0.93607796 +H 2.01738195 0.24991828 0.59881811 +H -0.67740148 -2.01003995 0.74746441 +13 +Properties=species:S:1:pos:R:3 A=7.92874 B=3.8272 C=2.82581 mu=0.6578 alpha=51.2 epsHOMO=-0.2589 epsLUMO=0.0571 epsgap=0.316 =481.9854 zpve=0.112471 U0=-195.186772 U=-195.180446 H=-195.179502 G=-195.215658 Cv=22.569 pbc="F F F" +C -0.01763434 1.54859468 -0.00253584 +C -0.01719180 0.00735789 0.03269604 +C 0.75818463 -0.52820155 1.25278862 +C -1.38213303 -0.51768438 0.00775516 +C -2.50796392 -0.94005629 0.00478552 +H 1.00944396 1.92639613 -0.02632830 +H -0.54514614 1.92217052 -0.88393459 +H -0.51243000 1.95339892 0.88560642 +H 0.49366179 -0.34904155 -0.87265890 +H 0.28968458 -0.19345372 2.18334679 +H 0.77833392 -1.62102506 1.25783727 +H 1.78952492 -0.16181727 1.23574954 +H -3.49998244 -1.31810165 -0.00723572 +12 +Properties=species:S:1:pos:R:3 A=7.90009 B=3.95466 C=2.88643 mu=3.9512 alpha=46.23 epsHOMO=-0.318 epsLUMO=0.0365 epsgap=0.3545 =460.694 zpve=0.102281 U0=-211.295796 U=-211.289821 H=-211.288877 G=-211.324525 Cv=20.467 pbc="F F F" +C -0.00242350 1.55035837 0.01056187 +C -0.01856600 0.01004352 -0.02261770 +C -1.45045318 -0.55766178 0.01293347 +C 0.70125749 -0.48583168 -1.20172764 +N 1.26190946 -0.87126766 -2.13619740 +H 1.01959496 1.93673215 0.00349668 +H -0.52972844 1.96141639 -0.85498386 +H -0.50134251 1.90346839 0.91755862 +H 0.52481877 -0.36222456 0.85525462 +H -2.02427903 -0.21428716 -0.85251869 +H -1.44438296 -1.65026927 0.00754107 +H -1.95805494 -0.21725882 0.91996760 +11 +Properties=species:S:1:pos:R:3 A=8.30095 B=3.97615 C=2.93764 mu=2.7429 alpha=42.87 epsHOMO=-0.2704 epsLUMO=0.0278 epsgap=0.2983 =440.0738 zpve=0.091554 U0=-227.338075 U=-227.332253 H=-227.331309 G=-227.366638 Cv=19.946 pbc="F F F" +C -0.01064295 1.54128691 0.00722087 +C -0.00203811 0.00756015 0.03863463 +N 0.76268677 -0.47101822 1.19250856 +C -1.38541954 -0.52099256 -0.00194097 +N -2.45680847 -0.95636072 0.00694969 +H 1.02043523 1.90106589 0.00392365 +H -0.52873161 1.91193452 -0.88031480 +H -0.51849536 1.94150431 0.89013197 +H 0.50418828 -0.35805500 -0.86276072 +H 0.30070681 -0.18364934 2.05155305 +H 0.78148992 -1.48670299 1.20428183 +11 +Properties=species:S:1:pos:R:3 A=8.55919 B=3.89952 C=2.91869 mu=1.3582 alpha=44.03 epsHOMO=-0.2665 epsLUMO=0.0336 epsgap=0.3001 =444.6452 zpve=0.088908 U0=-231.108368 U=-231.102292 H=-231.101348 G=-231.137061 Cv=21.306 pbc="F F F" +C -0.00754278 1.55566144 0.08650633 +C 0.01207541 0.02096553 0.04935916 +O -1.29150251 -0.52785128 0.22126386 +C 0.65093362 -0.47718651 -1.17430507 +C 1.13902673 -0.86577343 -2.20205518 +H 1.00824243 1.95969458 0.04929733 +H -0.56272245 1.95192010 -0.76978085 +H -0.49328161 1.88929438 1.00704123 +H 0.57811967 -0.34464907 0.91458864 +H -1.78516532 -0.35903262 -0.58909400 +H 1.58078877 -1.22365060 -3.09919447 +10 +Properties=species:S:1:pos:R:3 A=8.55908 B=4.01671 C=2.97882 mu=3.269 alpha=39.28 epsHOMO=-0.3051 epsLUMO=0.0115 epsgap=0.3166 =424.3395 zpve=0.078602 U0=-247.214861 U=-247.209162 H=-247.208218 G=-247.243338 Cv=19.252 pbc="F F F" +C -0.00992462 1.54951908 0.00732200 +C 0.00740485 0.01722020 0.05031270 +O 0.72085722 -0.49630479 1.15864583 +C -1.36995095 -0.52355929 0.00894432 +N -2.45081572 -0.93233138 0.00716912 +H 1.01892314 1.91630447 0.02724149 +H -0.50261490 1.90969362 -0.89997951 +H -0.54800426 1.95377611 0.87053664 +H 0.53602846 -0.36494195 -0.82970945 +H 0.27702643 -0.20644534 1.96421773 +13 +Properties=species:S:1:pos:R:3 A=7.66289 B=3.70925 C=2.79937 mu=2.6921 alpha=46.58 epsHOMO=-0.2469 epsLUMO=-0.0188 epsgap=0.2281 =482.0475 zpve=0.112328 U0=-232.364952 U=-232.358577 H=-232.357633 G=-232.394589 Cv=21.434 pbc="F F F" +C -0.02629647 1.53877116 -0.00783747 +C 0.01468904 0.00095310 0.00166037 +C 0.74737862 -0.53388859 1.24420471 +C -1.40211264 -0.53859257 -0.01557820 +O -1.87620455 -1.23582351 -0.87399559 +H 0.98823696 1.94625080 0.03705522 +H -0.50725090 1.92153422 -0.91285565 +H -0.57756514 1.92360883 0.85796386 +H 0.50584680 -0.36421091 -0.90619124 +H 0.22958380 -0.23909403 2.16431052 +H 0.81692736 -1.62559499 1.22973438 +H 1.76267153 -0.12846566 1.29050755 +H -2.00861510 -0.22792742 0.87121666 +11 +Properties=species:S:1:pos:R:3 A=8.31096 B=4.07963 C=2.92918 mu=2.8354 alpha=39.16 epsHOMO=-0.255 epsLUMO=-0.0279 epsgap=0.2271 =432.1489 zpve=0.088443 U0=-268.287661 U=-268.281505 H=-268.280561 G=-268.316982 Cv=20.344 pbc="F F F" +C -0.05483118 1.46032843 -0.17068647 +C 0.07105714 -0.03524485 0.11225841 +O 0.58204486 -0.33316656 1.40176256 +C -1.27639279 -0.74383027 0.03051185 +O -2.11855775 -0.49643175 -0.79163412 +H 0.93158072 1.93091398 -0.23353204 +H -0.57713995 1.61290939 -1.11835562 +H -0.62444315 1.94776119 0.62573670 +H 0.69687521 -0.50071620 -0.67159471 +H 1.45603316 0.06066659 1.47858584 +H -1.40723217 -1.53388627 0.80211181 +12 +Properties=species:S:1:pos:R:3 A=8.9331 B=4.19405 C=2.96046 mu=3.7163 alpha=44.42 epsHOMO=-0.2424 epsLUMO=0.0327 epsgap=0.2751 =441.85 zpve=0.10227 U0=-248.430371 U=-248.424309 H=-248.423365 G=-248.459383 Cv=19.918 pbc="F F F" +C -0.09776499 1.44917268 0.04561984 +N 0.01043053 0.00373818 -0.00070600 +C 0.05323028 -0.71432112 1.25264891 +C 0.06728716 -0.64067225 -1.20060778 +O 0.03636432 -0.11006515 -2.29109228 +H 0.75650660 1.88747109 0.57647421 +H -0.11648577 1.81632103 -0.98049172 +H -1.01641106 1.75521548 0.56151415 +H -0.85453556 -0.53657675 1.84390168 +H 0.13297722 -1.78696336 1.05851351 +H 0.91637348 -0.40737846 1.85761345 +H 0.14762240 -1.73801339 -1.06805139 +11 +Properties=species:S:1:pos:R:3 A=10.13982 B=3.81346 C=2.86965 mu=2.9514 alpha=39.33 epsHOMO=-0.2699 epsLUMO=-0.0262 epsgap=0.2437 =440.1727 zpve=0.088924 U0=-268.301176 U=-268.295084 H=-268.29414 G=-268.331307 Cv=19.824 pbc="F F F" +C -0.01708808 1.44281136 0.03225501 +C -0.05544542 -0.06560572 -0.02375999 +O -0.17379913 -0.76196233 0.96223129 +C 0.06068107 -0.73615179 -1.38825252 +O 0.00877771 -2.12574390 -1.27749486 +H 0.92062950 1.81463913 -0.39522215 +H -0.83050523 1.86542281 -0.56780350 +H -0.10911975 1.78049045 1.06493675 +H -0.75015067 -0.35235060 -2.03056074 +H 1.00312898 -0.40389029 -1.85592942 +H -0.09063255 -2.29450960 -0.32608019 +13 +Properties=species:S:1:pos:R:3 A=9.55216 B=3.57844 C=2.7341 mu=2.6168 alpha=46.19 epsHOMO=-0.2423 epsLUMO=-0.0072 epsgap=0.2351 =489.8518 zpve=0.112006 U0=-232.377706 U=-232.371073 H=-232.370129 G=-232.408256 Cv=21.716 pbc="F F F" +C -0.04384102 1.54099793 -0.03913814 +C 0.02765132 0.01885817 0.04778773 +C 0.76814644 -0.48842218 1.28000847 +C 0.87850208 -1.99746312 1.42393323 +O 1.24179142 0.26651340 2.09788702 +H 0.95796454 1.97690198 -0.06816012 +H -0.58447717 1.85356031 -0.93690369 +H -0.55173626 1.95737759 0.83440529 +H -0.97692824 -0.42709877 0.05353178 +H 0.51853486 -0.40806507 -0.83809409 +H 1.39924980 -2.42678188 0.56053177 +H 1.42001187 -2.24449367 2.33776426 +H -0.11750602 -2.45360651 1.45141965 +12 +Properties=species:S:1:pos:R:3 A=9.78802 B=3.78441 C=2.84416 mu=3.499 alpha=42.54 epsHOMO=-0.2438 epsLUMO=0.0355 epsgap=0.2793 =457.447 zpve=0.101847 U0=-248.448467 U=-248.441988 H=-248.441044 G=-248.478935 Cv=21.374 pbc="F F F" +C -0.00220144 1.53344329 -0.05960369 +C -0.03235837 0.01206167 0.06840265 +C 0.69666086 -0.48196462 1.31828322 +N 0.98104007 -1.82034162 1.31984698 +O 1.00392124 0.24606232 2.24136749 +H 1.02111190 1.89525169 -0.19182672 +H -0.59707204 1.86206427 -0.91645604 +H -0.39728839 1.99920712 0.84545201 +H -1.06936083 -0.34552935 0.13040806 +H 0.39947165 -0.46906818 -0.81777020 +H 0.63281951 -2.43917647 0.60874785 +H 1.35708014 -2.22217793 2.16303522 +12 +Properties=species:S:1:pos:R:3 A=10.01185 B=3.84195 C=2.87635 mu=3.5402 alpha=43.61 epsHOMO=-0.2418 epsLUMO=0.0387 epsgap=0.2805 =458.9803 zpve=0.101735 U0=-248.443503 U=-248.436899 H=-248.435955 G=-248.474272 Cv=20.967 pbc="F F F" +C 0.00619941 1.45933202 -0.03893859 +N -0.06411616 0.01422963 -0.12046901 +C -1.20595394 -0.68196796 0.16117120 +C -1.08982704 -2.19284899 0.02095775 +O -2.24482911 -0.14443077 0.49994418 +H 0.75053989 1.78372520 0.69715573 +H 0.25692001 1.90395263 -1.00876215 +H -0.97856026 1.81121438 0.27082194 +H 0.75820982 -0.49543660 -0.39464024 +H -0.09909703 -2.53543104 -0.28970003 +H -1.34097671 -2.65395851 0.97972885 +H -1.82903100 -2.53483935 -0.70813076 +11 +Properties=species:S:1:pos:R:3 A=10.44045 B=4.04692 C=2.98547 mu=3.5648 alpha=39.51 epsHOMO=-0.2436 epsLUMO=0.0599 epsgap=0.3034 =428.404 zpve=0.091713 U0=-264.504487 U=-264.498452 H=-264.497508 G=-264.533633 Cv=20.274 pbc="F F F" +C 0.00922630 1.45021814 -0.03194543 +N -0.01429488 -0.00054646 -0.01782667 +C -1.21576579 -0.67252403 0.04421896 +N -1.09733352 -2.04644898 -0.15997741 +O -2.28599299 -0.12893711 0.24196027 +H 0.89659038 1.81885022 0.49120949 +H 0.00233950 1.86388504 -1.04914155 +H -0.88562684 1.79943346 0.48353298 +H 0.75257305 -0.47506631 -0.46719265 +H -0.24882333 -2.48772194 0.16375654 +H -1.93419610 -2.53898393 0.11482880 +12 +Properties=species:S:1:pos:R:3 A=9.69683 B=4.1533 C=3.01703 mu=1.1876 alpha=43.57 epsHOMO=-0.2595 epsLUMO=0.0352 epsgap=0.2948 =443.1687 zpve=0.102062 U0=-248.416462 U=-248.410358 H=-248.409414 G=-248.445651 Cv=20.256 pbc="F F F" +C -0.09160695 1.37540046 -0.00729426 +O -0.06964534 -0.04831581 -0.11148812 +C 0.68114625 -0.56142673 -1.10732458 +C 0.60405298 -2.06701550 -1.08766892 +N 1.32645533 0.19266343 -1.89967731 +H 0.91463916 1.76843655 0.16257460 +H -0.48562826 1.82624615 -0.92235216 +H -0.73971430 1.60007423 0.84110605 +H -0.43430065 -2.39242162 -1.20325205 +H 1.20627110 -2.50049977 -1.88745640 +H 0.95651337 -2.44976670 -0.12501838 +H 1.83504115 -0.36104361 -2.58486055 +11 +Properties=species:S:1:pos:R:3 A=10.20392 B=4.16834 C=3.07281 mu=1.7569 alpha=39.33 epsHOMO=-0.2685 epsLUMO=0.0174 epsgap=0.2859 =427.6606 zpve=0.089436 U0=-268.32127 U=-268.315051 H=-268.314106 G=-268.351214 Cv=19.501 pbc="F F F" +C 0.07295254 1.38840919 0.06442046 +O 0.21049114 -0.03740028 0.04612736 +C 0.50102805 -0.57224288 -1.15975346 +C 0.62218715 -2.07378323 -1.06030684 +O 0.63922154 0.08224418 -2.16157325 +H 1.00029720 1.87062570 -0.25532064 +H -0.73338957 1.70782089 -0.60080454 +H -0.15751280 1.65154060 1.09692954 +H -0.31330001 -2.50202851 -0.68941906 +H 0.85646588 -2.48430275 -2.04157352 +H 1.40651685 -2.34055496 -0.34634301 +10 +Properties=species:S:1:pos:R:3 A=10.68182 B=4.39532 C=3.17969 mu=2.329 alpha=35.5 epsHOMO=-0.2669 epsLUMO=0.0575 epsgap=0.3244 =398.5908 zpve=0.079271 U0=-284.385189 U=-284.379361 H=-284.378417 G=-284.414085 Cv=19.084 pbc="F F F" +C -0.02645771 1.38305753 0.02085660 +O 0.04139566 -0.04439503 -0.00277452 +C 0.66775338 -0.56001625 -1.09000344 +N 0.72605063 -1.92219983 -0.98822954 +O 1.12952381 0.09648143 -1.99322180 +H 0.97529779 1.82044307 0.02387306 +H -0.57085698 1.76214621 -0.84790425 +H -0.55410722 1.63835731 0.94043594 +H 0.12662696 -2.38401590 -0.32566929 +H 0.97230070 -2.41798421 -1.82732573 +10 +Properties=species:S:1:pos:R:3 A=10.20696 B=3.93135 C=2.9219 mu=4.5676 alpha=35.87 epsHOMO=-0.246 epsLUMO=0.0273 epsgap=0.2733 =418.5967 zpve=0.078222 U0=-284.360325 U=-284.354148 H=-284.353204 G=-284.390143 Cv=20.089 pbc="F F F" +N -0.08115607 1.31807026 0.01276742 +C 0.05442259 -0.04477733 0.08155388 +O 1.12163074 -0.59783950 0.20940553 +C -1.28687786 -0.79011642 0.06178435 +O -1.04605114 -2.13954195 -0.23957158 +H 0.76623366 1.85991128 -0.04483425 +H -0.93021346 1.74567447 -0.31565615 +H -1.74559696 -0.65521334 1.05526700 +H -1.96026061 -0.31149222 -0.67026256 +H -1.85637296 -2.63017646 -0.07625478 +10 +Properties=species:S:1:pos:R:3 A=10.14389 B=4.0714 C=3.02223 mu=5.3004 alpha=35.19 epsHOMO=-0.2527 epsLUMO=0.0208 epsgap=0.2735 =408.0279 zpve=0.080317 U0=-284.372483 U=-284.367172 H=-284.366228 G=-284.40095 Cv=17.931 pbc="F F F" +N 0.01838100 0.93057357 0.04943058 +H -0.70674506 0.56116798 -0.55550134 +H 0.68248307 0.18430002 0.21487099 +H 0.21928084 2.79858558 -0.32670585 +C -0.54586914 1.44688585 1.30274586 +C -0.80466975 2.96030569 1.21052835 +O -0.21468822 3.53648884 0.15531687 +O -1.45295676 3.56338618 2.02143528 +H -1.47662579 0.96512183 1.62072169 +H 0.17781209 1.30324286 2.11157176 +13 +Properties=species:S:1:pos:R:3 A=6.6508 B=4.19317 C=3.39441 mu=2.2854 alpha=42.01 epsHOMO=-0.2597 epsLUMO=0.0631 epsgap=0.3228 =438.3028 zpve=0.11345 U0=-269.479234 U=-269.472993 H=-269.472049 G=-269.508213 Cv=22.072 pbc="F F F" +C -0.02775167 1.53421433 0.00651151 +C 0.08343246 0.01218277 0.05258986 +O 0.95856392 -0.48200710 -0.96694113 +C 0.69767574 -0.49516451 1.35948812 +O 2.02247062 -0.02842530 1.52222186 +H 0.94333763 1.98841434 0.21841589 +H -0.37320516 1.87354041 -0.97716908 +H -0.74773817 1.89256394 0.74926853 +H -0.91671848 -0.43697036 -0.06040528 +H 0.76726710 -0.01412177 -1.78502043 +H 0.11650002 -0.13351914 2.21392431 +H 0.65814685 -1.59563445 1.36274474 +H 2.46378566 -0.22762476 0.68698431 +17 +Properties=species:S:1:pos:R:3 A=7.26511 B=3.32874 C=2.54491 mu=0.0618 alpha=56.26 epsHOMO=-0.3085 epsLUMO=0.085 epsgap=0.3934 =565.8412 zpve=0.159632 U0=-197.629387 U=-197.622325 H=-197.621381 G=-197.659365 Cv=25.169 pbc="F F F" +C 0.05879035 1.56174169 -0.01945240 +C -0.01544024 0.03182306 0.00308332 +C -1.44103237 -0.54862028 0.05028629 +C -1.40777007 -2.07231030 -0.13012010 +C -2.18400529 -0.17659759 1.34114303 +H 1.09435638 1.90233607 -0.11900072 +H -0.50689566 1.97217319 -0.86388098 +H -0.34407951 2.00628082 0.89579522 +H 0.48926758 -0.36011214 -0.88953902 +H 0.55254938 -0.35139446 0.86269887 +H -2.00031575 -0.12153682 -0.79559684 +H -0.85501131 -2.55065810 0.68775438 +H -2.41787513 -2.49543628 -0.13720127 +H -0.91928762 -2.35363802 -1.06914014 +H -3.19100197 -0.60699559 1.35137051 +H -1.65197294 -0.56010584 2.22063481 +H -2.28793759 0.90575132 1.46079572 +15 +Properties=species:S:1:pos:R:3 A=8.11598 B=3.43678 C=2.66869 mu=1.3894 alpha=49.45 epsHOMO=-0.2617 epsLUMO=0.0733 epsgap=0.335 =521.8605 zpve=0.136091 U0=-233.555951 U=-233.549143 H=-233.548199 G=-233.585602 Cv=23.99 pbc="F F F" +C -0.03841227 1.55621396 0.00935837 +C 0.01907135 0.02716079 -0.02074203 +C 0.73141506 -0.52709328 -1.25454422 +C 0.83975868 -2.05206973 -1.22872655 +O -0.01226470 -0.08514394 -2.38973758 +H 0.96845664 1.98863424 0.03526234 +H -0.54377006 1.93824134 -0.88074407 +H -0.57652955 1.91383386 0.89256698 +H 0.53166867 -0.34991486 0.87240999 +H -0.99727186 -0.38638654 -0.00185289 +H 1.74909346 -0.09943025 -1.28826184 +H 1.34143968 -2.42487137 -2.12957624 +H 1.41822012 -2.39229108 -0.36340871 +H -0.15616765 -2.50443859 -1.18308875 +H 0.43090825 -0.40901616 -3.17979642 +15 +Properties=species:S:1:pos:R:3 A=7.55335 B=3.78323 C=2.80566 mu=1.0758 alpha=49.8 epsHOMO=-0.2476 epsLUMO=0.086 epsgap=0.3336 =507.9614 zpve=0.135681 U0=-233.545899 U=-233.539034 H=-233.53809 G=-233.57582 Cv=23.573 pbc="F F F" +C -0.00857605 1.38097833 0.09454670 +O -0.02255106 -0.00739461 -0.13062576 +C -1.30243341 -0.61997805 -0.02088309 +C -1.18687286 -1.97885751 -0.70303918 +C -1.75587824 -0.74662760 1.43714508 +H 1.00321550 1.73258087 -0.12443496 +H -0.71511468 1.90838275 -0.56674280 +H -0.24909065 1.64967237 1.13384681 +H -2.03737978 -0.00432241 -0.56884198 +H -0.44792779 -2.59945605 -0.18596870 +H -2.14751041 -2.50225525 -0.69102581 +H -0.86524212 -1.85860679 -1.74077836 +H -2.73321482 -1.23674908 1.49205117 +H -1.03621350 -1.34483828 2.00544671 +H -1.84898410 0.22935483 1.92198087 +15 +Properties=species:S:1:pos:R:3 A=6.14183 B=5.17679 C=3.79297 mu=0.1068 alpha=52.92 epsHOMO=-0.2633 epsLUMO=0.0893 epsgap=0.3526 =439.1643 zpve=0.137025 U0=-196.409349 U=-196.403398 H=-196.402453 G=-196.437676 Cv=22.258 pbc="F F F" +C -0.03913204 1.50456305 0.04978913 +C 0.00985856 -0.01113880 0.01248619 +C -1.24167981 -0.66663044 -0.53989979 +C 1.33803387 -0.66895748 -0.27400430 +C 0.75126442 -0.72221586 1.11862571 +H 0.88633040 1.92490040 0.45579358 +H -0.18183878 1.92045743 -0.95489291 +H -0.86832076 1.85815718 0.67439648 +H -1.43570890 -0.34344387 -1.56976411 +H -1.15326757 -1.75757800 -0.54433832 +H -2.12221416 -0.40570820 0.05951361 +H 1.34398374 -1.58235208 -0.86005084 +H 2.20196897 -0.03329084 -0.43929326 +H 1.21949576 -0.12246018 1.89246580 +H 0.36153319 -1.67153717 1.47171932 +13 +Properties=species:S:1:pos:R:3 A=6.49534 B=5.52329 C=3.9108 mu=1.8235 alpha=45.7 epsHOMO=-0.2596 epsLUMO=0.091 epsgap=0.3505 =405.521 zpve=0.112851 U0=-232.335768 U=-232.329981 H=-232.329037 G=-232.364091 Cv=20.719 pbc="F F F" +C -0.03970330 1.50954998 -0.04112233 +C -0.03023556 -0.00335329 -0.06498361 +C -1.24613189 -0.68356648 0.52531190 +C 0.78937201 -0.71129193 -1.06027559 +O 1.22546798 -0.60059036 0.29712283 +H 0.88992772 1.90948478 -0.45296558 +H -0.87952723 1.90350108 -0.62376642 +H -0.14448576 1.87590604 0.98604429 +H -2.15218727 -0.41012616 -0.02629495 +H -1.13412584 -1.77005431 0.49747032 +H -1.38586631 -0.38055453 1.56882757 +H 0.50712690 -1.71386874 -1.38085018 +H 1.37088104 -0.14369322 -1.78640853 +13 +Properties=species:S:1:pos:R:3 A=6.57648 B=5.31603 C=3.98453 mu=1.3822 alpha=45.85 epsHOMO=-0.2494 epsLUMO=0.0776 epsgap=0.327 =401.4888 zpve=0.113237 U0=-232.33341 U=-232.327734 H=-232.32679 G=-232.36147 Cv=21.17 pbc="F F F" +C -0.03714052 1.53215920 0.01332310 +C 0.01344335 0.02117019 0.01922005 +O -1.17476104 -0.59735284 -0.40931260 +C 1.30744798 -0.69902106 -0.27389650 +C 0.70844749 -0.71373504 1.12226318 +H 0.89026861 1.96240279 0.40276135 +H -0.17846151 1.91561262 -1.00518522 +H -0.87011232 1.88955201 0.62730953 +H -1.28352265 -0.40225391 -1.34690641 +H 1.23973813 -1.61642115 -0.84783774 +H 2.20543993 -0.11523820 -0.44744474 +H 1.19539159 -0.13889834 1.90240284 +H 0.24735439 -1.63670241 1.45378480 +10 +Properties=species:S:1:pos:R:3 A=12.18399 B=5.1384 C=3.78868 mu=2.5732 alpha=40.19 epsHOMO=-0.263 epsLUMO=0.0277 epsgap=0.2907 =352.0378 zpve=0.080558 U0=-247.201165 U=-247.196699 H=-247.195755 G=-247.22841 Cv=15.51 pbc="F F F" +N -0.04445152 1.25623533 0.31681915 +C -0.03914559 0.04913710 -0.01670558 +C -1.08749619 -1.04164446 -0.11488033 +C 0.09869688 -1.90754844 -0.58225894 +O 0.99647508 -0.76306629 -0.44028010 +H 0.88935980 1.66481350 0.24916893 +H -1.54324114 -1.32235812 0.83572172 +H -1.86910036 -0.85713839 -0.85325910 +H 0.39739753 -2.72227350 0.08158721 +H 0.06945080 -2.25418345 -1.61796084 +11 +Properties=species:S:1:pos:R:3 A=10.79571 B=4.78732 C=3.54292 mu=2.7119 alpha=42.77 epsHOMO=-0.2415 epsLUMO=-0.0194 epsgap=0.2222 =379.6371 zpve=0.090544 U0=-231.15578 U=-231.150804 H=-231.149859 G=-231.184401 Cv=17.076 pbc="F F F" +O -0.00101746 0.01846628 -0.20114742 +C -0.01405811 1.19658059 0.00205306 +C -1.13542911 2.22979148 0.21957980 +C -0.03500884 3.29097637 0.53052223 +C 1.08469664 2.25947795 0.18994331 +H -1.71642814 2.41772662 -0.68901078 +H -1.83392757 1.98415059 1.02444244 +H -0.05534741 4.17585264 -0.10670963 +H -0.02541477 3.61399048 1.57278917 +H 1.81074389 2.03288534 0.97578973 +H 1.63600801 2.46255380 -0.73376244 +10 +Properties=species:S:1:pos:R:3 A=12.23436 B=4.97895 C=3.70561 mu=3.6671 alpha=39.73 epsHOMO=-0.25 epsLUMO=0.038 epsgap=0.288 =355.0934 zpve=0.080442 U0=-247.225618 U=-247.220897 H=-247.219953 G=-247.253218 Cv=16.1 pbc="F F F" +O 0.03184134 -0.03461682 0.00849836 +C -0.02440334 1.16440533 0.02160811 +C -1.14541322 2.23712809 0.03012901 +C 0.00928805 3.27945878 0.04557758 +N 0.93843838 2.14652109 0.03554396 +H -1.77030529 2.23779730 -0.86428437 +H -1.77632809 2.21765386 0.92007934 +H 0.07170564 3.91863516 -0.84072193 +H 0.06622603 3.89826625 0.94657952 +H 1.94616840 2.09535713 0.03855945 +9 +Properties=species:S:1:pos:R:3 A=12.44643 B=5.23328 C=3.86599 mu=3.9339 alpha=35.4 epsHOMO=-0.2788 epsLUMO=0.0089 epsgap=0.2878 =336.0792 zpve=0.068574 U0=-267.106213 U=-267.101929 H=-267.100985 G=-267.13332 Cv=14.483 pbc="F F F" +O 0.04424004 0.03933243 -0.33071104 +C -0.04854479 1.17416230 0.00410076 +C -1.14211656 2.23611850 0.15338361 +C 0.02332538 3.12357010 0.61681625 +O 0.95663808 2.01190122 0.42603746 +H -1.62573755 2.51482212 -0.78401102 +H -1.90009213 1.99553386 0.90028663 +H 0.28368077 3.96172751 -0.03229287 +H 0.00782250 3.43942655 1.66162725 +10 +Properties=species:S:1:pos:R:3 A=11.54185 B=4.90368 C=3.63309 mu=2.5257 alpha=39.34 epsHOMO=-0.2437 epsLUMO=-0.0258 epsgap=0.2179 =359.152 zpve=0.07965 U0=-247.190194 U=-247.185543 H=-247.184599 G=-247.217688 Cv=16.083 pbc="F F F" +O 0.00053891 0.00665475 -0.09743049 +C -0.01371990 1.19213881 0.03139936 +C -1.10668681 2.26612284 0.17247913 +N -0.03543445 3.25123475 0.48533693 +C 1.05328615 2.29521506 0.14383904 +H -1.65047970 2.42796175 -0.76886054 +H -1.83606786 2.11370530 0.97567410 +H -0.05434600 4.07193609 -0.10727051 +H 1.80754206 2.16277968 0.92736250 +H 1.56737937 2.47130277 -0.81152688 +9 +Properties=species:S:1:pos:R:3 A=12.18264 B=4.94228 C=3.68162 mu=0.8477 alpha=35.94 epsHOMO=-0.2647 epsLUMO=-0.0352 epsgap=0.2295 =344.5473 zpve=0.066989 U0=-267.068488 U=-267.063976 H=-267.063032 G=-267.095887 Cv=15.115 pbc="F F F" +O -0.00031692 0.00676321 -0.17139774 +C -0.01417098 1.18672865 -0.00980279 +C -1.08283120 2.27653381 0.15574094 +O -0.03856115 3.26460423 0.27513986 +C 1.02857270 2.30515693 0.12774311 +H -1.72923855 2.43647507 -0.71671274 +H -1.70219898 2.19346725 1.05800754 +H 1.67366781 2.23923293 1.01324179 +H 1.64705068 2.48224548 -0.76148355 +15 +Properties=species:S:1:pos:R:3 A=9.63416 B=4.10254 C=3.28665 mu=0.095 alpha=52.94 epsHOMO=-0.2896 epsLUMO=0.0927 epsgap=0.3823 =455.0215 zpve=0.138424 U0=-196.407957 U=-196.40245 H=-196.401505 G=-196.436159 Cv=20.299 pbc="F F F" +C -0.47005934 1.44206547 -0.06684548 +C 0.14287306 0.05485144 0.03747984 +C 1.37254693 -0.30295273 -0.84679168 +C 1.95947994 -1.09364561 0.35326039 +C 0.99887433 -0.30768787 1.28563099 +H 0.29210839 2.21844829 0.06844100 +H -0.93614604 1.60094605 -1.04612799 +H -1.24148161 1.59707353 0.69627593 +H -0.63517341 -0.70560132 -0.10055095 +H 1.19338376 -0.84118177 -1.78129390 +H 1.96867777 0.58857708 -1.07004184 +H 1.69873597 -2.15500718 0.30521100 +H 3.03253510 -1.01077235 0.54148058 +H 1.48290386 0.58242428 1.70211026 +H 0.51304296 -0.84979686 2.10121062 +13 +Properties=species:S:1:pos:R:3 A=9.93129 B=4.56362 C=3.63704 mu=1.6826 alpha=45.72 epsHOMO=-0.241 epsLUMO=0.0915 epsgap=0.3325 =407.6909 zpve=0.11459 U0=-232.33123 U=-232.325877 H=-232.324933 G=-232.359589 Cv=18.582 pbc="F F F" +C -0.32308228 1.53751451 0.01947624 +C 0.11847214 0.08900172 -0.00523140 +C 0.87489284 -0.43763303 -1.24721738 +C 1.97841048 -0.80657483 -0.23740356 +O 1.27344926 -0.15419108 0.83794040 +H 0.49211985 2.19658795 -0.29527664 +H -1.17615835 1.68923490 -0.65134808 +H -0.62776963 1.82747912 1.03013205 +H -0.71410200 -0.56389187 0.29597305 +H 0.41033314 -1.26269706 -1.78910031 +H 1.15560811 0.34596269 -1.95459519 +H 2.09501012 -1.88349764 -0.05606083 +H 2.96970344 -0.36924147 -0.40408009 +13 +Properties=species:S:1:pos:R:3 A=10.13697 B=4.32512 C=3.50912 mu=1.8995 alpha=45.45 epsHOMO=-0.2419 epsLUMO=0.0814 epsgap=0.3233 =414.1575 zpve=0.114694 U0=-232.325947 U=-232.320548 H=-232.319604 G=-232.354576 Cv=18.443 pbc="F F F" +C -0.18371124 1.54668909 -0.01671129 +C 0.04513382 0.04386724 0.02270494 +C 1.20301104 -0.54034580 -0.81983464 +O 1.80061686 -1.15530615 0.33924991 +C 0.83391983 -0.54313754 1.21648003 +H 0.75102609 2.09256015 0.15344911 +H -0.58319248 1.86155707 -0.98694169 +H -0.89843724 1.85917126 0.75244153 +H -0.89506736 -0.48768372 -0.14843995 +H 0.93560472 -1.27857588 -1.58543917 +H 1.86392938 0.21379924 -1.27081649 +H 1.29414565 0.20948993 1.87273952 +H 0.31517042 -1.28326856 1.83756393 +13 +Properties=species:S:1:pos:R:3 A=10.09703 B=4.26865 C=3.40487 mu=1.4604 alpha=45.55 epsHOMO=-0.256 epsLUMO=0.0801 epsgap=0.3361 =413.9118 zpve=0.115063 U0=-232.333258 U=-232.328097 H=-232.327153 G=-232.361103 Cv=19.023 pbc="F F F" +O 0.54622847 1.03270424 -0.58398083 +C -0.00112269 -0.11036621 0.01898238 +C 0.13945950 -0.34310429 1.54777117 +C -1.26863784 -0.99876517 1.51632654 +C -1.53844651 -0.26418236 0.17423510 +H 0.20332379 1.80433874 -0.12071219 +H 0.41296647 -0.95857955 -0.53560402 +H 0.98890738 -0.92719626 1.90921867 +H 0.11786549 0.61323034 2.08356053 +H -1.20828813 -2.08191064 1.38036699 +H -1.94143614 -0.79119746 2.35014116 +H -2.00495682 0.71307933 0.34581493 +H -2.09160311 -0.78230202 -0.61249171 +11 +Properties=species:S:1:pos:R:3 A=10.85394 B=4.52867 C=3.62328 mu=2.4158 alpha=38.58 epsHOMO=-0.2465 epsLUMO=0.0604 epsgap=0.3069 =374.4087 zpve=0.091005 U0=-268.248371 U=-268.243234 H=-268.24229 G=-268.276572 Cv=17.344 pbc="F F F" +O 0.07721714 1.41738724 -0.14439016 +C 0.01675156 0.02484791 0.03233817 +C 0.11193823 -0.54392541 1.47000744 +O -1.16387804 -1.20093191 1.32441605 +C -1.41930178 -0.52040930 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This will be done by taking internal representations of a +NanoPET model (as an example) and using them inside a user-defined torch ``Module``. + +Only architectures which can output internal representations ("features" output) can +be used in this way. + +.. GENERATED FROM PYTHON SOURCE LINES 15-28 + +.. code-block:: Python + + + import torch + from metatomic.torch import ModelOutput + + from metatrain.pet import PET + from metatrain.utils.architectures import get_default_hypers + from metatrain.utils.data import DatasetInfo, read_systems + from metatrain.utils.neighbor_lists import ( + get_requested_neighbor_lists, + get_system_with_neighbor_lists, + ) + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 29-31 + +Read some sample systems. Metatrain always reads systems in float64, while torch +uses float32 by default. We will convert the systems to float32. + +.. GENERATED FROM PYTHON SOURCE LINES 32-37 + +.. code-block:: Python + + + systems = read_systems("qm9_reduced_100.xyz") + systems = [s.to(torch.float32) for s in systems] + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 38-41 + +Define the custom model using the PET architecture as a building block. +The dummy architecture here adds a linear layer and a tanh activation function +on top of the PET model. + +.. GENERATED FROM PYTHON SOURCE LINES 42-68 + +.. code-block:: Python + + + + class PETWithTanh(torch.nn.Module): + def __init__(self): + super().__init__() + self.pet = PET( + get_default_hypers("pet")["model"], + DatasetInfo( + length_unit="angstrom", + atomic_types=[1, 6, 7, 8, 9], + targets={}, + ), + ) + self.linear = torch.nn.Linear(384, 1) + self.tanh = torch.nn.Tanh() + + def forward(self, systems): + model_outputs = self.pet( + systems, + {"features": ModelOutput()}, + # ModelOutput(per_atom=True) would give per-atom features + ) + features = model_outputs["features"].block().values + return self.tanh(self.linear(features)) + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 69-71 + +Now we can train the custom model. Here is one training step executed with +some random targets. + +.. GENERATED FROM PYTHON SOURCE LINES 72-91 + +.. code-block:: Python + + my_targets = torch.randn(100, 1) + + # instantiate the model + model = PETWithTanh() + + # all metatrain models require neighbor lists to be present in the input systems + systems = [ + get_system_with_neighbor_lists(sys, get_requested_neighbor_lists(model)) + for sys in systems + ] + + # define an optimizer + optimizer = torch.optim.SGD(model.parameters(), lr=1e-3) + + # this is one training step + predictions = model(systems) + loss = torch.nn.functional.mse_loss(predictions, my_targets) + loss.backward() + optimizer.step() + + + + + + + + +.. _sphx_glr_download_examples_programmatic_use_architectures_outside_use_outside.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: use_outside.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: use_outside.py ` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: use_outside.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/programmatic/use_architectures_outside/use_outside.zip b/docs/src/examples/programmatic/use_architectures_outside/use_outside.zip new file mode 100644 index 0000000000000000000000000000000000000000..6a075f505bc2699e4a479fa393b86291801c082d GIT binary patch literal 7022 zcmdT}+iv8<77dpJl93QcLP9!EB}WKb>M;`6-#@B#bE zvbkUhF`RFh;TNQ33ND4h`Gr_PJ22%Yg&=e$cRuGTKmPE9WqBeb^SEYQG4Yg>06sn^ z5Rtf;rO3dFY{)o?UEE2;B2^F&>cva;=+S3+QcCgYQQ$aVD7hI5&eqEmR57?ig;il(KvosDW zDQgOdD&!Yd4yQ**u@I~TWUCm#FyqzIq*4dNc&Vw+g^6GjUP==!H!D%--J26`cyq!7 zeAdEzq89lC4&q6qH`<77&sHVU^W{7*BAIGq!-Vwp9EOXIFT~?gV1E(~DJLyO#Xef6 zX1PVyTQ}V zcdp-~KGpEQXG0*Ux%q677YsI3%~5B4^&i;awvxo9HWW%WL)vU$lr{Z0V3a#-UZygk 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+ +.. only:: html + + .. image:: /examples/train_from_scratch/images/thumb/sphx_glr_train_from_scratch_thumb.png + :alt: + + :ref:`sphx_glr_examples_train_from_scratch_train_from_scratch.py` + +.. raw:: html + +
Training a model from scratch
+
+ + +.. thumbnail-parent-div-close + +.. raw:: html + + + + +.. toctree:: + :hidden: + + /examples/train_from_scratch/train_from_scratch + + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-gallery + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download all examples in Python source code: train_from_scratch_python.zip ` + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download all examples in Jupyter notebooks: train_from_scratch_jupyter.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/examples/train_from_scratch/options.yaml b/docs/src/examples/train_from_scratch/options.yaml similarity index 100% rename from examples/train_from_scratch/options.yaml rename to docs/src/examples/train_from_scratch/options.yaml diff --git a/docs/src/examples/train_from_scratch/train_from_scratch.ipynb b/docs/src/examples/train_from_scratch/train_from_scratch.ipynb new file mode 100644 index 0000000000..d0f0dadee6 --- /dev/null +++ b/docs/src/examples/train_from_scratch/train_from_scratch.ipynb @@ -0,0 +1,32 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n\n# Training a model from scratch\nThis tutorial explains how to train a model with ``metatrain`` from scratch and evaluate\nit. [This dataset](./ethanol_reduced_100.xyz) is used here as an example of the\npreferred dataset format. If you have your own dataset, you can simply replace the\ndataset file name with yours.\n\n## Train the model\n\n### Configure the ``options.yaml`` and run the training\nBelow is an example ``options.yaml`` for training a PET model. In order to train other\nmodels, simply replace the architecture name with other models' architecture name. For\nthe supported models, please check `Available Architectures`_ .\n\n\n.. literalinclude:: ./options.yaml\n :language: yaml\n :linenos:\n\nStart the training by running\n\n```bash\nmtt train options.yaml\n```\nIt will start training. ``metatrain`` will automatically read the atomic forces from the\ntraining set, if they are stored in it and named as \"forces\". The model can also be\ntrained to learn other properties through transfer learning. For this, please refer to\nthis `transfer learning tutorial`_.\n\n\nOnce the training is started, a folder named ``outputs`` will be created automatically\nunder the folder where you run the command. Under this ``outputs`` folder, there is a\nfolder with the timestamp. Below is a normal structure of that folder of a successful\ntraining run.\n\n```bash\noutputs/2025-10-07/17-08-25/\n\u251c\u2500\u2500 indices # the results of dataset-spliting\n\u2502 \u251c\u2500\u2500 test.txt\n\u2502 \u251c\u2500\u2500 training.txt\n\u2502 \u2514\u2500\u2500 validation.txt\n\u251c\u2500\u2500 model_0.ckpt # the intermediate model saved at the 0th training step\n\u251c\u2500\u2500 model.ckpt # the final model\n\u251c\u2500\u2500 model.pt # the final model , usable directly by ASE and LAMMPS\n\u251c\u2500\u2500 options_restart.yaml # an expanded options file\n\u251c\u2500\u2500 train.csv # structured log of training metrics (loss, MAE, RMSE,...)\n\u2514\u2500\u2500 train.log # a human-friendly output\n```\nThe ``train.log`` provides information of the training procedure. For example, by\nchecking the following:\n\n```bash\n[2025-10-07 17:08:25][INFO] - Setting up training set [2025-10-07 17:08:25][INFO] -\nForces found in section 'energy', we will use this gradient to train the model\n[2025-10-07 17:08:25][WARNING] - No stress found in section 'energy'.\n```\nYou can know that the forces are identified by ``metatrain`` and are used during the\ntraining, and it fails to find stress. The following provides some statistical of the\ntraining, validation, and the test set\n\n```bash\n[2025-10-07 17:08:25][INFO] - Training dataset:\n Dataset containing 80 structures Mean and standard deviation of targets: -\n energy:\n - mean -9.708e+04 eV\n - std 3.97 eV\n[2025-10-07 17:08:25][INFO] - Validation dataset:\n Dataset containing 10 structures Mean and standard deviation of targets: -\n energy:\n - mean -9.708e+04 eV\n - std 3.73 eV\n[2025-10-07 17:08:25][INFO] - Test dataset:\n Dataset containing 10 structures Mean and standard deviation of targets: -\n energy:\n - mean -9.708e+04 eV\n - std 3.535 eV\n```\nThe training metrics are outputted every epoch, like\n\n```bash\n[2025-10-07 17:08:28][INFO] - Epoch: 0 | learning rate: 0.000e+00 | training\nloss: 6.305e+03 | training energy RMSE (per atom): 884.08 meV | training energy MAE\n(per atom): 773.44 meV | training forces RMSE: 28059.9 meV/A | training forces MAE:\n20581.1 meV/A | validation loss: 7.725e+02 | validation energy RMSE (per atom):\n877.08 meV | validation energy MAE (per atom): 772.04 meV | validation forces RMSE:\n27779.2 meV/A | validation forces MAE: 20201.9 meV/A\n```\nThese metrics are also outputted into ``train.csv`` in a formatted way, which can be\nused for plotting graph like loss curve.\n\nIt is easy to restart the training from the last step, by running\n\n```bash\nmtt train options.yaml --restart model.ckpt\n```\n## Evaluate the trained model\nIn order to evaluate the model on the test set, we can use the mtt eval sub-command.\nFirst, create the input file ``eval.yaml`` with the following options:\n\n.. literalinclude:: ./eval.yaml\n :language: yaml\n :linenos:\n\nand run\n\n```bash\nmtt eval PATH_TO_YOUR_MODEL/model.pt eval.yaml # be sure to replace the path\n```\nAfter this, a file named ``output.xyz`` will be created, with the atom positions and the\npredicted forces recorded in it. Also, you should see these statistical on your screen\n\n```bash\n[2025-10-07 17:11:47][INFO] - energy RMSE (per atom): 436.50 meV | energy MAE (per\natom): 341.32 meV | forces RMSE: 27823.1 meV/A | forces MAE: 20392.7 meV/A\n[2025-10-07 17:11:47][INFO] - Evaluation time: 1.10 s [1.2185 \u00b1 1.2768 ms per atom]\n```\nFurther analysis can be performed now that the model is trained. We provide a `Python\nscript`_ that can be used to generate a parity plot of the target vs predicted energies,\nbut otherwise leave this open-ended.\n\n\nTo run the script, download it from the repository, modify the paths as necessary\n(indicated with a #TODO), and run. This will generate a plot saved at parity_plot.png.\n\n\n## Use the model\nWith the trained model, you can run molecular dynamics. Please refer to these two\ntutorials for `ASE`_ and `LAMMPS`_ to see how to do that.\n\n\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/examples/train_from_scratch/train_from_scratch.py b/docs/src/examples/train_from_scratch/train_from_scratch.py similarity index 100% rename from examples/train_from_scratch/train_from_scratch.py rename to docs/src/examples/train_from_scratch/train_from_scratch.py diff --git a/docs/src/examples/train_from_scratch/train_from_scratch.py.md5 b/docs/src/examples/train_from_scratch/train_from_scratch.py.md5 new file mode 100644 index 0000000000..d77b561efc --- /dev/null +++ b/docs/src/examples/train_from_scratch/train_from_scratch.py.md5 @@ -0,0 +1 @@ +6dabc07a5102263b98df5d6e7b15c10a \ No newline at end of file diff --git a/docs/src/examples/train_from_scratch/train_from_scratch.rst b/docs/src/examples/train_from_scratch/train_from_scratch.rst new file mode 100644 index 0000000000..c57f518ceb --- /dev/null +++ b/docs/src/examples/train_from_scratch/train_from_scratch.rst @@ -0,0 +1,195 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "examples/train_from_scratch/train_from_scratch.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code. + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_examples_train_from_scratch_train_from_scratch.py: + + +.. _train-from-scratch: + +Training a model from scratch +############################# +This tutorial explains how to train a model with ``metatrain`` from scratch and evaluate +it. `This dataset <./ethanol_reduced_100.xyz>`_ is used here as an example of the +preferred dataset format. If you have your own dataset, you can simply replace the +dataset file name with yours. + +Train the model +--------------- + +Configure the ``options.yaml`` and run the training +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Below is an example ``options.yaml`` for training a PET model. In order to train other +models, simply replace the architecture name with other models' architecture name. For +the supported models, please check `Available Architectures`_ . + +.. _`Available Architectures`: https://metatensor.github.io/metatrain/latest/architectures/index.html + +.. literalinclude:: ./options.yaml + :language: yaml + :linenos: + +Start the training by running + +.. code-block:: bash + + mtt train options.yaml + +It will start training. ``metatrain`` will automatically read the atomic forces from the +training set, if they are stored in it and named as "forces". The model can also be +trained to learn other properties through transfer learning. For this, please refer to +this `transfer learning tutorial`_. + +.. _`transfer learning tutorial`: https://metatensor.github.io/metatrain/latest/advanced-concepts/transfer-learning.html + +Once the training is started, a folder named ``outputs`` will be created automatically +under the folder where you run the command. Under this ``outputs`` folder, there is a +folder with the timestamp. Below is a normal structure of that folder of a successful +training run. + +.. code-block:: bash + + outputs/2025-10-07/17-08-25/ + ├── indices # the results of dataset-spliting + │ ├── test.txt + │ ├── training.txt + │ └── validation.txt + ├── model_0.ckpt # the intermediate model saved at the 0th training step + ├── model.ckpt # the final model + ├── model.pt # the final model , usable directly by ASE and LAMMPS + ├── options_restart.yaml # an expanded options file + ├── train.csv # structured log of training metrics (loss, MAE, RMSE,...) + └── train.log # a human-friendly output + +The ``train.log`` provides information of the training procedure. For example, by +checking the following: + +.. code-block:: bash + + [2025-10-07 17:08:25][INFO] - Setting up training set [2025-10-07 17:08:25][INFO] - + Forces found in section 'energy', we will use this gradient to train the model + [2025-10-07 17:08:25][WARNING] - No stress found in section 'energy'. + +You can know that the forces are identified by ``metatrain`` and are used during the +training, and it fails to find stress. The following provides some statistical of the +training, validation, and the test set + +.. code-block:: bash + + [2025-10-07 17:08:25][INFO] - Training dataset: + Dataset containing 80 structures Mean and standard deviation of targets: - + energy: + - mean -9.708e+04 eV + - std 3.97 eV + [2025-10-07 17:08:25][INFO] - Validation dataset: + Dataset containing 10 structures Mean and standard deviation of targets: - + energy: + - mean -9.708e+04 eV + - std 3.73 eV + [2025-10-07 17:08:25][INFO] - Test dataset: + Dataset containing 10 structures Mean and standard deviation of targets: - + energy: + - mean -9.708e+04 eV + - std 3.535 eV + +The training metrics are outputted every epoch, like + +.. code-block:: bash + + [2025-10-07 17:08:28][INFO] - Epoch: 0 | learning rate: 0.000e+00 | training + loss: 6.305e+03 | training energy RMSE (per atom): 884.08 meV | training energy MAE + (per atom): 773.44 meV | training forces RMSE: 28059.9 meV/A | training forces MAE: + 20581.1 meV/A | validation loss: 7.725e+02 | validation energy RMSE (per atom): + 877.08 meV | validation energy MAE (per atom): 772.04 meV | validation forces RMSE: + 27779.2 meV/A | validation forces MAE: 20201.9 meV/A + +These metrics are also outputted into ``train.csv`` in a formatted way, which can be +used for plotting graph like loss curve. + +It is easy to restart the training from the last step, by running + +.. code-block:: bash + + mtt train options.yaml --restart model.ckpt + +Evaluate the trained model +-------------------------- +In order to evaluate the model on the test set, we can use the mtt eval sub-command. +First, create the input file ``eval.yaml`` with the following options: + +.. literalinclude:: ./eval.yaml + :language: yaml + :linenos: + +and run + +.. code-block:: bash + + mtt eval PATH_TO_YOUR_MODEL/model.pt eval.yaml # be sure to replace the path + +After this, a file named ``output.xyz`` will be created, with the atom positions and the +predicted forces recorded in it. Also, you should see these statistical on your screen + +.. code-block:: bash + + [2025-10-07 17:11:47][INFO] - energy RMSE (per atom): 436.50 meV | energy MAE (per + atom): 341.32 meV | forces RMSE: 27823.1 meV/A | forces MAE: 20392.7 meV/A + [2025-10-07 17:11:47][INFO] - Evaluation time: 1.10 s [1.2185 ± 1.2768 ms per atom] + +Further analysis can be performed now that the model is trained. We provide a `Python +script`_ that can be used to generate a parity plot of the target vs predicted energies, +but otherwise leave this open-ended. + +.. _`Python script`: https://raw.githubusercontent.com/metatensor/Workshop-spring-2025/refs/heads/main/training-custom-models/part-1-gap/parity_plot.py + +To run the script, download it from the repository, modify the paths as necessary +(indicated with a #TODO), and run. This will generate a plot saved at parity_plot.png. + + +Use the model +------------------------- +With the trained model, you can run molecular dynamics. Please refer to these two +tutorials for `ASE`_ and `LAMMPS`_ to see how to do that. + +.. _`ASE`: https://docs.metatensor.org/metatomic/latest/examples/2-running-ase-md.html + +.. _`LAMMPS`: https://atomistic-cookbook.org/examples/pet-mad-nc/pet-mad-nc.html#running-lammps-on-gpus-with-kokkos + + +.. _sphx_glr_download_examples_train_from_scratch_train_from_scratch.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: train_from_scratch.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: train_from_scratch.py ` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: train_from_scratch.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git 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+ +.. only:: html + + .. image:: /examples/validation/images/thumb/sphx_glr_parity_plot_thumb.png + :alt: + + :ref:`sphx_glr_examples_validation_parity_plot.py` + +.. raw:: html + +
Model validation with parity plots for energies and forces
+
+ + +.. thumbnail-parent-div-close + +.. raw:: html + + + + +.. toctree:: + :hidden: + + /examples/validation/parity_plot + + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-gallery + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download all examples in Python source code: validation_python.zip ` + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download all examples in Jupyter notebooks: validation_jupyter.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/examples/validation/output.xyz b/docs/src/examples/validation/output.xyz similarity index 100% rename from examples/validation/output.xyz rename to docs/src/examples/validation/output.xyz diff --git a/docs/src/examples/validation/parity_plot.codeobj.json b/docs/src/examples/validation/parity_plot.codeobj.json new file mode 100644 index 0000000000..8337a1b7da --- /dev/null +++ b/docs/src/examples/validation/parity_plot.codeobj.json @@ -0,0 +1,382 @@ +{ + "ase.io.read": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase.io", + "module_short": "ase.io", + "name": "read" + } + ], + "axs": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "ndarray" + } + ], + "chemiscope.ase_vectors_to_arrows": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "chemiscope", + "module_short": "chemiscope", + "name": "ase_vectors_to_arrows" + } + ], + "chemiscope.quick_settings": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "chemiscope", + "module_short": "chemiscope", + "name": "quick_settings" + } + ], + "chemiscope.show": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "chemiscope", + "module_short": "chemiscope", + "name": "show" + } + ], + "cs": [ + { + "is_class": false, + "is_explicit": false, + "module": "chemiscope.jupyter", + "module_short": "chemiscope.jupyter", + "name": "ChemiscopeWidget" + }, + { + "is_class": false, + "is_explicit": false, + "module": "chemiscope", + "module_short": "chemiscope", + "name": "ChemiscopeWidget" + } + ], + "e_predictions": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "ndarray" + } + ], + "e_targets": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "ndarray" + } + ], + "f_predictions": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "ndarray" + } + ], + "f_targets": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "ndarray" + } + ], + "fig": [ + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib.figure", + "module_short": "matplotlib.figure", + "name": "Figure" + }, + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib", + "module_short": "matplotlib", + "name": "Figure" + } + ], + "frame": [ + { + "is_class": false, + "is_explicit": false, + "module": "ase.atoms", + "module_short": "ase", + "name": "Atoms" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Atoms" + } + ], + "frame.arrays": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "dict" + } + ], + "frame.get_forces": [ + { + "is_class": false, + "is_explicit": false, + "module": "ase.atoms", + "module_short": "ase", + "name": "Atoms.get_forces" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Atoms.get_forces" + } + ], + "frame.get_total_energy": [ + { + "is_class": false, + "is_explicit": false, + "module": "ase.atoms", + "module_short": "ase", + "name": "Atoms.get_total_energy" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Atoms.get_total_energy" + } + ], + "max_e": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "float64" + } + ], + "max_f": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "float64" + } + ], + "min_e": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "float64" + } + ], + "min_f": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "float64" + } + ], + "np.array": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "builtin_function_or_method" + }, + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "array" + } + ], + "np.max": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "_ArrayFunctionDispatcher" + }, + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "max" + } + ], + "np.mean": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "_ArrayFunctionDispatcher" + }, + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "mean" + } + ], + "np.min": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "_ArrayFunctionDispatcher" + }, + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "min" + } + ], + "np.sqrt": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "ufunc" + }, + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "sqrt" + } + ], + "plt.show": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib.pyplot", + "module_short": "matplotlib.pyplot", + "name": "show" + } + ], + "plt.subplots": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib.pyplot", + "module_short": "matplotlib.pyplot", + "name": "subplots" + } + ], + "plt.tight_layout": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib.pyplot", + "module_short": "matplotlib.pyplot", + "name": "tight_layout" + } + ], + "predictions": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "list" + } + ], + "targets": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "list" + } + ] +} diff --git a/docs/src/examples/validation/parity_plot.ipynb b/docs/src/examples/validation/parity_plot.ipynb new file mode 100644 index 0000000000..7b3b971834 --- /dev/null +++ b/docs/src/examples/validation/parity_plot.ipynb @@ -0,0 +1,154 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Model validation with parity plots for energies and forces\n\nThis tutorial shows how to visualise your model output using parity plots. In the\n`train-from-scratch` we learned how to evaluate a trained model on a test set and\nsave the results to an output file. Here we will show how to create parity plots from\nthese results.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Import necessary libraries\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import ase.io\nimport chemiscope\nimport matplotlib.pyplot as plt\nimport numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the target and predicted data\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "targets = ase.io.read(\n \"../train_from_scratch/ethanol_reduced_100.xyz\", \":\"\n) # reference data (ground truth)\npredictions = ase.io.read(\"output.xyz\", \":\") # predicted data from the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Extract the energies from the loaded frames\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "e_targets = np.array(\n [frame.get_total_energy() / len(frame) for frame in targets]\n) # target energies\ne_predictions = np.array(\n [frame.get_total_energy() / len(frame) for frame in predictions]\n) # predicted energies\nf_targets = np.array(\n [frame.get_forces().flatten() for frame in targets]\n).flatten() # target forces\nf_predictions = np.array(\n [frame.get_forces().flatten() for frame in predictions]\n).flatten() # predicted forces" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create parity plots to compare predicted vs. target energies and forces\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "fig, axs = plt.subplots(1, 2, figsize=(12, 5))\n\n# Parity plot for energies\naxs[0].scatter(e_targets, e_predictions)\naxs[0].axline((np.min(e_targets), np.min(e_targets)), slope=1, ls=\"--\", color=\"red\")\naxs[0].set_xlabel(\"Target energy / kcal\")\naxs[0].set_ylabel(\"Predicted energy / kcal\")\nmin_e = np.min(np.array([e_targets, e_predictions])) - 2\nmax_e = np.max(np.array([e_targets, e_predictions])) + 2\naxs[0].set_xlim([min_e, max_e])\naxs[0].set_ylim([min_e, max_e])\naxs[0].set_title(\"Energy Parity Plot\")\n\n# Parity plot for forces\naxs[1].scatter(f_targets, f_predictions, alpha=0.5)\naxs[1].axline((np.min(f_targets), np.min(f_targets)), slope=1, ls=\"--\", color=\"red\")\naxs[1].set_xlabel(\"Target force / kcal/\u00c5\")\naxs[1].set_ylabel(\"Predicted force / kcal/\u00c5\")\nmin_f = np.min(np.array([f_targets, f_predictions])) - 2\nmax_f = np.max(np.array([f_targets, f_predictions])) + 2\naxs[1].set_xlim([min_f, max_f])\naxs[1].set_ylim([min_f, max_f])\naxs[1].set_title(\"Force Parity Plot\")\n\nplt.tight_layout()\nplt.show()\n\nprint(\n \"RMSE energy (per atom):\",\n np.sqrt(np.mean((e_targets - e_predictions) ** 2)),\n \"kcal\",\n)\nprint(\"RMSE forces:\", np.sqrt(np.mean((f_targets - f_predictions) ** 2)), \"kcal/\u00c5 \")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The results are a bit poor here because the model was not trained well enough and\nwas created only for demonstration purposes. In the case of a well-trained model, the\npoints should be closer to the diagonal line.\n\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Check outliers with ``Chemiscope``\nWith the approach above, you can inspect the whole dataset, but it might be difficult\nto identify outliers. `Chemiscope ` is a\nvisualisation tool, allowing you to explore the dataset interactively. The following\nexample shows how to use it to check the structure of probable outliers and the atomic\nforces.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "for frame in targets + predictions:\n frame.arrays[\"forces\"] = frame.get_forces()\n# a workaround, because the chemiscope interface for getting forces is broken with ASE\n# 3.23" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot the energy parity plot with Chemiscope. This can be rendered as a widget in a\nJupyter notebook.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "cs = chemiscope.show(\n targets, # reading structures from the dataset\n properties={\n \"Target energy\": {\"values\": e_targets, \"target\": \"structure\", \"units\": \"kcal\"},\n \"Predicted energy\": {\n \"values\": e_predictions,\n \"target\": \"structure\",\n \"units\": \"kcal\",\n },\n }, # plotting the energy parity plot\n mode=\"default\",\n shapes={\n \"target_forces\": chemiscope.ase_vectors_to_arrows(\n targets, key=\"forces\", scale=0.05, radius=0.15\n ),\n \"predicted_forces\": chemiscope.ase_vectors_to_arrows(\n predictions, key=\"forces\", scale=0.05, radius=0.15\n ),\n }, # plotting the atomic forces\n settings=chemiscope.quick_settings(\n trajectory=True,\n map_settings={\"joinPoints\": False},\n structure_settings={\n \"unitCell\": True,\n \"environments\": {\"activated\": False},\n \"shape\": \"predicted_forces\", # show predicted forces by defalut\n },\n ),\n)\ncs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can check the structures by clicking the red dots on the parity plot, or\ndragging the bar in the bottom right corner. Currently, plotting the diagonal line is\nnot supported in chemiscope, so please check the parity plot above to see the\noutliers.\n\nThe atomic forces are shown in arrows. The predicted forces are shown here. The target\nforces can be toggled by clicking the \"target_forces\" option in the menu in the upper\nright corner of the right panel.\n\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/examples/validation/parity_plot.py b/docs/src/examples/validation/parity_plot.py similarity index 100% rename from examples/validation/parity_plot.py rename to docs/src/examples/validation/parity_plot.py diff --git a/docs/src/examples/validation/parity_plot.py.md5 b/docs/src/examples/validation/parity_plot.py.md5 new file mode 100644 index 0000000000..c1a3ea37a7 --- /dev/null +++ b/docs/src/examples/validation/parity_plot.py.md5 @@ -0,0 +1 @@ +4381fe7e0c0bc39cf97ff93007ea6114 \ No newline at end of file diff --git a/docs/src/examples/validation/parity_plot.rst b/docs/src/examples/validation/parity_plot.rst new file mode 100644 index 0000000000..de747cd147 --- /dev/null +++ b/docs/src/examples/validation/parity_plot.rst @@ -0,0 +1,281 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "examples/validation/parity_plot.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code. + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_examples_validation_parity_plot.py: + + +Model validation with parity plots for energies and forces +========================================================== + +This tutorial shows how to visualise your model output using parity plots. In the +:ref:`train-from-scratch` we learned how to evaluate a trained model on a test set and +save the results to an output file. Here we will show how to create parity plots from +these results. + +.. GENERATED FROM PYTHON SOURCE LINES 13-14 + +Import necessary libraries + +.. GENERATED FROM PYTHON SOURCE LINES 14-20 + +.. code-block:: Python + + import ase.io + import chemiscope + import matplotlib.pyplot as plt + import numpy as np + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 21-22 + +Load the target and predicted data + +.. GENERATED FROM PYTHON SOURCE LINES 22-28 + +.. code-block:: Python + + targets = ase.io.read( + "../train_from_scratch/ethanol_reduced_100.xyz", ":" + ) # reference data (ground truth) + predictions = ase.io.read("output.xyz", ":") # predicted data from the model + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 29-30 + +Extract the energies from the loaded frames + +.. GENERATED FROM PYTHON SOURCE LINES 30-44 + +.. code-block:: Python + + e_targets = np.array( + [frame.get_total_energy() / len(frame) for frame in targets] + ) # target energies + e_predictions = np.array( + [frame.get_total_energy() / len(frame) for frame in predictions] + ) # predicted energies + f_targets = np.array( + [frame.get_forces().flatten() for frame in targets] + ).flatten() # target forces + f_predictions = np.array( + [frame.get_forces().flatten() for frame in predictions] + ).flatten() # predicted forces + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 45-47 + +Create parity plots to compare predicted vs. target energies and forces +----------------------------------------------------------------------- + +.. GENERATED FROM PYTHON SOURCE LINES 47-81 + +.. code-block:: Python + + + fig, axs = plt.subplots(1, 2, figsize=(12, 5)) + + # Parity plot for energies + axs[0].scatter(e_targets, e_predictions) + axs[0].axline((np.min(e_targets), np.min(e_targets)), slope=1, ls="--", color="red") + axs[0].set_xlabel("Target energy / kcal") + axs[0].set_ylabel("Predicted energy / kcal") + min_e = np.min(np.array([e_targets, e_predictions])) - 2 + max_e = np.max(np.array([e_targets, e_predictions])) + 2 + axs[0].set_xlim([min_e, max_e]) + axs[0].set_ylim([min_e, max_e]) + axs[0].set_title("Energy Parity Plot") + + # Parity plot for forces + axs[1].scatter(f_targets, f_predictions, alpha=0.5) + axs[1].axline((np.min(f_targets), np.min(f_targets)), slope=1, ls="--", color="red") + axs[1].set_xlabel("Target force / kcal/Ã…") + axs[1].set_ylabel("Predicted force / kcal/Ã…") + min_f = np.min(np.array([f_targets, f_predictions])) - 2 + max_f = np.max(np.array([f_targets, f_predictions])) + 2 + axs[1].set_xlim([min_f, max_f]) + axs[1].set_ylim([min_f, max_f]) + axs[1].set_title("Force Parity Plot") + + plt.tight_layout() + plt.show() + + print( + "RMSE energy (per atom):", + np.sqrt(np.mean((e_targets - e_predictions) ** 2)), + "kcal", + ) + print("RMSE forces:", np.sqrt(np.mean((f_targets - f_predictions) ** 2)), "kcal/Ã… ") + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + RMSE energy (per atom): 0.3662913918764794 kcal + RMSE forces: 15.042111936421486 kcal/Ã… + + + + +.. GENERATED FROM PYTHON SOURCE LINES 82-85 + +The results are a bit poor here because the model was not trained well enough and +was created only for demonstration purposes. In the case of a well-trained model, the +points should be closer to the diagonal line. + +.. GENERATED FROM PYTHON SOURCE LINES 87-94 + +Check outliers with ``Chemiscope`` +---------------------------------- +With the approach above, you can inspect the whole dataset, but it might be difficult +to identify outliers. `Chemiscope ` is a +visualisation tool, allowing you to explore the dataset interactively. The following +example shows how to use it to check the structure of probable outliers and the atomic +forces. + +.. GENERATED FROM PYTHON SOURCE LINES 94-100 + +.. code-block:: Python + + + for frame in targets + predictions: + frame.arrays["forces"] = frame.get_forces() + # a workaround, because the chemiscope interface for getting forces is broken with ASE + # 3.23 + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 101-103 + +Plot the energy parity plot with Chemiscope. This can be rendered as a widget in a +Jupyter notebook. + +.. GENERATED FROM PYTHON SOURCE LINES 103-134 + +.. code-block:: Python + + cs = chemiscope.show( + targets, # reading structures from the dataset + properties={ + "Target energy": {"values": e_targets, "target": "structure", "units": "kcal"}, + "Predicted energy": { + "values": e_predictions, + "target": "structure", + "units": "kcal", + }, + }, # plotting the energy parity plot + mode="default", + shapes={ + "target_forces": chemiscope.ase_vectors_to_arrows( + targets, key="forces", scale=0.05, radius=0.15 + ), + "predicted_forces": chemiscope.ase_vectors_to_arrows( + predictions, key="forces", scale=0.05, radius=0.15 + ), + }, # plotting the atomic forces + settings=chemiscope.quick_settings( + trajectory=True, + map_settings={"joinPoints": False}, + structure_settings={ + "unitCell": True, + "environments": {"activated": False}, + "shape": "predicted_forces", # show predicted forces by defalut + }, + ), + ) + cs + + + +.. chemiscope:: _datasets/fig_parity_plot_001.json.gz + :mode: default + :warning_timeout: 2000 + + + +.. raw:: html + +
+ +
+
+
+ +.. GENERATED FROM PYTHON SOURCE LINES 135-143 + +You can check the structures by clicking the red dots on the parity plot, or +dragging the bar in the bottom right corner. Currently, plotting the diagonal line is +not supported in chemiscope, so please check the parity plot above to see the +outliers. + +The atomic forces are shown in arrows. The predicted forces are shown here. The target +forces can be toggled by clicking the "target_forces" option in the menu in the upper +right corner of the right panel. + + +.. _sphx_glr_download_examples_validation_parity_plot.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: parity_plot.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: parity_plot.py ` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: parity_plot.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/validation/parity_plot.zip b/docs/src/examples/validation/parity_plot.zip new file mode 100644 index 0000000000000000000000000000000000000000..f3d9dbd83d2f478cceb628d85b1850ddb7b94f50 GIT binary patch literal 12844 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"module_short": "builtins", + "name": "builtin_function_or_method" + } + ], + "structures_for_pair": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "list" + } + ], + "torch.jit.optimized_execution": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "torch.jit", + "module_short": "torch.jit", + "name": "optimized_execution" + } + ] +} diff --git a/docs/src/examples/zbl/dimers.ipynb b/docs/src/examples/zbl/dimers.ipynb new file mode 100644 index 0000000000..b241500ab9 --- /dev/null +++ b/docs/src/examples/zbl/dimers.ipynb @@ -0,0 +1,140 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Training a model with ZBL corrections\n\nThis tutorial demonstrates how to train a model with ZBL corrections.\n\nThe training set for this example consists of a\nsubset of the ethanol moleculs from the [rMD17 dataset](https://iopscience.iop.org/article/10.1088/2632-2153/abba6f/meta).\n\nThe models are trained using the following training options, respectively:\n\n.. literalinclude:: options_no_zbl.yaml\n :language: yaml\n\n.. literalinclude:: options_zbl.yaml\n :language: yaml\n\nAs you can see, they are identical, except for the ``zbl`` key in the\n``model`` section.\nYou can train the same models yourself with\n\n.. literalinclude:: train.sh\n :language: bash\n\nA detailed step-by-step introduction on how to train a model is provided in\nthe `label_basic_usage` tutorial.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we start by importing the necessary libraries, including the integration of ASE\ncalculators for metatensor atomistic models.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import ase\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\nfrom metatomic.torch.ase_calculator import MetatomicCalculator" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting up the dimers\n\nWe set up a series of dimers with different atom pairs and distances. We will\ncalculate the energies of these dimers using the models trained with and without ZBL\ncorrections.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "distances = np.linspace(0.5, 6.0, 200)\npairs = {}\nfor pair in [(\"H\", \"H\"), (\"H\", \"C\"), (\"C\", \"C\"), (\"C\", \"O\"), (\"O\", \"O\"), (\"H\", \"O\")]:\n structures = []\n for distance in distances:\n atoms = ase.Atoms(\n symbols=[pair[0], pair[1]],\n positions=[[0, 0, 0], [0, 0, distance]],\n )\n structures.append(atoms)\n pairs[pair] = structures" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now load the two exported models, one with and one without ZBL corrections\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "calc_no_zbl = MetatomicCalculator(\"model_no_zbl.pt\", extensions_directory=\"extensions/\")\ncalc_zbl = MetatomicCalculator(\"model_zbl.pt\", extensions_directory=\"extensions/\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculate and plot energies without ZBL\n\nWe calculate the energies of the dimer curves for each pair of atoms and\nplot the results, using the non-ZBL-corrected model.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "for pair, structures_for_pair in pairs.items():\n energies = []\n for atoms in structures_for_pair:\n atoms.set_calculator(calc_no_zbl)\n with torch.jit.optimized_execution(False):\n energies.append(atoms.get_potential_energy())\n energies = np.array(energies) - energies[-1]\n plt.plot(distances, energies, label=f\"{pair[0]}-{pair[1]}\")\nplt.title(\"Dimer curves - no ZBL\")\nplt.xlabel(\"Distance (\u00c5)\")\nplt.ylabel(\"Energy (eV)\")\nplt.legend()\nplt.tight_layout()\nplt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculate and plot energies from the ZBL-corrected model\n\nWe repeat the same procedure as above, but this time with the ZBL-corrected model.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "for pair, structures_for_pair in pairs.items():\n energies = []\n for atoms in structures_for_pair:\n atoms.set_calculator(calc_zbl)\n with torch.jit.optimized_execution(False):\n energies.append(atoms.get_potential_energy())\n energies = np.array(energies) - energies[-1]\n plt.plot(distances, energies, label=f\"{pair[0]}-{pair[1]}\")\nplt.title(\"Dimer curves - with ZBL\")\nplt.xlabel(\"Distance (\u00c5)\")\nplt.ylabel(\"Energy (eV)\")\nplt.legend()\nplt.tight_layout()\nplt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It can be seen that all the dimer curves include a strong repulsion\nat short distances, which is due to the ZBL contribution. Even the H-H dimer,\nwhose ZBL correction is very weak due to the small covalent radii of hydrogen,\nwould show a strong repulsion closer to the origin (here, we only plotted\nstarting from a distance of 0.5 \u00c5). Let's zoom in on the H-H dimer to see\nthis effect more clearly.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "new_distances = np.linspace(0.1, 2.0, 200)\n\nstructures = []\nfor distance in new_distances:\n atoms = ase.Atoms(\n symbols=[\"H\", \"H\"],\n positions=[[0, 0, 0], [0, 0, distance]],\n )\n structures.append(atoms)\n\nfor atoms in structures:\n atoms.set_calculator(calc_zbl)\nwith torch.jit.optimized_execution(False):\n energies = [atoms.get_potential_energy() for atoms in structures]\nenergies = np.array(energies) - energies[-1]\nplt.plot(new_distances, energies, label=\"H-H\")\nplt.title(\"Dimer curve - H-H with ZBL\")\nplt.xlabel(\"Distance (\u00c5)\")\nplt.ylabel(\"Energy (eV)\")\nplt.legend()\nplt.tight_layout()\nplt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/examples/zbl/dimers.py b/docs/src/examples/zbl/dimers.py similarity index 100% rename from examples/zbl/dimers.py rename to docs/src/examples/zbl/dimers.py diff --git a/docs/src/examples/zbl/dimers.py.md5 b/docs/src/examples/zbl/dimers.py.md5 new file mode 100644 index 0000000000..2d0c746956 --- /dev/null +++ b/docs/src/examples/zbl/dimers.py.md5 @@ -0,0 +1 @@ +fc55a8030c89ecfae84a5d1c4dce758f \ No newline at end of file diff --git a/docs/src/examples/zbl/dimers.rst b/docs/src/examples/zbl/dimers.rst new file mode 100644 index 0000000000..c529edc435 --- /dev/null +++ b/docs/src/examples/zbl/dimers.rst @@ -0,0 +1,287 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "examples/zbl/dimers.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code. + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_examples_zbl_dimers.py: + + +Training a model with ZBL corrections +===================================== + +This tutorial demonstrates how to train a model with ZBL corrections. + +The training set for this example consists of a +subset of the ethanol moleculs from the `rMD17 dataset +`_. + +The models are trained using the following training options, respectively: + +.. literalinclude:: options_no_zbl.yaml + :language: yaml + +.. literalinclude:: options_zbl.yaml + :language: yaml + +As you can see, they are identical, except for the ``zbl`` key in the +``model`` section. +You can train the same models yourself with + +.. literalinclude:: train.sh + :language: bash + +A detailed step-by-step introduction on how to train a model is provided in +the :ref:`label_basic_usage` tutorial. + +.. GENERATED FROM PYTHON SOURCE LINES 31-33 + +First, we start by importing the necessary libraries, including the integration of ASE +calculators for metatensor atomistic models. + +.. GENERATED FROM PYTHON SOURCE LINES 34-42 + +.. code-block:: Python + + + import ase + import matplotlib.pyplot as plt + import numpy as np + import torch + from metatomic.torch.ase_calculator import MetatomicCalculator + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 43-49 + +Setting up the dimers +--------------------- + +We set up a series of dimers with different atom pairs and distances. We will +calculate the energies of these dimers using the models trained with and without ZBL +corrections. + +.. GENERATED FROM PYTHON SOURCE LINES 50-63 + +.. code-block:: Python + + + distances = np.linspace(0.5, 6.0, 200) + pairs = {} + for pair in [("H", "H"), ("H", "C"), ("C", "C"), ("C", "O"), ("O", "O"), ("H", "O")]: + structures = [] + for distance in distances: + atoms = ase.Atoms( + symbols=[pair[0], pair[1]], + positions=[[0, 0, 0], [0, 0, distance]], + ) + structures.append(atoms) + pairs[pair] = structures + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 64-65 + +We now load the two exported models, one with and one without ZBL corrections + +.. GENERATED FROM PYTHON SOURCE LINES 66-71 + +.. code-block:: Python + + + calc_no_zbl = MetatomicCalculator("model_no_zbl.pt", extensions_directory="extensions/") + calc_zbl = MetatomicCalculator("model_zbl.pt", extensions_directory="extensions/") + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 72-77 + +Calculate and plot energies without ZBL +--------------------------------------- + +We calculate the energies of the dimer curves for each pair of atoms and +plot the results, using the non-ZBL-corrected model. + +.. GENERATED FROM PYTHON SOURCE LINES 78-94 + +.. code-block:: Python + + + for pair, structures_for_pair in pairs.items(): + energies = [] + for atoms in structures_for_pair: + atoms.set_calculator(calc_no_zbl) + with torch.jit.optimized_execution(False): + energies.append(atoms.get_potential_energy()) + energies = np.array(energies) - energies[-1] + plt.plot(distances, energies, label=f"{pair[0]}-{pair[1]}") + plt.title("Dimer curves - no ZBL") + plt.xlabel("Distance (Ã…)") + plt.ylabel("Energy (eV)") + plt.legend() + plt.tight_layout() + plt.show() + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + /Users/philiploche/repos/lab-cosmo/metatrain/examples/zbl/dimers.py:82: FutureWarning: Please use atoms.calc = calc + atoms.set_calculator(calc_no_zbl) + + + + +.. GENERATED FROM PYTHON SOURCE LINES 95-99 + +Calculate and plot energies from the ZBL-corrected model +-------------------------------------------------------- + +We repeat the same procedure as above, but this time with the ZBL-corrected model. + +.. GENERATED FROM PYTHON SOURCE LINES 100-116 + +.. code-block:: Python + + + for pair, structures_for_pair in pairs.items(): + energies = [] + for atoms in structures_for_pair: + atoms.set_calculator(calc_zbl) + with torch.jit.optimized_execution(False): + energies.append(atoms.get_potential_energy()) + energies = np.array(energies) - energies[-1] + plt.plot(distances, energies, label=f"{pair[0]}-{pair[1]}") + plt.title("Dimer curves - with ZBL") + plt.xlabel("Distance (Ã…)") + plt.ylabel("Energy (eV)") + plt.legend() + plt.tight_layout() + plt.show() + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + /Users/philiploche/repos/lab-cosmo/metatrain/examples/zbl/dimers.py:104: FutureWarning: Please use atoms.calc = calc + atoms.set_calculator(calc_zbl) + + + + +.. GENERATED FROM PYTHON SOURCE LINES 117-123 + +It can be seen that all the dimer curves include a strong repulsion +at short distances, which is due to the ZBL contribution. Even the H-H dimer, +whose ZBL correction is very weak due to the small covalent radii of hydrogen, +would show a strong repulsion closer to the origin (here, we only plotted +starting from a distance of 0.5 Ã…). Let's zoom in on the H-H dimer to see +this effect more clearly. + +.. GENERATED FROM PYTHON SOURCE LINES 124-147 + +.. code-block:: Python + + + new_distances = np.linspace(0.1, 2.0, 200) + + structures = [] + for distance in new_distances: + atoms = ase.Atoms( + symbols=["H", "H"], + positions=[[0, 0, 0], [0, 0, distance]], + ) + structures.append(atoms) + + for atoms in structures: + atoms.set_calculator(calc_zbl) + with torch.jit.optimized_execution(False): + energies = [atoms.get_potential_energy() for atoms in structures] + energies = np.array(energies) - energies[-1] + plt.plot(new_distances, energies, label="H-H") + plt.title("Dimer curve - H-H with ZBL") + plt.xlabel("Distance (Ã…)") + plt.ylabel("Energy (eV)") + plt.legend() + plt.tight_layout() + plt.show() + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + /Users/philiploche/repos/lab-cosmo/metatrain/examples/zbl/dimers.py:136: FutureWarning: Please use atoms.calc = calc + atoms.set_calculator(calc_zbl) + + + + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** (0 minutes 15.025 seconds) + + +.. _sphx_glr_download_examples_zbl_dimers.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: dimers.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: dimers.py ` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: dimers.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/zbl/dimers.zip b/docs/src/examples/zbl/dimers.zip new file mode 100644 index 0000000000000000000000000000000000000000..6fc0c82974246cb822013bf756b95c1fb876a0d3 GIT binary patch literal 11424 zcmeHNTW{OQ6;3y8fwJvGfqiKoiUEVg%78@Mag%O^xH{CL^J)Fy#GiT0izTwkHH^2M-4SH_= z{*Qwne;$1Jx1Zd&K|eo0-6WlhqKxuY7>2>Kf~P7~r;M|CHW897Q$1sU{_SIyWJMto zoo1>Gj<+LazEj5JgO;S`=KS 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+ +.. only:: html + + .. image:: /examples/zbl/images/thumb/sphx_glr_dimers_thumb.png + :alt: + + :ref:`sphx_glr_examples_zbl_dimers.py` + +.. raw:: html + +
Training a model with ZBL corrections
+
+ + +.. thumbnail-parent-div-close + +.. raw:: html + + + + +.. toctree:: + :hidden: + + /examples/zbl/dimers + + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-gallery + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download all examples as source code: zbl.zip ` + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download all examples in Jupyter notebooks: zbl_jupyter.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/examples/zbl/options_no_zbl.yaml b/docs/src/examples/zbl/options_no_zbl.yaml similarity index 100% rename from examples/zbl/options_no_zbl.yaml rename to docs/src/examples/zbl/options_no_zbl.yaml diff --git a/examples/zbl/options_zbl.yaml b/docs/src/examples/zbl/options_zbl.yaml similarity index 100% rename from examples/zbl/options_zbl.yaml rename to docs/src/examples/zbl/options_zbl.yaml diff --git a/examples/zbl/train.sh b/docs/src/examples/zbl/train.sh old mode 100755 new mode 100644 similarity index 100% rename from examples/zbl/train.sh rename to docs/src/examples/zbl/train.sh diff --git a/docs/src/examples/zbl/zbl.zip b/docs/src/examples/zbl/zbl.zip new file mode 100644 index 0000000000000000000000000000000000000000..1e804b005e3441c902c5f04b4b933aeec40abd00 GIT binary patch literal 4789 zcmd^D-;WbF5WbeSYPaeG73#Aagh-@hb9V@eqF$vdKSTwHt3c2;Iwj7olel=-tF`xX zf%3pZpZibnpY$K=H}>vk!z~nEP^AWPalH1-c;@?N#yPrs?vo2U^!oDWU&c?)e{)G< zOz+R}wmFw+XqFeh9nNGroN==Zg2YFT3qZNiN^@>?tp7?FLLJ}kVLbA&U%jP;~mSl@l zo@7eEw$w;t4byYR15?as6~-1jg<{+$u3j1P*^FPC4-;Ye57S9$ zt&5dmJTDCwF)IvNPX^7kQhMdSRn9I$-eBUQv=G-`uK zz!)1To-PW$5F$Bg!zK)VEv0&#lP?TUga?|_nH!=W3mNDYRVOn`hDm0kn@y-o&9SHpzcOlb?HU^$KO zkY&nN&(k8w5Mjg8teS$ZXc;)FIBn1pg>Ev0;K|0K@`&s{*+i>u)W!x4-#ir7xuM7$ z2Yk-K(FScV=glW@BUp?L90T$dImB=DSt*wDxyS)Y=LME=_@D7K#v%j&^fe)al$BJf z;Y3i9vO+5IMJW^Cji@AVcv_O!VGgKpiIAkzKo^#hB8lCkMfkqf%nsnp5VSQJk3@Gj zyxM2i!reaG-`(v6-qZv3{6#?Vq_0Gqakq1)(`We8>$7rv!;f#!d~^K3&p)8~a_shE zd^~czMydtq3#1enGd^~6q^+`%G*)Bdw@^BuDAEpuIXt9s*X^W7^fK#Yrj{vm`j55cuu|LH)T_5|3=eshi8SsyoBRUjI;Z>?MsAii zu!fkYC@)I$*u~neH1ZV@5n=RE?IRHoGEmznq}7))tj(YX0b#wYyU^Hz^KQop-PC5p zM4S>qT^)}l)lHqR4?3IKVW;N>BX7Q4s;}YiD6-*&iig6T(6PP-{WgT#DA+Prv9jWR zE0H{nSXAUEz#YL&@M!4~PQ}@Q3oZw9oJqt*Ahb{xsc^%+RHp;*Yf!SQiW?YK@ulC0 z{RESfibM_|A*v>j+w-ul&%MP1ZwpAU0Z}dJH9CaIM2(!+Z9rlJTZbl}B7c`Qq?(?{ zXCj`6Q#2mLh3;*xj3|xrbYgWT+QNFcfWk}zLl&*m#2sMW?RjO5;~>ZIJm>3fHP>T< zTH<)H=UIg%Nc4u(M=j=Y@t(F>{5S-DdfF$Zf zpl4kP{cwbCf-=xP(ukaA2Kr>-Tmm}3+LYan-PAeaj#b|>7%*_H+QxDwBz^e e?7VvU7l!`sge&0D-A_NGM<3%~g3tZ0>DxaLdIuN) literal 0 HcmV?d00001 diff --git a/docs/src/examples/zbl/zbl_jupyter.zip b/docs/src/examples/zbl/zbl_jupyter.zip new file mode 100644 index 0000000000000000000000000000000000000000..a9dbfb39d4d3302871790d89a169d8616113ede9 GIT binary patch literal 6854 zcmeHMTaO#J72Y&yAJRVNsSgDqKU51yBgtF4$pZV}CJtQKK^-LMO%k-!3?;GVkOPvl z+Ogq>{+Ira{G|Mbeuw0Yq+QwVB1K>qkuT!)@bK`Q?_Bi!>4zVEazdY9e*M?!cfaX> z|LoT%C-m=E7%OBctjlE8=;f=A8SCXjDc3t^Q*;=kA^L>jg>Nc>sb0zLi^6PmZ_rMc z!t;WAj@ehQyHRIqo70X+ED)OMd+b}wrIvclI4eyd6x&L_VSoR_GnN}`Mee21Zl-7Y z+l_S0*WOsk6)QxEac{X7j&01AdBf0?`mJ=bNJzw`bgC1ct&C-!sEcb}R!ZPx?WFUL znHA$R?dl~hL&t9f6Mn;+chZLwpfFE4{QFzTXuKumv2bfXH8R2(lw)TnT(bwaIzZoNqgVQc0#H?O|rG&a+Z zpmUx}1=%|}ejplOg@@ZSYHJogc6*B<7)&YzaY^m4SXH_0l32&T-p7ah8@_Dck^eAmwS7 zKm=k9Q-rCKl|1_9@l4}2VI3Ui3JZaOkwSZ+9hw*{VO}y%?}N+U>AE>^Vh1qfwG5K^Sa?S56dIzT(P>G_}JkQ~Rp+0sZD3v11?>rNv(^4$VmmlagsFyu3uqFA1tH~G7|lR@u@h)|AodlBlvaJ- z$@)i9ntS)yH^K)MsjC1&(Ra{8{so!@w8S_^gVcp|BF;x}FXU<^EKELV0;@Pwka1mL z5p)nR=`sc?Td9CU?X`h?1io6>bs~wr6D{E#944#`<_v2K?u3X_1CL6s1ypyw<^9&a zPGsx?%9H`sUBz?JA7>8+>|r(@u>0fj=}bo7o--? 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Furthermore, outliers, such as large forces complicate training, so looking at the distribution of the forces and removing structures with large forces (e.g. all structures with forces with an absolute force > 20 eV/Å) from the dataset can help to stabilize training. For these tasks parity plots can be useful to find outliers. -See our :ref:`sphx_glr_examples_validation_parity_plot.py` for how to create them. +See our :ref:`sphx_glr_generated_examples_0-beginner_04-parity_plot.py` for how to create them. General training concepts ------------------------- @@ -63,7 +63,7 @@ correlations up to roughly 10 Å. **Q: In what format should I provide my data?** **A:** You can find everything on how to prepare your data in -:ref:`sphx_glr_examples_programmatic_data_preparation_data_preparation.py`. +:ref:`sphx_glr_generated_examples_0-beginner_01-data_preparation.py`. **Q: How small should my errors be before I can use my model to run Molecular Dynamics simulations?** diff --git a/docs/src/getting-started/index.rst b/docs/src/getting-started/index.rst index fc111aaf31..a64c3e3f1d 100644 --- a/docs/src/getting-started/index.rst +++ b/docs/src/getting-started/index.rst @@ -7,7 +7,6 @@ This sections describes how to install the package, and its most basic commands. :maxdepth: 1 quickstart - ../examples/basic_usage/usage custom_dataset_conf advanced_base_config override diff --git a/docs/src/getting-started/override.rst b/docs/src/getting-started/override.rst index 5296db7972..eb6725361c 100644 --- a/docs/src/getting-started/override.rst +++ b/docs/src/getting-started/override.rst @@ -4,7 +4,7 @@ Override Architecture's Default Parameters In our initial tutorial, we used default parameters to train a model employing the SOAP-BPNN architecture, as shown in the following config: -.. literalinclude:: ../../../examples/basic_usage/options.yaml +.. literalinclude:: ../../../examples/0-beginner/options-basic.yaml :language: yaml While default parameters often serve as a good starting point, depending on your diff --git a/docs/src/index.rst b/docs/src/index.rst index c1379fdacb..0ef1288481 100644 --- a/docs/src/index.rst +++ b/docs/src/index.rst @@ -19,7 +19,7 @@ getting-started/index configuration/index architectures/index - tutorials/index + generated_examples/index advanced-concepts/index faq cite diff --git a/docs/src/tutorials/advanced_tutorials/index.rst b/docs/src/tutorials/advanced_tutorials/index.rst deleted file mode 100644 index 075a804027..0000000000 --- a/docs/src/tutorials/advanced_tutorials/index.rst +++ /dev/null @@ -1,17 +0,0 @@ -.. _advanced_tutorials: - -Advanced Tutorials -================== - -This sections includes the advanced tutorials on the usage of the -``metatrain`` package. - -.. toctree:: - :maxdepth: 1 - - ../../advanced-concepts/transfer-learning - ../../examples/llpr/llpr - ../../examples/zbl/dimers - ../../advanced-concepts/fitting-generic-targets - ../../examples/programmatic/flashmd/flashmd - ../../examples/multi-gpu/multi-gpu diff --git a/docs/src/tutorials/beginner_tutorials/index.rst b/docs/src/tutorials/beginner_tutorials/index.rst deleted file mode 100644 index 5cec504471..0000000000 --- a/docs/src/tutorials/beginner_tutorials/index.rst +++ /dev/null @@ -1,16 +0,0 @@ -.. _beginner_tutorials: - -Beginner Tutorials -================== - -This sections includes the beginner tutorials on the usage of the -``metatrain`` package. - -.. toctree:: - :maxdepth: 1 - - ../../examples/programmatic/data_preparation/data_preparation - ../../advanced-concepts/fine-tuning - ../../examples/train_from_scratch/train_from_scratch - ../../examples/ase/run_ase - ../../examples/validation/parity_plot diff --git a/docs/src/tutorials/index.rst b/docs/src/tutorials/index.rst deleted file mode 100644 index 59abcd6d3b..0000000000 --- a/docs/src/tutorials/index.rst +++ /dev/null @@ -1,14 +0,0 @@ -.. _tutorials: - -Tutorials -========= - -This sections includes all tutorials on the usage of the -``metatrain`` package. There are beginner tutorials to help you get started -and advanced tutorials for more complex applications and workflows. - -.. toctree:: - :maxdepth: 2 - - beginner_tutorials/index - advanced_tutorials/index diff --git a/examples/programmatic/data_preparation/.gitignore b/examples/0-beginner/.gitignore similarity index 61% rename from examples/programmatic/data_preparation/.gitignore rename to examples/0-beginner/.gitignore index afae9d0eaf..6153de5dc0 100644 --- a/examples/programmatic/data_preparation/.gitignore +++ b/examples/0-beginner/.gitignore @@ -1,3 +1,5 @@ data.xyz +output.xyz qm9_reduced_100.zip qm9_reduced_100_all_at_once.zip +carbon_reduced_100_memmap/* diff --git a/examples/0-beginner/00-basic-usage.sh b/examples/0-beginner/00-basic-usage.sh new file mode 100755 index 0000000000..cc35dfc1e2 --- /dev/null +++ b/examples/0-beginner/00-basic-usage.sh @@ -0,0 +1,125 @@ +# .. _label_basic_usage: +# +# Basic Usage +# =========== +# +# ``metatrain`` is designed for a direct usage from the command line (cli). The program +# is registered via the abbreviation ``mtt`` to your command line. The general help of +# ``metatrain`` can be accessed using +# + +mtt --help + +# %% +# +# We now demonstrate how to ``train`` and ``evaluate`` a model from the command line. +# For this example we use the :ref:`architecture-soap-bpnn` architecture and a subset of +# the `QM9 dataset `_. You can obtain the +# dataset for this example here: :download:`qm9_reduced_100.xyz `. +# +# +# Training +# -------- +# +# To train models, ``metatrain`` uses a dynamic override strategy for your training +# options. We allow a dynamical composition and override of the default architecture +# with either your custom ``options.yaml`` and even command line override grammar. For +# reference and reproducibility purposes ``metatrain`` always writes the fully expanded, +# including the overwritten option to ``options_restart.yaml``. The restart options file +# is written into a subfolder named with the current *date* and *time* inside the +# ``output`` directory of your current training run. +# +# The sub-command to start a model training is +# +# .. code-block:: bash +# +# mtt train +# +# To train a model you have to define your options. This includes the specific +# architecture you want to use and the data including the training systems and target +# values +# +# The default model and training hyperparameter for each model are listed in their +# corresponding documentation page. We will use these minimal options to run an example +# training using the default hyperparameters of an SOAP BPNN model +# +# .. literalinclude:: options-basic.yaml +# :language: yaml +# +# For each training run a new output directory in the format +# ``outputs/YYYY-MM-DD/HH-MM-SS`` based on the current *date* and *time* is created. We +# use this output directory to store checkpoints, the restart ``options_restart.yaml`` +# file as well as the log files. To start the training, create an ``options.yaml`` file +# in the current directory and type + + +mtt train options-basic.yaml + +# %% +# +# The functions saves the final model `model.pt` to the current output folder for later +# evaluation. An `extensions/` folder, which contains the compiled extensions for the +# model, might also be saved depending on the architecture. All command line flags of +# the train sub-command can be listed via +# + +mtt train --help + +# %% +# +# After the training has finished, the ``mtt train`` command generates the +# ``model.ckpt`` (final checkpoint) and ``model.pt`` (exported model) files in the +# current directory, as well as in the ``output/YYYY-MM-DD/HH-MM-SS`` directory. +# +# Evaluation +# ---------- +# +# The sub-command to evaluate an already trained model is +# +# .. code-block:: bash +# +# mtt eval +# +# Besides the trained ``model``, you will also have to provide a file containing the +# system and possible target values for evaluation. The system section of this +# ``eval.yaml`` is exactly the same as for a dataset in the ``options.yaml`` file. +# +# .. literalinclude:: eval-basic.yaml +# :language: yaml +# +# Note that the ``targets`` section is optional. If the ``targets`` section is present, +# the function will calculate and report RMSE values of the predictions with respect to +# the real values as loaded from the ``targets`` section. You can run an evaluation by +# typing +# +# We now evaluate the model on the training dataset, where the first arguments specifies +# trained model and the second an option file containing the path of the dataset for +# evaulation. The extensions of the model, if any, can be specified via the ``-e`` flag. + +mtt eval model.pt eval-basic.yaml -e extensions/ + +# %% +# +# The evaluation command predicts those properties the model was trained against; here +# ``"U0"``. The predictions together with the systems have been written in a file named +# ``output.xyz`` in the current directory. The written file starts with the following +# lines + +head -n 20 output.xyz + +# %% +# +# All command line flags of the eval sub-command can be listed via + +mtt eval --help + +# %% +# +# An important parameter of ``mtt eval`` is the ``-b`` (or ``--batch-size``) option, +# which allows you to specify the batch size for the evaluation. +# +# Molecular simulations +# --------------------- +# +# The trained model can also be used to run molecular simulations. +# You can find how in the :ref:`tutorials` section. diff --git a/examples/0-beginner/01-data_preparation.py b/examples/0-beginner/01-data_preparation.py new file mode 100644 index 0000000000..a8dfa80f25 --- /dev/null +++ b/examples/0-beginner/01-data_preparation.py @@ -0,0 +1,242 @@ +""" +How to prepare data for training +================================ + +.. attention:: + + This tutorial is only relevant for users who need to prepare their data from scratch + from several files or for big datasets. If you already have your data in a common + file format (like XYZ or `ASE database`_), you can skip this tutorial and directly + start training. + +.. _ASE database: https://ase-lib.org/ase/db/db.html + +XYZ, ASE databases, and also from metrain's +:class:`metatrain.utils.data.dataset.DiskDataset ` file. + +For the small datasets (<10k structures), you can simply provide an XYZ file or an ASE +database to ``metatrain``, and it will handle the data loading for you. Large datasets +(>10k structures) may not fit into the GPU memory. In such cases, it is useful to +pre-process the dataset, save it to disk and load it on the fly during training. + +In this tutorial, we will show how to prepare data for training using three different +formats. You can choose the one that best fits your needs. + +We start by importing the necessary packages. +""" + +# %% +# +import subprocess +from pathlib import Path + +import ase.io +import numpy as np +import torch +from metatensor.torch import Labels, TensorBlock, TensorMap +from metatomic.torch import NeighborListOptions, systems_to_torch + +from metatrain.utils.data.writers import DiskDatasetWriter +from metatrain.utils.neighbor_lists import get_system_with_neighbor_lists + + +# %% +# Create a XYZ training file (small datasets) +# ------------------------------------------- +# +# First, we will show how to create a XYZ file with fields corresponding to the target +# properties. On modern HPC systems, this format is suitable for datasets up to around +# 1M structures. As an example, we will use 100 structures from a file read by ASE_. +# Since files from reference calculations may be located in different directories, we +# first create a list of all path that we want to read from. Here, for simplicity, we +# assume that all files are located in the same directory. +# +# .. _ASE: https://ase-lib.org/ + +filelist = 100 * ["qm9_reduced_100.xyz"] + +# %% +# +# We will now read the structures using the ASE package. Check the ase documentation for +# more details on how to read different file formats. Instead of creating the ``atoms`` +# object by reading from disk, you can also create an +# :class:`ase.Atoms` object containing the chemical ``symbols``, ``positions``, the +# ``cell`` and the periodic boundary conditions (``pbc``) by hand using its constructor. +# +# .. hint:: +# +# If a property is not read by the :func:`ase.io.read` function, you can add custom +# scalar properties to the ``info`` dictionary. Vector properties (e.g. forces) can be +# added to the ``arrays`` dictionary. Tensor properties (e.g. stress) must +# be flattened before adding them to the ``arrays`` dictionary. + +frames = [] +for i, fname in enumerate(filelist): + atoms = ase.io.read(fname, index=i) + + n_atoms = len(atoms) + # scalar + atoms.info["U0"] = -100.0 + # vector + atoms.arrays["forces"] = np.zeros((n_atoms, 3)) + # tensor + atoms.arrays["my_tensor"] = np.zeros((n_atoms, 3, 3)).reshape(n_atoms, 9) + + frames.append(atoms) + +ase.io.write("data.xyz", frames) + +# %% +# +# .. note:: +# +# The names of the added properties (like, ``U0``, etc.) must be referenced correctly +# in the ``options.yaml`` file. +# +# Create a ``DiskDataset`` (large datasets) +# ----------------------------------------- +# +# In addition to the systems and targets (as above), we also save the neighbor +# lists that the model will use during training. We first create the writer object that +# will write the data to a zip file. + +disk_dataset_writer = DiskDatasetWriter("qm9_reduced_100.zip") + +# %% +# +# Then we loop over all structures, convert them to the internal torch format using +# :func:`metatomic.torch.systems_to_torch`, compute the neighbor lists using +# :func:`metatrain.utils.neighbor_lists.get_system_with_neighbor_lists` and write +# everything to disk using the writer's ``write()`` method. + +for i, fname in enumerate(filelist): + atoms = ase.io.read(fname, index=i) + + system = systems_to_torch(atoms, dtype=torch.float64) + system = get_system_with_neighbor_lists( + system, + [NeighborListOptions(cutoff=5.0, full_list=True, strict=True)], + ) + energy = TensorMap( + keys=Labels.single(), + blocks=[ + TensorBlock( + values=torch.tensor([[atoms.info["U0"]]], dtype=torch.float64), + samples=Labels( + names=["system"], + values=torch.tensor([[i]]), + ), + components=[], + properties=Labels("energy", torch.tensor([[0]])), + ) + ], + ) + disk_dataset_writer.write([system], {"energy": energy}) + +disk_dataset_writer.finish() + +# %% +# +# Alternatively, you can also write the whole dataset at once, which might be more +# efficient (but also potentially run into memory issues). We use the same ``frames`` +# that we created above. + +disk_dataset_writer = DiskDatasetWriter("qm9_reduced_100_all_at_once.zip") + +systems = systems_to_torch(frames, dtype=torch.float64) +systems = [ + get_system_with_neighbor_lists( + system, + [NeighborListOptions(cutoff=5.0, full_list=True, strict=True)], + ) + for system in systems +] +energy = TensorMap( + keys=Labels.single(), + blocks=[ + TensorBlock( + values=torch.tensor( + [frame.info["U0"] for frame in frames], dtype=torch.float64 + ).reshape(-1, 1), + samples=Labels.range("system", len(frames)), + components=[], + properties=Labels("energy", torch.tensor([[0]])), + ) + ], +) + +disk_dataset_writer.write(systems, {"energy": energy}) +disk_dataset_writer.finish() + +# %% +# +# The dataset is saved to disk. You can now provide it to ``metatrain`` as a +# dataset to train from, simply by replacing your ``.xyz`` file with the newly created +# zip file (e.g. ``read_from: qm9_reduced_100.zip``). +# +# Create a ``MemmapDataset`` (large datasets, parallel filesystems) +# ----------------------------------------------------------------- +# +# If your dataset is large and you are using a parallel filesystem (e.g. on an HPC +# cluster), it is recommended to use a ``MemmapDataset`` instead of a ``DiskDataset``. +# The ``MemmapDataset`` stores the data inside memory-mapped numpy arrays instead of a +# zip file. Reading from this format avoids I/O bottlenecks, but it does not support +# spherical targets or storing neighbor lists. +# +# As an example, we will use 100 structures from a dataset of carbon structures. The +# numpy arrays must be saved inside a directory, using the following format. + +structures = ase.io.read("carbon_reduced_100.xyz", index=":") + +root = Path("carbon_reduced_100_memmap/") +root.mkdir(exist_ok=True) + +ns_path = root / "ns.npy" +na_path = root / "na.npy" +a_path = root / "a.bin" +x_path = root / "x.bin" +c_path = root / "c.bin" +e_path = root / "e.bin" +f_path = root / "f.bin" +s_path = root / "s.bin" + +ns = len(structures) +na = np.cumsum(np.array([0] + [len(s) for s in structures], dtype=np.int64)) +np.save(ns_path, ns) +np.save(na_path, na) + +a_mm = np.memmap(a_path, dtype="int32", mode="w+", shape=(na[-1],)) +x_mm = np.memmap(x_path, dtype="float32", mode="w+", shape=(na[-1], 3)) +c_mm = np.memmap(c_path, dtype="float32", mode="w+", shape=(ns, 3, 3)) +e_mm = np.memmap(e_path, dtype="float32", mode="w+", shape=(ns, 1)) +f_mm = np.memmap(f_path, dtype="float32", mode="w+", shape=(na[-1], 3)) +s_mm = np.memmap(s_path, dtype="float32", mode="w+", shape=(ns, 3, 3)) + +for i, s in enumerate(structures): + a_mm[na[i] : na[i + 1]] = s.numbers + x_mm[na[i] : na[i + 1]] = s.get_positions() + c_mm[i] = s.get_cell()[:] + e_mm[i] = s.get_potential_energy() + f_mm[na[i] : na[i + 1]] = s.arrays["force"] + s_mm[i] = -s.info["virial"] / s.get_volume() + +a_mm.flush() +x_mm.flush() +c_mm.flush() +e_mm.flush() +f_mm.flush() +s_mm.flush() + +# %% +# +# The dataset is saved to disk. You can now provide it to ``metatrain`` as a +# dataset to train from, simply by specifying the newly created +# directory as the path from which to read the systems +# (e.g. ``read_from: carbon_reduced_100_memmap/``). +# +# For example, you can use the following options file: +# +# .. literalinclude:: options-memmap.yaml +# :language: yaml + +subprocess.run(["mtt", "train", "options-memmap.yaml"]) diff --git a/docs/src/advanced-concepts/fine-tuning.rst b/examples/0-beginner/02-fine-tuning.py similarity index 80% rename from docs/src/advanced-concepts/fine-tuning.rst rename to examples/0-beginner/02-fine-tuning.py index 2d20f322e9..7391cf1e0c 100644 --- a/docs/src/advanced-concepts/fine-tuning.rst +++ b/examples/0-beginner/02-fine-tuning.py @@ -1,3 +1,4 @@ +""" .. _fine-tuning: Fine-tuning @@ -15,7 +16,6 @@ There is a complete example in :ref:`Fine-tune example `. - .. note:: Please note that the fine-tuning recommendations in this section are not universal @@ -44,8 +44,8 @@ method: "full" # This stands for the full fine-tuning read_from: path/to/checkpoint.ckpt -We recommend to use a lower learning rate than the one used for the original training, as -this will help stabilizing the training process. I.e. if the default learning rate is +We recommend to use a lower learning rate than the one used for the original training, +as this will help stabilizing the training process. I.e. if the default learning rate is ``1e-4``, you can set it to ``1e-5`` or even lower, using the following in the ``options.yaml`` file: @@ -59,19 +59,19 @@ will be taken from the checkpoint and not adapted to the new dataset. The basic fine-tuning strategy is a good choice in the case when the level of theory -which is used for the original training is the same, or at least similar to the one used for -the new dataset. However, since this is not always the case, we also provide more advanced -fine-tuning strategies described below. +which is used for the original training is the same, or at least similar to the one used +for the new dataset. However, since this is not always the case, we also provide more +advanced fine-tuning strategies described below. Fine-tuning model Heads ----------------------- -Adapting all the model weights to a new dataset is not always the best approach. If the new -dataset consist of the same or similar data computed with a slightly different level of theory -compared to the pre-trained models' dataset, you might want to keep the learned representations -of the crystal structures and only adapt the readout layers (i.e. the model heads) to the new -dataset. +Adapting all the model weights to a new dataset is not always the best approach. If the +new dataset consist of the same or similar data computed with a slightly different level +of theory compared to the pre-trained models' dataset, you might want to keep the +learned representations of the crystal structures and only adapt the readout layers +(i.e. the model heads) to the new dataset. In this case, the ``mtt train`` command needs to be accompanied by the specific training options in the ``options.yaml`` file. The following options need to be set: @@ -98,18 +98,19 @@ edge heads and last layers to be fine-tuned. We recommend to first start the fine-tuning including all the modules listed above and -experiment with their different combinations if needed. You might also consider using a lower -learning rate, e.g. ``1e-5`` or even lower, to stabilize the training process. +experiment with their different combinations if needed. You might also consider using a +lower learning rate, e.g. ``1e-5`` or even lower, to stabilize the training process. LoRA Fine-tuning ---------------- -If the conceptually new type of structures is introduced in the new dataset, tuning only the -model heads might not be sufficient. In this case, you might need to adapt the internal -representations of the crystal structures. This can be done using the LoRA technique. However, -in this case the model heads will be not adapted to the new dataset, so conceptually the -level of theory should be consistent with the one used for the pre-trained model. +If the conceptually new type of structures is introduced in the new dataset, tuning only +the model heads might not be sufficient. In this case, you might need to adapt the +internal representations of the crystal structures. This can be done using the LoRA +technique. However, in this case the model heads will be not adapted to the new dataset, +so conceptually the level of theory should be consistent with the one used for the +pre-trained model. What is LoRA? ^^^^^^^^^^^^^ @@ -161,9 +162,9 @@ ------------------------------------ If the new dataset is computed with a totally different level of theory compared to the -pre-trained model, which includes, for instance, the different composition energies, -or you want to fine-tune the model on a completely new target, you might need to consider -the transfer learning approach and introduce a new target in the -``options.yaml`` file. More details about this approach can be found in the -:ref:`Transfer Learning ` section of the documentation. - +pre-trained model, which includes, for instance, the different composition energies, or +you want to fine-tune the model on a completely new target, you might need to consider +the transfer learning approach and introduce a new target in the ``options.yaml`` file. +More details about this approach can be found in the :ref:`Transfer Learning +` section of the documentation. +""" diff --git a/examples/0-beginner/03-train_from_scratch.py b/examples/0-beginner/03-train_from_scratch.py new file mode 100644 index 0000000000..5f977545ef --- /dev/null +++ b/examples/0-beginner/03-train_from_scratch.py @@ -0,0 +1,151 @@ +""" +Training a model from scratch +============================= + +This tutorial explains how to train a model with ``metatrain`` from scratch and evaluate +it. `This dataset <./ethanol_reduced_100.xyz>`_ is used here as an example of the +preferred dataset format. If you have your own dataset, you can simply replace the +dataset file name with yours. + +Train the model +--------------- + +Configure the ``options.yaml`` and run the training +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Below is an example ``options.yaml`` for training a PET model. In order to train other +models, simply replace the architecture name with other models' architecture name. For +the supported models, please check `Available Architectures`_ . + +.. _`Available Architectures`: https://metatensor.github.io/metatrain/latest/architectures/index.html + +.. literalinclude:: options-scratch.yaml + :language: yaml + :linenos: + +Start the training by running + +.. code-block:: bash + + mtt train options-scratch.yaml + +It will start training. ``metatrain`` will automatically read the atomic forces from the +training set, if they are stored in it and named as "forces". The model can also be +trained to learn other properties through transfer learning. For this, please refer to +this `transfer learning tutorial`_. + +.. _`transfer learning tutorial`: https://metatensor.github.io/metatrain/latest/advanced-concepts/transfer-learning.html + +Once the training is started, a folder named ``outputs`` will be created automatically +under the folder where you run the command. Under this ``outputs`` folder, there is a +folder with the timestamp. Below is a normal structure of that folder of a successful +training run. + +.. code-block:: bash + + outputs/2025-10-07/17-08-25/ + ├── indices # the results of dataset-spliting + │ ├── test.txt + │ ├── training.txt + │ └── validation.txt + ├── model_0.ckpt # the intermediate model saved at the 0th training step + ├── model.ckpt # the final model + ├── model.pt # the final model , usable directly by ASE and LAMMPS + ├── options_restart.yaml # an expanded options file + ├── train.csv # structured log of training metrics (loss, MAE, RMSE,...) + └── train.log # a human-friendly output + +The ``train.log`` provides information of the training procedure. For example, by +checking the following: + +.. code-block:: bash + + [2025-10-07 17:08:25][INFO] - Setting up training set [2025-10-07 17:08:25][INFO] - + Forces found in section 'energy', we will use this gradient to train the model + [2025-10-07 17:08:25][WARNING] - No stress found in section 'energy'. + +You can know that the forces are identified by ``metatrain`` and are used during the +training, and it fails to find stress. The following provides some statistical of the +training, validation, and the test set + +.. code-block:: bash + + [2025-10-07 17:08:25][INFO] - Training dataset: + Dataset containing 80 structures Mean and standard deviation of targets: - + energy: + - mean -9.708e+04 eV + - std 3.97 eV + [2025-10-07 17:08:25][INFO] - Validation dataset: + Dataset containing 10 structures Mean and standard deviation of targets: - + energy: + - mean -9.708e+04 eV + - std 3.73 eV + [2025-10-07 17:08:25][INFO] - Test dataset: + Dataset containing 10 structures Mean and standard deviation of targets: - + energy: + - mean -9.708e+04 eV + - std 3.535 eV + +The training metrics are outputted every epoch, like + +.. code-block:: bash + + [2025-10-07 17:08:28][INFO] - Epoch: 0 | learning rate: 0.000e+00 | training + loss: 6.305e+03 | training energy RMSE (per atom): 884.08 meV | training energy MAE + (per atom): 773.44 meV | training forces RMSE: 28059.9 meV/A | training forces MAE: + 20581.1 meV/A | validation loss: 7.725e+02 | validation energy RMSE (per atom): + 877.08 meV | validation energy MAE (per atom): 772.04 meV | validation forces RMSE: + 27779.2 meV/A | validation forces MAE: 20201.9 meV/A + +These metrics are also outputted into ``train.csv`` in a formatted way, which can be +used for plotting graph like loss curve. + +It is easy to restart the training from the last step, by running + +.. code-block:: bash + + mtt train options-scratch.yaml --restart model.ckpt + +Evaluate the trained model +-------------------------- + +In order to evaluate the model on the test set, we can use the mtt eval sub-command. +First, create the input file ``eval-scratch.yaml`` with the following options: + +.. literalinclude:: ./eval-scratch.yaml + :language: yaml + :linenos: + +and run (be sure to replace the path to the ``model.pt``) + +.. code-block:: bash + + mtt eval model.pt eval-scratch.yaml + +After this, a file named ``output.xyz`` will be created, with the atom positions and the +predicted forces recorded in it. Also, you should see these statistical on your screen + +.. code-block:: bash + + [2025-10-07 17:11:47][INFO] - energy RMSE (per atom): 436.50 meV | energy MAE (per + atom): 341.32 meV | forces RMSE: 27823.1 meV/A | forces MAE: 20392.7 meV/A + [2025-10-07 17:11:47][INFO] - Evaluation time: 1.10 s [1.2185 ± 1.2768 ms per atom] + +Further analysis can be performed now that the model is trained. We provide a `Python +script`_ that can be used to generate a parity plot of the target vs predicted energies, +but otherwise leave this open-ended. + +.. _`Python script`: https://raw.githubusercontent.com/metatensor/Workshop-spring-2025/refs/heads/main/training-custom-models/part-1-gap/parity_plot.py + +To run the script, download it from the repository, modify the paths as necessary +(indicated with a #TODO), and run. This will generate a plot saved at parity_plot.png. + +Use the model +------------- + +With the trained model, you can run molecular dynamics. Please refer to these two +tutorials for `ASE`_ and `LAMMPS`_ to see how to do that. + +.. _`ASE`: https://docs.metatensor.org/metatomic/latest/examples/2-running-ase-md.html + +.. _`LAMMPS`: https://atomistic-cookbook.org/examples/pet-mad-nc/pet-mad-nc.html#running-lammps-on-gpus-with-kokkos +""" diff --git a/examples/0-beginner/04-parity_plot.py b/examples/0-beginner/04-parity_plot.py new file mode 100644 index 0000000000..5e2d136a03 --- /dev/null +++ b/examples/0-beginner/04-parity_plot.py @@ -0,0 +1,146 @@ +""" +Model validation with parity plots +================================== + +This tutorial shows how to visualise your model output using parity plots. In the +:ref:`sphx_glr_generated_examples_0-beginner_03-train_from_scratch.py` we learned how to +evaluate a trained model on a test set and save the results to an output file. Here we +will show how to create parity plots from these results. + +""" + +# %% +# Import necessary libraries +import ase.io +import chemiscope +import matplotlib.pyplot as plt +import numpy as np + + +# %% +# Load the reference data (ground truth) +targets = ase.io.read("ethanol_reduced_100.xyz", ":") + +# %% +# predicted data from a model +predictions = ase.io.read("ethanol_reduced_100_predicted.xyz", ":") + +# %% +# Extract the energies from the loaded frames +e_targets = np.array( + [frame.get_total_energy() / len(frame) for frame in targets] +) # target energies +e_predictions = np.array( + [frame.get_total_energy() / len(frame) for frame in predictions] +) # predicted energies +f_targets = np.array( + [frame.get_forces().flatten() for frame in targets] +).flatten() # target forces +f_predictions = np.array( + [frame.get_forces().flatten() for frame in predictions] +).flatten() # predicted forces + + +# %% +# Create parity plots to compare predicted vs. target energies and forces +# ----------------------------------------------------------------------- + +fig, axs = plt.subplots(1, 2, figsize=(12, 5)) + +# Parity plot for energies +axs[0].scatter(e_targets, e_predictions) +axs[0].axline((np.min(e_targets), np.min(e_targets)), slope=1, ls="--", color="red") +axs[0].set_xlabel("Target energy / kcal") +axs[0].set_ylabel("Predicted energy / kcal") +min_e = np.min(np.array([e_targets, e_predictions])) - 2 +max_e = np.max(np.array([e_targets, e_predictions])) + 2 +axs[0].set_xlim([min_e, max_e]) +axs[0].set_ylim([min_e, max_e]) +axs[0].set_title("Energy Parity Plot") + +# Parity plot for forces +axs[1].scatter(f_targets, f_predictions, alpha=0.5) +axs[1].axline((np.min(f_targets), np.min(f_targets)), slope=1, ls="--", color="red") +axs[1].set_xlabel("Target force / kcal/Å") +axs[1].set_ylabel("Predicted force / kcal/Å") +min_f = np.min(np.array([f_targets, f_predictions])) - 2 +max_f = np.max(np.array([f_targets, f_predictions])) + 2 +axs[1].set_xlim([min_f, max_f]) +axs[1].set_ylim([min_f, max_f]) +axs[1].set_title("Force Parity Plot") + +fig.tight_layout() +plt.show() + +# %% +# We can also compute and print RMSE for energies and forces + +print( + "RMSE energy (per atom):", + np.sqrt(np.mean((e_targets - e_predictions) ** 2)), + "kcal", +) +print("RMSE forces:", np.sqrt(np.mean((f_targets - f_predictions) ** 2)), "kcal/Å ") + +# %% +# +# The results are a bit poor here because the model was not trained well enough and +# was created only for demonstration purposes. In the case of a well-trained model, the +# points should be closer to the diagonal line. +# +# Check outliers with ``Chemiscope`` +# ---------------------------------- +# With the approach above, you can inspect the whole dataset, but it might be difficult +# to identify outliers. `Chemiscope ` is a +# visualisation tool, allowing you to explore the dataset interactively. The following +# example shows how to use it to check the structure of probable outliers and the atomic +# forces. + +for frame in targets + predictions: + frame.arrays["forces"] = frame.get_forces() +# a workaround, because the chemiscope interface for getting forces is broken with ASE +# 3.23 + +# %% +# Plot the energy parity plot with Chemiscope. This can be rendered as a widget in a +# Jupyter notebook. +cs = chemiscope.show( + targets, # reading structures from the dataset + properties={ + "Target energy": {"values": e_targets, "target": "structure", "units": "kcal"}, + "Predicted energy": { + "values": e_predictions, + "target": "structure", + "units": "kcal", + }, + }, # plotting the energy parity plot + mode="default", + shapes={ + "target_forces": chemiscope.ase_vectors_to_arrows( + targets, key="forces", scale=0.05, radius=0.15 + ), + "predicted_forces": chemiscope.ase_vectors_to_arrows( + predictions, key="forces", scale=0.05, radius=0.15 + ), + }, # plotting the atomic forces + settings=chemiscope.quick_settings( + trajectory=True, + map_settings={"joinPoints": False}, + structure_settings={ + "unitCell": True, + "environments": {"activated": False}, + "shape": "predicted_forces", # show predicted forces by defalut + }, + ), +) +cs + +# %% +# You can check the structures by clicking the red dots on the parity plot, or +# dragging the bar in the bottom right corner. Currently, plotting the diagonal line is +# not supported in chemiscope, so please check the parity plot above to see the +# outliers. +# +# The atomic forces are shown in arrows. The predicted forces are shown here. The target +# forces can be toggled by clicking the "target_forces" option in the menu in the upper +# right corner of the right panel. diff --git a/examples/0-beginner/05-run_ase.py b/examples/0-beginner/05-run_ase.py new file mode 100644 index 0000000000..9310d95b25 --- /dev/null +++ b/examples/0-beginner/05-run_ase.py @@ -0,0 +1,207 @@ +""" +Running molecular dynamics with ASE +=================================== + +This tutorial demonstrates how to use an already trained and exported model to run an +ASE simulation of a single ethanol molecule in vacuum. We use a model that was trained +using the :ref:`architecture-soap-bpnn` architecture on 100 ethanol systems +containing energies and forces. You can obtain the :download:`dataset file +` used in this example from our website. The dataset is a +subset of the `rMD17 dataset +`_. + +The model was trained using the following training options. + +.. literalinclude:: options-ase.yaml + :language: yaml + +We first train the model same model but and before import the necessary libraries and +run the training process and the integration of ASE. + +""" +# %% +# + +import subprocess + +import ase.md +import ase.md.velocitydistribution +import ase.units +import ase.visualize.plot +import matplotlib.pyplot as plt +import numpy as np +from ase.geometry.analysis import Analysis +from metatomic.torch.ase_calculator import MetatomicCalculator + + +# %% +# + +subprocess.run(["mtt", "train", "options-ase.yaml", "--output", "model-md.pt"]) + +# %% +# +# A detailed step-by-step introduction on how to train a model is provided in the +# :ref:`label_basic_usage` tutorial. +# +# Setting up the simulation +# ------------------------- +# +# Next, we initialize the simulation by extracting the initial positions from the +# dataset file which we initially trained the model on. + +train_frames = ase.io.read("ethanol_reduced_100.xyz", ":") +atoms = train_frames[0].copy() + +# %% +# +# Below we show the initial configuration of a single ethanol molecule in vacuum. + +ase.visualize.plot.plot_atoms(atoms) + +plt.xlabel("Å") +plt.ylabel("Å") + +plt.show() + + +# %% +# +# Our initial coordinates do not include velocities. We initialize the velocities +# according to a Maxwell-Boltzmann Distribution at 300 K. + +ase.md.velocitydistribution.MaxwellBoltzmannDistribution(atoms, temperature_K=300) + +# %% +# +# We now register our exported model as the energy calculator to obtain energies and +# forces. + +atoms.calc = MetatomicCalculator("model-md.pt", extensions_directory="extensions/") + +# %% +# +# Finally, we define the integrator which we use to obtain new positions and velocities +# based on our energy calculator. We use a common timestep of 0.5 fs. + +integrator = ase.md.VelocityVerlet(atoms, timestep=0.5 * ase.units.fs) + + +# %% +# +# Run the simulation +# ------------------ +# +# We now have everything ready to run the MD simulation at constant energy (NVE). To +# keep the execution time of this tutorial small we run the simulations only for 100 +# steps. If you want to run a longer simulation you can increase the ``n_steps`` +# variable. +# +# During the simulation loop we collect data about the simulation for later analysis. + + +n_steps = 100 + +potential_energy = np.zeros(n_steps) +kinetic_energy = np.zeros(n_steps) +total_energy = np.zeros(n_steps) +trajectory = [] + +for step in range(n_steps): + # run a single simulation step + integrator.run(1) + + trajectory.append(atoms.copy()) + potential_energy[step] = atoms.get_potential_energy() + kinetic_energy[step] = atoms.get_kinetic_energy() + total_energy[step] = atoms.get_total_energy() + +# %% +# +# Analyse the results +# ------------------- +# +# Energy conservation +# ################### +# +# For a first analysis, we plot the evolution of the mean of the kinetic, potential, and +# total energy which is an important measure for the stability of a simulation. +# +# As shown below we see that both the kinetic, potential, and total energy +# fluctuate but the total energy is conserved over the length of the simulation. + + +plt.plot(potential_energy - potential_energy.mean(), label="potential energy") +plt.plot(kinetic_energy - kinetic_energy.mean(), label="kinetic energy") +plt.plot(total_energy - total_energy.mean(), label="total energy") + +plt.xlabel("step") +plt.ylabel("energy / kcal/mol") +plt.legend() + +plt.show() + +# %% +# +# Inspect the systems +# ################### +# +# Even though the total energy is conserved, we also have to verify that the ethanol +# molecule is stable and the bonds did not break. + +animation = ase.visualize.plot.animate(trajectory, interval=100, save_count=None) +plt.show() + +# %% +# +# Carbon-hydrogen radial distribution function +# ############################################ +# +# As a final analysis we also calculate and plot the carbon-hydrogen radial distribution +# function (RDF) from the trajectory and compare this to the RDF from the training set. +# +# To use the RDF code from ase we first have to define a unit cell for our systems. +# We choose a cubic one with a side length of 10 Å. + +for atoms in train_frames: + atoms.cell = 10 * np.ones(3) + atoms.pbc = True + +for atoms in trajectory: + atoms.cell = 10 * np.ones(3) + atoms.pbc = True + +# %% +# +# We now can initilize the :py:class:`ase.geometry.analysis.Analysis` objects and +# compute the the RDF using the :py:meth:`ase.geometry.analysis.Analysis.get_rdf` +# method. + +ana_traj = Analysis(trajectory) +ana_train = Analysis(train_frames) + +rdf_traj = ana_traj.get_rdf(rmax=5, nbins=50, elements=["C", "H"], return_dists=True) +rdf_train = ana_train.get_rdf(rmax=5, nbins=50, elements=["C", "H"], return_dists=True) + +# %% +# +# We extract the bin positions from the returned values and and averege the RDF over the +# whole trajectory and dataset, respectively. + +bins = rdf_traj[0][1] +rdf_traj_mean = np.mean([rdf_traj[i][0] for i in range(n_steps)], axis=0) +rdf_train_mean = np.mean([rdf_train[i][0] for i in range(n_steps)], axis=0) + +# %% +# +# Plotting the RDF verifies that the hydrogen bonds are stable, confirming that we +# performed an energy-conserving and stable simulation. + +plt.plot(bins, rdf_traj_mean, label="trajectory") +plt.plot(bins, rdf_train_mean, label="training set") + +plt.legend() +plt.xlabel("r / Å") +plt.ylabel("radial distribution function") + +plt.show() diff --git a/examples/0-beginner/GALLERY_HEADER.rst b/examples/0-beginner/GALLERY_HEADER.rst new file mode 100644 index 0000000000..6bc08086ab --- /dev/null +++ b/examples/0-beginner/GALLERY_HEADER.rst @@ -0,0 +1,7 @@ +.. _beginner_tutorials: + +Beginner Tutorials +------------------ + +This sections includes the beginner tutorials on the usage of the ``metatrain`` package. + diff --git a/examples/0-beginner/carbon_reduced_100.xyz b/examples/0-beginner/carbon_reduced_100.xyz new file mode 120000 index 0000000000..5f4bb4cb8a --- /dev/null +++ b/examples/0-beginner/carbon_reduced_100.xyz @@ -0,0 +1 @@ +../../tests/resources/carbon_reduced_100.xyz \ No newline at end of file diff --git a/examples/ase/ethanol_reduced_100.xyz b/examples/0-beginner/ethanol_reduced_100.xyz similarity index 100% rename from examples/ase/ethanol_reduced_100.xyz rename to examples/0-beginner/ethanol_reduced_100.xyz diff --git a/examples/0-beginner/ethanol_reduced_100_predicted.xyz b/examples/0-beginner/ethanol_reduced_100_predicted.xyz new file mode 100644 index 0000000000..a07f3fddcc --- /dev/null +++ b/examples/0-beginner/ethanol_reduced_100_predicted.xyz @@ -0,0 +1,1100 @@ +9 +Properties=species:S:1:pos:R:3:forces:R:3 energy=-97079.8125 pbc="F F F" +H 0.16712676 -1.19985032 0.84958249 -7.49372673 12.43364716 -17.29284668 +H -0.93496192 -0.92263609 -0.68150204 5.89590979 -5.37698555 -3.40375805 +H -1.75179350 0.29946017 1.28426456 -0.59080982 -1.71196353 1.63215256 +H -0.20725261 1.35456109 1.05486238 -7.42889786 -10.10517120 -0.95196557 +H -1.47331512 1.17441297 -0.27787521 -4.87997389 -2.30470848 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length_unit: Angstrom + +targets: + energy: + key: energy # name of the target value + unit: eV # unit of the target value diff --git a/examples/0-beginner/options-ase.yaml b/examples/0-beginner/options-ase.yaml new file mode 100644 index 0000000000..87300b9263 --- /dev/null +++ b/examples/0-beginner/options-ase.yaml @@ -0,0 +1,15 @@ +seed: 42 + +architecture: + name: gap + +# training set section +training_set: + systems: ethanol_reduced_100.xyz + targets: + energy: + key: energy + unit: kcal/mol # very important to run simulations + +validation_set: 0.1 +test_set: 0.0 diff --git a/examples/0-beginner/options-basic.yaml b/examples/0-beginner/options-basic.yaml new file mode 100644 index 0000000000..c0c0ddf7bf --- /dev/null +++ b/examples/0-beginner/options-basic.yaml @@ -0,0 +1,18 @@ +# architecture used to train the model +architecture: + name: soap_bpnn + training: + num_epochs: 5 # a very short training run + batch_size: 10 + +# Mandatory section defining the parameters for system and target data of the +# training set +training_set: + systems: qm9_reduced_100.xyz # file where the positions are stored + targets: + energy: + key: U0 # name of the target value + unit: eV # unit of the target value + +test_set: 0.1 # 10 % of the training_set are randomly split and taken for test set +validation_set: 0.1 # 10 % of the training_set are randomly split and for validation diff --git a/examples/0-beginner/options-memmap.yaml b/examples/0-beginner/options-memmap.yaml new file mode 100644 index 0000000000..f8de24c036 --- /dev/null +++ b/examples/0-beginner/options-memmap.yaml @@ -0,0 +1,38 @@ +seed: 42 + +architecture: + name: pet + training: + num_epochs: 1 + batch_size: 8 + +training_set: + systems: + read_from: carbon_reduced_100_memmap/ + length_unit: angstrom + targets: + energy: + key: e + unit: eV + forces: + key: f + stress: + key: s + non_conservative_forces: + key: f + quantity: force + unit: eV/A + per_atom: true + type: + cartesian: + rank: 1 + non_conservative_stress: + key: s + quantity: pressure + unit: eV/A^3 + type: + cartesian: + rank: 2 + +test_set: 0.1 +validation_set: 0.1 diff --git a/examples/0-beginner/options-scratch.yaml b/examples/0-beginner/options-scratch.yaml new file mode 100644 index 0000000000..ceef2551ed --- /dev/null +++ b/examples/0-beginner/options-scratch.yaml @@ -0,0 +1,22 @@ +architecture: + name: pet # the architecture name for PET, or the name for other architectures, if you want to train other models + model: + cutoff: 4.5 # the cutoff + training: + num_epochs: 10 # this is for a reasonable time of a tutorial, for a good model, consider increasing the number + batch_size: 10 # the size of the training data feed to the model per batch, determining the GPU memory usage during the training + log_interval: 1 + checkpoint_interval: 10 # it saves checkpoints of the model every 10 epochs + +# this needs specifying based on the specific dataset +training_set: + systems: + read_from: ./ethanol_reduced_100.xyz + length_unit: Angstrom + targets: + energy: + key: energy # name of the target value + unit: eV # unit of the target value + +test_set: 0.1 # 10 % of the training_set are randomly split and taken for test set +validation_set: 0.1 # 10 % of the training_set are randomly split and for validation set diff --git a/examples/basic_usage/qm9_reduced_100.xyz b/examples/0-beginner/qm9_reduced_100.xyz similarity index 100% rename from examples/basic_usage/qm9_reduced_100.xyz rename to examples/0-beginner/qm9_reduced_100.xyz diff --git a/examples/1-advanced/.gitignore b/examples/1-advanced/.gitignore new file mode 100644 index 0000000000..d0920d4284 --- /dev/null +++ b/examples/1-advanced/.gitignore @@ -0,0 +1 @@ +flashmd.xyz diff --git a/docs/src/advanced-concepts/transfer-learning.rst b/examples/1-advanced/00-transfer-learning.py similarity index 73% rename from docs/src/advanced-concepts/transfer-learning.rst rename to examples/1-advanced/00-transfer-learning.py index eec6ea2cbd..c7b2b059c5 100644 --- a/docs/src/advanced-concepts/transfer-learning.rst +++ b/examples/1-advanced/00-transfer-learning.py @@ -1,3 +1,4 @@ +""" .. _transfer-learning: Transfer Learning (experimental) @@ -32,12 +33,13 @@ -------------------------------- Training on a new level of theory is a common use case for transfer learning. It -requires using a pre-trained model checkpoint with the ``mtt train`` command and setting the -new targets corresponding to the new level of theory in the ``options.yaml`` file. Let's -assume that the training is done on the dataset computed with the hybrid DFT functional -(e.g. PBE0) stored in the ``new_train_dataset.xyz`` file, where the corresponsing -energies and forces are written in the ``energy`` and ``forces`` key of the ``info`` dictionary -of the ``ase.Atoms`` object. Then, the ``options.yaml`` file should look like this: +requires using a pre-trained model checkpoint with the ``mtt train`` command and setting +the new targets corresponding to the new level of theory in the ``options.yaml`` file. +Let's assume that the training is done on the dataset computed with the hybrid DFT +functional (e.g. PBE0) stored in the ``new_train_dataset.xyz`` file, where the +corresponsing energies and forces are written in the ``energy`` and ``forces`` key of +the ``info`` dictionary of the ``ase.Atoms`` object. Then, the ``options.yaml`` file +should look like this: .. code-block:: yaml @@ -71,11 +73,9 @@ Fitting to a new set of properties ---------------------------------- -Training on a new set of properties is another common use case for -transfer learning. It can be done in a similar way as training on a new -level of theory. The only difference is that the new targets need to be -properly set in the ``options.yaml`` file. More information about fitting the -generic targets can be found in the :ref:`Fitting generic targets ` -section of the documentation. - - +Training on a new set of properties is another common use case for transfer learning. It +can be done in a similar way as training on a new level of theory. The only difference +is that the new targets need to be properly set in the ``options.yaml`` file. More +information about fitting the generic targets can be found in the :ref:`Fitting generic +targets ` section of the documentation. +""" diff --git a/examples/1-advanced/01-llpr.py b/examples/1-advanced/01-llpr.py new file mode 100644 index 0000000000..8979340c17 --- /dev/null +++ b/examples/1-advanced/01-llpr.py @@ -0,0 +1,121 @@ +""".. _llprexample: + +Computing LLPR uncertainties +============================ + +This tutorial demonstrates how to train a model with uncertainties using metatrain. +It involves the computation of the uncertainties on ethanol molecules, using the +last-layer prediction rigidity (`LLPR `_) approximation. Both total and local +(`LPR `_) uncertainties are computed + +.. _LLPR: https://arxiv.org/html/2403.02251v1 +.. _LPR: https://pubs.acs.org/doi/10.1021/acs.jctc.3c00704 + +The baseline model was trained using the following training options, where the training +set consists of 100 structures from the QM9 dataset. + +.. literalinclude:: options-no-llpr.yaml + :language: yaml + +Once a model is trained, you can add LLPR uncertainties to it by launching a training +run with the "llpr" architecture, on the same data. In this case, the training options +to add LLPR uncertainties are as follows: + +.. literalinclude:: options-llpr.yaml + :language: yaml + +Adding LLPR uncertainties is very cheap compared to training a model, as it only +involves one pass through the training data (equivalent to one epoch of training). + +You can repeat the same training yourself with + +""" +# %% +# + +import subprocess + +import ase.io +import matplotlib.pyplot as plt +from ase.visualize.plot import plot_atoms +from matplotlib.colors import LogNorm +from metatomic.torch import ModelOutput +from metatomic.torch.ase_calculator import MetatomicCalculator + + +# %% +# +# We first train the baseline model without uncertainties and then the LLPR model. + + +subprocess.run(["mtt", "train", "options-no-llpr.yaml", "-o", "model.pt"]) +subprocess.run(["mtt", "train", "options-llpr.yaml", "-o", "model-llpr.pt"]) + +# %% +# +# A detailed step-by-step introduction on how to train a model is provided in +# the :ref:`label_basic_usage` tutorial. +# +# As an example, we will compute the energies and uncertainties of the LLPR model on a +# few ethanol structures. +# +# Next we load 5 ethanol structures +structures = ase.io.read("ethanol_reduced_100.xyz", ":5") + +# %% +# +# and load the model as an ASE calculator +calc = MetatomicCalculator( + "model-llpr.pt", extensions_directory="extensions/", device="cpu" +) + +# the uncertainties are available throguh the ``run_model`` method of the calculator +predictions = calc.run_model( + structures, + { + "energy": ModelOutput(per_atom=False), + "energy_uncertainty": ModelOutput(per_atom=False), + }, +) + +# print the energies and uncertainties + +energies = predictions["energy"].block().values.squeeze().numpy() +uncertainties = predictions["energy_uncertainty"].block().values.squeeze().numpy() + +print(energies) +print(uncertainties) + + +# %% +# +# We can also obtain per-atom uncertainties (local prediction rigidity, LPR). As an +# example, we will compute the uncertainties on an ethanol structure. + +structure = structures[0] +predictions = calc.run_model( + structure, + { + # here, we use per_atom=True to request per-atom uncertainties + "energy_uncertainty": ModelOutput(per_atom=True), + }, +) +local_uncertainty = predictions["energy_uncertainty"].block().values.squeeze().numpy() +local_uncertainty = local_uncertainty * 1000.0 # convert from eV to meV + +norm = LogNorm(vmin=min(local_uncertainty), vmax=max(local_uncertainty)) +colormap = plt.get_cmap("viridis") +colors = colormap(norm(local_uncertainty)) +ax = plot_atoms(structure, colors=colors, rotation="180x,0y,0z") +custom_ticks = [0.7, 0.8, 0.9, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5] +cbar = plt.colorbar( + plt.cm.ScalarMappable(norm=norm, cmap=colormap), + ax=ax, + label="Local energy uncertainty (meV)", + ticks=custom_ticks, +) +cbar.ax.set_yticklabels([f"{tick}" for tick in custom_ticks]) +cbar.minorticks_off() +ax.set_xticks([]) +ax.set_yticks([]) +plt.show() diff --git a/examples/1-advanced/02-zbl.py b/examples/1-advanced/02-zbl.py new file mode 100644 index 0000000000..3fa6b5acdc --- /dev/null +++ b/examples/1-advanced/02-zbl.py @@ -0,0 +1,147 @@ +""" +Training a model with ZBL corrections +===================================== + +This tutorial demonstrates how to train a model with ZBL corrections. + +The training set for this example consists of a +subset of the ethanol moleculs from the `rMD17 dataset +`_. + +The models are trained using the following training options, respectively: + +.. literalinclude:: options-no-zbl.yaml + :language: yaml + +.. literalinclude:: options_zbl.yaml + :language: yaml + +As you can see, they are identical, except for the ``zbl`` key in the ``model`` section. +A detailed step-by-step introduction on how to train a model is provided in the +:ref:`label_basic_usage` tutorial. +""" + +# %% +# +# First, we start by importing the necessary libraries, including the integration of ASE +# calculators for metatensor atomistic models. + +import subprocess + +import ase +import matplotlib.pyplot as plt +import numpy as np +import torch +from metatomic.torch.ase_calculator import MetatomicCalculator + + +# %% + +subprocess.run(["mtt", "train", "options-no-zbl.yaml", "-o", "model_no_zbl.pt"]) +subprocess.run(["mtt", "train", "options_zbl.yaml", "-o", "model_zbl.pt"]) + +# %% +# +# Setting up the dimers +# --------------------- +# +# We set up a series of dimers with different atom pairs and distances. We will +# calculate the energies of these dimers using the models trained with and without ZBL +# corrections. + +distances = np.linspace(0.5, 6.0, 200) +pairs = {} +for pair in [("H", "H"), ("H", "C"), ("C", "C"), ("C", "O"), ("O", "O"), ("H", "O")]: + structures = [] + for distance in distances: + atoms = ase.Atoms( + symbols=[pair[0], pair[1]], + positions=[[0, 0, 0], [0, 0, distance]], + ) + structures.append(atoms) + pairs[pair] = structures + +# %% +# +# We now load the two exported models, one with and one without ZBL corrections + +calc_no_zbl = MetatomicCalculator("model_no_zbl.pt", extensions_directory="extensions/") +calc_zbl = MetatomicCalculator("model_zbl.pt", extensions_directory="extensions/") + + +# %% +# +# Calculate and plot energies without ZBL +# --------------------------------------- +# +# We calculate the energies of the dimer curves for each pair of atoms and +# plot the results, using the non-ZBL-corrected model. + +for pair, structures_for_pair in pairs.items(): + energies = [] + for atoms in structures_for_pair: + atoms.set_calculator(calc_no_zbl) + with torch.jit.optimized_execution(False): + energies.append(atoms.get_potential_energy()) + energies = np.array(energies) - energies[-1] + plt.plot(distances, energies, label=f"{pair[0]}-{pair[1]}") +plt.title("Dimer curves - no ZBL") +plt.xlabel("Distance (Å)") +plt.ylabel("Energy (eV)") +plt.legend() +plt.tight_layout() +plt.show() + +# %% +# +# Calculate and plot energies from the ZBL-corrected model +# -------------------------------------------------------- +# +# We repeat the same procedure as above, but this time with the ZBL-corrected model. + +for pair, structures_for_pair in pairs.items(): + energies = [] + for atoms in structures_for_pair: + atoms.set_calculator(calc_zbl) + with torch.jit.optimized_execution(False): + energies.append(atoms.get_potential_energy()) + energies = np.array(energies) - energies[-1] + plt.plot(distances, energies, label=f"{pair[0]}-{pair[1]}") +plt.title("Dimer curves - with ZBL") +plt.xlabel("Distance (Å)") +plt.ylabel("Energy (eV)") +plt.legend() +plt.tight_layout() +plt.show() + +# %% +# +# It can be seen that all the dimer curves include a strong repulsion +# at short distances, which is due to the ZBL contribution. Even the H-H dimer, +# whose ZBL correction is very weak due to the small covalent radii of hydrogen, +# would show a strong repulsion closer to the origin (here, we only plotted +# starting from a distance of 0.5 Å). Let's zoom in on the H-H dimer to see +# this effect more clearly. + +new_distances = np.linspace(0.1, 2.0, 200) + +structures = [] +for distance in new_distances: + atoms = ase.Atoms( + symbols=["H", "H"], + positions=[[0, 0, 0], [0, 0, distance]], + ) + structures.append(atoms) + +for atoms in structures: + atoms.set_calculator(calc_zbl) +with torch.jit.optimized_execution(False): + energies = [atoms.get_potential_energy() for atoms in structures] +energies = np.array(energies) - energies[-1] +plt.plot(new_distances, energies, label="H-H") +plt.title("Dimer curve - H-H with ZBL") +plt.xlabel("Distance (Å)") +plt.ylabel("Energy (eV)") +plt.legend() +plt.tight_layout() +plt.show() diff --git a/docs/src/advanced-concepts/fitting-generic-targets.rst b/examples/1-advanced/03-fitting-generic-targets.py similarity index 99% rename from docs/src/advanced-concepts/fitting-generic-targets.rst rename to examples/1-advanced/03-fitting-generic-targets.py index 595a6b991c..ab84e6d0d1 100644 --- a/docs/src/advanced-concepts/fitting-generic-targets.rst +++ b/examples/1-advanced/03-fitting-generic-targets.py @@ -1,3 +1,4 @@ +""" .. _fitting-generic-targets: Fitting generic targets @@ -155,3 +156,4 @@ the ``TensorMap`` should have the ``o3_lambda`` and ``o3_sigma`` names, corresponding to the values provided in the input file, and each ``TensorBlock`` should be one component label, with name ``o3_mu`` and values going from -L to L. +""" diff --git a/examples/1-advanced/04-flashmd.py b/examples/1-advanced/04-flashmd.py new file mode 100644 index 0000000000..970b523723 --- /dev/null +++ b/examples/1-advanced/04-flashmd.py @@ -0,0 +1,120 @@ +""" +Training a FlashMD model +======================== + +This tutorial demonstrates how to train a FlashMD model for the direct prediction +of molecular dynamics. This type of model affords faster MD simulations compared to +MLIPs by a factor between 10 and 30 (https://arxiv.org/abs/2505.19350). +""" + +# %% +# + +import copy +import subprocess + +import ase +import ase.build +import ase.io +import ase.units +from ase.calculators.emt import EMT +from ase.md import VelocityVerlet +from ase.md.langevin import Langevin +from ase.md.velocitydistribution import MaxwellBoltzmannDistribution + + +# %% +# +# Data generation +# --------------- +# +# FlashMD models train on molecular dynamics trajectories in the NVE ensemble (i.e., +# most often with the velocity Verlet integrator). These trajectories can be generated +# with almost any MD code (i-PI, LAMMPS, etc.). Here, for simplicity, we will use ASE +# and its built-in EMT potential. In reality, you might want to use a more accurate +# baseline such as ab initio MD or a machine-learned interatomic potential (MLIP). + +# We start by creating a simple system (a small box of aluminum). +atoms = ase.build.bulk("Al", "fcc", cubic=True) * (2, 2, 2) + +# We first equilibrate the system at 300K using a Langevin thermostat. +MaxwellBoltzmannDistribution(atoms, temperature_K=300) +atoms.calc = EMT() +dyn = Langevin( + atoms, 2 * ase.units.fs, temperature_K=300, friction=1 / (100 * ase.units.fs) +) +dyn.run(1000) # 2 ps equilibration (around 10 ps is better in practice) + +# Then, we run a production simulation in the NVE ensemble. +trajectory = [] + + +def store_trajectory(): + trajectory.append(copy.deepcopy(atoms)) + + +dyn = VelocityVerlet(atoms, 1 * ase.units.fs) +dyn.attach(store_trajectory, interval=1) +dyn.run(2000) # 2 ps NVE run + + +# %% +# +# Data preparation +# ---------------- +# +# Now, we need to generate the training data from the trajectory. FlashMD models +# require future positions and momenta as targets. We will save them in an `.xyz` +# file under the `future_positions` and `future_momenta` keys. + +# The FlashMD model will be trained to predict 30 steps into the future, i.e., 30 fs +# since we ran the reference simulation with a time step of 1 fs. For this type +# of system, FlashMD is expected to perform well up to around 60-80 fs. +time_lag = 30 + +# We pick starting structures that are 200 steps apart. To avoid wasting training +# structures, this should be set to be around the expected velocity-velocity +# autocorrelation time for the system. This is the time scale that quantifies how long +# it takes for the system to forget its original velocities. +spacing = 200 + + +def get_structure_for_dataset(frame_now, frame_ahead): + s = copy.deepcopy(frame_now) + s.arrays["future_positions"] = frame_ahead.get_positions() + s.arrays["future_momenta"] = frame_ahead.get_momenta() + return s + + +structures_for_dataset = [] +for i in range(0, len(trajectory) - time_lag, spacing): + frame_now = trajectory[i] + frame_ahead = trajectory[i + time_lag] + s = get_structure_for_dataset(frame_now, frame_ahead) + structures_for_dataset.append(s) + + # Here, we also add the time-reversed pair (optional) + frame_now_trev = copy.deepcopy(frame_now) + frame_ahead_trev = copy.deepcopy(frame_ahead) + frame_now_trev.set_momenta(-frame_now_trev.get_momenta()) + frame_ahead_trev.set_momenta(-frame_ahead_trev.get_momenta()) + s = get_structure_for_dataset(frame_ahead_trev, frame_now_trev) + structures_for_dataset.append(s) + +# Write the structures to an xyz file +ase.io.write("flashmd.xyz", structures_for_dataset) + +# %% +# +# Training the model +# ------------------ +# +# The dataset is now ready for training. You can now provide it to ``metatrain`` and +# train your FlashMD model! +# +# For example, you can use the following options file: +# +# .. literalinclude:: options-flashmd.yaml +# :language: yaml + +subprocess.run(["mtt", "train", "options-flashmd.yaml"]) diff --git a/examples/1-advanced/05-multi-gpu.py b/examples/1-advanced/05-multi-gpu.py new file mode 100644 index 0000000000..393c946d67 --- /dev/null +++ b/examples/1-advanced/05-multi-gpu.py @@ -0,0 +1,90 @@ +""" +================== +Multi-GPU training +================== + +``metatrain`` supports training a model with several GPUs, which can accelerate the +training, especially when the training dataset is large / there are many training +epochs. This feature is enabled by the :py:mod:`torch.distributed` module, and thus can +do multiprocess parallelism across several nodes. + +In multi-GPU training, every batch of samples is split into smaller mini-batches and the +computation is run for each of the smaller mini-batches in parallel on different GPUs. +The different gradients obtained on each device are then summed. This approach allows +the user to reduce the time it takes to train models. + +To know if the model supports multi-GPU training, please check `Available Architectures +<../../docs/src/architectures/index.rst>`_ and see if the default hyperparameters have +the ``distributed`` option. + +Input file +---------- +To do this, you only need to switch on the ``distributed`` option in the ``.yaml`` file +for the training. Let's take `this tutorial +<../beginner_tutorials/train-from-scratch.rst>`_ as an example. Now, the +``options.yaml`` is + +.. literalinclude:: options-distributed.yaml + :language: yaml + :linenos: + +Slurm script +------------ + +Below is an example Slurm script for submitting the job. Please be aware that the actual +configurations vary from clusters to clusters, so you have to modify it. Different +scheduler will require similar options. ``metatrain`` will automatically use all the +GPUs that you have asked for. You should make a single GPU visible for each process +(setting `--gpus-per-node` equal to the number of GPUs, or setting `--gpus-per-task=1`, +depending on your cluster configuration). + +.. code-block:: bash + + #!/bin/bash + #SBATCH --nodes 1 + #SBATCH --ntasks 2 # must equal to the number of GPUs + #SBATCH --ntasks-per-node 2 + #SBATCH --gpus-per-node 2 # use 2 GPUs + #SBATCH --cpus-per-task 8 + #SBATCH --exclusive + #SBATCH --partition=h100 # adapt this to your cluster + #SBATCH --time=1:00:00 + + # load modules and/or virtual environments and/or containers here + + srun mtt train options-distributed.yaml + +Performance +----------- + +If the multi-GPU training runs successfully, you should see this in the training log: + +.. code-block:: bash + + [2025-10-08 11:34:22][INFO] - Distributed environment set up with MASTER_ADDR=kh080, + MASTER_PORT=39591, WORLD_SIZE=2, RANK=0, LOCAL_RANK=0 + [2025-10-08 11:34:23][INFO] - Training on 2 devices with dtype torch.float32 + +This 100-epoch training takes 23 seconds. + +.. code-block:: bash + + [2025-10-08 11:34:22][INFO] - Starting training from scratch + ... + [2025-10-08 11:34:45][INFO] - Training finished! + +Now let's switch off the multi-GPU training by writing ``distributed: false``, and +submit this job again. The training takes 69 seconds. + +.. code-block:: bash + + [2025-10-08 11:37:38][INFO] - Setting up model + ... + [2025-10-08 11:38:47][INFO] - Training finished! + +Multi-GPU fine-tuning +--------------------- +You can use multi-GPU for fine-tuning too, by writing ``distributed: True`` in the +``.yaml`` input. For information about fine-tuning, please refer to `this tutorial on +fine-tuning <../../getting-started/finetuning-example.rst>`_. +""" diff --git a/examples/1-advanced/GALLERY_HEADER.rst b/examples/1-advanced/GALLERY_HEADER.rst new file mode 100644 index 0000000000..564319f571 --- /dev/null +++ b/examples/1-advanced/GALLERY_HEADER.rst @@ -0,0 +1,8 @@ +.. _advanced_tutorials: + +Advanced Tutorials +------------------ + +This sections includes the advanced tutorials on the usage of the ``metatrain`` package. +If you are new to ``metatrain``, we recommend to start with the :ref:`label_quickstart` +and the :ref:`beginner_tutorials`. diff --git a/examples/llpr/ethanol_reduced_100.xyz b/examples/1-advanced/ethanol_reduced_100.xyz similarity index 100% rename from examples/llpr/ethanol_reduced_100.xyz rename to examples/1-advanced/ethanol_reduced_100.xyz diff --git a/examples/1-advanced/options-distributed-soap.yaml b/examples/1-advanced/options-distributed-soap.yaml new file mode 120000 index 0000000000..9b9da26ac2 --- /dev/null +++ b/examples/1-advanced/options-distributed-soap.yaml @@ -0,0 +1 @@ +../../tests/distributed/options-distributed.yaml \ No newline at end of file diff --git a/examples/1-advanced/options-distributed.yaml b/examples/1-advanced/options-distributed.yaml new file mode 120000 index 0000000000..9b9da26ac2 --- /dev/null +++ b/examples/1-advanced/options-distributed.yaml @@ -0,0 +1 @@ +../../tests/distributed/options-distributed.yaml \ No newline at end of file diff --git a/examples/1-advanced/options-flashmd.yaml b/examples/1-advanced/options-flashmd.yaml new file mode 100644 index 0000000000..cc73011ea5 --- /dev/null +++ b/examples/1-advanced/options-flashmd.yaml @@ -0,0 +1,43 @@ +seed: 42 + +architecture: + name: experimental.flashmd + training: + timestep: 30 # in this case 30 (time lag) * 1 fs (timestep of reference MD) + batch_size: 2 # to be increased in a production scenario + num_epochs: 5 # to be increased (at least 1000-10000) in a production scenario + log_interval: 1 + loss: + positions: + type: mse + weight: 1.0 + reduction: mean + momenta: + type: mse + weight: 1.0 + reduction: mean + +training_set: + systems: + read_from: flashmd.xyz + length_unit: A + targets: + positions: + key: future_positions + quantity: length + unit: A + type: + cartesian: + rank: 1 + per_atom: true + momenta: + key: future_momenta + quantity: momentum + unit: (eV*u)^1/2 + type: + cartesian: + rank: 1 + per_atom: true + +validation_set: 0.1 +test_set: 0.1 diff --git a/examples/1-advanced/options-llpr.yaml b/examples/1-advanced/options-llpr.yaml new file mode 100644 index 0000000000..4eaf6145ff --- /dev/null +++ b/examples/1-advanced/options-llpr.yaml @@ -0,0 +1,20 @@ +device: cpu +base_precision: 64 +seed: 42 + +architecture: + name: llpr + training: + model_checkpoint: model.ckpt + batch_size: 4 + +# Section defining the parameters for system and target data +training_set: + systems: qm9_reduced_100.xyz + targets: + energy: + key: U0 + unit: hartree # very important to run simulations + +validation_set: 0.1 +test_set: 0.0 diff --git a/examples/1-advanced/options-no-llpr.yaml b/examples/1-advanced/options-no-llpr.yaml new file mode 100644 index 0000000000..fde4d39a9b --- /dev/null +++ b/examples/1-advanced/options-no-llpr.yaml @@ -0,0 +1,21 @@ +device: cpu +base_precision: 64 +seed: 42 + +architecture: + name: soap_bpnn + training: + batch_size: 10 + num_epochs: 10 + learning_rate: 0.01 + +# Section defining the parameters for system and target data +training_set: + systems: qm9_reduced_100.xyz + targets: + energy: + key: U0 + unit: hartree # very important to run simulations + +validation_set: 0.1 +test_set: 0.0 diff --git a/examples/1-advanced/options-no-zbl.yaml b/examples/1-advanced/options-no-zbl.yaml new file mode 100644 index 0000000000..efc0bf57ce --- /dev/null +++ b/examples/1-advanced/options-no-zbl.yaml @@ -0,0 +1,22 @@ +seed: 42 + +architecture: + name: soap_bpnn + model: + zbl: false + training: + num_epochs: 10 + batch_size: 10 + +# training set section +training_set: + systems: + read_from: ethanol_reduced_100.xyz + length_unit: angstrom + targets: + energy: + key: energy + unit: eV # very important to run simulations + +validation_set: 0.1 +test_set: 0.0 diff --git a/examples/1-advanced/options_zbl.yaml b/examples/1-advanced/options_zbl.yaml new file mode 100644 index 0000000000..f7969f9d90 --- /dev/null +++ b/examples/1-advanced/options_zbl.yaml @@ -0,0 +1,22 @@ +seed: 42 + +architecture: + name: soap_bpnn + model: + zbl: true + training: + num_epochs: 10 + batch_size: 10 + +# training set section +training_set: + systems: + read_from: ethanol_reduced_100.xyz + length_unit: angstrom + targets: + energy: + key: energy + unit: eV # very important to run simulations + +validation_set: 0.1 +test_set: 0.0 diff --git a/examples/llpr/qm9_reduced_100.xyz b/examples/1-advanced/qm9_reduced_100.xyz similarity index 100% rename from examples/llpr/qm9_reduced_100.xyz rename to examples/1-advanced/qm9_reduced_100.xyz diff --git a/examples/GALLERY_HEADER.rst b/examples/GALLERY_HEADER.rst new file mode 100644 index 0000000000..f5de0c2442 --- /dev/null +++ b/examples/GALLERY_HEADER.rst @@ -0,0 +1,6 @@ +Tutorials +========= + +Here we present beginner and advanced examples of how to use ``metatrain`` for +various applications. The beginner examples cover basic usage and data +preparation, while the advanced examples demonstrate more complex scenarios. diff --git a/examples/README.rst b/examples/README.rst deleted file mode 100644 index e7c763f8e1..0000000000 --- a/examples/README.rst +++ /dev/null @@ -1,4 +0,0 @@ -Metatrain Examples -================== - -This folder consists of introductory and advanced examples. diff --git a/examples/ase/README.rst b/examples/ase/README.rst deleted file mode 100644 index 50f7bdb413..0000000000 --- a/examples/ase/README.rst +++ /dev/null @@ -1,2 +0,0 @@ -Running molecular dynamics with ASE -=================================== diff --git a/examples/basic_usage/README.rst b/examples/basic_usage/README.rst deleted file mode 100644 index a93f89a85c..0000000000 --- a/examples/basic_usage/README.rst +++ /dev/null @@ -1,2 +0,0 @@ -Basic usage of the metatrain CLI -================================ diff --git a/examples/beginner/README.rst b/examples/beginner/README.rst deleted file mode 100644 index 6193a314f0..0000000000 --- a/examples/beginner/README.rst +++ /dev/null @@ -1,2 +0,0 @@ -Beginner tutorials -================== diff --git a/examples/llpr/README.rst b/examples/llpr/README.rst deleted file mode 100644 index f623186ad5..0000000000 --- a/examples/llpr/README.rst +++ /dev/null @@ -1,2 +0,0 @@ -Computing LLPR uncertainties -============================ diff --git a/examples/multi-gpu/README.rst b/examples/multi-gpu/README.rst deleted file mode 100644 index 099549c1e0..0000000000 --- a/examples/multi-gpu/README.rst +++ /dev/null @@ -1,3 +0,0 @@ -================== -Multi-GPU training -================== diff --git a/examples/multi-gpu/ethanol_reduced_100.xyz b/examples/multi-gpu/ethanol_reduced_100.xyz deleted file mode 120000 index f01afa4c67..0000000000 --- a/examples/multi-gpu/ethanol_reduced_100.xyz +++ /dev/null @@ -1 +0,0 @@ -../ase/ethanol_reduced_100.xyz \ No newline at end of file diff --git a/examples/multi-gpu/soap-bpnn/options-distributed.yaml b/examples/multi-gpu/soap-bpnn/options-distributed.yaml deleted file mode 120000 index d317b95012..0000000000 --- a/examples/multi-gpu/soap-bpnn/options-distributed.yaml +++ /dev/null @@ -1 +0,0 @@ -../../../tests/distributed/options-distributed.yaml \ No newline at end of file diff --git a/examples/multi-gpu/soap-bpnn/submit-distributed.sh b/examples/multi-gpu/soap-bpnn/submit-distributed.sh deleted file mode 120000 index 5b31a6b9bc..0000000000 --- a/examples/multi-gpu/soap-bpnn/submit-distributed.sh +++ /dev/null @@ -1 +0,0 @@ -../../../tests/distributed/submit-distributed.sh \ No newline at end of file diff --git a/examples/programmatic/data_preparation/README.rst b/examples/programmatic/data_preparation/README.rst deleted file mode 100644 index eccaed31e6..0000000000 --- a/examples/programmatic/data_preparation/README.rst +++ /dev/null @@ -1,2 +0,0 @@ -How to prepare data for training -================================ diff --git a/examples/programmatic/data_preparation/carbon_reduced_100.xyz b/examples/programmatic/data_preparation/carbon_reduced_100.xyz deleted file mode 120000 index a702ba7615..0000000000 --- a/examples/programmatic/data_preparation/carbon_reduced_100.xyz +++ /dev/null @@ -1 +0,0 @@ -../../../tests/resources/carbon_reduced_100.xyz \ No newline at end of file diff --git a/examples/programmatic/data_preparation/data.xyz b/examples/programmatic/data_preparation/data.xyz new file mode 100644 index 0000000000..4f7575ee81 --- /dev/null +++ b/examples/programmatic/data_preparation/data.xyz @@ -0,0 +1,1201 @@ +5 +Properties=species:S:1:pos:R:3:forces:R:3:my_tensor:R:9 A=157.7118 B=157.70997 C=157.70699 mu=0 alpha=13.21 epsHOMO=-0.3877 epsLUMO=0.1171 epsgap=0.5048 =35.3641 zpve=0.044749 U0=-100.0 U=-40.476062 H=-40.475117 G=-40.498597 Cv=6.469 pbc="F F F" +C -0.01269814 1.08580416 0.00800100 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 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z+ahNjeb!*b7;2ssJ?swZ*BaQ^$xz4A+%)6;zP9?T?RpqP&9fFM)g_z$+*G}<7HVm3 zIx#NdoIdN=WyVnRtXBD3@7906wqK=%TAG{s@4GWcpY{AHW2kx7s7}NE^;vz-YN3|q zCZDP+^6InZes2sl&$=f2ls@{bagkc6rMYSEvI;ZwS^MSALZRkai-j&WGfTJ^&_XTE zO@p(J_^8jiyrwbKJZm23hY|YvI`Df_MC}XH8tDW)5!OMor23uEyL5-)*(*Fm(>RsXh literal 0 HcmV?d00001 diff --git a/examples/programmatic/flashmd/README.rst b/examples/programmatic/flashmd/README.rst deleted file mode 100644 index 870044d0b5..0000000000 --- a/examples/programmatic/flashmd/README.rst +++ /dev/null @@ -1,2 +0,0 @@ -Training a FlashMD model -======================== diff --git a/examples/programmatic/use_architectures_outside/README.rst b/examples/programmatic/use_architectures_outside/README.rst deleted file mode 100644 index b3c4c2c56d..0000000000 --- a/examples/programmatic/use_architectures_outside/README.rst +++ /dev/null @@ -1,2 +0,0 @@ -Using metatrain architectures outside of metatrain -================================================== diff --git a/examples/programmatic/use_architectures_outside/qm9_reduced_100.xyz b/examples/programmatic/use_architectures_outside/qm9_reduced_100.xyz deleted file mode 120000 index a98d028721..0000000000 --- a/examples/programmatic/use_architectures_outside/qm9_reduced_100.xyz +++ /dev/null @@ -1 +0,0 @@ -../../../tests/resources/qm9_reduced_100.xyz \ No newline at end of file diff --git a/examples/train_from_scratch/README.rst b/examples/train_from_scratch/README.rst deleted file mode 100644 index ac545fe554..0000000000 --- a/examples/train_from_scratch/README.rst +++ /dev/null @@ -1,2 +0,0 @@ -Training a model from scratch -============================= diff --git a/examples/train_from_scratch/ethanol_reduced_100.xyz b/examples/train_from_scratch/ethanol_reduced_100.xyz deleted file mode 120000 index f01afa4c67..0000000000 --- a/examples/train_from_scratch/ethanol_reduced_100.xyz +++ /dev/null @@ -1 +0,0 @@ -../ase/ethanol_reduced_100.xyz \ No newline at end of file diff --git a/examples/validation/README.rst b/examples/validation/README.rst deleted file mode 100644 index 4340241618..0000000000 --- a/examples/validation/README.rst +++ /dev/null @@ -1,2 +0,0 @@ -Creating parity plots for validation -==================================== diff --git a/examples/zbl/README.rst b/examples/zbl/README.rst deleted file mode 100644 index 50f7bdb413..0000000000 --- a/examples/zbl/README.rst +++ /dev/null @@ -1,2 +0,0 @@ -Running molecular dynamics with ASE -=================================== diff --git a/examples/zbl/ethanol_reduced_100.xyz b/examples/zbl/ethanol_reduced_100.xyz deleted file mode 120000 index acaad11d93..0000000000 --- a/examples/zbl/ethanol_reduced_100.xyz +++ /dev/null @@ -1 +0,0 @@ -../../tests/resources/ethanol_reduced_100.xyz \ No newline at end of file diff --git a/src/metatrain/gap/tests/ethanol_reduced_100.xyz b/src/metatrain/gap/tests/ethanol_reduced_100.xyz deleted file mode 120000 index 8084906dc2..0000000000 --- a/src/metatrain/gap/tests/ethanol_reduced_100.xyz +++ /dev/null @@ -1 +0,0 @@ -../../../../examples/ase/ethanol_reduced_100.xyz \ No newline at end of file diff --git a/tox.ini b/tox.ini index c915475d08..0081a723d0 100644 --- a/tox.ini +++ b/tox.ini @@ -224,11 +224,10 @@ extras = # these architectures are used in the documentation soap-bpnn commands = {env:ENV_INSTALLER:python -m pip} install --no-build-isolation sphericart torch-spex>=0.1,<0.2 wigners - # Run example and usage scripts. - bash -c "set -e && cd {toxinidir}/examples/basic_usage && bash usage.sh" - bash -c "set -e && cd {toxinidir}/examples/ase && bash train.sh" - bash -c "set -e && cd {toxinidir}/examples/llpr && bash train.sh" - bash -c "set -e && cd {toxinidir}/examples/zbl && bash train.sh" + + # Run all .sh files in the example folder (including subfolders) + bash -c "set -e && for f in $(find examples -name '*.sh'); do cd $(dirname $f); bash $(basename $f); cd -; done" + sphinx-build \ {posargs:-E} \ --builder html \ From 46decec0f95f51985455c14a0fde1ba0a72363e6 Mon Sep 17 00:00:00 2001 From: Philip Loche Date: Mon, 27 Oct 2025 12:06:34 +0100 Subject: [PATCH 2/7] Suggestions from code review --- docs/src/examples/ase/ase.zip | Bin 6176 -> 0 bytes docs/src/examples/ase/ase_jupyter.zip | Bin 10186 -> 0 bytes docs/src/examples/ase/ethanol_reduced_100.xyz | 1100 ------------- .../images/thumb/sphx_glr_run_ase_thumb.png | Bin 41946 -> 0 bytes docs/src/examples/ase/index.rst | 61 - docs/src/examples/ase/options.yaml | 15 - docs/src/examples/ase/run_ase.codeobj.json | 926 ----------- docs/src/examples/ase/run_ase.ipynb | 266 ---- docs/src/examples/ase/run_ase.py | 204 --- docs/src/examples/ase/run_ase.py.md5 | 1 - docs/src/examples/ase/run_ase.rst | 403 ----- docs/src/examples/ase/run_ase.zip | Bin 16212 -> 0 bytes docs/src/examples/ase/train.sh | 3 - docs/src/examples/basic_usage/basic_usage.zip | Bin 4718 -> 0 bytes docs/src/examples/basic_usage/eval.yaml | 6 - 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- -.. only:: html - - .. image:: /examples/ase/images/thumb/sphx_glr_run_ase_thumb.png - :alt: - - :ref:`sphx_glr_examples_ase_run_ase.py` - -.. raw:: html - -
Running molecular dynamics with ASE
-
- - -.. thumbnail-parent-div-close - -.. raw:: html - - - - -.. toctree:: - :hidden: - - /examples/ase/run_ase - - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-gallery - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download all examples as source code: ase.zip ` - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download all examples in Jupyter notebooks: ase_jupyter.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/ase/options.yaml b/docs/src/examples/ase/options.yaml deleted file mode 100644 index 87300b9263..0000000000 --- a/docs/src/examples/ase/options.yaml +++ /dev/null @@ -1,15 +0,0 @@ -seed: 42 - -architecture: - name: gap - -# training set section -training_set: - systems: ethanol_reduced_100.xyz - targets: - energy: - key: energy - unit: kcal/mol # very important to run simulations - -validation_set: 0.1 -test_set: 0.0 diff --git a/docs/src/examples/ase/run_ase.codeobj.json b/docs/src/examples/ase/run_ase.codeobj.json deleted file mode 100644 index 44011ab7cd..0000000000 --- a/docs/src/examples/ase/run_ase.codeobj.json +++ /dev/null @@ -1,926 +0,0 @@ -{ - "Analysis": [ - { - "is_class": true, - "is_explicit": false, - "module": "ase.geometry.analysis", - "module_short": "ase.geometry.analysis", - "name": "Analysis" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.geometry", - "module_short": "ase.geometry", - "name": "Analysis" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Analysis" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.geometry.analysis", - "module_short": "ase.geometry.analysis", - "name": "Analysis" - } - ], - "MetatomicCalculator": [ - { - "is_class": true, - "is_explicit": false, - "module": "metatomic.torch.ase_calculator", - "module_short": "metatomic.torch.ase_calculator", - "name": "MetatomicCalculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "metatomic.torch", - "module_short": "metatomic.torch", - "name": "MetatomicCalculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "metatomic", - "module_short": "metatomic", - "name": "MetatomicCalculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.calculators.calculator", - "module_short": "ase.calculators.calculator", - "name": "Calculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.calculators", - "module_short": "ase.calculators", - "name": "Calculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Calculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.calculators.calculator", - "module_short": "ase.calculators.calculator", - "name": "BaseCalculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.calculators", - "module_short": "ase.calculators", - "name": "BaseCalculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "BaseCalculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.calculators.abc", - "module_short": "ase.calculators.abc", - "name": "GetPropertiesMixin" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.calculators", - "module_short": "ase.calculators", - "name": "GetPropertiesMixin" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "GetPropertiesMixin" - }, - { - "is_class": true, - "is_explicit": false, - "module": "abc", - "module_short": "abc", - "name": "ABC" - }, - { - "is_class": false, - "is_explicit": false, - "module": "metatomic.torch.ase_calculator", - "module_short": "metatomic.torch.ase_calculator", - "name": "MetatomicCalculator" - } - ], - "ana_train": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.geometry.analysis", - "module_short": "ase.geometry.analysis", - "name": "Analysis" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.geometry", - "module_short": "ase.geometry", - "name": "Analysis" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Analysis" - } - ], - "ana_train.get_rdf": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.geometry.analysis", - "module_short": "ase.geometry.analysis", - "name": "Analysis.get_rdf" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.geometry", - "module_short": "ase.geometry", - "name": "Analysis.get_rdf" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Analysis.get_rdf" - } - ], - "ana_traj": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.geometry.analysis", - "module_short": "ase.geometry.analysis", - "name": "Analysis" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.geometry", - "module_short": "ase.geometry", - "name": "Analysis" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Analysis" - } - ], - "ana_traj.get_rdf": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.geometry.analysis", - "module_short": "ase.geometry.analysis", - "name": "Analysis.get_rdf" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.geometry", - "module_short": "ase.geometry", - "name": "Analysis.get_rdf" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Analysis.get_rdf" - } - ], - "animation": [ - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.animation", - "module_short": "matplotlib.animation", - "name": "FuncAnimation" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib", - "module_short": "matplotlib", - "name": "FuncAnimation" - } - ], - "ase.geometry.analysis.Analysis": [ - { - "is_class": false, - "is_explicit": true, - "module": "ase.geometry.analysis", - "module_short": "ase.geometry.analysis", - "name": "Analysis" - } - ], - "ase.geometry.analysis.Analysis.get_rdf": [ - { - "is_class": false, - "is_explicit": true, - "module": "ase.geometry.analysis.Analysis", - "module_short": "ase.geometry.analysis.Analysis", - "name": "get_rdf" - } - ], - "ase.io.read": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - } - ], - "ase.md.VelocityVerlet": [ - { - "is_class": true, - "is_explicit": false, - "module": "ase.md.verlet", - "module_short": "ase.md", - "name": "VelocityVerlet" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.md", - "module_short": "ase.md", - "name": "VelocityVerlet" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "VelocityVerlet" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.md.md", - "module_short": "ase.md.md", - "name": "MolecularDynamics" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.md", - "module_short": "ase.md", - "name": "MolecularDynamics" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "MolecularDynamics" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.optimize.optimize", - "module_short": "ase.optimize.optimize", - "name": "Dynamics" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.optimize", - "module_short": "ase.optimize", - "name": "Dynamics" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Dynamics" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.utils", - "module_short": "ase.utils", - "name": "IOContext" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "IOContext" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.md", - "module_short": "ase.md", - "name": "VelocityVerlet" - } - ], - "ase.md.velocitydistribution.MaxwellBoltzmannDistribution": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.md.velocitydistribution", - "module_short": "ase.md.velocitydistribution", - "name": "MaxwellBoltzmannDistribution" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.md.velocitydistribution", - "module_short": "ase.md.velocitydistribution", - "name": "MaxwellBoltzmannDistribution" - } - ], - "ase.units.fs": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "float" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.units", - "module_short": "ase.units", - "name": "fs" - } - ], - "ase.visualize.plot.animate": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.visualize.plot", - "module_short": "ase.visualize.plot", - "name": "animate" - } - ], - "ase.visualize.plot.plot_atoms": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.visualize.plot", - "module_short": "ase.visualize.plot", - "name": "plot_atoms" - } - ], - "atoms": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms" - } - ], - "atoms.calc": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.calc" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.calc" - } - ], - "atoms.cell": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.cell" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.cell" - } - ], - "atoms.copy": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.copy" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.copy" - } - ], - "atoms.get_kinetic_energy": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.get_kinetic_energy" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.get_kinetic_energy" - } - ], - "atoms.get_potential_energy": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.get_potential_energy" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.get_potential_energy" - } - ], - "atoms.get_total_energy": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.get_total_energy" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.get_total_energy" - } - ], - "atoms.pbc": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.pbc" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.pbc" - } - ], - "bins": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray" - } - ], - "integrator": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.md.verlet", - "module_short": "ase.md", - "name": "VelocityVerlet" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.md", - "module_short": "ase.md", - "name": "VelocityVerlet" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "VelocityVerlet" - } - ], - "integrator.run": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.md.verlet", - "module_short": "ase.md", - "name": "VelocityVerlet.run" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.md", - "module_short": "ase.md", - "name": "VelocityVerlet.run" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "VelocityVerlet.run" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.md.md", - "module_short": "ase.md.md", - "name": "MolecularDynamics.run" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.md", - "module_short": "ase.md", - "name": "MolecularDynamics.run" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "MolecularDynamics.run" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.optimize.optimize", - "module_short": "ase.optimize.optimize", - "name": "Dynamics.run" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.optimize", - "module_short": "ase.optimize", - "name": "Dynamics.run" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Dynamics.run" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.utils", - "module_short": "ase.utils", - "name": "IOContext.run" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "IOContext.run" - } - ], - "kinetic_energy": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray" - } - ], - "kinetic_energy.mean": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "builtin_function_or_method" - } - ], - "n_steps": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "int" - } - ], - "np.mean": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "_ArrayFunctionDispatcher" - }, - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "mean" - } - ], - "np.ones": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ones" - } - ], - "np.zeros": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "builtin_function_or_method" - }, - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "zeros" - } - ], - "plt.legend": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot", - "module_short": "matplotlib.pyplot", - "name": "legend" - } - ], - "plt.plot": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot", - "module_short": "matplotlib.pyplot", - "name": "plot" - } - ], - "plt.show": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot", - "module_short": "matplotlib.pyplot", - "name": "show" - } - ], - "plt.xlabel": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot", - "module_short": "matplotlib.pyplot", - "name": "xlabel" - } - ], - "plt.ylabel": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot", - "module_short": "matplotlib.pyplot", - "name": "ylabel" - } - ], - "potential_energy": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray" - } - ], - "potential_energy.mean": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "builtin_function_or_method" - } - ], - "rdf_train": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "list" - } - ], - "rdf_train_mean": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray" - } - ], - "rdf_traj": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "list" - } - ], - "rdf_traj_mean": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray" - } - ], - "step": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "int" - } - ], - "total_energy": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray" - } - ], - "total_energy.mean": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "builtin_function_or_method" - } - ], - "train_frames": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "list" - } - ], - "trajectory": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "list" - } - ], - "trajectory.append": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "builtin_function_or_method" - } - ] -} diff --git a/docs/src/examples/ase/run_ase.ipynb b/docs/src/examples/ase/run_ase.ipynb deleted file mode 100644 index 61d76e3cb0..0000000000 --- a/docs/src/examples/ase/run_ase.ipynb +++ /dev/null @@ -1,266 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# Running molecular dynamics with ASE\n\nThis tutorial demonstrates how to use an already trained and exported model to run an\nASE simulation of a single ethanol molecule in vacuum. We use a model that was trained\nusing the `architecture-soap-bpnn` architecture on 100 ethanol systems\ncontaining energies and forces. You can obtain the :download:`dataset file\n` used in this example from our website. The dataset is a\nsubset of the [rMD17 dataset](https://iopscience.iop.org/article/10.1088/2632-2153/abba6f/meta).\n\nThe model was trained using the following training options.\n\n.. literalinclude:: options.yaml\n :language: yaml\n\nYou can train the same model yourself with\n\n.. literalinclude:: train.sh\n :language: bash\n\nA detailed step-by-step introduction on how to train a model is provided in\nthe `label_basic_usage` tutorial.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, we start by importing the necessary libraries, including the integration of ASE\ncalculators for metatensor atomistic models.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import ase.md\nimport ase.md.velocitydistribution\nimport ase.units\nimport ase.visualize.plot\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom ase.geometry.analysis import Analysis\nfrom metatomic.torch.ase_calculator import MetatomicCalculator" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setting up the simulation\n\nNext, we initialize the simulation by extracting the initial positions from the\ndataset file which we initially trained the model on.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "train_frames = ase.io.read(\"ethanol_reduced_100.xyz\", \":\")\natoms = train_frames[0].copy()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below we show the initial configuration of a single ethanol molecule in vacuum.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "ase.visualize.plot.plot_atoms(atoms)\n\nplt.xlabel(\"\u00c5\")\nplt.ylabel(\"\u00c5\")\n\nplt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our initial coordinates do not include velocities. We initialize the velocities\naccording to a Maxwell-Boltzmann Distribution at 300 K.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "ase.md.velocitydistribution.MaxwellBoltzmannDistribution(atoms, temperature_K=300)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now register our exported model as the energy calculator to obtain energies and\nforces.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "atoms.calc = MetatomicCalculator(\"model.pt\", extensions_directory=\"extensions/\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we define the integrator which we use to obtain new positions and velocities\nbased on our energy calculator. We use a common timestep of 0.5 fs.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "integrator = ase.md.VelocityVerlet(atoms, timestep=0.5 * ase.units.fs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run the simulation\n\nWe now have everything ready to run the MD simulation at constant energy (NVE). To\nkeep the execution time of this tutorial small we run the simulations only for 100\nsteps. If you want to run a longer simulation you can increase the ``n_steps``\nvariable.\n\nDuring the simulation loop we collect data about the simulation for later analysis.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n_steps = 100\n\npotential_energy = np.zeros(n_steps)\nkinetic_energy = np.zeros(n_steps)\ntotal_energy = np.zeros(n_steps)\ntrajectory = []\n\nfor step in range(n_steps):\n # run a single simulation step\n integrator.run(1)\n\n trajectory.append(atoms.copy())\n potential_energy[step] = atoms.get_potential_energy()\n kinetic_energy[step] = atoms.get_kinetic_energy()\n total_energy[step] = atoms.get_total_energy()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyse the results\n\n### Energy conservation\n\nFor a first analysis, we plot the evolution of the mean of the kinetic, potential, and\ntotal energy which is an important measure for the stability of a simulation.\n\nAs shown below we see that both the kinetic, potential, and total energy\nfluctuate but the total energy is conserved over the length of the simulation.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "plt.plot(potential_energy - potential_energy.mean(), label=\"potential energy\")\nplt.plot(kinetic_energy - kinetic_energy.mean(), label=\"kinetic energy\")\nplt.plot(total_energy - total_energy.mean(), label=\"total energy\")\n\nplt.xlabel(\"step\")\nplt.ylabel(\"energy / kcal/mol\")\nplt.legend()\n\nplt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Inspect the systems\n\nEven though the total energy is conserved, we also have to verify that the ethanol\nmolecule is stable and the bonds did not break.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "animation = ase.visualize.plot.animate(trajectory, interval=100, save_count=None)\nplt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Carbon-hydrogen radial distribution function\n\nAs a final analysis we also calculate and plot the carbon-hydrogen radial distribution\nfunction (RDF) from the trajectory and compare this to the RDF from the training set.\n\nTo use the RDF code from ase we first have to define a unit cell for our systems.\nWe choose a cubic one with a side length of 10 \u00c5.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "for atoms in train_frames:\n atoms.cell = 10 * np.ones(3)\n atoms.pbc = True\n\nfor atoms in trajectory:\n atoms.cell = 10 * np.ones(3)\n atoms.pbc = True" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now can initilize the :py:class:`ase.geometry.analysis.Analysis` objects and\ncompute the the RDF using the :py:meth:`ase.geometry.analysis.Analysis.get_rdf`\nmethod.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "ana_traj = Analysis(trajectory)\nana_train = Analysis(train_frames)\n\nrdf_traj = ana_traj.get_rdf(rmax=5, nbins=50, elements=[\"C\", \"H\"], return_dists=True)\nrdf_train = ana_train.get_rdf(rmax=5, nbins=50, elements=[\"C\", \"H\"], return_dists=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We extract the bin positions from the returned values and and averege the RDF over the\nwhole trajectory and dataset, respectively.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "bins = rdf_traj[0][1]\nrdf_traj_mean = np.mean([rdf_traj[i][0] for i in range(n_steps)], axis=0)\nrdf_train_mean = np.mean([rdf_train[i][0] for i in range(n_steps)], axis=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plotting the RDF verifies that the hydrogen bonds are stable, confirming that we\nperformed an energy-conserving and stable simulation.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "plt.plot(bins, rdf_traj_mean, label=\"trajectory\")\nplt.plot(bins, rdf_train_mean, label=\"training set\")\n\nplt.legend()\nplt.xlabel(\"r / \u00c5\")\nplt.ylabel(\"radial distribution function\")\n\nplt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/src/examples/ase/run_ase.py b/docs/src/examples/ase/run_ase.py deleted file mode 100644 index 06e9952fd7..0000000000 --- a/docs/src/examples/ase/run_ase.py +++ /dev/null @@ -1,204 +0,0 @@ -""" -Running molecular dynamics with ASE -=================================== - -This tutorial demonstrates how to use an already trained and exported model to run an -ASE simulation of a single ethanol molecule in vacuum. We use a model that was trained -using the :ref:`architecture-soap-bpnn` architecture on 100 ethanol systems -containing energies and forces. You can obtain the :download:`dataset file -` used in this example from our website. The dataset is a -subset of the `rMD17 dataset -`_. - -The model was trained using the following training options. - -.. literalinclude:: options.yaml - :language: yaml - -You can train the same model yourself with - -.. literalinclude:: train.sh - :language: bash - -A detailed step-by-step introduction on how to train a model is provided in -the :ref:`label_basic_usage` tutorial. -""" - -# %% -# -# First, we start by importing the necessary libraries, including the integration of ASE -# calculators for metatensor atomistic models. - -import ase.md -import ase.md.velocitydistribution -import ase.units -import ase.visualize.plot -import matplotlib.pyplot as plt -import numpy as np -from ase.geometry.analysis import Analysis -from metatomic.torch.ase_calculator import MetatomicCalculator - - -# %% -# -# Setting up the simulation -# ------------------------- -# -# Next, we initialize the simulation by extracting the initial positions from the -# dataset file which we initially trained the model on. - -train_frames = ase.io.read("ethanol_reduced_100.xyz", ":") -atoms = train_frames[0].copy() - -# %% -# -# Below we show the initial configuration of a single ethanol molecule in vacuum. - -ase.visualize.plot.plot_atoms(atoms) - -plt.xlabel("Ã…") -plt.ylabel("Ã…") - -plt.show() - - -# %% -# -# Our initial coordinates do not include velocities. We initialize the velocities -# according to a Maxwell-Boltzmann Distribution at 300 K. - -ase.md.velocitydistribution.MaxwellBoltzmannDistribution(atoms, temperature_K=300) - -# %% -# -# We now register our exported model as the energy calculator to obtain energies and -# forces. - -atoms.calc = MetatomicCalculator("model.pt", extensions_directory="extensions/") - -# %% -# -# Finally, we define the integrator which we use to obtain new positions and velocities -# based on our energy calculator. We use a common timestep of 0.5 fs. - -integrator = ase.md.VelocityVerlet(atoms, timestep=0.5 * ase.units.fs) - - -# %% -# -# Run the simulation -# ------------------ -# -# We now have everything ready to run the MD simulation at constant energy (NVE). To -# keep the execution time of this tutorial small we run the simulations only for 100 -# steps. If you want to run a longer simulation you can increase the ``n_steps`` -# variable. -# -# During the simulation loop we collect data about the simulation for later analysis. - - -n_steps = 100 - -potential_energy = np.zeros(n_steps) -kinetic_energy = np.zeros(n_steps) -total_energy = np.zeros(n_steps) -trajectory = [] - -for step in range(n_steps): - # run a single simulation step - integrator.run(1) - - trajectory.append(atoms.copy()) - potential_energy[step] = atoms.get_potential_energy() - kinetic_energy[step] = atoms.get_kinetic_energy() - total_energy[step] = atoms.get_total_energy() - -# %% -# -# Analyse the results -# ------------------- -# -# Energy conservation -# ################### -# -# For a first analysis, we plot the evolution of the mean of the kinetic, potential, and -# total energy which is an important measure for the stability of a simulation. -# -# As shown below we see that both the kinetic, potential, and total energy -# fluctuate but the total energy is conserved over the length of the simulation. - - -plt.plot(potential_energy - potential_energy.mean(), label="potential energy") -plt.plot(kinetic_energy - kinetic_energy.mean(), label="kinetic energy") -plt.plot(total_energy - total_energy.mean(), label="total energy") - -plt.xlabel("step") -plt.ylabel("energy / kcal/mol") -plt.legend() - -plt.show() - -# %% -# -# Inspect the systems -# ################### -# -# Even though the total energy is conserved, we also have to verify that the ethanol -# molecule is stable and the bonds did not break. - -animation = ase.visualize.plot.animate(trajectory, interval=100, save_count=None) -plt.show() - -# %% -# -# Carbon-hydrogen radial distribution function -# ############################################ -# -# As a final analysis we also calculate and plot the carbon-hydrogen radial distribution -# function (RDF) from the trajectory and compare this to the RDF from the training set. -# -# To use the RDF code from ase we first have to define a unit cell for our systems. -# We choose a cubic one with a side length of 10 Ã…. - -for atoms in train_frames: - atoms.cell = 10 * np.ones(3) - atoms.pbc = True - -for atoms in trajectory: - atoms.cell = 10 * np.ones(3) - atoms.pbc = True - -# %% -# -# We now can initilize the :py:class:`ase.geometry.analysis.Analysis` objects and -# compute the the RDF using the :py:meth:`ase.geometry.analysis.Analysis.get_rdf` -# method. - -ana_traj = Analysis(trajectory) -ana_train = Analysis(train_frames) - -rdf_traj = ana_traj.get_rdf(rmax=5, nbins=50, elements=["C", "H"], return_dists=True) -rdf_train = ana_train.get_rdf(rmax=5, nbins=50, elements=["C", "H"], return_dists=True) - -# %% -# -# We extract the bin positions from the returned values and and averege the RDF over the -# whole trajectory and dataset, respectively. - -bins = rdf_traj[0][1] -rdf_traj_mean = np.mean([rdf_traj[i][0] for i in range(n_steps)], axis=0) -rdf_train_mean = np.mean([rdf_train[i][0] for i in range(n_steps)], axis=0) - -# %% -# -# Plotting the RDF verifies that the hydrogen bonds are stable, confirming that we -# performed an energy-conserving and stable simulation. - -plt.plot(bins, rdf_traj_mean, label="trajectory") -plt.plot(bins, rdf_train_mean, label="training set") - -plt.legend() -plt.xlabel("r / Ã…") -plt.ylabel("radial distribution function") - -plt.show() diff --git a/docs/src/examples/ase/run_ase.py.md5 b/docs/src/examples/ase/run_ase.py.md5 deleted file mode 100644 index 31f5391e73..0000000000 --- a/docs/src/examples/ase/run_ase.py.md5 +++ /dev/null @@ -1 +0,0 @@ -ac05d2993cd2bb401fd7750e31d970c6 \ No newline at end of file diff --git a/docs/src/examples/ase/run_ase.rst b/docs/src/examples/ase/run_ase.rst deleted file mode 100644 index b1401d1108..0000000000 --- a/docs/src/examples/ase/run_ase.rst +++ /dev/null @@ -1,403 +0,0 @@ - -.. DO NOT EDIT. -.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. -.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: -.. "examples/ase/run_ase.py" -.. LINE NUMBERS ARE GIVEN BELOW. - -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - :ref:`Go to the end ` - to download the full example code. - -.. rst-class:: sphx-glr-example-title - -.. _sphx_glr_examples_ase_run_ase.py: - - -Running molecular dynamics with ASE -=================================== - -This tutorial demonstrates how to use an already trained and exported model to run an -ASE simulation of a single ethanol molecule in vacuum. We use a model that was trained -using the :ref:`architecture-soap-bpnn` architecture on 100 ethanol systems -containing energies and forces. You can obtain the :download:`dataset file -` used in this example from our website. The dataset is a -subset of the `rMD17 dataset -`_. - -The model was trained using the following training options. - -.. literalinclude:: options.yaml - :language: yaml - -You can train the same model yourself with - -.. literalinclude:: train.sh - :language: bash - -A detailed step-by-step introduction on how to train a model is provided in -the :ref:`label_basic_usage` tutorial. - -.. GENERATED FROM PYTHON SOURCE LINES 28-30 - -First, we start by importing the necessary libraries, including the integration of ASE -calculators for metatensor atomistic models. - -.. GENERATED FROM PYTHON SOURCE LINES 31-42 - -.. code-block:: Python - - - import ase.md - import ase.md.velocitydistribution - import ase.units - import ase.visualize.plot - import matplotlib.pyplot as plt - import numpy as np - from ase.geometry.analysis import Analysis - from metatomic.torch.ase_calculator import MetatomicCalculator - - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 43-48 - -Setting up the simulation -------------------------- - -Next, we initialize the simulation by extracting the initial positions from the -dataset file which we initially trained the model on. - -.. GENERATED FROM PYTHON SOURCE LINES 49-53 - -.. code-block:: Python - - - train_frames = ase.io.read("ethanol_reduced_100.xyz", ":") - atoms = train_frames[0].copy() - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 54-55 - -Below we show the initial configuration of a single ethanol molecule in vacuum. - -.. GENERATED FROM PYTHON SOURCE LINES 56-65 - -.. code-block:: Python - - - ase.visualize.plot.plot_atoms(atoms) - - plt.xlabel("Ã…") - plt.ylabel("Ã…") - - plt.show() - - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 66-68 - -Our initial coordinates do not include velocities. We initialize the velocities -according to a Maxwell-Boltzmann Distribution at 300 K. - -.. GENERATED FROM PYTHON SOURCE LINES 69-72 - -.. code-block:: Python - - - ase.md.velocitydistribution.MaxwellBoltzmannDistribution(atoms, temperature_K=300) - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 73-75 - -We now register our exported model as the energy calculator to obtain energies and -forces. - -.. GENERATED FROM PYTHON SOURCE LINES 76-79 - -.. code-block:: Python - - - atoms.calc = MetatomicCalculator("model.pt", extensions_directory="extensions/") - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 80-82 - -Finally, we define the integrator which we use to obtain new positions and velocities -based on our energy calculator. We use a common timestep of 0.5 fs. - -.. GENERATED FROM PYTHON SOURCE LINES 83-87 - -.. code-block:: Python - - - integrator = ase.md.VelocityVerlet(atoms, timestep=0.5 * ase.units.fs) - - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 88-97 - -Run the simulation ------------------- - -We now have everything ready to run the MD simulation at constant energy (NVE). To -keep the execution time of this tutorial small we run the simulations only for 100 -steps. If you want to run a longer simulation you can increase the ``n_steps`` -variable. - -During the simulation loop we collect data about the simulation for later analysis. - -.. GENERATED FROM PYTHON SOURCE LINES 98-116 - -.. code-block:: Python - - - - n_steps = 100 - - potential_energy = np.zeros(n_steps) - kinetic_energy = np.zeros(n_steps) - total_energy = np.zeros(n_steps) - trajectory = [] - - for step in range(n_steps): - # run a single simulation step - integrator.run(1) - - trajectory.append(atoms.copy()) - potential_energy[step] = atoms.get_potential_energy() - kinetic_energy[step] = atoms.get_kinetic_energy() - total_energy[step] = atoms.get_total_energy() - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 117-128 - -Analyse the results -------------------- - -Energy conservation -################### - -For a first analysis, we plot the evolution of the mean of the kinetic, potential, and -total energy which is an important measure for the stability of a simulation. - -As shown below we see that both the kinetic, potential, and total energy -fluctuate but the total energy is conserved over the length of the simulation. - -.. GENERATED FROM PYTHON SOURCE LINES 129-141 - -.. code-block:: Python - - - - plt.plot(potential_energy - potential_energy.mean(), label="potential energy") - plt.plot(kinetic_energy - kinetic_energy.mean(), label="kinetic energy") - plt.plot(total_energy - total_energy.mean(), label="total energy") - - plt.xlabel("step") - plt.ylabel("energy / kcal/mol") - plt.legend() - - plt.show() - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 142-147 - -Inspect the systems -################### - -Even though the total energy is conserved, we also have to verify that the ethanol -molecule is stable and the bonds did not break. - -.. GENERATED FROM PYTHON SOURCE LINES 148-152 - -.. code-block:: Python - - - animation = ase.visualize.plot.animate(trajectory, interval=100, save_count=None) - plt.show() - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 153-161 - -Carbon-hydrogen radial distribution function -############################################ - -As a final analysis we also calculate and plot the carbon-hydrogen radial distribution -function (RDF) from the trajectory and compare this to the RDF from the training set. - -To use the RDF code from ase we first have to define a unit cell for our systems. -We choose a cubic one with a side length of 10 Ã…. - -.. GENERATED FROM PYTHON SOURCE LINES 162-171 - -.. code-block:: Python - - - for atoms in train_frames: - atoms.cell = 10 * np.ones(3) - atoms.pbc = True - - for atoms in trajectory: - atoms.cell = 10 * np.ones(3) - atoms.pbc = True - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 172-175 - -We now can initilize the :py:class:`ase.geometry.analysis.Analysis` objects and -compute the the RDF using the :py:meth:`ase.geometry.analysis.Analysis.get_rdf` -method. - -.. GENERATED FROM PYTHON SOURCE LINES 176-183 - -.. code-block:: Python - - - ana_traj = Analysis(trajectory) - ana_train = Analysis(train_frames) - - rdf_traj = ana_traj.get_rdf(rmax=5, nbins=50, elements=["C", "H"], return_dists=True) - rdf_train = ana_train.get_rdf(rmax=5, nbins=50, elements=["C", "H"], return_dists=True) - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 184-186 - -We extract the bin positions from the returned values and and averege the RDF over the -whole trajectory and dataset, respectively. - -.. GENERATED FROM PYTHON SOURCE LINES 187-192 - -.. code-block:: Python - - - bins = rdf_traj[0][1] - rdf_traj_mean = np.mean([rdf_traj[i][0] for i in range(n_steps)], axis=0) - rdf_train_mean = np.mean([rdf_train[i][0] for i in range(n_steps)], axis=0) - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 193-195 - -Plotting the RDF verifies that the hydrogen bonds are stable, confirming that we -performed an energy-conserving and stable simulation. - -.. GENERATED FROM PYTHON SOURCE LINES 196-205 - -.. code-block:: Python - - - plt.plot(bins, rdf_traj_mean, label="trajectory") - plt.plot(bins, rdf_train_mean, label="training set") - - plt.legend() - plt.xlabel("r / Ã…") - plt.ylabel("radial distribution function") - - plt.show() - - - - - - - - -.. _sphx_glr_download_examples_ase_run_ase.py: - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-example - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: run_ase.ipynb ` - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: run_ase.py ` - - .. container:: sphx-glr-download sphx-glr-download-zip - - :download:`Download zipped: run_ase.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/ase/run_ase.zip b/docs/src/examples/ase/run_ase.zip deleted file mode 100644 index 074a0cf90b7fc095beb556e85cee490919d5a9a0..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 16212 zcmdU0+iv5=8FrE$00O-W(AxoxpmJc-cHC`KAr7$F0}?EDyG<5p3)7HCF=%0s&MJ-3X6hS`sec$`KmNJo3mZ?-kb;=7C&n3^2sAOkJHDeFIdEy;j z{o{GBW=YAax>7~LQx=O%$+9YVB}z6^XRK1JE(PO~@w5;;o-@2mq=@k-X5u_oMTHNU zibYC`fhFE}IG&YB2E?jFNv0-@+3qFGXjsHzLGQ!1YCjB_bZSob-@`TKi&?%Z--Rw66CNXZH} zA%H@PVw&K_bf<|bB2fnHuc~Gd?ybhO%p4l4GnpzL4^LuVff*H>B&qN|vv(R5BCaD5 zk8oaaK7aH130)_q8&*VSan7?GB$yN`W2!FLnHZPAKVYwLr3NGxa<8n%1RjK=+nyB9 zAMgLZS>b&)tE#*l?(QZkFQY`rNCfyZP{nkY7gZ9aVt0Qp*x&o~)7?*gcks#1C;JZ$ zcKLYBe>d69M8!Wp83n|8VF}%#F)(guJW*+?&h#guKmDO{;%AA?LBLX=Uhp)LQCi1h zICO94JWD;s*f8aCTJxzGGX2PFXsY?CZ&vcGL1GSylp>vI0bYH8-WQa!1>j?jN8Uq7 zFIbg=;NUPsVZKAZLH(*w;ERz&*@(5lfm;fI=y{<|lUTFMYeg*OW08(gVd!7>sXw@S}8?VEPZ$zc3Ugt*2 zoGAh|cP{$TOnf2E4G$sk6-*+oz5s&wibVy7L|JQO*Roteok>BAibINUUZdA!D%ja9 ziDo1sc1zkCRFXMkN0kH{wS0~y1r(%Yhnk&<3djZz{0r=jzs>xizvU5SXipc!(cWX7LRh{Kr{SZ(WpKywpV$4N4+TSA}4FKtCyg6rl?mS`GUnY-Wq0DxfynQ^kr&2%)t^%t|@bOS=@iqv2%x7Jzva zX)y3A3O?#NKR**`y7Prft2Y^!l0EKd3bc(Kz?D1$dyIOmRH(p0aX`8-3=y^&d{7SS zNuF!;>=019e1RX7U`!#VI9(LlS1kBi@`~V>c3tz1g`j&_m(_J^xT<@Fw7K5KmwNT+zA{al_7#*RRXOc;|~YA7d&7SZ5XX>1r|o1 zvN-tG8pLl!k&4PO$8JBQy&r4c)0Q!qls$Pw6t-X-K#FDaq794X>5QL(Yp0@^BP4+N zutAlHoQR{(A9o`sNE}oV{zO*Iy$3J8eX@m+LE)^|0(aDMaE>rgdkwm}i5p;HU0_#c z;0tLw1!%OPjm}EY6=sMW3*r_4CBi{-zM3$&WW--Mx{2&qs^k;`+3wbBC{BX%;V#5f zKtDN=BMs){1OQLr>i9SnfhNS`x@hcX2TZC|PWVL#Iv{1*YBD}nH4g8gs}Y4W1Qv}W zfjWBjJaCpS;pGZqOZIzYrR5O5D0m|ZRSxXZE$=nt6Rz{JC6%f!hXEV=ozV&$eRS-3 zL`N-pi24e|Kf;|iq_Bl;ni#nWhdU&oU3y8YE&aLR7Xcxy}A6OS3d;@I3jaIV@^c0MbeRq)|OM18f~!wb|xs&H^H{O=Qiw- zm7#4)3~->nO{0d!kWsJ>+1FtnDGC6UFzH%IwcJ*GoFGu16Lj*SO-^KV;bFKlI%eOD*f;HMqVUv7P3E7l5+MR{-h?7r=BsF5=TA z1v#v41VS3J5QA8ovCCe=OYS1aw(nChCAG5vn)t+pCtt}jC)=eNZ_~_+#q`OkAXBXB zso`@MN7iMRmR+8fifQKoTMYIk6GV`jIpjabfc?DEp?K%TD6yz-9FJe4@DxCNA%%Tp1g^sT9Z8(QaB?J zU5yf{l|_mTzJC04%fW+xyD?x5%WX8p83iINLXfI$48=pXq5-H>K#68bQN@Nu^%!X(SYj$dWPET!tMU7L z?57_g0pzf>cK~C1DTDD>Hd>?u$+sdLf+Gdga&TZAhgp!1DVls$)M6Q;b&xlP=M)Ur zqqx|F8_2X=b~eoC!zkrtiJH)=R5NID%o7C1gpRKDfMCS88a9)-&~T!yfDl;ZqqD1^ zBDbwZMLeNkht{iDYZf1o+JG$0=^ZsfmL~Ok5WT-3kv8jtnE}iOhF$($bJn29`1#?3 zZ6?QwEDs+*6-6pCL_OuLgaAtF-Xqy6LN_TP=P)7NpK_FhMBU2=@2TD=m|iGCCZlJfJUJVe4~H-KRmfJ)@i z+rnyT_YywWRTjvGXlqa9NoVh!88tSY439DP+vMb7I5HDq!#$i)2?CiWv^)c)5v&;J zwqv6KT1Ygv&Y~9rkeAdhn+OT&T4B_;b*{ek3|%MR+hGaR-QsLvzp{yr+||JeWgzh3-iV}t(vs#8%<^0^#;k0975vs(H?cBCU>dPG0^ z@u-^T0~A-(q4AtV5O2T#!96Wunj_j_fN4OX++@fZud3XKGL*Mcmk;H` zTPe(kvbCA-Qj`xRq;gGF`B1LEEFa2#UH+(7mp>l7Yh8XTFsyG1f!D6f)Ap+>^Pz++ zE|lhJ-`f}GFFFOLAdx7~FP=d4dD^qa3VkTaft*}coe!mNHS}2jwyL>)t-hVQKXk#B zwy!Sx6M3(3T?#f^116f{k%{ABvt*s7&XRTQo24ijMHFgyjM5osNEp8 z()RS&0b6AjdmW1q2?;e#bkz+NI$H`#WOk zt@K($xrSEjy_Sf!`G(RF+5a1LiSI}T5YsL9po#oe-`Ch}4(0lr&B&+M+G`G_Yc&%Q zZlTkBuSL7pBd43VHeJ)%ThS#4->+PmUG+MmZf%od*(tIA<}mK7WO3z}_RO0C$`eLc7ar7}>uRz?VAtAXC)}>#C;q=`Hm*0Ys2=FVFFso6 z#)nJe0WMd%!GJ*M_a_AUu)6AW`HVp5>$O(!G?azBj!czkL?! zQc!TO#hs!wQ=!Ehb6FT==Mpq6OIl*}M=^_Ve9pgoJBvb1iT|!`VlgXufEmtnF z>Wxqtd1@x8kgK?Aic2?^C$$#luJkT9c-6^p+C+s8x8KYPeVV?ysF!uw5LO5B%9IC# z0(u_6Um7g%UyDMDl!o|dn95ER_?TVHQ$8Qz3(k53>m|NBgMsCPc7mb#qA7OyxqjQe zYj&CE2Syd_l!D)E93_ZIx@S_hoh3z~iV=Mqs`Co74-H66ju9sAEWE<79(`L1>kpO& zBIp}{BiAb+TdU&$^A`r@j5KGpM~ zqZAV#dWF3}hmtM+{z0&R5d6W?3zyL}^W_+!8%8N`z;6k4_h5vfLq$;cydPdZd++@Z zKe+hI?B-kU8yo-p8~ueZx!A9^clWIt*=zTGjjzQw`}6?c%V$6T1-*VB|CRXt@6YMe Fe*q$4Rrvq_ diff --git a/docs/src/examples/ase/train.sh b/docs/src/examples/ase/train.sh deleted file mode 100644 index f73b048e61..0000000000 --- a/docs/src/examples/ase/train.sh +++ /dev/null @@ -1,3 +0,0 @@ -#!/bin/bash - -mtt train options.yaml diff --git a/docs/src/examples/basic_usage/basic_usage.zip b/docs/src/examples/basic_usage/basic_usage.zip deleted file mode 100644 index 29173744cf9d766480cb629534615cf1c3358a9b..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 4718 zcmb7IO^+ML5tZX42=?Z{m)r^x3`oLqsN@j9jAKB89oP_7Sx0LmMh-j8nI@; 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- -.. only:: html - - .. image:: /examples/basic_usage/images/thumb/sphx_glr_usage_thumb.png - :alt: - - :ref:`sphx_glr_examples_basic_usage_usage.sh` - -.. raw:: html - -
Basic Usage
-
- - -.. thumbnail-parent-div-close - -.. raw:: html - - - - -.. toctree:: - :hidden: - - /examples/basic_usage/usage - - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-gallery - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download all examples as source code: basic_usage.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/basic_usage/options.yaml b/docs/src/examples/basic_usage/options.yaml deleted file mode 100644 index c0c0ddf7bf..0000000000 --- a/docs/src/examples/basic_usage/options.yaml +++ /dev/null @@ -1,18 +0,0 @@ -# architecture used to train the model -architecture: - name: soap_bpnn - training: - num_epochs: 5 # a very short training run - batch_size: 10 - -# Mandatory section defining the parameters for system and target data of the -# training set -training_set: - systems: qm9_reduced_100.xyz # file where the positions are stored - targets: - energy: - key: U0 # name of the target value - unit: eV # unit of the target value - -test_set: 0.1 # 10 % of the training_set are randomly split and taken for test set -validation_set: 0.1 # 10 % of the training_set are randomly split and for validation diff --git a/docs/src/examples/basic_usage/output.xyz b/docs/src/examples/basic_usage/output.xyz deleted file mode 100644 index 37cb4a2653..0000000000 --- a/docs/src/examples/basic_usage/output.xyz +++ /dev/null @@ -1,1201 +0,0 @@ -5 -Properties=species:S:1:pos:R:3 energy=-40.47401479185843 pbc="F F F" -C -0.01269814 1.08580416 0.00800100 -H 0.00215042 -0.00603132 0.00197612 -H 1.01173084 1.46375116 0.00027657 -H -0.54081507 1.44752661 -0.87664372 -H -0.52381363 1.43793264 0.90639729 -4 -Properties=species:S:1:pos:R:3 energy=-56.53954696998065 pbc="F F F" -N -0.04042605 1.02410775 0.06256380 -H 0.01725746 0.01254521 -0.02737716 -H 0.91578937 1.35874519 -0.02875776 -H -0.52027774 1.34353213 -0.77554261 -3 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0.00807391 -11 -Properties=species:S:1:pos:R:3 A=29.45018 B=8.37701 C=7.42076 mu=0.0597 alpha=34.75 epsHOMO=-0.323 epsLUMO=0.0949 epsgap=0.4179 =227.1361 zpve=0.103182 U0=-119.052475 U=-119.047927 H=-119.046983 G=-119.078157 Cv=14.84 pbc="F F F" -C -0.03113825 1.54081582 0.03192126 -C 0.01215347 0.01092235 -0.01603259 -C 0.72169129 -0.52583353 -1.26230570 -H 0.97955987 1.96459116 0.03098367 -H -0.55840223 1.94831192 -0.83816075 -H -0.54252252 1.90153531 0.93005671 -H 0.51522791 -0.36840234 0.88231134 -H -1.01070641 -0.38456999 0.02051783 -H 1.75851210 -0.17376585 -1.30871516 -H 0.74087192 -1.62024959 -1.27516511 -H 0.22023351 -0.19051179 -2.17729020 -9 -Properties=species:S:1:pos:R:3 A=35.09545 B=9.3686 C=8.1497 mu=1.4131 alpha=27.87 epsHOMO=-0.2619 epsLUMO=0.0798 epsgap=0.3417 =193.1659 zpve=0.079754 U0=-154.972731 U=-154.968412 H=-154.967467 G=-154.998148 Cv=13.546 pbc="F F F" -C -0.00860504 1.50203829 -0.00681217 -C 0.01099310 -0.01764877 -0.01377035 -O 0.68088841 -0.44041803 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H=-117.820482 G=-117.849087 Cv=11.041 pbc="F F F" -C -0.01193280 1.51433198 0.01031700 -C 1.30299118 0.77886562 -0.00617842 -C 0.00867174 0.00767042 0.00201032 -H -0.30541464 2.01702117 0.92533239 -H -0.32275498 2.02680156 -0.89347839 -H 1.88493396 0.79145020 -0.92119393 -H 1.90226631 0.78165391 0.89766373 -H -0.27088043 -0.51289125 0.91137474 -H -0.28821983 -0.50310409 -0.90740824 -7 -Properties=species:S:1:pos:R:3 A=25.77385 B=22.12109 C=14.18066 mu=1.7675 alpha=24.04 epsHOMO=-0.2682 epsLUMO=0.1042 epsgap=0.3724 =129.891 zpve=0.057289 U0=-153.742562 U=-153.73941 H=-153.738466 G=-153.766642 Cv=9.176 pbc="F F F" -C 0.01537720 1.41764380 0.00956287 -C 1.26478161 0.64923984 -0.00655044 -O -0.00024018 -0.00771006 0.00204055 -H -0.31763326 1.88586598 0.93475751 -H -0.33527583 1.89576673 -0.90397578 -H 1.83246682 0.56256848 -0.93193290 -H 1.85008258 0.55267938 0.90680147 -10 -Properties=species:S:1:pos:R:3 A=10.12193 B=8.49011 C=4.89615 mu=2.7362 alpha=35.53 epsHOMO=-0.2431 epsLUMO=-0.0087 epsgap=0.2344 =292.4367 zpve=0.083382 U0=-193.08834 U=-193.082969 H=-193.082024 G=-193.116476 Cv=16.893 pbc="F F F" -C -0.00310189 1.48050328 -0.17259867 -C -0.04527202 -0.02887059 0.00139493 -C 1.29705800 -0.72194415 0.16889042 -O -1.08643172 -0.64273343 0.00630393 -H 0.59403529 1.74668579 -1.05225043 -H -1.01540407 1.87034023 -0.28382265 -H 0.48099600 1.95070605 0.69099376 -H 1.82512144 -0.32470731 1.04328658 -H 1.14892507 -1.79597689 0.28554393 -H 1.93682635 -0.52949692 -0.69995403 -9 -Properties=species:S:1:pos:R:3 A=10.78886 B=9.27509 C=5.14977 mu=3.6367 alpha=31.83 epsHOMO=-0.2436 epsLUMO=0.0347 epsgap=0.2783 =267.6148 zpve=0.07319 U0=-209.159302 U=-209.15402 H=-209.153076 G=-209.187468 Cv=16.561 pbc="F F F" -C 0.00679889 1.49969736 -0.02567030 -C -0.02740385 -0.01752051 -0.14894811 -N 1.17826667 -0.63324298 0.05691721 -O -1.03721965 -0.63980649 -0.40727572 -H 0.91390280 1.92923755 -0.46100067 -H -0.87080896 1.91124186 -0.52345176 -H -0.02826124 1.78517271 1.03149125 -H 1.98405139 -0.13175861 0.38674906 -H 1.19049421 -1.63919563 0.10006209 -8 -Properties=species:S:1:pos:R:3 A=11.1475 B=10.36388 C=5.42091 mu=3.4869 alpha=28.07 epsHOMO=-0.2495 epsLUMO=0.0556 epsgap=0.3051 =244.2308 zpve=0.063824 U0=-225.221461 U=-225.217075 H=-225.216131 G=-225.247724 Cv=15.292 pbc="F F F" -N 0.03605270 1.36077873 -0.12416403 -C -0.02591138 -0.02076560 0.00200645 -N 1.21968479 -0.62334220 0.11963222 -O -1.06822866 -0.64174597 0.00865573 -H 0.80749439 1.83455148 0.32144915 -H -0.85825775 1.80373500 0.02243770 -H 1.99927148 -0.17303054 -0.33602496 -H 1.18204327 -1.62147901 -0.02109234 -14 -Properties=species:S:1:pos:R:3 A=7.75166 B=7.74847 C=4.48668 mu=0.0897 alpha=45.46 epsHOMO=-0.3167 epsLUMO=0.0843 epsgap=0.401 =355.0621 zpve=0.131146 U0=-158.342346 U=-158.336603 H=-158.335658 G=-158.370016 Cv=20.273 pbc="F F F" -C -0.03215888 1.54021598 0.01074456 -C 0.03381741 0.00745852 0.00180716 -C 0.71375572 -0.50856404 -1.27296949 -C 0.73848999 -0.52220886 1.25750907 -H 0.97522736 1.97414341 0.00528362 -H -0.56196769 1.92232326 -0.86835418 -H -0.54817239 1.91268414 0.90208039 -H -0.99752525 -0.37310578 0.00977297 -H 1.75070021 -0.15586784 -1.33269631 -H 0.73446691 -1.60328288 -1.29939696 -H 0.19367011 -0.16085589 -2.17195508 -H 0.23611990 -0.18382017 2.17005260 -H 0.75964083 -1.61713153 1.27226101 -H 1.77635866 -0.16985318 1.30053019 -12 -Properties=species:S:1:pos:R:3 A=8.67553 B=8.00568 C=4.75542 mu=1.4259 alpha=38.58 epsHOMO=-0.2612 epsLUMO=0.074 epsgap=0.3351 =318.3721 zpve=0.107673 U0=-194.267232 U=-194.261748 H=-194.260804 G=-194.294663 Cv=19.052 pbc="F F F" -C -0.03315871 1.54782566 -0.00438815 -C -0.01108534 0.01859095 0.01676117 -C 0.70938384 -0.53596711 1.23978444 -O -1.33219841 -0.51712554 0.05236057 -H 0.98215692 1.95715900 -0.03182225 -H -0.56388100 1.92087623 -0.88845865 -H -0.54082548 1.93340945 0.88575489 -H 0.51042122 -0.33767241 -0.88819176 -H 0.22240492 -0.18785860 2.15650245 -H 0.68242024 -1.62887638 1.23305141 -H 1.75434301 -0.21246950 1.25524188 -H -1.81818681 -0.15577435 -0.69553962 -6 -Properties=species:S:1:pos:R:3 A=0 B=4.4259726 C=4.4259726 mu=0 alpha=38.52 epsHOMO=-0.2599 epsLUMO=-0.0214 epsgap=0.2386 =278.6264 zpve=0.037354 U0=-153.459846 U=-153.455442 H=-153.454498 G=-153.482621 Cv=15.312 pbc="F F F" -C 0.68098021 0.00000000 0.00000000 -C -0.68098021 0.00000000 0.00000000 -C -1.88766603 0.00000000 0.00000000 -C 1.88766603 0.00000000 0.00000000 -H -2.94960000 0.00000000 0.00000000 -H 2.94960000 0.00000000 0.00000000 -5 -Properties=species:S:1:pos:R:3 A=0 B=4.5793222 C=4.5793222 mu=3.792 alpha=32.66 epsHOMO=-0.3102 epsLUMO=-0.0543 epsgap=0.2559 =260.1896 zpve=0.027259 U0=-169.557758 U=-169.553764 H=-169.55282 G=-169.581024 Cv=12.93 pbc="F F F" -C 0.01510390 0.00000000 1.00000000 -C 1.38233149 0.00000000 1.00000000 -C -1.18862525 0.00000000 1.00000000 -H -2.25177201 0.00000000 1.00000000 -N 2.54296187 0.00000000 1.00000000 -4 -Properties=species:S:1:pos:R:3 A=0 B=4.73269 C=4.73269 mu=0.0023 alpha=27.7 epsHOMO=-0.3696 epsLUMO=-0.0926 epsgap=0.277 =242.9308 zpve=0.015951 U0=-185.648533 U=-185.644825 H=-185.64388 G=-185.667652 Cv=10.398 pbc="F F F" -N 0.01745734 -1.16134217 -0.00415342 -C 0.00253212 -0.00344278 0.00179939 -C -0.01611419 1.37220987 0.00934873 -N -0.03264302 2.53008276 0.01609090 -6 -Properties=species:S:1:pos:R:3 A=68.5171 B=4.8345 C=4.51586 mu=2.7824 alpha=31.14 epsHOMO=-0.2777 epsLUMO=-0.0735 epsgap=0.2042 =268.3921 zpve=0.037208 U0=-190.624631 U=-190.620363 H=-190.619419 G=-190.650543 Cv=13.049 pbc="F F F" -O -0.06776120 -0.04233838 0.00282489 -C -0.00562945 1.16382879 0.00743862 -C 1.22985272 1.91339997 0.00028781 -C 2.23625322 2.57430970 -0.00529277 -H -0.91670701 1.79467546 0.01781352 -H 3.13429662 3.14370267 -0.01037656 -5 -Properties=species:S:1:pos:R:3 A=67.88408 B=5.00823 C=4.66413 mu=2.3112 alpha=26.25 epsHOMO=-0.3166 epsLUMO=-0.11 epsgap=0.2066 =251.0007 zpve=0.02654 U0=-206.721858 U=-206.717875 H=-206.716931 G=-206.747625 Cv=11.329 pbc="F F F" -O -0.04055978 -0.03799144 0.00266513 -C -0.01768870 1.16193251 0.00746008 -C 1.23315534 1.93381336 0.00043371 -N 2.19857378 2.57055097 -0.00483320 -H -0.92627534 1.78959196 0.01764859 -6 -Properties=species:S:1:pos:R:3 A=56.25048 B=4.77441 C=4.40088 mu=0.002 alpha=26.12 epsHOMO=-0.2668 epsLUMO=-0.1113 epsgap=0.1555 =266.8164 zpve=0.036943 U0=-227.798785 U=-227.79457 H=-227.793626 G=-227.825074 Cv=12.147 pbc="F F F" -O 0.00337122 -0.03146586 0.00157206 -C -0.01713174 1.17081409 0.00676444 -C 1.26555783 1.99719410 -0.00007703 -O 1.24507069 3.19946589 0.00681275 -H -0.94690483 1.77678142 0.01852668 -H 2.19534410 1.39121374 -0.01006739 -10 -Properties=species:S:1:pos:R:3 A=80.28766 B=3.3688 C=3.3688 mu=0 alpha=42.32 epsHOMO=-0.2412 epsLUMO=0.0684 epsgap=0.3096 =400.2236 zpve=0.083896 U0=-155.908941 U=-155.90318 H=-155.902236 G=-155.937641 Cv=17.447 pbc="F F F" -C -0.01787959 1.46676893 0.01011180 -C 0.00186644 0.01072913 0.00211109 -C 0.01819704 -1.19272861 -0.00450723 -C 0.03795170 -2.64876793 -0.01251193 -H 0.86460312 1.87589979 0.51479719 -H -0.03217624 1.87210581 -1.00791002 -H -0.90233115 1.85185524 0.53003953 -H -0.83477011 -3.06339007 0.50451436 -H 0.03276422 -3.04315564 -1.03491223 -H 0.93213486 -3.03931642 0.48626606 -10 -Properties=species:S:1:pos:R:3 A=27.39459 B=4.53005 C=4.08342 mu=0.7067 alpha=40.09 epsHOMO=-0.2592 epsLUMO=0.0566 epsgap=0.3157 =333.9589 zpve=0.084338 U0=-155.897345 U=-155.892291 H=-155.891347 G=-155.924226 Cv=17.13 pbc="F F F" -C -0.03095840 1.54775025 0.03167945 -C 0.01485375 0.00962471 -0.02082154 -C 0.69099073 -0.49954575 -1.20857624 -C 1.25222275 -0.89514467 -2.19527879 -H 0.97899300 1.96621609 0.03373834 -H -0.56082354 1.95002902 -0.83580745 -H -0.54576708 1.88225519 0.93709062 -H 0.51920135 -0.37425398 0.87496224 -H -1.00644802 -0.39030838 0.01340632 -H 1.74594541 -1.25604187 -3.06299415 -9 -Properties=species:S:1:pos:R:3 A=28.0016 B=4.67752 C=4.21765 mu=3.9233 alpha=35.38 epsHOMO=-0.3213 epsLUMO=0.034 epsgap=0.3553 =314.5335 zpve=0.07419 U0=-172.006141 U=-172.001467 H=-172.000523 G=-172.032826 Cv=14.988 pbc="F F F" -C -0.02522005 1.54773054 0.02133370 -C 0.01183939 0.00999278 -0.01556702 -C 0.68915114 -0.50836846 -1.20331488 -N 1.22565733 -0.90360846 -2.14737482 -H 0.98571620 1.96212990 0.02370153 -H -0.55522192 1.94599662 -0.84711771 -H -0.53962556 1.88294354 0.92536604 -H 0.52481910 -0.38226048 0.86933355 -H -1.00453542 -0.39832109 0.00530071 -8 -Properties=species:S:1:pos:R:3 A=32.19893 B=4.76299 C=4.30696 mu=4.4361 alpha=31.81 epsHOMO=-0.2683 epsLUMO=0.0173 epsgap=0.2855 =295.6635 zpve=0.063305 U0=-188.042067 U=-188.037478 H=-188.036534 G=-188.06863 Cv=14.488 pbc="F F F" -N -0.03608099 1.48762533 0.03678072 -C -0.01039471 0.02517605 -0.01506608 -C -1.37180956 -0.52108920 0.02739272 -N -2.44026695 -0.95956444 0.03370519 -H 0.91729752 1.83547547 0.05202682 -H -0.47162178 1.85677205 -0.80275569 -H 0.48946161 -0.40880401 -0.89715168 -H 0.52827150 -0.34533338 0.86469547 -8 -Properties=species:S:1:pos:R:3 A=35.11615 B=4.66117 C=4.22354 mu=1.7211 alpha=33.1 epsHOMO=-0.2595 epsLUMO=0.0277 epsgap=0.2872 =300.0993 zpve=0.060632 U0=-191.810916 U=-191.806025 H=-191.805081 G=-191.837634 Cv=15.855 pbc="F F F" -O -0.02013197 1.45738137 0.01010195 -C 0.00168478 0.03579980 0.00224736 -C -1.36366691 -0.47481101 0.01255056 -C -2.47902835 -0.91970202 0.02082451 -H 0.89446911 1.75736553 0.00327363 -H 0.51979028 -0.35175413 -0.88980302 -H 0.53670458 -0.36127661 0.88000894 -H -3.46773502 -1.30737009 0.02818120 -7 -Properties=species:S:1:pos:R:3 A=35.93172 B=4.83443 C=4.37827 mu=4.6788 alpha=28.56 epsHOMO=-0.3018 epsLUMO=0.0022 epsgap=0.3039 =280.6659 zpve=0.050262 U0=-207.916786 U=-207.912215 H=-207.911271 G=-207.943384 Cv=13.845 pbc="F F F" -O -0.03738221 1.44547841 0.01010769 -C 0.01292906 0.03441661 0.00209165 -C -1.35747452 -0.48874523 0.01239738 -N -2.42135277 -0.93685184 0.02016053 -H 0.86898057 1.76972949 0.00417577 -H 0.51692713 -0.36329790 -0.89215814 -H 0.53387616 -0.37278749 0.88223921 -10 -Properties=species:S:1:pos:R:3 A=26.14564 B=4.2893 C=4.12773 mu=2.6741 alpha=35.83 epsHOMO=-0.25 epsLUMO=-0.0205 epsgap=0.2295 =333.3276 zpve=0.084175 U0=-193.075202 U=-193.070116 H=-193.069171 G=-193.102798 Cv=15.954 pbc="F F F" -C 0.16879340 1.52513042 -0.15747056 -C -0.18737625 0.06194763 0.14679374 -C 0.50917645 -0.43996215 1.39125850 -O 1.18191079 -1.43565585 1.45816384 -H 1.23126511 1.63137254 -0.39632693 -H -0.40744668 1.89431681 -1.00969246 -H -0.04931034 2.17266260 0.69857432 -H -1.26905160 -0.01668067 0.32854433 -H 0.06279062 -0.60250479 -0.68474036 -H 0.35384841 0.20663370 2.28871052 -9 -Properties=species:S:1:pos:R:3 A=19.98749 B=6.11589 C=4.82584 mu=3.7071 alpha=32.78 epsHOMO=-0.2516 epsLUMO=0.0335 epsgap=0.2851 =279.7863 zpve=0.074166 U0=-209.144909 U=-209.139976 H=-209.139032 G=-209.172305 Cv=15.058 pbc="F F F" -C 0.01474916 1.47888256 -0.03509326 -N 0.18708077 0.03831317 -0.02906851 -C 0.45703661 -0.66858895 1.10069001 -O 0.58457996 -0.19876202 2.21119705 -H 0.76025289 1.96736104 -0.67198126 -H -0.98462423 1.75984483 -0.38485103 -H 0.14269849 1.82387718 0.99149397 -H 0.10386349 -0.46937726 -0.89461379 -H 0.55047568 -1.75253452 0.88868242 -8 -Properties=species:S:1:pos:R:3 A=48.62351 B=4.67298 C=4.38187 mu=3.92 alpha=29.47 epsHOMO=-0.2814 epsLUMO=0.0074 epsgap=0.2888 =293.917 zpve=0.061327 U0=-229.013797 U=-229.009003 H=-229.008059 G=-229.041086 Cv=13.885 pbc="F F F" -C 0.10653781 1.43512049 0.03336100 -O 0.42686564 0.04716929 0.09309945 -C 0.50714862 -0.63652665 -1.06318297 -O 0.76927014 -1.79955017 -1.11010475 -H 0.92349251 2.00385139 0.48465728 -H -0.03757815 1.76927183 -1.00057425 -H -0.81269569 1.60801916 0.59873287 -H 0.30458460 -0.00231902 -1.94965553 -8 -Properties=species:S:1:pos:R:3 A=36.49282 B=4.31175 C=4.04493 mu=1.7341 alpha=28.53 epsHOMO=-0.2537 epsLUMO=-0.0341 epsgap=0.2196 =303.8129 zpve=0.060508 U0=-228.992613 U=-228.987769 H=-228.986825 G=-229.019918 Cv=14.78 pbc="F F F" -O 0.19375856 1.37170995 0.31398297 -C 0.04992637 0.01405481 -0.04526239 -C -1.40888025 -0.39635680 -0.20601574 -O -1.77729462 -1.37233860 -0.80361689 -H -0.01723186 1.91269132 -0.45337758 -H 0.60440478 -0.26519766 -0.95205725 -H 0.46740406 -0.57521679 0.78176270 -H -2.11835055 0.30075962 0.29870515 -14 -Properties=species:S:1:pos:R:3 A=23.44662 B=3.60588 C=3.38683 mu=0 alpha=45.71 epsHOMO=-0.317 epsLUMO=0.0937 epsgap=0.4107 =426.2996 zpve=0.131708 U0=-158.340943 U=-158.33517 H=-158.334226 G=-158.36894 Cv=19.668 pbc="F F F" -C -0.03007624 1.55804912 0.02980774 -C 0.00707277 0.02834877 -0.00712529 -C 0.71320792 -0.52989340 -1.24765581 -C 0.75036895 -2.05959366 -1.28458957 -H 0.98195330 1.97845113 0.02800797 -H -0.55582433 1.96212343 -0.84270157 -H -0.54083762 1.92692755 0.92493697 -H 0.50928672 -0.34909049 0.89361319 -H -1.01733481 -0.36542299 0.03207393 -H 0.21098948 -0.15245770 -2.14839302 -H 1.73761331 -0.13611625 -1.28685672 -H 1.27616854 -2.46366410 -0.41210980 -H -0.26165699 -2.48000447 -1.28273552 -H 1.26108491 -2.42846444 -2.17974803 -12 -Properties=species:S:1:pos:R:3 A=26.59789 B=3.79186 C=3.53779 mu=1.3402 alpha=38.61 epsHOMO=-0.2619 epsLUMO=0.081 epsgap=0.3429 =382.8628 zpve=0.108241 U0=-194.261089 U=-194.255495 H=-194.254551 G=-194.28893 Cv=18.431 pbc="F F F" -C -0.00379012 1.52196790 0.01027996 -C -0.03589372 -0.00778254 0.00225671 -C -1.45219016 -0.56653711 0.01284386 -O -1.36799785 -1.98394604 0.00453606 -H 1.02477576 1.89411231 0.00239793 -H -0.51286674 1.93764614 -0.86653888 -H -0.49584080 1.92801344 0.90121721 -H 0.48140784 -0.39530552 -0.88316064 -H 0.49828589 -0.40486256 0.87330348 -H -1.98734220 -0.20399494 0.90666062 -H -2.00431485 -0.19459473 -0.86666460 -H -2.26171819 -2.33824298 0.01034748 -12 -Properties=species:S:1:pos:R:3 A=28.29567 B=4.14901 C=3.88349 mu=1.0363 alpha=39.34 epsHOMO=-0.2503 epsLUMO=0.0925 epsgap=0.3428 =368.9331 zpve=0.107895 U0=-194.254127 U=-194.248585 H=-194.247641 G=-194.281899 Cv=17.888 pbc="F F F" -C -0.00519551 1.50868224 0.03308760 -C 0.01316580 -0.01037819 0.01988643 -O 0.69758944 -0.46344140 1.16868979 -C 0.76688491 -1.86508183 1.24602669 -H 1.01445476 1.90423799 0.02996018 -H -0.52990866 1.89184059 -0.84726911 -H -0.51263765 1.87805843 0.92875424 -H -1.01599507 -0.40998729 0.00714282 -H 0.51093143 -0.38369435 -0.89219395 -H -0.23415841 -2.32451489 1.28977780 -H 1.30129276 -2.29853663 0.38486095 -H 1.30990287 -2.11811998 2.16013206 -10 -Properties=species:S:1:pos:R:3 A=14.79671 B=5.6956 C=4.58846 mu=0.0075 alpha=31.42 epsHOMO=-0.2594 epsLUMO=0.0584 epsgap=0.3179 =297.8398 zpve=0.085172 U0=-230.183076 U=-230.177723 H=-230.176779 G=-230.211195 Cv=16.837 pbc="F F F" -O -0.01418735 1.42642242 -0.05423651 -C -0.00391839 0.00717714 0.03496062 -C -1.42317019 -0.55560600 -0.01256639 -O -2.11229413 -0.10563875 -1.17228313 -H -0.18987882 1.64154705 -0.97555148 -H 0.59659504 -0.43315430 -0.77409095 -H 0.47311116 -0.24903733 0.98747216 -H -1.39494859 -1.64975279 -0.06283188 -H -1.96497417 -0.27216915 0.90131972 -H -2.35925991 0.80944396 -1.00614597 -12 -Properties=species:S:1:pos:R:3 A=15.61472 B=6.31363 C=5.54514 mu=0.1136 alpha=41.96 epsHOMO=-0.2727 epsLUMO=0.1012 epsgap=0.3738 =298.6061 zpve=0.109284 U0=-157.116735 U=-157.11209 H=-157.111146 G=-157.143262 Cv=16.49 pbc="F F F" -C -0.03602245 1.52092949 0.01569161 -C 0.01938487 0.01004403 0.04814953 -C 1.30891671 -0.71251995 -0.24920416 -C 0.75347423 -0.70267714 1.15564676 -H 0.90105691 1.95389291 0.38283338 -H -0.19640365 1.89437369 -1.00208569 -H -0.84685273 1.90588930 0.64451322 -H -0.88416809 -0.48001889 -0.30566335 -H 1.26900901 -1.63903068 -0.81150371 -H 2.19010738 -0.11131209 -0.44927513 -H 1.26248862 -0.09487455 1.89690028 -H 0.33540836 -1.62248918 1.54980695 -10 -Properties=species:S:1:pos:R:3 A=18.21931 B=6.63877 C=5.92459 mu=1.812 alpha=35.01 epsHOMO=-0.2633 epsLUMO=0.1052 epsgap=0.3685 =267.2979 zpve=0.085275 U0=-193.039603 U=-193.035186 H=-193.034242 G=-193.065979 Cv=14.764 pbc="F F F" -C -0.01646343 1.51830705 0.00771445 -C -0.02743227 0.01657694 -0.10889741 -C 0.77296553 -0.70131886 -1.10863115 -O 1.17212293 -0.66702969 0.26278829 -H 0.91865980 1.92914217 -0.38285880 -H -0.85158014 1.95456506 -0.55109460 -H -0.11487702 1.82596470 1.05397777 -H -0.93582598 -0.47350600 0.24682793 -H 0.44443922 -1.67201495 -1.47810622 -H 1.40538284 -0.13600339 -1.79271491 -11 -Properties=species:S:1:pos:R:3 A=16.58914 B=7.18798 C=6.11415 mu=1.1353 alpha=39.02 epsHOMO=-0.2304 epsLUMO=0.0968 epsgap=0.3271 =270.5508 zpve=0.097671 U0=-173.147782 U=-173.143343 H=-173.142399 G=-173.174073 Cv=15.298 pbc="F F F" -C -0.05275541 1.47419574 0.00999108 -N 0.01940516 0.02189947 0.06334866 -C 1.32511048 -0.56446850 -0.21420725 -C 0.78051759 -0.54057993 1.17242760 -H 0.86784202 1.97176076 0.36297285 -H -0.23845155 1.79095005 -1.02142866 -H -0.88683448 1.81938959 0.62950398 -H 1.31992909 -1.48186782 -0.79602038 -H 2.15202820 0.11020204 -0.43285248 -H 1.24563570 0.14996098 1.87499245 -H 0.37013358 -1.44020447 1.62233365 -10 -Properties=species:S:1:pos:R:3 A=16.79477 B=6.71015 C=5.86781 mu=1.3894 alpha=34.64 epsHOMO=-0.239 epsLUMO=0.0775 epsgap=0.3166 =263.966 zpve=0.085106 U0=-193.034988 U=-193.030356 H=-193.029411 G=-193.061689 Cv=15.679 pbc="F F F" -O 0.21577583 1.35386859 -0.10695107 -C 0.01628099 -0.02832718 -0.02915694 -C -0.39081465 -0.71497009 1.24315669 -C -1.35118138 -0.64280380 0.06273589 -H -0.43642338 1.79062849 0.45006761 -H 0.74981167 -0.52404050 -0.65624767 -H 0.05959375 -1.66840483 1.49729237 -H -0.61369353 -0.08848006 2.10170640 -H -2.19821932 0.03058430 0.15411451 -H -1.56395912 -1.54640426 -0.49826515 -12 -Properties=species:S:1:pos:R:3 A=10.66107 B=10.66107 C=6.30452 mu=0 alpha=41.83 epsHOMO=-0.2982 epsLUMO=0.0956 epsgap=0.3938 =268.4432 zpve=0.110511 U0=-157.115484 U=-157.111322 H=-157.110378 G=-157.141657 Cv=14.696 pbc="F F F" -C -0.02542957 1.54032578 -0.04215944 -C 1.52564399 1.50419989 0.04121276 -C 1.42146727 0.06146790 0.60844885 -C -0.02638833 -0.01070956 0.04935088 -H -0.46591372 1.99639894 0.84962894 -H -0.47900239 2.00028207 -0.92365389 -H 1.98248278 1.51375334 -0.95303183 -H 2.02191365 2.25986579 0.65522129 -H 2.14291679 -0.67956358 0.25531480 -H 1.43105853 0.05751479 1.70261879 -H -0.79053440 -0.48530296 0.66996573 -H -0.05233245 -0.47238446 -0.94236394 -10 -Properties=species:S:1:pos:R:3 A=12.06545 B=11.77731 C=6.71997 mu=1.7978 alpha=34.56 epsHOMO=-0.2424 epsLUMO=0.0859 epsgap=0.3283 =236.9759 zpve=0.086675 U0=-193.034094 U=-193.029968 H=-193.029024 G=-193.060777 Cv=12.915 pbc="F F F" -C -0.03489789 1.55259455 -0.08507691 -C 1.49349183 1.45459700 0.10114677 -O 1.42725884 0.02348990 0.27254886 -C -0.00634758 0.02236494 0.10944630 -H -0.56579210 2.11301721 0.68591527 -H -0.36628352 1.89393380 -1.06692327 -H 2.10031724 1.73525720 -0.76898349 -H 1.90023090 1.95506611 0.98896748 -H -0.51987849 -0.35595179 1.00235948 -H -0.31970062 -0.57567421 -0.75559275 -12 -Properties=species:S:1:pos:R:3 A=8.63483 B=4.11977 C=2.88825 mu=0.6875 alpha=45.37 epsHOMO=-0.2392 epsLUMO=0.0192 epsgap=0.2584 =452.5112 zpve=0.100501 U0=-248.375248 U=-248.368823 H=-248.367879 G=-248.405354 Cv=21.616 pbc="F F F" -C -0.10255370 1.49907127 -0.00133642 -C -0.00519774 -0.00343114 -0.02723470 -C -0.00688536 -0.76890740 1.26434744 -N 0.08186799 -0.69202791 -1.10087306 -O 0.07439067 0.13215394 -2.24195258 -H 0.78342093 1.94690862 -0.46354050 -H -0.96150430 1.83614082 -0.59021965 -H -0.19854173 1.87462989 1.01931607 -H -0.92620012 -0.57297946 1.82982479 -H 0.07256154 -1.84064782 1.07516863 -H 0.82937178 -0.45553340 1.90133089 -H 0.13975924 -0.51254299 -2.95389260 -10 -Properties=species:S:1:pos:R:3 A=9.17136 B=9.04195 C=4.5531 mu=1.8689 alpha=43.14 epsHOMO=-0.2029 epsLUMO=0.0499 epsgap=0.2528 =303.9808 zpve=0.082433 U0=-210.101789 U=-210.097816 H=-210.096872 G=-210.12818 Cv=14.821 pbc="F F F" -N -0.00829694 1.35362838 0.00995971 -C 1.28033254 1.82457421 -0.00022459 -C 2.12216639 0.73698898 -0.01445600 -C 1.30849904 -0.43109580 -0.01284154 -C -0.00342782 -0.01836884 0.00235736 -H -0.83243234 1.92772668 0.02122995 -H 1.48471820 2.88312630 0.00354313 -H 3.20066918 0.77343077 -0.02488416 -H 1.64810599 -1.45539683 -0.02180394 -H -0.92551647 -0.57697054 0.00839402 -9 -Properties=species:S:1:pos:R:3 A=9.77329 B=9.41192 C=4.7946 mu=3.6193 alpha=39.13 epsHOMO=-0.2253 epsLUMO=0.0332 epsgap=0.2585 =283.6817 zpve=0.071145 U0=-226.160842 U=-226.157088 H=-226.156144 G=-226.187104 Cv=13.371 pbc="F F F" -N -0.00996090 1.35636172 0.01000855 -C 1.29535422 1.79575578 -0.00047017 -C 2.05550519 0.65637438 -0.01422927 -N 1.25385745 -0.46275132 -0.01260894 -C 0.02301170 -0.00706382 0.00200146 -H -0.83195718 1.93618436 0.02132575 -H 1.54553961 2.84344998 0.00289054 -H 3.13177434 0.57569766 -0.02524155 -H -0.87667864 -0.60414531 0.00750794 -9 -Properties=species:S:1:pos:R:3 A=9.50228 B=9.29497 C=4.69874 mu=0.5571 alpha=39.2 epsHOMO=-0.2246 epsLUMO=0.0199 epsgap=0.2445 =289.005 zpve=0.069883 U0=-229.969129 U=-229.965414 H=-229.96447 G=-229.995393 Cv=13.358 pbc="F F F" -O -0.03255819 1.35065130 0.00967376 -C 1.25319237 1.79167710 -0.00030351 -C 2.11664681 0.74375623 -0.01441494 -C 1.30397796 -0.43852387 -0.01283316 -C 0.01629568 -0.00777185 0.00191294 -H 1.37951089 2.86161135 0.00427433 -H 3.19376406 0.79865501 -0.02458604 -H 1.63858893 -1.46383271 -0.02156949 -H -0.93736295 -0.50899925 0.00843891 -8 -Properties=species:S:1:pos:R:3 A=10.10926 B=9.69372 C=4.94857 mu=1.5081 alpha=35.17 epsHOMO=-0.2509 epsLUMO=0.001 epsgap=0.2519 =269.2444 zpve=0.058593 U0=-246.02915 U=-246.025614 H=-246.024669 G=-246.055309 Cv=12.04 pbc="F F F" -O -0.03808298 1.35643965 0.00975193 -C 1.26496982 1.76855319 -0.00061744 -C 2.04955750 0.66630033 -0.01406047 -N 1.24227075 -0.46732066 -0.01237541 -C 0.04019820 0.00561577 0.00183569 -H 1.43513907 2.83095265 0.00340823 -H 3.12532898 0.59783015 -0.02480757 -H -0.89387707 -0.53342154 0.00796935 -17 -Properties=species:S:1:pos:R:3 A=4.4178 B=4.41753 C=4.41717 mu=0.0003 alpha=56.01 epsHOMO=-0.3145 epsLUMO=0.0737 epsgap=0.3882 =486.2719 zpve=0.158836 U0=-197.632222 U=-197.625241 H=-197.624297 G=-197.661411 Cv=26.084 pbc="F F F" -C -0.01859029 1.53486266 0.01039904 -C 0.00210320 -0.00413672 0.00196980 -C 0.74643954 -0.51390760 1.24887638 -C -1.44190166 -0.53673615 0.01311457 -C 0.72234971 -0.50049606 -1.26445632 -H 0.99776928 1.94420529 0.00435474 -H -0.54181883 1.92841415 -0.86819911 -H -0.52758402 1.91888376 0.90148530 -H 0.25549576 -0.17602728 2.16833067 -H 0.77815389 -1.60892372 1.27088904 -H 1.77964786 -0.14975834 1.26944035 -H -1.99871895 -0.18888794 -0.86408066 -H -1.45956909 -1.63227235 0.00649916 -H -1.98088827 -0.19979639 0.90561580 -H 1.75482473 -0.13554018 -1.30118083 -H 0.75396989 -1.59524036 -1.29822280 -H 0.21358221 -0.15343900 -2.17067181 -15 -Properties=species:S:1:pos:R:3 A=4.69474 B=4.66938 C=4.47909 mu=1.384 alpha=49.04 epsHOMO=-0.2601 epsLUMO=0.0664 epsgap=0.3265 =449.0573 zpve=0.134977 U0=-233.560626 U=-233.553779 H=-233.552834 G=-233.589759 Cv=25.128 pbc="F F F" -C -0.00855999 1.54270147 0.00152717 -C 0.00506780 0.00702635 0.01856160 -C 0.76137731 -0.51839196 1.24766443 -C -1.42012774 -0.54933183 -0.02116008 -O 0.62590477 -0.48363745 -1.17657057 -H 1.01361406 1.94020776 0.00281128 -H -0.51459669 1.90702466 -0.89705781 -H -0.52214815 1.94882514 0.87918344 -H 0.28130613 -0.20165548 2.17943598 -H 0.79965506 -1.61131746 1.23022549 -H 1.79096916 -0.14072935 1.26083003 -H -1.93854829 -0.19910724 -0.91828281 -H -1.39937632 -1.64259062 -0.04563833 -H -1.98850725 -0.23059656 0.85726520 -H 1.53111188 -0.15488046 -1.19210222 -9 -Properties=species:S:1:pos:R:3 A=10.21876 B=4.04324 C=2.94978 mu=2.8579 alpha=42.02 epsHOMO=-0.2654 epsLUMO=-0.0575 epsgap=0.2079 =416.7799 zpve=0.065175 U0=-229.927277 U=-229.921622 H=-229.920677 G=-229.955843 Cv=18.723 pbc="F F F" -C -0.00693851 1.49559567 -0.01063358 -C -0.02986170 -0.01594623 0.06248673 -O -0.08815239 -0.63215062 1.10334812 -C 0.02336981 -0.70256747 -1.22062379 -C 0.06738132 -1.26058341 -2.28623129 -H 0.90429806 1.83281887 -0.51655384 -H -0.85338524 1.85556926 -0.60552819 -H -0.05237764 1.91099807 0.99695949 -H 0.10466868 -1.77036047 -3.21832617 -8 -Properties=species:S:1:pos:R:3 A=10.16063 B=4.15847 C=3.0056 mu=3.3351 alpha=37.08 epsHOMO=-0.3007 epsLUMO=-0.0889 epsgap=0.2118 =397.7757 zpve=0.05451 U0=-246.027383 U=-246.022024 H=-246.02108 G=-246.055769 Cv=16.963 pbc="F F F" -C -0.01171097 1.49973776 -0.01213268 -C -0.02919740 -0.00338745 0.07151218 -O -0.09164028 -0.64592927 1.08732241 -C 0.03831104 -0.69690258 -1.23748557 -N 0.09136286 -1.22982650 -2.26228068 -H 0.90093132 1.83370670 -0.51814928 -H -0.85663933 1.84864400 -0.61618135 -H -0.06416540 1.92131542 0.99203818 -8 -Properties=species:S:1:pos:R:3 A=10.68423 B=4.3198 C=3.07993 mu=5.1815 alpha=38.31 epsHOMO=-0.2739 epsLUMO=-0.0438 epsgap=0.2301 =384.5574 zpve=0.056619 U0=-242.19573 U=-242.190591 H=-242.189646 G=-242.223513 Cv=17.465 pbc="F F F" -N -0.05232108 1.34991769 0.15535538 -C 0.08549378 -0.01476951 0.02668890 -N 1.14555215 -0.71993906 -0.03966037 -C -1.19426556 -0.71152088 0.01802151 -N -2.22747610 -1.22790543 0.00132148 -H 0.75474469 1.91998737 -0.04411334 -H -0.94458527 1.76600473 -0.05932293 -H 1.98312429 -0.13415500 -0.02878551 -9 -Properties=species:S:1:pos:R:3 A=9.08854 B=4.42607 C=3.03169 mu=0.9461 alpha=36.51 epsHOMO=-0.2538 epsLUMO=-0.0964 epsgap=0.1574 =399.222 zpve=0.064992 U0=-267.10335 U=-267.097658 H=-267.096714 G=-267.132534 Cv=17.806 pbc="F F F" -C -0.43126066 1.30586664 -0.12758585 -C 0.25285715 -0.01026988 0.13138867 -O 0.69872099 -0.36890333 1.19604690 -C 0.38978943 -0.96773680 -1.06226208 -O -0.01611268 -0.71564293 -2.16583718 -H 0.10006565 1.85324490 -0.91332597 -H -1.44270433 1.13069361 -0.50923371 -H -0.46757554 1.89548158 0.78836479 -H 0.90329886 -1.91558845 -0.79531972 -9 -Properties=species:S:1:pos:R:3 A=10.08927 B=4.54115 C=3.13487 mu=2.7707 alpha=38.09 epsHOMO=-0.2561 epsLUMO=-0.064 epsgap=0.192 =384.4827 zpve=0.067668 U0=-263.278851 U=-263.273589 H=-263.272645 G=-263.306835 Cv=17.972 pbc="F F F" -N -0.16032929 1.08528962 -0.45398652 -C 0.18649127 -0.08762327 0.14758021 -N 1.28979627 -0.46427423 0.67927030 -C -0.94204384 -1.08440264 0.20423621 -O -2.01802205 -0.89007687 -0.30583149 -H 0.54574437 1.73837930 -0.74689246 -H -1.04684920 1.09570857 -0.93607796 -H 2.01738195 0.24991828 0.59881811 -H -0.67740148 -2.01003995 0.74746441 -13 -Properties=species:S:1:pos:R:3 A=7.92874 B=3.8272 C=2.82581 mu=0.6578 alpha=51.2 epsHOMO=-0.2589 epsLUMO=0.0571 epsgap=0.316 =481.9854 zpve=0.112471 U0=-195.186772 U=-195.180446 H=-195.179502 G=-195.215658 Cv=22.569 pbc="F F F" -C -0.01763434 1.54859468 -0.00253584 -C -0.01719180 0.00735789 0.03269604 -C 0.75818463 -0.52820155 1.25278862 -C -1.38213303 -0.51768438 0.00775516 -C -2.50796392 -0.94005629 0.00478552 -H 1.00944396 1.92639613 -0.02632830 -H -0.54514614 1.92217052 -0.88393459 -H -0.51243000 1.95339892 0.88560642 -H 0.49366179 -0.34904155 -0.87265890 -H 0.28968458 -0.19345372 2.18334679 -H 0.77833392 -1.62102506 1.25783727 -H 1.78952492 -0.16181727 1.23574954 -H -3.49998244 -1.31810165 -0.00723572 -12 -Properties=species:S:1:pos:R:3 A=7.90009 B=3.95466 C=2.88643 mu=3.9512 alpha=46.23 epsHOMO=-0.318 epsLUMO=0.0365 epsgap=0.3545 =460.694 zpve=0.102281 U0=-211.295796 U=-211.289821 H=-211.288877 G=-211.324525 Cv=20.467 pbc="F F F" -C -0.00242350 1.55035837 0.01056187 -C -0.01856600 0.01004352 -0.02261770 -C -1.45045318 -0.55766178 0.01293347 -C 0.70125749 -0.48583168 -1.20172764 -N 1.26190946 -0.87126766 -2.13619740 -H 1.01959496 1.93673215 0.00349668 -H -0.52972844 1.96141639 -0.85498386 -H -0.50134251 1.90346839 0.91755862 -H 0.52481877 -0.36222456 0.85525462 -H -2.02427903 -0.21428716 -0.85251869 -H -1.44438296 -1.65026927 0.00754107 -H -1.95805494 -0.21725882 0.91996760 -11 -Properties=species:S:1:pos:R:3 A=8.30095 B=3.97615 C=2.93764 mu=2.7429 alpha=42.87 epsHOMO=-0.2704 epsLUMO=0.0278 epsgap=0.2983 =440.0738 zpve=0.091554 U0=-227.338075 U=-227.332253 H=-227.331309 G=-227.366638 Cv=19.946 pbc="F F F" -C -0.01064295 1.54128691 0.00722087 -C -0.00203811 0.00756015 0.03863463 -N 0.76268677 -0.47101822 1.19250856 -C -1.38541954 -0.52099256 -0.00194097 -N -2.45680847 -0.95636072 0.00694969 -H 1.02043523 1.90106589 0.00392365 -H -0.52873161 1.91193452 -0.88031480 -H -0.51849536 1.94150431 0.89013197 -H 0.50418828 -0.35805500 -0.86276072 -H 0.30070681 -0.18364934 2.05155305 -H 0.78148992 -1.48670299 1.20428183 -11 -Properties=species:S:1:pos:R:3 A=8.55919 B=3.89952 C=2.91869 mu=1.3582 alpha=44.03 epsHOMO=-0.2665 epsLUMO=0.0336 epsgap=0.3001 =444.6452 zpve=0.088908 U0=-231.108368 U=-231.102292 H=-231.101348 G=-231.137061 Cv=21.306 pbc="F F F" -C -0.00754278 1.55566144 0.08650633 -C 0.01207541 0.02096553 0.04935916 -O -1.29150251 -0.52785128 0.22126386 -C 0.65093362 -0.47718651 -1.17430507 -C 1.13902673 -0.86577343 -2.20205518 -H 1.00824243 1.95969458 0.04929733 -H -0.56272245 1.95192010 -0.76978085 -H -0.49328161 1.88929438 1.00704123 -H 0.57811967 -0.34464907 0.91458864 -H -1.78516532 -0.35903262 -0.58909400 -H 1.58078877 -1.22365060 -3.09919447 -10 -Properties=species:S:1:pos:R:3 A=8.55908 B=4.01671 C=2.97882 mu=3.269 alpha=39.28 epsHOMO=-0.3051 epsLUMO=0.0115 epsgap=0.3166 =424.3395 zpve=0.078602 U0=-247.214861 U=-247.209162 H=-247.208218 G=-247.243338 Cv=19.252 pbc="F F F" -C -0.00992462 1.54951908 0.00732200 -C 0.00740485 0.01722020 0.05031270 -O 0.72085722 -0.49630479 1.15864583 -C -1.36995095 -0.52355929 0.00894432 -N -2.45081572 -0.93233138 0.00716912 -H 1.01892314 1.91630447 0.02724149 -H -0.50261490 1.90969362 -0.89997951 -H -0.54800426 1.95377611 0.87053664 -H 0.53602846 -0.36494195 -0.82970945 -H 0.27702643 -0.20644534 1.96421773 -13 -Properties=species:S:1:pos:R:3 A=7.66289 B=3.70925 C=2.79937 mu=2.6921 alpha=46.58 epsHOMO=-0.2469 epsLUMO=-0.0188 epsgap=0.2281 =482.0475 zpve=0.112328 U0=-232.364952 U=-232.358577 H=-232.357633 G=-232.394589 Cv=21.434 pbc="F F F" -C -0.02629647 1.53877116 -0.00783747 -C 0.01468904 0.00095310 0.00166037 -C 0.74737862 -0.53388859 1.24420471 -C -1.40211264 -0.53859257 -0.01557820 -O -1.87620455 -1.23582351 -0.87399559 -H 0.98823696 1.94625080 0.03705522 -H -0.50725090 1.92153422 -0.91285565 -H -0.57756514 1.92360883 0.85796386 -H 0.50584680 -0.36421091 -0.90619124 -H 0.22958380 -0.23909403 2.16431052 -H 0.81692736 -1.62559499 1.22973438 -H 1.76267153 -0.12846566 1.29050755 -H -2.00861510 -0.22792742 0.87121666 -11 -Properties=species:S:1:pos:R:3 A=8.31096 B=4.07963 C=2.92918 mu=2.8354 alpha=39.16 epsHOMO=-0.255 epsLUMO=-0.0279 epsgap=0.2271 =432.1489 zpve=0.088443 U0=-268.287661 U=-268.281505 H=-268.280561 G=-268.316982 Cv=20.344 pbc="F F F" -C -0.05483118 1.46032843 -0.17068647 -C 0.07105714 -0.03524485 0.11225841 -O 0.58204486 -0.33316656 1.40176256 -C -1.27639279 -0.74383027 0.03051185 -O -2.11855775 -0.49643175 -0.79163412 -H 0.93158072 1.93091398 -0.23353204 -H -0.57713995 1.61290939 -1.11835562 -H -0.62444315 1.94776119 0.62573670 -H 0.69687521 -0.50071620 -0.67159471 -H 1.45603316 0.06066659 1.47858584 -H -1.40723217 -1.53388627 0.80211181 -12 -Properties=species:S:1:pos:R:3 A=8.9331 B=4.19405 C=2.96046 mu=3.7163 alpha=44.42 epsHOMO=-0.2424 epsLUMO=0.0327 epsgap=0.2751 =441.85 zpve=0.10227 U0=-248.430371 U=-248.424309 H=-248.423365 G=-248.459383 Cv=19.918 pbc="F F F" -C -0.09776499 1.44917268 0.04561984 -N 0.01043053 0.00373818 -0.00070600 -C 0.05323028 -0.71432112 1.25264891 -C 0.06728716 -0.64067225 -1.20060778 -O 0.03636432 -0.11006515 -2.29109228 -H 0.75650660 1.88747109 0.57647421 -H -0.11648577 1.81632103 -0.98049172 -H -1.01641106 1.75521548 0.56151415 -H -0.85453556 -0.53657675 1.84390168 -H 0.13297722 -1.78696336 1.05851351 -H 0.91637348 -0.40737846 1.85761345 -H 0.14762240 -1.73801339 -1.06805139 -11 -Properties=species:S:1:pos:R:3 A=10.13982 B=3.81346 C=2.86965 mu=2.9514 alpha=39.33 epsHOMO=-0.2699 epsLUMO=-0.0262 epsgap=0.2437 =440.1727 zpve=0.088924 U0=-268.301176 U=-268.295084 H=-268.29414 G=-268.331307 Cv=19.824 pbc="F F F" -C -0.01708808 1.44281136 0.03225501 -C -0.05544542 -0.06560572 -0.02375999 -O -0.17379913 -0.76196233 0.96223129 -C 0.06068107 -0.73615179 -1.38825252 -O 0.00877771 -2.12574390 -1.27749486 -H 0.92062950 1.81463913 -0.39522215 -H -0.83050523 1.86542281 -0.56780350 -H -0.10911975 1.78049045 1.06493675 -H -0.75015067 -0.35235060 -2.03056074 -H 1.00312898 -0.40389029 -1.85592942 -H -0.09063255 -2.29450960 -0.32608019 -13 -Properties=species:S:1:pos:R:3 A=9.55216 B=3.57844 C=2.7341 mu=2.6168 alpha=46.19 epsHOMO=-0.2423 epsLUMO=-0.0072 epsgap=0.2351 =489.8518 zpve=0.112006 U0=-232.377706 U=-232.371073 H=-232.370129 G=-232.408256 Cv=21.716 pbc="F F F" -C -0.04384102 1.54099793 -0.03913814 -C 0.02765132 0.01885817 0.04778773 -C 0.76814644 -0.48842218 1.28000847 -C 0.87850208 -1.99746312 1.42393323 -O 1.24179142 0.26651340 2.09788702 -H 0.95796454 1.97690198 -0.06816012 -H -0.58447717 1.85356031 -0.93690369 -H -0.55173626 1.95737759 0.83440529 -H -0.97692824 -0.42709877 0.05353178 -H 0.51853486 -0.40806507 -0.83809409 -H 1.39924980 -2.42678188 0.56053177 -H 1.42001187 -2.24449367 2.33776426 -H -0.11750602 -2.45360651 1.45141965 -12 -Properties=species:S:1:pos:R:3 A=9.78802 B=3.78441 C=2.84416 mu=3.499 alpha=42.54 epsHOMO=-0.2438 epsLUMO=0.0355 epsgap=0.2793 =457.447 zpve=0.101847 U0=-248.448467 U=-248.441988 H=-248.441044 G=-248.478935 Cv=21.374 pbc="F F F" -C -0.00220144 1.53344329 -0.05960369 -C -0.03235837 0.01206167 0.06840265 -C 0.69666086 -0.48196462 1.31828322 -N 0.98104007 -1.82034162 1.31984698 -O 1.00392124 0.24606232 2.24136749 -H 1.02111190 1.89525169 -0.19182672 -H -0.59707204 1.86206427 -0.91645604 -H -0.39728839 1.99920712 0.84545201 -H -1.06936083 -0.34552935 0.13040806 -H 0.39947165 -0.46906818 -0.81777020 -H 0.63281951 -2.43917647 0.60874785 -H 1.35708014 -2.22217793 2.16303522 -12 -Properties=species:S:1:pos:R:3 A=10.01185 B=3.84195 C=2.87635 mu=3.5402 alpha=43.61 epsHOMO=-0.2418 epsLUMO=0.0387 epsgap=0.2805 =458.9803 zpve=0.101735 U0=-248.443503 U=-248.436899 H=-248.435955 G=-248.474272 Cv=20.967 pbc="F F F" -C 0.00619941 1.45933202 -0.03893859 -N -0.06411616 0.01422963 -0.12046901 -C -1.20595394 -0.68196796 0.16117120 -C -1.08982704 -2.19284899 0.02095775 -O -2.24482911 -0.14443077 0.49994418 -H 0.75053989 1.78372520 0.69715573 -H 0.25692001 1.90395263 -1.00876215 -H -0.97856026 1.81121438 0.27082194 -H 0.75820982 -0.49543660 -0.39464024 -H -0.09909703 -2.53543104 -0.28970003 -H -1.34097671 -2.65395851 0.97972885 -H -1.82903100 -2.53483935 -0.70813076 -11 -Properties=species:S:1:pos:R:3 A=10.44045 B=4.04692 C=2.98547 mu=3.5648 alpha=39.51 epsHOMO=-0.2436 epsLUMO=0.0599 epsgap=0.3034 =428.404 zpve=0.091713 U0=-264.504487 U=-264.498452 H=-264.497508 G=-264.533633 Cv=20.274 pbc="F F F" -C 0.00922630 1.45021814 -0.03194543 -N -0.01429488 -0.00054646 -0.01782667 -C -1.21576579 -0.67252403 0.04421896 -N -1.09733352 -2.04644898 -0.15997741 -O -2.28599299 -0.12893711 0.24196027 -H 0.89659038 1.81885022 0.49120949 -H 0.00233950 1.86388504 -1.04914155 -H -0.88562684 1.79943346 0.48353298 -H 0.75257305 -0.47506631 -0.46719265 -H -0.24882333 -2.48772194 0.16375654 -H -1.93419610 -2.53898393 0.11482880 -12 -Properties=species:S:1:pos:R:3 A=9.69683 B=4.1533 C=3.01703 mu=1.1876 alpha=43.57 epsHOMO=-0.2595 epsLUMO=0.0352 epsgap=0.2948 =443.1687 zpve=0.102062 U0=-248.416462 U=-248.410358 H=-248.409414 G=-248.445651 Cv=20.256 pbc="F F F" -C -0.09160695 1.37540046 -0.00729426 -O -0.06964534 -0.04831581 -0.11148812 -C 0.68114625 -0.56142673 -1.10732458 -C 0.60405298 -2.06701550 -1.08766892 -N 1.32645533 0.19266343 -1.89967731 -H 0.91463916 1.76843655 0.16257460 -H -0.48562826 1.82624615 -0.92235216 -H -0.73971430 1.60007423 0.84110605 -H -0.43430065 -2.39242162 -1.20325205 -H 1.20627110 -2.50049977 -1.88745640 -H 0.95651337 -2.44976670 -0.12501838 -H 1.83504115 -0.36104361 -2.58486055 -11 -Properties=species:S:1:pos:R:3 A=10.20392 B=4.16834 C=3.07281 mu=1.7569 alpha=39.33 epsHOMO=-0.2685 epsLUMO=0.0174 epsgap=0.2859 =427.6606 zpve=0.089436 U0=-268.32127 U=-268.315051 H=-268.314106 G=-268.351214 Cv=19.501 pbc="F F F" -C 0.07295254 1.38840919 0.06442046 -O 0.21049114 -0.03740028 0.04612736 -C 0.50102805 -0.57224288 -1.15975346 -C 0.62218715 -2.07378323 -1.06030684 -O 0.63922154 0.08224418 -2.16157325 -H 1.00029720 1.87062570 -0.25532064 -H -0.73338957 1.70782089 -0.60080454 -H -0.15751280 1.65154060 1.09692954 -H -0.31330001 -2.50202851 -0.68941906 -H 0.85646588 -2.48430275 -2.04157352 -H 1.40651685 -2.34055496 -0.34634301 -10 -Properties=species:S:1:pos:R:3 A=10.68182 B=4.39532 C=3.17969 mu=2.329 alpha=35.5 epsHOMO=-0.2669 epsLUMO=0.0575 epsgap=0.3244 =398.5908 zpve=0.079271 U0=-284.385189 U=-284.379361 H=-284.378417 G=-284.414085 Cv=19.084 pbc="F F F" -C -0.02645771 1.38305753 0.02085660 -O 0.04139566 -0.04439503 -0.00277452 -C 0.66775338 -0.56001625 -1.09000344 -N 0.72605063 -1.92219983 -0.98822954 -O 1.12952381 0.09648143 -1.99322180 -H 0.97529779 1.82044307 0.02387306 -H -0.57085698 1.76214621 -0.84790425 -H -0.55410722 1.63835731 0.94043594 -H 0.12662696 -2.38401590 -0.32566929 -H 0.97230070 -2.41798421 -1.82732573 -10 -Properties=species:S:1:pos:R:3 A=10.20696 B=3.93135 C=2.9219 mu=4.5676 alpha=35.87 epsHOMO=-0.246 epsLUMO=0.0273 epsgap=0.2733 =418.5967 zpve=0.078222 U0=-284.360325 U=-284.354148 H=-284.353204 G=-284.390143 Cv=20.089 pbc="F F F" -N -0.08115607 1.31807026 0.01276742 -C 0.05442259 -0.04477733 0.08155388 -O 1.12163074 -0.59783950 0.20940553 -C -1.28687786 -0.79011642 0.06178435 -O -1.04605114 -2.13954195 -0.23957158 -H 0.76623366 1.85991128 -0.04483425 -H -0.93021346 1.74567447 -0.31565615 -H -1.74559696 -0.65521334 1.05526700 -H -1.96026061 -0.31149222 -0.67026256 -H -1.85637296 -2.63017646 -0.07625478 -10 -Properties=species:S:1:pos:R:3 A=10.14389 B=4.0714 C=3.02223 mu=5.3004 alpha=35.19 epsHOMO=-0.2527 epsLUMO=0.0208 epsgap=0.2735 =408.0279 zpve=0.080317 U0=-284.372483 U=-284.367172 H=-284.366228 G=-284.40095 Cv=17.931 pbc="F F F" -N 0.01838100 0.93057357 0.04943058 -H -0.70674506 0.56116798 -0.55550134 -H 0.68248307 0.18430002 0.21487099 -H 0.21928084 2.79858558 -0.32670585 -C -0.54586914 1.44688585 1.30274586 -C -0.80466975 2.96030569 1.21052835 -O -0.21468822 3.53648884 0.15531687 -O -1.45295676 3.56338618 2.02143528 -H -1.47662579 0.96512183 1.62072169 -H 0.17781209 1.30324286 2.11157176 -13 -Properties=species:S:1:pos:R:3 A=6.6508 B=4.19317 C=3.39441 mu=2.2854 alpha=42.01 epsHOMO=-0.2597 epsLUMO=0.0631 epsgap=0.3228 =438.3028 zpve=0.11345 U0=-269.479234 U=-269.472993 H=-269.472049 G=-269.508213 Cv=22.072 pbc="F F F" -C -0.02775167 1.53421433 0.00651151 -C 0.08343246 0.01218277 0.05258986 -O 0.95856392 -0.48200710 -0.96694113 -C 0.69767574 -0.49516451 1.35948812 -O 2.02247062 -0.02842530 1.52222186 -H 0.94333763 1.98841434 0.21841589 -H -0.37320516 1.87354041 -0.97716908 -H -0.74773817 1.89256394 0.74926853 -H -0.91671848 -0.43697036 -0.06040528 -H 0.76726710 -0.01412177 -1.78502043 -H 0.11650002 -0.13351914 2.21392431 -H 0.65814685 -1.59563445 1.36274474 -H 2.46378566 -0.22762476 0.68698431 -17 -Properties=species:S:1:pos:R:3 A=7.26511 B=3.32874 C=2.54491 mu=0.0618 alpha=56.26 epsHOMO=-0.3085 epsLUMO=0.085 epsgap=0.3934 =565.8412 zpve=0.159632 U0=-197.629387 U=-197.622325 H=-197.621381 G=-197.659365 Cv=25.169 pbc="F F F" -C 0.05879035 1.56174169 -0.01945240 -C -0.01544024 0.03182306 0.00308332 -C -1.44103237 -0.54862028 0.05028629 -C -1.40777007 -2.07231030 -0.13012010 -C -2.18400529 -0.17659759 1.34114303 -H 1.09435638 1.90233607 -0.11900072 -H -0.50689566 1.97217319 -0.86388098 -H -0.34407951 2.00628082 0.89579522 -H 0.48926758 -0.36011214 -0.88953902 -H 0.55254938 -0.35139446 0.86269887 -H -2.00031575 -0.12153682 -0.79559684 -H -0.85501131 -2.55065810 0.68775438 -H -2.41787513 -2.49543628 -0.13720127 -H -0.91928762 -2.35363802 -1.06914014 -H -3.19100197 -0.60699559 1.35137051 -H -1.65197294 -0.56010584 2.22063481 -H -2.28793759 0.90575132 1.46079572 -15 -Properties=species:S:1:pos:R:3 A=8.11598 B=3.43678 C=2.66869 mu=1.3894 alpha=49.45 epsHOMO=-0.2617 epsLUMO=0.0733 epsgap=0.335 =521.8605 zpve=0.136091 U0=-233.555951 U=-233.549143 H=-233.548199 G=-233.585602 Cv=23.99 pbc="F F F" -C -0.03841227 1.55621396 0.00935837 -C 0.01907135 0.02716079 -0.02074203 -C 0.73141506 -0.52709328 -1.25454422 -C 0.83975868 -2.05206973 -1.22872655 -O -0.01226470 -0.08514394 -2.38973758 -H 0.96845664 1.98863424 0.03526234 -H -0.54377006 1.93824134 -0.88074407 -H -0.57652955 1.91383386 0.89256698 -H 0.53166867 -0.34991486 0.87240999 -H -0.99727186 -0.38638654 -0.00185289 -H 1.74909346 -0.09943025 -1.28826184 -H 1.34143968 -2.42487137 -2.12957624 -H 1.41822012 -2.39229108 -0.36340871 -H -0.15616765 -2.50443859 -1.18308875 -H 0.43090825 -0.40901616 -3.17979642 -15 -Properties=species:S:1:pos:R:3 A=7.55335 B=3.78323 C=2.80566 mu=1.0758 alpha=49.8 epsHOMO=-0.2476 epsLUMO=0.086 epsgap=0.3336 =507.9614 zpve=0.135681 U0=-233.545899 U=-233.539034 H=-233.53809 G=-233.57582 Cv=23.573 pbc="F F F" -C -0.00857605 1.38097833 0.09454670 -O -0.02255106 -0.00739461 -0.13062576 -C -1.30243341 -0.61997805 -0.02088309 -C -1.18687286 -1.97885751 -0.70303918 -C -1.75587824 -0.74662760 1.43714508 -H 1.00321550 1.73258087 -0.12443496 -H -0.71511468 1.90838275 -0.56674280 -H -0.24909065 1.64967237 1.13384681 -H -2.03737978 -0.00432241 -0.56884198 -H -0.44792779 -2.59945605 -0.18596870 -H -2.14751041 -2.50225525 -0.69102581 -H -0.86524212 -1.85860679 -1.74077836 -H -2.73321482 -1.23674908 1.49205117 -H -1.03621350 -1.34483828 2.00544671 -H -1.84898410 0.22935483 1.92198087 -15 -Properties=species:S:1:pos:R:3 A=6.14183 B=5.17679 C=3.79297 mu=0.1068 alpha=52.92 epsHOMO=-0.2633 epsLUMO=0.0893 epsgap=0.3526 =439.1643 zpve=0.137025 U0=-196.409349 U=-196.403398 H=-196.402453 G=-196.437676 Cv=22.258 pbc="F F F" -C -0.03913204 1.50456305 0.04978913 -C 0.00985856 -0.01113880 0.01248619 -C -1.24167981 -0.66663044 -0.53989979 -C 1.33803387 -0.66895748 -0.27400430 -C 0.75126442 -0.72221586 1.11862571 -H 0.88633040 1.92490040 0.45579358 -H -0.18183878 1.92045743 -0.95489291 -H -0.86832076 1.85815718 0.67439648 -H -1.43570890 -0.34344387 -1.56976411 -H -1.15326757 -1.75757800 -0.54433832 -H -2.12221416 -0.40570820 0.05951361 -H 1.34398374 -1.58235208 -0.86005084 -H 2.20196897 -0.03329084 -0.43929326 -H 1.21949576 -0.12246018 1.89246580 -H 0.36153319 -1.67153717 1.47171932 -13 -Properties=species:S:1:pos:R:3 A=6.49534 B=5.52329 C=3.9108 mu=1.8235 alpha=45.7 epsHOMO=-0.2596 epsLUMO=0.091 epsgap=0.3505 =405.521 zpve=0.112851 U0=-232.335768 U=-232.329981 H=-232.329037 G=-232.364091 Cv=20.719 pbc="F F F" -C -0.03970330 1.50954998 -0.04112233 -C -0.03023556 -0.00335329 -0.06498361 -C -1.24613189 -0.68356648 0.52531190 -C 0.78937201 -0.71129193 -1.06027559 -O 1.22546798 -0.60059036 0.29712283 -H 0.88992772 1.90948478 -0.45296558 -H -0.87952723 1.90350108 -0.62376642 -H -0.14448576 1.87590604 0.98604429 -H -2.15218727 -0.41012616 -0.02629495 -H -1.13412584 -1.77005431 0.49747032 -H -1.38586631 -0.38055453 1.56882757 -H 0.50712690 -1.71386874 -1.38085018 -H 1.37088104 -0.14369322 -1.78640853 -13 -Properties=species:S:1:pos:R:3 A=6.57648 B=5.31603 C=3.98453 mu=1.3822 alpha=45.85 epsHOMO=-0.2494 epsLUMO=0.0776 epsgap=0.327 =401.4888 zpve=0.113237 U0=-232.33341 U=-232.327734 H=-232.32679 G=-232.36147 Cv=21.17 pbc="F F F" -C -0.03714052 1.53215920 0.01332310 -C 0.01344335 0.02117019 0.01922005 -O -1.17476104 -0.59735284 -0.40931260 -C 1.30744798 -0.69902106 -0.27389650 -C 0.70844749 -0.71373504 1.12226318 -H 0.89026861 1.96240279 0.40276135 -H -0.17846151 1.91561262 -1.00518522 -H -0.87011232 1.88955201 0.62730953 -H -1.28352265 -0.40225391 -1.34690641 -H 1.23973813 -1.61642115 -0.84783774 -H 2.20543993 -0.11523820 -0.44744474 -H 1.19539159 -0.13889834 1.90240284 -H 0.24735439 -1.63670241 1.45378480 -10 -Properties=species:S:1:pos:R:3 A=12.18399 B=5.1384 C=3.78868 mu=2.5732 alpha=40.19 epsHOMO=-0.263 epsLUMO=0.0277 epsgap=0.2907 =352.0378 zpve=0.080558 U0=-247.201165 U=-247.196699 H=-247.195755 G=-247.22841 Cv=15.51 pbc="F F F" -N -0.04445152 1.25623533 0.31681915 -C -0.03914559 0.04913710 -0.01670558 -C -1.08749619 -1.04164446 -0.11488033 -C 0.09869688 -1.90754844 -0.58225894 -O 0.99647508 -0.76306629 -0.44028010 -H 0.88935980 1.66481350 0.24916893 -H -1.54324114 -1.32235812 0.83572172 -H -1.86910036 -0.85713839 -0.85325910 -H 0.39739753 -2.72227350 0.08158721 -H 0.06945080 -2.25418345 -1.61796084 -11 -Properties=species:S:1:pos:R:3 A=10.79571 B=4.78732 C=3.54292 mu=2.7119 alpha=42.77 epsHOMO=-0.2415 epsLUMO=-0.0194 epsgap=0.2222 =379.6371 zpve=0.090544 U0=-231.15578 U=-231.150804 H=-231.149859 G=-231.184401 Cv=17.076 pbc="F F F" -O -0.00101746 0.01846628 -0.20114742 -C -0.01405811 1.19658059 0.00205306 -C -1.13542911 2.22979148 0.21957980 -C -0.03500884 3.29097637 0.53052223 -C 1.08469664 2.25947795 0.18994331 -H -1.71642814 2.41772662 -0.68901078 -H -1.83392757 1.98415059 1.02444244 -H -0.05534741 4.17585264 -0.10670963 -H -0.02541477 3.61399048 1.57278917 -H 1.81074389 2.03288534 0.97578973 -H 1.63600801 2.46255380 -0.73376244 -10 -Properties=species:S:1:pos:R:3 A=12.23436 B=4.97895 C=3.70561 mu=3.6671 alpha=39.73 epsHOMO=-0.25 epsLUMO=0.038 epsgap=0.288 =355.0934 zpve=0.080442 U0=-247.225618 U=-247.220897 H=-247.219953 G=-247.253218 Cv=16.1 pbc="F F F" -O 0.03184134 -0.03461682 0.00849836 -C -0.02440334 1.16440533 0.02160811 -C -1.14541322 2.23712809 0.03012901 -C 0.00928805 3.27945878 0.04557758 -N 0.93843838 2.14652109 0.03554396 -H -1.77030529 2.23779730 -0.86428437 -H -1.77632809 2.21765386 0.92007934 -H 0.07170564 3.91863516 -0.84072193 -H 0.06622603 3.89826625 0.94657952 -H 1.94616840 2.09535713 0.03855945 -9 -Properties=species:S:1:pos:R:3 A=12.44643 B=5.23328 C=3.86599 mu=3.9339 alpha=35.4 epsHOMO=-0.2788 epsLUMO=0.0089 epsgap=0.2878 =336.0792 zpve=0.068574 U0=-267.106213 U=-267.101929 H=-267.100985 G=-267.13332 Cv=14.483 pbc="F F F" -O 0.04424004 0.03933243 -0.33071104 -C -0.04854479 1.17416230 0.00410076 -C -1.14211656 2.23611850 0.15338361 -C 0.02332538 3.12357010 0.61681625 -O 0.95663808 2.01190122 0.42603746 -H -1.62573755 2.51482212 -0.78401102 -H -1.90009213 1.99553386 0.90028663 -H 0.28368077 3.96172751 -0.03229287 -H 0.00782250 3.43942655 1.66162725 -10 -Properties=species:S:1:pos:R:3 A=11.54185 B=4.90368 C=3.63309 mu=2.5257 alpha=39.34 epsHOMO=-0.2437 epsLUMO=-0.0258 epsgap=0.2179 =359.152 zpve=0.07965 U0=-247.190194 U=-247.185543 H=-247.184599 G=-247.217688 Cv=16.083 pbc="F F F" -O 0.00053891 0.00665475 -0.09743049 -C -0.01371990 1.19213881 0.03139936 -C -1.10668681 2.26612284 0.17247913 -N -0.03543445 3.25123475 0.48533693 -C 1.05328615 2.29521506 0.14383904 -H -1.65047970 2.42796175 -0.76886054 -H -1.83606786 2.11370530 0.97567410 -H -0.05434600 4.07193609 -0.10727051 -H 1.80754206 2.16277968 0.92736250 -H 1.56737937 2.47130277 -0.81152688 -9 -Properties=species:S:1:pos:R:3 A=12.18264 B=4.94228 C=3.68162 mu=0.8477 alpha=35.94 epsHOMO=-0.2647 epsLUMO=-0.0352 epsgap=0.2295 =344.5473 zpve=0.066989 U0=-267.068488 U=-267.063976 H=-267.063032 G=-267.095887 Cv=15.115 pbc="F F F" -O -0.00031692 0.00676321 -0.17139774 -C -0.01417098 1.18672865 -0.00980279 -C -1.08283120 2.27653381 0.15574094 -O -0.03856115 3.26460423 0.27513986 -C 1.02857270 2.30515693 0.12774311 -H -1.72923855 2.43647507 -0.71671274 -H -1.70219898 2.19346725 1.05800754 -H 1.67366781 2.23923293 1.01324179 -H 1.64705068 2.48224548 -0.76148355 -15 -Properties=species:S:1:pos:R:3 A=9.63416 B=4.10254 C=3.28665 mu=0.095 alpha=52.94 epsHOMO=-0.2896 epsLUMO=0.0927 epsgap=0.3823 =455.0215 zpve=0.138424 U0=-196.407957 U=-196.40245 H=-196.401505 G=-196.436159 Cv=20.299 pbc="F F F" -C -0.47005934 1.44206547 -0.06684548 -C 0.14287306 0.05485144 0.03747984 -C 1.37254693 -0.30295273 -0.84679168 -C 1.95947994 -1.09364561 0.35326039 -C 0.99887433 -0.30768787 1.28563099 -H 0.29210839 2.21844829 0.06844100 -H -0.93614604 1.60094605 -1.04612799 -H -1.24148161 1.59707353 0.69627593 -H -0.63517341 -0.70560132 -0.10055095 -H 1.19338376 -0.84118177 -1.78129390 -H 1.96867777 0.58857708 -1.07004184 -H 1.69873597 -2.15500718 0.30521100 -H 3.03253510 -1.01077235 0.54148058 -H 1.48290386 0.58242428 1.70211026 -H 0.51304296 -0.84979686 2.10121062 -13 -Properties=species:S:1:pos:R:3 A=9.93129 B=4.56362 C=3.63704 mu=1.6826 alpha=45.72 epsHOMO=-0.241 epsLUMO=0.0915 epsgap=0.3325 =407.6909 zpve=0.11459 U0=-232.33123 U=-232.325877 H=-232.324933 G=-232.359589 Cv=18.582 pbc="F F F" -C -0.32308228 1.53751451 0.01947624 -C 0.11847214 0.08900172 -0.00523140 -C 0.87489284 -0.43763303 -1.24721738 -C 1.97841048 -0.80657483 -0.23740356 -O 1.27344926 -0.15419108 0.83794040 -H 0.49211985 2.19658795 -0.29527664 -H -1.17615835 1.68923490 -0.65134808 -H -0.62776963 1.82747912 1.03013205 -H -0.71410200 -0.56389187 0.29597305 -H 0.41033314 -1.26269706 -1.78910031 -H 1.15560811 0.34596269 -1.95459519 -H 2.09501012 -1.88349764 -0.05606083 -H 2.96970344 -0.36924147 -0.40408009 -13 -Properties=species:S:1:pos:R:3 A=10.13697 B=4.32512 C=3.50912 mu=1.8995 alpha=45.45 epsHOMO=-0.2419 epsLUMO=0.0814 epsgap=0.3233 =414.1575 zpve=0.114694 U0=-232.325947 U=-232.320548 H=-232.319604 G=-232.354576 Cv=18.443 pbc="F F F" -C -0.18371124 1.54668909 -0.01671129 -C 0.04513382 0.04386724 0.02270494 -C 1.20301104 -0.54034580 -0.81983464 -O 1.80061686 -1.15530615 0.33924991 -C 0.83391983 -0.54313754 1.21648003 -H 0.75102609 2.09256015 0.15344911 -H -0.58319248 1.86155707 -0.98694169 -H -0.89843724 1.85917126 0.75244153 -H -0.89506736 -0.48768372 -0.14843995 -H 0.93560472 -1.27857588 -1.58543917 -H 1.86392938 0.21379924 -1.27081649 -H 1.29414565 0.20948993 1.87273952 -H 0.31517042 -1.28326856 1.83756393 -13 -Properties=species:S:1:pos:R:3 A=10.09703 B=4.26865 C=3.40487 mu=1.4604 alpha=45.55 epsHOMO=-0.256 epsLUMO=0.0801 epsgap=0.3361 =413.9118 zpve=0.115063 U0=-232.333258 U=-232.328097 H=-232.327153 G=-232.361103 Cv=19.023 pbc="F F F" -O 0.54622847 1.03270424 -0.58398083 -C -0.00112269 -0.11036621 0.01898238 -C 0.13945950 -0.34310429 1.54777117 -C -1.26863784 -0.99876517 1.51632654 -C -1.53844651 -0.26418236 0.17423510 -H 0.20332379 1.80433874 -0.12071219 -H 0.41296647 -0.95857955 -0.53560402 -H 0.98890738 -0.92719626 1.90921867 -H 0.11786549 0.61323034 2.08356053 -H -1.20828813 -2.08191064 1.38036699 -H -1.94143614 -0.79119746 2.35014116 -H -2.00495682 0.71307933 0.34581493 -H -2.09160311 -0.78230202 -0.61249171 -11 -Properties=species:S:1:pos:R:3 A=10.85394 B=4.52867 C=3.62328 mu=2.4158 alpha=38.58 epsHOMO=-0.2465 epsLUMO=0.0604 epsgap=0.3069 =374.4087 zpve=0.091005 U0=-268.248371 U=-268.243234 H=-268.24229 G=-268.276572 Cv=17.344 pbc="F F F" -O 0.07721714 1.41738724 -0.14439016 -C 0.01675156 0.02484791 0.03233817 -C 0.11193823 -0.54392541 1.47000744 -O -1.16387804 -1.20093191 1.32441605 -C -1.41930178 -0.52040930 0.07750673 -H 1.00022214 1.67889479 -0.21697466 -H 0.66036495 -0.50380879 -0.68180327 -H 0.91806589 -1.25385265 1.68878628 -H 0.09146208 0.24427487 2.23499232 -H -2.16139683 0.28183960 0.18222436 -H -1.72867456 -1.20941157 -0.71550887 -15 -Properties=species:S:1:pos:R:3 A=7.14221 B=4.37083 C=3.30265 mu=0.1023 alpha=52.92 epsHOMO=-0.2632 epsLUMO=0.094 epsgap=0.3573 =465.3301 zpve=0.137341 U0=-196.406419 U=-196.40034 H=-196.399396 G=-196.435152 Cv=22.005 pbc="F F F" -C -0.05189850 1.55217435 -0.10318438 -C 0.01226648 0.04690675 0.03747520 -C 1.30115692 -0.70661553 -0.19725724 -C 0.70431939 -0.64893239 1.19003562 -C 1.39750324 0.09481280 2.31078011 -H 0.87404209 2.03543171 0.22162228 -H -0.21641357 1.83695278 -1.14852958 -H -0.87157646 1.97673684 0.48797046 -H -0.88508206 -0.45596941 -0.31580786 -H 1.27663658 -1.64149022 -0.74697967 -H 2.19424067 -0.11404121 -0.37569359 -H 0.21672461 -1.56377934 1.51902400 -H 1.91317356 0.99051175 1.95271747 -H 0.68738009 0.40901468 3.08446972 -H 2.14934633 -0.54151807 2.79111543 -13 -Properties=species:S:1:pos:R:3 A=7.36997 B=4.78662 C=3.53123 mu=1.3092 alpha=45.84 epsHOMO=-0.2518 epsLUMO=0.0822 epsgap=0.334 =418.298 zpve=0.113827 U0=-232.32907 U=-232.323327 H=-232.322383 G=-232.357426 Cv=20.702 pbc="F F F" -C -0.07992786 1.55914739 -0.03875994 -C 0.00042206 0.05111569 0.04346551 -C 1.31748624 -0.66230034 -0.20269965 -C 0.71624326 -0.61854924 1.18128126 -O 1.28134126 0.21474798 2.15341739 -H 0.80207677 2.01979988 0.41147972 -H -0.15235410 1.89225261 -1.07968732 -H -0.95836082 1.93564781 0.49643179 -H -0.88615174 -0.46939753 -0.30918496 -H 1.32384464 -1.60086657 -0.74666557 -H 2.18619315 -0.03316813 -0.37068758 -H 0.28509951 -1.54025035 1.57291630 -H 2.12931661 -0.16234387 2.40895930 -14 -Properties=species:S:1:pos:R:3 A=7.53724 B=4.74514 C=3.54049 mu=1.2483 alpha=49.79 epsHOMO=-0.2199 epsLUMO=0.0931 epsgap=0.313 =432.5081 zpve=0.125556 U0=-212.438188 U=-212.432286 H=-212.431342 G=-212.466725 Cv=20.993 pbc="F F F" -C -0.17034561 1.55765735 -0.10065470 -C 0.01075256 0.05574638 -0.05225833 -C 0.71794239 -0.67802425 -1.14204150 -N 1.29811574 -0.59777508 0.19386866 -C 2.50047290 0.20256923 0.36428818 -H 0.60876703 2.05717913 -0.68119301 -H -1.13238698 1.79957181 -0.56489127 -H -0.17350949 1.98946323 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a/docs/src/examples/basic_usage/usage.rst b/docs/src/examples/basic_usage/usage.rst deleted file mode 100644 index a65bd19678..0000000000 --- a/docs/src/examples/basic_usage/usage.rst +++ /dev/null @@ -1,189 +0,0 @@ - -.. DO NOT EDIT. -.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. -.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: -.. "examples/basic_usage/usage.sh" -.. LINE NUMBERS ARE GIVEN BELOW. - -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - :ref:`Go to the end ` - to download the full example code. - -.. rst-class:: sphx-glr-example-title - -.. _sphx_glr_examples_basic_usage_usage.sh: - -.. _label_basic_usage: - -Basic Usage -=========== - -``metatrain`` is designed for a direct usage from the command line (cli). The program -is registered via the abbreviation ``mtt`` to your command line. The general help of -``metatrain`` can be accessed using - -.. GENERATED FROM PYTHON SOURCE LINES 10-11 - -.. code-block:: Bash - - mtt --help - -.. GENERATED FROM PYTHON SOURCE LINES 12-52 - -We now demonstrate how to ``train`` and ``evaluate`` a model from the command line. -For this example we use the :ref:`architecture-soap-bpnn` architecture and a subset of -the `QM9 dataset `_. You can obtain the -dataset for this example here: :download:`qm9_reduced_100.xyz -<../../../../examples/basic_usage/qm9_reduced_100.xyz>`. - - -Training --------- - -To train models, ``metatrain`` uses a dynamic override strategy for your training -options. We allow a dynamical composition and override of the default architecture -with either your custom ``options.yaml`` and even command line override grammar. For -reference and reproducibility purposes ``metatrain`` always writes the fully expanded, -including the overwritten option to ``options_restart.yaml``. The restart options file -is written into a subfolder named with the current *date* and *time* inside the -``output`` directory of your current training run. - -The sub-command to start a model training is - -.. code-block:: bash - - mtt train - -To train a model you have to define your options. This includes the specific -architecture you want to use and the data including the training systems and target -values - -The default model and training hyperparameter for each model are listed in their -corresponding documentation page. We will use these minimal options to run an example -training using the default hyperparameters of an SOAP BPNN model - -.. literalinclude:: ../../../../examples/basic_usage/options.yaml - :language: yaml - -For each training run a new output directory in the format -``outputs/YYYY-MM-DD/HH-MM-SS`` based on the current *date* and *time* is created. We -use this output directory to store checkpoints, the restart ``options_restart.yaml`` -file as well as the log files. To start the training, create an ``options.yaml`` file -in the current directory and type - -.. GENERATED FROM PYTHON SOURCE LINES 55-57 - -.. code-block:: Bash - - - mtt train options.yaml - -.. GENERATED FROM PYTHON SOURCE LINES 58-62 - -The functions saves the final model `model.pt` to the current output folder for later -evaluation. An `extensions/` folder, which contains the compiled extensions for the -model, might also be saved depending on the architecture. All command line flags of -the train sub-command can be listed via - -.. GENERATED FROM PYTHON SOURCE LINES 66-67 - -.. code-block:: Bash - - mtt train --help - -.. GENERATED FROM PYTHON SOURCE LINES 68-96 - -After the training has finished, the ``mtt train`` command generates the -``model.ckpt`` (final checkpoint) and ``model.pt`` (exported model) files in the -current directory, as well as in the ``output/YYYY-MM-DD/HH-MM-SS`` directory. - -Evaluation ----------- - -The sub-command to evaluate an already trained model is - -.. code-block:: bash - - mtt eval - -Besides the trained ``model``, you will also have to provide a file containing the -system and possible target values for evaluation. The system section of this -``eval.yaml`` is exactly the same as for a dataset in the ``options.yaml`` file. - -.. literalinclude:: ../../../../examples/basic_usage/eval.yaml - :language: yaml - -Note that the ``targets`` section is optional. If the ``targets`` section is present, -the function will calculate and report RMSE values of the predictions with respect to -the real values as loaded from the ``targets`` section. You can run an evaluation by -typing - -We now evaluate the model on the training dataset, where the first arguments specifies -trained model and the second an option file containing the path of the dataset for -evaulation. The extensions of the model, if any, can be specified via the ``-e`` flag. - -.. GENERATED FROM PYTHON SOURCE LINES 99-100 - -.. code-block:: Bash - - mtt eval model.pt eval.yaml -e extensions/ - -.. GENERATED FROM PYTHON SOURCE LINES 101-105 - -The evaluation command predicts those properties the model was trained against; here -``"U0"``. The predictions together with the systems have been written in a file named -``output.xyz`` in the current directory. The written file starts with the following -lines - -.. GENERATED FROM PYTHON SOURCE LINES 108-109 - -.. code-block:: Bash - - head -n 20 output.xyz - -.. GENERATED FROM PYTHON SOURCE LINES 110-111 - -All command line flags of the eval sub-command can be listed via - -.. GENERATED FROM PYTHON SOURCE LINES 114-115 - -.. code-block:: Bash - - mtt eval --help - -.. GENERATED FROM PYTHON SOURCE LINES 115-123 - -An important parameter of ``mtt eval`` is the ``-b`` (or ``--batch-size``) option, -which allows you to specify the batch size for the evaluation. - -Molecular simulations ---------------------- - -The trained model can also be used to run molecular simulations. -You can find how in the :ref:`tutorials` section. - - -.. _sphx_glr_download_examples_basic_usage_usage.sh: - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-example - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Bash source code: usage.sh ` - - .. container:: sphx-glr-download sphx-glr-download-zip - - :download:`Download zipped: usage.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/basic_usage/usage.sh b/docs/src/examples/basic_usage/usage.sh deleted file mode 100644 index c3ffc0acbf..0000000000 --- a/docs/src/examples/basic_usage/usage.sh +++ /dev/null @@ -1,126 +0,0 @@ -# .. _label_basic_usage: -# -# Basic Usage -# =========== -# -# ``metatrain`` is designed for a direct usage from the command line (cli). The program -# is registered via the abbreviation ``mtt`` to your command line. The general help of -# ``metatrain`` can be accessed using -# - -mtt --help - -# %% -# -# We now demonstrate how to ``train`` and ``evaluate`` a model from the command line. -# For this example we use the :ref:`architecture-soap-bpnn` architecture and a subset of -# the `QM9 dataset `_. You can obtain the -# dataset for this example here: :download:`qm9_reduced_100.xyz -# <../../../../examples/basic_usage/qm9_reduced_100.xyz>`. -# -# -# Training -# -------- -# -# To train models, ``metatrain`` uses a dynamic override strategy for your training -# options. We allow a dynamical composition and override of the default architecture -# with either your custom ``options.yaml`` and even command line override grammar. For -# reference and reproducibility purposes ``metatrain`` always writes the fully expanded, -# including the overwritten option to ``options_restart.yaml``. The restart options file -# is written into a subfolder named with the current *date* and *time* inside the -# ``output`` directory of your current training run. -# -# The sub-command to start a model training is -# -# .. code-block:: bash -# -# mtt train -# -# To train a model you have to define your options. This includes the specific -# architecture you want to use and the data including the training systems and target -# values -# -# The default model and training hyperparameter for each model are listed in their -# corresponding documentation page. We will use these minimal options to run an example -# training using the default hyperparameters of an SOAP BPNN model -# -# .. literalinclude:: ../../../../examples/basic_usage/options.yaml -# :language: yaml -# -# For each training run a new output directory in the format -# ``outputs/YYYY-MM-DD/HH-MM-SS`` based on the current *date* and *time* is created. We -# use this output directory to store checkpoints, the restart ``options_restart.yaml`` -# file as well as the log files. To start the training, create an ``options.yaml`` file -# in the current directory and type - - -mtt train options.yaml - -# %% -# -# The functions saves the final model `model.pt` to the current output folder for later -# evaluation. An `extensions/` folder, which contains the compiled extensions for the -# model, might also be saved depending on the architecture. All command line flags of -# the train sub-command can be listed via -# - -mtt train --help - -# %% -# -# After the training has finished, the ``mtt train`` command generates the -# ``model.ckpt`` (final checkpoint) and ``model.pt`` (exported model) files in the -# current directory, as well as in the ``output/YYYY-MM-DD/HH-MM-SS`` directory. -# -# Evaluation -# ---------- -# -# The sub-command to evaluate an already trained model is -# -# .. code-block:: bash -# -# mtt eval -# -# Besides the trained ``model``, you will also have to provide a file containing the -# system and possible target values for evaluation. The system section of this -# ``eval.yaml`` is exactly the same as for a dataset in the ``options.yaml`` file. -# -# .. literalinclude:: ../../../../examples/basic_usage/eval.yaml -# :language: yaml -# -# Note that the ``targets`` section is optional. If the ``targets`` section is present, -# the function will calculate and report RMSE values of the predictions with respect to -# the real values as loaded from the ``targets`` section. You can run an evaluation by -# typing -# -# We now evaluate the model on the training dataset, where the first arguments specifies -# trained model and the second an option file containing the path of the dataset for -# evaulation. The extensions of the model, if any, can be specified via the ``-e`` flag. - -mtt eval model.pt eval.yaml -e extensions/ - -# %% -# -# The evaluation command predicts those properties the model was trained against; here -# ``"U0"``. The predictions together with the systems have been written in a file named -# ``output.xyz`` in the current directory. 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- -.. only:: html - - .. image:: /examples/llpr/images/thumb/sphx_glr_llpr_thumb.png - :alt: - - :ref:`sphx_glr_examples_llpr_llpr.py` - -.. raw:: html - -
Computing LLPR uncertainties
-
- - -.. thumbnail-parent-div-close - -.. raw:: html - - - - -.. toctree:: - :hidden: - - /examples/llpr/llpr - - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-gallery - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download all examples as source code: llpr.zip ` - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download all examples in Jupyter notebooks: llpr_jupyter.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/llpr/llpr.codeobj.json b/docs/src/examples/llpr/llpr.codeobj.json deleted file mode 100644 index bd7f237401..0000000000 --- a/docs/src/examples/llpr/llpr.codeobj.json +++ /dev/null @@ -1,728 +0,0 @@ -{ - "LogNorm": [ - { - "is_class": true, - "is_explicit": false, - "module": "matplotlib.colors", - "module_short": "matplotlib.colors", - "name": "LogNorm" - }, - { - "is_class": true, - "is_explicit": false, - "module": "matplotlib", - "module_short": "matplotlib", - "name": "LogNorm" - }, - { - "is_class": true, - "is_explicit": false, - "module": "matplotlib.colors", - "module_short": "matplotlib.colors", - "name": "Normalize" - }, - { - "is_class": true, - "is_explicit": false, - "module": "matplotlib", - "module_short": "matplotlib", - "name": "Normalize" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.colors", - "module_short": "matplotlib.colors", - "name": "LogNorm" - } - ], - "MetatomicCalculator": [ - { - "is_class": true, - "is_explicit": false, - "module": "metatomic.torch.ase_calculator", - "module_short": "metatomic.torch.ase_calculator", - "name": "MetatomicCalculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "metatomic.torch", - "module_short": "metatomic.torch", - "name": "MetatomicCalculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "metatomic", - "module_short": "metatomic", - "name": "MetatomicCalculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.calculators.calculator", - "module_short": "ase.calculators.calculator", - "name": "Calculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.calculators", - "module_short": "ase.calculators", - "name": "Calculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Calculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.calculators.calculator", - "module_short": "ase.calculators.calculator", - "name": "BaseCalculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.calculators", - "module_short": "ase.calculators", - "name": "BaseCalculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "BaseCalculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.calculators.abc", - "module_short": "ase.calculators.abc", - 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"is_explicit": false, - "module": "ase.calculators.abc", - "module_short": "ase.calculators.abc", - "name": "GetPropertiesMixin.run_model" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.calculators", - "module_short": "ase.calculators", - "name": "GetPropertiesMixin.run_model" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "GetPropertiesMixin.run_model" - }, - { - "is_class": false, - "is_explicit": false, - "module": "abc", - "module_short": "abc", - "name": "ABC.run_model" - } - ], - "cbar": [ - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.colorbar", - "module_short": "matplotlib.colorbar", - "name": "Colorbar" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib", - "module_short": "matplotlib", - "name": "Colorbar" - } - ], - "cbar.ax.set_yticklabels": [ - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.axes._axes", - "module_short": "matplotlib.axes", - "name": "Axes.set_yticklabels" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.axes", - "module_short": "matplotlib.axes", - "name": "Axes.set_yticklabels" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib", - "module_short": "matplotlib", - "name": "Axes.set_yticklabels" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.axes._base", - "module_short": "matplotlib.axes._base", - "name": "_AxesBase.set_yticklabels" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.axes", - "module_short": "matplotlib.axes", - "name": "_AxesBase.set_yticklabels" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib", - "module_short": "matplotlib", - "name": "_AxesBase.set_yticklabels" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.artist", - "module_short": "matplotlib.artist", - "name": "Artist.set_yticklabels" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib", - "module_short": "matplotlib", - "name": "Artist.set_yticklabels" - } - ], - "cbar.minorticks_off": [ - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.colorbar", - "module_short": "matplotlib.colorbar", - "name": "Colorbar.minorticks_off" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib", - "module_short": "matplotlib", - "name": "Colorbar.minorticks_off" - } - ], - "colormap": [ - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.colors", - "module_short": "matplotlib.colors", - "name": "ListedColormap" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib", - "module_short": "matplotlib", - "name": "ListedColormap" - } - ], - "colors": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray" - } - ], - "custom_ticks": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "list" - } - ], - "energies": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray" - } - ], - "local_uncertainty": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray" - } - ], - "norm": [ - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.colors", - "module_short": "matplotlib.colors", - "name": "LogNorm" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib", - "module_short": "matplotlib", - "name": "LogNorm" - } - ], - "plot_atoms": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.visualize.plot", - "module_short": "ase.visualize.plot", - "name": "plot_atoms" - } - ], - "plt.cm.ScalarMappable": [ - { - "is_class": true, - "is_explicit": false, - "module": "matplotlib.colorizer", - "module_short": "matplotlib.colorizer", - "name": "_ScalarMappable" - }, - { - "is_class": true, - "is_explicit": false, - "module": "matplotlib", - "module_short": "matplotlib", - "name": "_ScalarMappable" - }, - { - "is_class": true, - "is_explicit": false, - "module": "matplotlib.colorizer", - "module_short": "matplotlib.colorizer", - "name": "_ColorizerInterface" - }, - { - "is_class": true, - "is_explicit": false, - "module": "matplotlib", - "module_short": "matplotlib", - "name": "_ColorizerInterface" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot.cm", - "module_short": "matplotlib.pyplot.cm", - "name": "ScalarMappable" - } - ], - "plt.colorbar": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot", - "module_short": "matplotlib.pyplot", - "name": "colorbar" - } - ], - "plt.get_cmap": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot", - "module_short": "matplotlib.pyplot", - "name": "get_cmap" - } - ], - "plt.show": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot", - "module_short": "matplotlib.pyplot", - "name": "show" - } - ], - "predictions": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "dict" - } - ], - "structure": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms" - } - ], - "structures": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "list" - } - ], - "uncertainties": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray" - } - ] -} diff --git a/docs/src/examples/llpr/llpr.ipynb b/docs/src/examples/llpr/llpr.ipynb deleted file mode 100644 index 5c74493b0f..0000000000 --- a/docs/src/examples/llpr/llpr.ipynb +++ /dev/null @@ -1,68 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# Computing LLPR uncertainties\n\nThis tutorial demonstrates how to train a model with uncertainties using metatrain.\nIt involves the computation of the uncertainties on ethanol molecules, using the\nlast-layer prediction rigidity ([LLPR](LLPR_)) approximation. Both total and local\n([LPR](LPR_)) uncertainties are computed\n\n\nThe baseline model was trained using the following training options, where the training\nset consists of 100 structures from the QM9 dataset.\n\n.. literalinclude:: options.yaml\n :language: yaml\n\nOnce a model is trained, you can add LLPR uncertainties to it by launching a training\nrun with the \"llpr\" architecture, on the same data. In this case, the training options\nto add LLPR uncertainties are as follows:\n\n.. literalinclude:: options-llpr.yaml\n :language: yaml\n\nAdding LLPR uncertainties is very cheap compared to training a model, as it only\ninvolves one pass through the training data (equivalent to one epoch of training).\n\nYou can repeat the same training yourself with\n\n.. literalinclude:: train.sh\n :language: bash\n\nA detailed step-by-step introduction on how to train a model is provided in\nthe `label_basic_usage` tutorial.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As an example, we will compute the energies and uncertainties of the LLPR model on a\nfew ethanol structures.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import ase.io\nimport matplotlib.pyplot as plt\nfrom ase.visualize.plot import plot_atoms\nfrom matplotlib.colors import LogNorm\nfrom metatomic.torch import ModelOutput\nfrom metatomic.torch.ase_calculator import MetatomicCalculator\n\n\n# load 5 ethanol structures\nstructures = ase.io.read(\"ethanol_reduced_100.xyz\", \":5\")\n\n# load the model as an ASE calculator\ncalc = MetatomicCalculator(\n \"model-llpr.pt\", extensions_directory=\"extensions/\", device=\"cpu\"\n)\n\n# the uncertainties are available throguh the ``run_model`` method of the calculator\npredictions = calc.run_model(\n structures,\n {\n \"energy\": ModelOutput(per_atom=False),\n \"energy_uncertainty\": ModelOutput(per_atom=False),\n },\n)\n\n# print the energies and uncertainties\n\nenergies = predictions[\"energy\"].block().values.squeeze().numpy()\nuncertainties = predictions[\"energy_uncertainty\"].block().values.squeeze().numpy()\n\nprint(energies)\nprint(uncertainties)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also obtain per-atom uncertainties (local prediction rigidity, LPR). As an\nexample, we will compute the uncertainties on an ethanol structure.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "structure = structures[0]\npredictions = calc.run_model(\n structure,\n {\n # here, we use per_atom=True to request per-atom uncertainties\n \"energy_uncertainty\": ModelOutput(per_atom=True),\n },\n)\nlocal_uncertainty = predictions[\"energy_uncertainty\"].block().values.squeeze().numpy()\nlocal_uncertainty = local_uncertainty * 1000.0 # convert from eV to meV\n\nnorm = LogNorm(vmin=min(local_uncertainty), vmax=max(local_uncertainty))\ncolormap = plt.get_cmap(\"viridis\")\ncolors = colormap(norm(local_uncertainty))\nax = plot_atoms(structure, colors=colors, rotation=\"180x,0y,0z\")\ncustom_ticks = [0.7, 0.8, 0.9, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5]\ncbar = plt.colorbar(\n plt.cm.ScalarMappable(norm=norm, cmap=colormap),\n ax=ax,\n label=\"Local energy uncertainty (meV)\",\n ticks=custom_ticks,\n)\ncbar.ax.set_yticklabels([f\"{tick}\" for tick in custom_ticks])\ncbar.minorticks_off()\nax.set_xticks([])\nax.set_yticks([])\nplt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/src/examples/llpr/llpr.py b/docs/src/examples/llpr/llpr.py deleted file mode 100644 index b40c8e68cd..0000000000 --- a/docs/src/examples/llpr/llpr.py +++ /dev/null @@ -1,109 +0,0 @@ -""".. _llprexample: - -Computing LLPR uncertainties -============================ - -This tutorial demonstrates how to train a model with uncertainties using metatrain. -It involves the computation of the uncertainties on ethanol molecules, using the -last-layer prediction rigidity (`LLPR `_) approximation. Both total and local -(`LPR `_) uncertainties are computed - -.. _LLPR: https://arxiv.org/html/2403.02251v1 -.. _LPR: https://pubs.acs.org/doi/10.1021/acs.jctc.3c00704 - -The baseline model was trained using the following training options, where the training -set consists of 100 structures from the QM9 dataset. - -.. literalinclude:: options.yaml - :language: yaml - -Once a model is trained, you can add LLPR uncertainties to it by launching a training -run with the "llpr" architecture, on the same data. In this case, the training options -to add LLPR uncertainties are as follows: - -.. literalinclude:: options-llpr.yaml - :language: yaml - -Adding LLPR uncertainties is very cheap compared to training a model, as it only -involves one pass through the training data (equivalent to one epoch of training). - -You can repeat the same training yourself with - -.. literalinclude:: train.sh - :language: bash - -A detailed step-by-step introduction on how to train a model is provided in -the :ref:`label_basic_usage` tutorial. -""" - -# %% -# -# As an example, we will compute the energies and uncertainties of the LLPR model on a -# few ethanol structures. - -import ase.io -import matplotlib.pyplot as plt -from ase.visualize.plot import plot_atoms -from matplotlib.colors import LogNorm -from metatomic.torch import ModelOutput -from metatomic.torch.ase_calculator import MetatomicCalculator - - -# load 5 ethanol structures -structures = ase.io.read("ethanol_reduced_100.xyz", ":5") - -# load the model as an ASE calculator -calc = MetatomicCalculator( - "model-llpr.pt", extensions_directory="extensions/", device="cpu" -) - -# the uncertainties are available throguh the ``run_model`` method of the calculator -predictions = calc.run_model( - structures, - { - "energy": ModelOutput(per_atom=False), - "energy_uncertainty": ModelOutput(per_atom=False), - }, -) - -# print the energies and uncertainties - -energies = predictions["energy"].block().values.squeeze().numpy() -uncertainties = predictions["energy_uncertainty"].block().values.squeeze().numpy() - -print(energies) -print(uncertainties) - - -# %% -# -# We can also obtain per-atom uncertainties (local prediction rigidity, LPR). As an -# example, we will compute the uncertainties on an ethanol structure. - -structure = structures[0] -predictions = calc.run_model( - structure, - { - # here, we use per_atom=True to request per-atom uncertainties - "energy_uncertainty": ModelOutput(per_atom=True), - }, -) -local_uncertainty = predictions["energy_uncertainty"].block().values.squeeze().numpy() -local_uncertainty = local_uncertainty * 1000.0 # convert from eV to meV - -norm = LogNorm(vmin=min(local_uncertainty), vmax=max(local_uncertainty)) -colormap = plt.get_cmap("viridis") -colors = colormap(norm(local_uncertainty)) -ax = plot_atoms(structure, colors=colors, rotation="180x,0y,0z") -custom_ticks = [0.7, 0.8, 0.9, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5] -cbar = plt.colorbar( - plt.cm.ScalarMappable(norm=norm, cmap=colormap), - ax=ax, - label="Local energy uncertainty (meV)", - ticks=custom_ticks, -) -cbar.ax.set_yticklabels([f"{tick}" for tick in custom_ticks]) -cbar.minorticks_off() -ax.set_xticks([]) -ax.set_yticks([]) -plt.show() diff --git a/docs/src/examples/llpr/llpr.py.md5 b/docs/src/examples/llpr/llpr.py.md5 deleted file mode 100644 index d9979d99e5..0000000000 --- a/docs/src/examples/llpr/llpr.py.md5 +++ /dev/null @@ -1 +0,0 @@ -76970f9c741099e3b7ea8946bb48d8e0 \ No newline at end of file diff --git a/docs/src/examples/llpr/llpr.rst b/docs/src/examples/llpr/llpr.rst deleted file mode 100644 index aed660fd85..0000000000 --- a/docs/src/examples/llpr/llpr.rst +++ /dev/null @@ -1,183 +0,0 @@ - -.. DO NOT EDIT. -.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. -.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: -.. "examples/llpr/llpr.py" -.. LINE NUMBERS ARE GIVEN BELOW. - -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - :ref:`Go to the end ` - to download the full example code. - -.. rst-class:: sphx-glr-example-title - -.. _sphx_glr_examples_llpr_llpr.py: - -.. _llprexample: - -Computing LLPR uncertainties -============================ - -This tutorial demonstrates how to train a model with uncertainties using metatrain. -It involves the computation of the uncertainties on ethanol molecules, using the -last-layer prediction rigidity (`LLPR `_) approximation. Both total and local -(`LPR `_) uncertainties are computed - -.. _LLPR: https://arxiv.org/html/2403.02251v1 -.. _LPR: https://pubs.acs.org/doi/10.1021/acs.jctc.3c00704 - -The baseline model was trained using the following training options, where the training -set consists of 100 structures from the QM9 dataset. - -.. literalinclude:: options.yaml - :language: yaml - -Once a model is trained, you can add LLPR uncertainties to it by launching a training -run with the "llpr" architecture, on the same data. In this case, the training options -to add LLPR uncertainties are as follows: - -.. literalinclude:: options-llpr.yaml - :language: yaml - -Adding LLPR uncertainties is very cheap compared to training a model, as it only -involves one pass through the training data (equivalent to one epoch of training). - -You can repeat the same training yourself with - -.. literalinclude:: train.sh - :language: bash - -A detailed step-by-step introduction on how to train a model is provided in -the :ref:`label_basic_usage` tutorial. - -.. GENERATED FROM PYTHON SOURCE LINES 40-42 - -As an example, we will compute the energies and uncertainties of the LLPR model on a -few ethanol structures. - -.. GENERATED FROM PYTHON SOURCE LINES 43-78 - -.. code-block:: Python - - - import ase.io - import matplotlib.pyplot as plt - from ase.visualize.plot import plot_atoms - from matplotlib.colors import LogNorm - from metatomic.torch import ModelOutput - from metatomic.torch.ase_calculator import MetatomicCalculator - - - # load 5 ethanol structures - structures = ase.io.read("ethanol_reduced_100.xyz", ":5") - - # load the model as an ASE calculator - calc = MetatomicCalculator( - "model-llpr.pt", extensions_directory="extensions/", device="cpu" - ) - - # the uncertainties are available throguh the ``run_model`` method of the calculator - predictions = calc.run_model( - structures, - { - "energy": ModelOutput(per_atom=False), - "energy_uncertainty": ModelOutput(per_atom=False), - }, - ) - - # print the energies and uncertainties - - energies = predictions["energy"].block().values.squeeze().numpy() - uncertainties = predictions["energy_uncertainty"].block().values.squeeze().numpy() - - print(energies) - print(uncertainties) - - - - - - -.. rst-class:: sphx-glr-script-out - - .. code-block:: none - - [-154.97468067 -154.97642503 -154.97632212 -154.97600337 -154.97451059] - [0.0097369 0.01553008 0.02451612 0.01447105 0.01356071] - - - - -.. GENERATED FROM PYTHON SOURCE LINES 79-81 - -We can also obtain per-atom uncertainties (local prediction rigidity, LPR). As an -example, we will compute the uncertainties on an ethanol structure. - -.. GENERATED FROM PYTHON SOURCE LINES 82-110 - -.. code-block:: Python - - - structure = structures[0] - predictions = calc.run_model( - structure, - { - # here, we use per_atom=True to request per-atom uncertainties - "energy_uncertainty": ModelOutput(per_atom=True), - }, - ) - local_uncertainty = predictions["energy_uncertainty"].block().values.squeeze().numpy() - local_uncertainty = local_uncertainty * 1000.0 # convert from eV to meV - - norm = LogNorm(vmin=min(local_uncertainty), vmax=max(local_uncertainty)) - colormap = plt.get_cmap("viridis") - colors = colormap(norm(local_uncertainty)) - ax = plot_atoms(structure, colors=colors, rotation="180x,0y,0z") - custom_ticks = [0.7, 0.8, 0.9, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5] - cbar = plt.colorbar( - plt.cm.ScalarMappable(norm=norm, cmap=colormap), - ax=ax, - label="Local energy uncertainty (meV)", - ticks=custom_ticks, - ) - cbar.ax.set_yticklabels([f"{tick}" for tick in custom_ticks]) - cbar.minorticks_off() - ax.set_xticks([]) - ax.set_yticks([]) - plt.show() - - - - - - - - -.. _sphx_glr_download_examples_llpr_llpr.py: - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-example - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: llpr.ipynb ` - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: llpr.py ` - - .. container:: sphx-glr-download sphx-glr-download-zip - - :download:`Download zipped: llpr.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/llpr/llpr.zip b/docs/src/examples/llpr/llpr.zip deleted file mode 100644 index 46bdfa75339844db082667a71e1500958b666a9a..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 3789 zcmb_fU5_NS60ZPG;#cG$Z~Vaggq-WD>W|$X!b4_M)vogO_5C>a_}0~vN3Xv5fc_qR^^fb;L(6;hoBTK_iEiY}MjHtuQBB#dG?oh6M 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A=127.83497 B=24.85872 C=23.97872 mu=1.5258 alpha=16.97 epsHOMO=-0.2653 epsLUMO=0.0784 epsgap=0.3437 =83.794 zpve=0.051208 U0=-115.679136 U=-115.675816 H=-115.674872 G=-115.701876 Cv=8.751 pbc="F F F" -C -0.00828817 1.39046978 -0.00560069 -O -0.00797038 -0.02504537 0.02030606 -H 1.00658338 1.81556366 0.00348335 -H -0.54657475 1.79916975 -0.87390126 -H -0.52288871 1.72555240 0.89907326 -H 0.44142019 -0.33354425 -0.77152059 -7 -Properties=species:S:1:pos:R:3 A=160.28041 B=8.59323 C=8.59321 mu=0.7156 alpha=28.78 epsHOMO=-0.2609 epsLUMO=0.0613 epsgap=0.3222 =177.1963 zpve=0.05541 U0=-116.609549 U=-116.60555 H=-116.604606 G=-116.633775 Cv=12.482 pbc="F F F" -C -0.01782102 1.46435788 0.01009397 -C 0.00208816 0.00950777 0.00201200 -C 0.01834085 -1.19180518 -0.00450508 -H 0.99782210 1.87425349 0.00260606 -H -0.54220434 1.85801178 -0.86721192 -H -0.52533306 1.84834356 0.90148138 -H 0.03231658 -2.25314823 -0.01025999 -6 -Properties=species:S:1:pos:R:3 A=159.03567 B=9.22327 C=9.22324 mu=3.8266 alpha=24.45 epsHOMO=-0.3264 epsLUMO=0.0376 epsgap=0.364 =160.7223 zpve=0.045286 U0=-132.71815 U=-132.714563 H=-132.713619 G=-132.742149 Cv=10.287 pbc="F F F" -C -0.01788629 1.46712787 0.01011311 -C 0.00173819 0.01035260 0.00207588 -N 0.01765235 -1.14452926 -0.00420445 -H 1.00202941 1.86089927 0.00245401 -H -0.54398848 1.84479901 -0.87075460 -H -0.52707783 1.83518166 0.90486329 -7 -Properties=species:S:1:pos:R:3 A=57.22434 B=10.11122 C=9.07368 mu=2.5682 alpha=25.11 epsHOMO=-0.254 epsLUMO=-0.0198 epsgap=0.2342 =166.9728 zpve=0.055355 U0=-153.787612 U=-153.783728 H=-153.782784 G=-153.812518 Cv=11.219 pbc="F F F" -C -0.00294482 1.50991366 0.00867278 -C 0.02608284 0.00327563 -0.03745912 -O 0.94228801 -0.65507035 -0.45682576 -H 0.92278802 1.92634242 -0.39146557 -H -0.86201540 1.87852481 -0.56479538 -H -0.15050638 1.84393383 1.04289100 -H -0.89443009 -0.48643408 0.35774865 -6 -Properties=species:S:1:pos:R:3 A=73.8472 B=11.34793 C=9.83639 mu=3.7286 alpha=21.57 epsHOMO=-0.2543 epsLUMO=0.0302 epsgap=0.2845 =145.3078 zpve=0.045279 U0=-169.860788 U=-169.856903 H=-169.855958 G=-169.885594 Cv=10.89 pbc="F F F" -N -0.02589986 1.34614561 0.00889391 -C 0.04646728 -0.01174341 0.00120425 -O 1.07183520 -0.65258782 -0.01113313 -H 0.82535497 1.88504948 0.00373769 -H -0.90837680 1.82679638 0.01891992 -H -0.96144100 -0.47500376 0.00807391 -11 -Properties=species:S:1:pos:R:3 A=29.45018 B=8.37701 C=7.42076 mu=0.0597 alpha=34.75 epsHOMO=-0.323 epsLUMO=0.0949 epsgap=0.4179 =227.1361 zpve=0.103182 U0=-119.052475 U=-119.047927 H=-119.046983 G=-119.078157 Cv=14.84 pbc="F F F" -C -0.03113825 1.54081582 0.03192126 -C 0.01215347 0.01092235 -0.01603259 -C 0.72169129 -0.52583353 -1.26230570 -H 0.97955987 1.96459116 0.03098367 -H -0.55840223 1.94831192 -0.83816075 -H -0.54252252 1.90153531 0.93005671 -H 0.51522791 -0.36840234 0.88231134 -H -1.01070641 -0.38456999 0.02051783 -H 1.75851210 -0.17376585 -1.30871516 -H 0.74087192 -1.62024959 -1.27516511 -H 0.22023351 -0.19051179 -2.17729020 -9 -Properties=species:S:1:pos:R:3 A=35.09545 B=9.3686 C=8.1497 mu=1.4131 alpha=27.87 epsHOMO=-0.2619 epsLUMO=0.0798 epsgap=0.3417 =193.1659 zpve=0.079754 U0=-154.972731 U=-154.968412 H=-154.967467 G=-154.998148 Cv=13.546 pbc="F F F" -C -0.00860504 1.50203829 -0.00681217 -C 0.01099310 -0.01764877 -0.01377035 -O 0.68088841 -0.44041803 -1.19313210 -H 1.01153334 1.89662030 -0.01920155 -H -0.53159862 1.88076109 -0.88974659 -H -0.51674572 1.87611771 0.88710739 -H 0.52377121 -0.38912295 0.88824082 -H -1.02027544 -0.40507261 0.01690670 -H 0.69529573 -1.40179568 -1.20148495 -9 -Properties=species:S:1:pos:R:3 A=39.37691 B=10.04033 C=8.90353 mu=1.1502 alpha=28.13 epsHOMO=-0.2525 epsLUMO=0.091 epsgap=0.3435 =187.1015 zpve=0.079534 U0=-154.960361 U=-154.956045 H=-154.9551 G=-154.985747 Cv=12.934 pbc="F F F" -C -0.01482147 1.39241234 0.00567104 -O -0.00471537 -0.01360660 0.01459658 -C 0.63794893 -0.55329676 -1.11358232 -H 1.00528358 1.81015807 0.00465631 -H -0.54689560 1.79343530 -0.87251051 -H -0.53002895 1.72291969 0.91101696 -H 0.13993792 -0.25599333 -2.05098375 -H 1.69265275 -0.23868382 -1.17477743 -H 0.59959435 -1.64180161 -1.02407616 -9 -Properties=species:S:1:pos:R:3 A=20.15852 B=20.15302 C=12.5891 mu=0.0005 alpha=30.82 epsHOMO=-0.2888 epsLUMO=0.1042 epsgap=0.393 =155.8145 zpve=0.081231 U0=-117.824798 U=-117.821426 H=-117.820482 G=-117.849087 Cv=11.041 pbc="F F F" -C -0.01193280 1.51433198 0.01031700 -C 1.30299118 0.77886562 -0.00617842 -C 0.00867174 0.00767042 0.00201032 -H -0.30541464 2.01702117 0.92533239 -H -0.32275498 2.02680156 -0.89347839 -H 1.88493396 0.79145020 -0.92119393 -H 1.90226631 0.78165391 0.89766373 -H -0.27088043 -0.51289125 0.91137474 -H -0.28821983 -0.50310409 -0.90740824 -7 -Properties=species:S:1:pos:R:3 A=25.77385 B=22.12109 C=14.18066 mu=1.7675 alpha=24.04 epsHOMO=-0.2682 epsLUMO=0.1042 epsgap=0.3724 =129.891 zpve=0.057289 U0=-153.742562 U=-153.73941 H=-153.738466 G=-153.766642 Cv=9.176 pbc="F F F" -C 0.01537720 1.41764380 0.00956287 -C 1.26478161 0.64923984 -0.00655044 -O -0.00024018 -0.00771006 0.00204055 -H -0.31763326 1.88586598 0.93475751 -H -0.33527583 1.89576673 -0.90397578 -H 1.83246682 0.56256848 -0.93193290 -H 1.85008258 0.55267938 0.90680147 -10 -Properties=species:S:1:pos:R:3 A=10.12193 B=8.49011 C=4.89615 mu=2.7362 alpha=35.53 epsHOMO=-0.2431 epsLUMO=-0.0087 epsgap=0.2344 =292.4367 zpve=0.083382 U0=-193.08834 U=-193.082969 H=-193.082024 G=-193.116476 Cv=16.893 pbc="F F F" -C -0.00310189 1.48050328 -0.17259867 -C -0.04527202 -0.02887059 0.00139493 -C 1.29705800 -0.72194415 0.16889042 -O -1.08643172 -0.64273343 0.00630393 -H 0.59403529 1.74668579 -1.05225043 -H -1.01540407 1.87034023 -0.28382265 -H 0.48099600 1.95070605 0.69099376 -H 1.82512144 -0.32470731 1.04328658 -H 1.14892507 -1.79597689 0.28554393 -H 1.93682635 -0.52949692 -0.69995403 -9 -Properties=species:S:1:pos:R:3 A=10.78886 B=9.27509 C=5.14977 mu=3.6367 alpha=31.83 epsHOMO=-0.2436 epsLUMO=0.0347 epsgap=0.2783 =267.6148 zpve=0.07319 U0=-209.159302 U=-209.15402 H=-209.153076 G=-209.187468 Cv=16.561 pbc="F F F" -C 0.00679889 1.49969736 -0.02567030 -C -0.02740385 -0.01752051 -0.14894811 -N 1.17826667 -0.63324298 0.05691721 -O -1.03721965 -0.63980649 -0.40727572 -H 0.91390280 1.92923755 -0.46100067 -H -0.87080896 1.91124186 -0.52345176 -H -0.02826124 1.78517271 1.03149125 -H 1.98405139 -0.13175861 0.38674906 -H 1.19049421 -1.63919563 0.10006209 -8 -Properties=species:S:1:pos:R:3 A=11.1475 B=10.36388 C=5.42091 mu=3.4869 alpha=28.07 epsHOMO=-0.2495 epsLUMO=0.0556 epsgap=0.3051 =244.2308 zpve=0.063824 U0=-225.221461 U=-225.217075 H=-225.216131 G=-225.247724 Cv=15.292 pbc="F F F" -N 0.03605270 1.36077873 -0.12416403 -C -0.02591138 -0.02076560 0.00200645 -N 1.21968479 -0.62334220 0.11963222 -O -1.06822866 -0.64174597 0.00865573 -H 0.80749439 1.83455148 0.32144915 -H -0.85825775 1.80373500 0.02243770 -H 1.99927148 -0.17303054 -0.33602496 -H 1.18204327 -1.62147901 -0.02109234 -14 -Properties=species:S:1:pos:R:3 A=7.75166 B=7.74847 C=4.48668 mu=0.0897 alpha=45.46 epsHOMO=-0.3167 epsLUMO=0.0843 epsgap=0.401 =355.0621 zpve=0.131146 U0=-158.342346 U=-158.336603 H=-158.335658 G=-158.370016 Cv=20.273 pbc="F F F" -C -0.03215888 1.54021598 0.01074456 -C 0.03381741 0.00745852 0.00180716 -C 0.71375572 -0.50856404 -1.27296949 -C 0.73848999 -0.52220886 1.25750907 -H 0.97522736 1.97414341 0.00528362 -H -0.56196769 1.92232326 -0.86835418 -H -0.54817239 1.91268414 0.90208039 -H -0.99752525 -0.37310578 0.00977297 -H 1.75070021 -0.15586784 -1.33269631 -H 0.73446691 -1.60328288 -1.29939696 -H 0.19367011 -0.16085589 -2.17195508 -H 0.23611990 -0.18382017 2.17005260 -H 0.75964083 -1.61713153 1.27226101 -H 1.77635866 -0.16985318 1.30053019 -12 -Properties=species:S:1:pos:R:3 A=8.67553 B=8.00568 C=4.75542 mu=1.4259 alpha=38.58 epsHOMO=-0.2612 epsLUMO=0.074 epsgap=0.3351 =318.3721 zpve=0.107673 U0=-194.267232 U=-194.261748 H=-194.260804 G=-194.294663 Cv=19.052 pbc="F F F" -C -0.03315871 1.54782566 -0.00438815 -C -0.01108534 0.01859095 0.01676117 -C 0.70938384 -0.53596711 1.23978444 -O -1.33219841 -0.51712554 0.05236057 -H 0.98215692 1.95715900 -0.03182225 -H -0.56388100 1.92087623 -0.88845865 -H -0.54082548 1.93340945 0.88575489 -H 0.51042122 -0.33767241 -0.88819176 -H 0.22240492 -0.18785860 2.15650245 -H 0.68242024 -1.62887638 1.23305141 -H 1.75434301 -0.21246950 1.25524188 -H -1.81818681 -0.15577435 -0.69553962 -6 -Properties=species:S:1:pos:R:3 A=0 B=4.4259726 C=4.4259726 mu=0 alpha=38.52 epsHOMO=-0.2599 epsLUMO=-0.0214 epsgap=0.2386 =278.6264 zpve=0.037354 U0=-153.459846 U=-153.455442 H=-153.454498 G=-153.482621 Cv=15.312 pbc="F F F" -C 0.68098021 0.00000000 0.00000000 -C -0.68098021 0.00000000 0.00000000 -C -1.88766603 0.00000000 0.00000000 -C 1.88766603 0.00000000 0.00000000 -H -2.94960000 0.00000000 0.00000000 -H 2.94960000 0.00000000 0.00000000 -5 -Properties=species:S:1:pos:R:3 A=0 B=4.5793222 C=4.5793222 mu=3.792 alpha=32.66 epsHOMO=-0.3102 epsLUMO=-0.0543 epsgap=0.2559 =260.1896 zpve=0.027259 U0=-169.557758 U=-169.553764 H=-169.55282 G=-169.581024 Cv=12.93 pbc="F F F" -C 0.01510390 0.00000000 1.00000000 -C 1.38233149 0.00000000 1.00000000 -C -1.18862525 0.00000000 1.00000000 -H -2.25177201 0.00000000 1.00000000 -N 2.54296187 0.00000000 1.00000000 -4 -Properties=species:S:1:pos:R:3 A=0 B=4.73269 C=4.73269 mu=0.0023 alpha=27.7 epsHOMO=-0.3696 epsLUMO=-0.0926 epsgap=0.277 =242.9308 zpve=0.015951 U0=-185.648533 U=-185.644825 H=-185.64388 G=-185.667652 Cv=10.398 pbc="F F F" -N 0.01745734 -1.16134217 -0.00415342 -C 0.00253212 -0.00344278 0.00179939 -C -0.01611419 1.37220987 0.00934873 -N -0.03264302 2.53008276 0.01609090 -6 -Properties=species:S:1:pos:R:3 A=68.5171 B=4.8345 C=4.51586 mu=2.7824 alpha=31.14 epsHOMO=-0.2777 epsLUMO=-0.0735 epsgap=0.2042 =268.3921 zpve=0.037208 U0=-190.624631 U=-190.620363 H=-190.619419 G=-190.650543 Cv=13.049 pbc="F F F" -O -0.06776120 -0.04233838 0.00282489 -C -0.00562945 1.16382879 0.00743862 -C 1.22985272 1.91339997 0.00028781 -C 2.23625322 2.57430970 -0.00529277 -H -0.91670701 1.79467546 0.01781352 -H 3.13429662 3.14370267 -0.01037656 -5 -Properties=species:S:1:pos:R:3 A=67.88408 B=5.00823 C=4.66413 mu=2.3112 alpha=26.25 epsHOMO=-0.3166 epsLUMO=-0.11 epsgap=0.2066 =251.0007 zpve=0.02654 U0=-206.721858 U=-206.717875 H=-206.716931 G=-206.747625 Cv=11.329 pbc="F F F" -O -0.04055978 -0.03799144 0.00266513 -C -0.01768870 1.16193251 0.00746008 -C 1.23315534 1.93381336 0.00043371 -N 2.19857378 2.57055097 -0.00483320 -H -0.92627534 1.78959196 0.01764859 -6 -Properties=species:S:1:pos:R:3 A=56.25048 B=4.77441 C=4.40088 mu=0.002 alpha=26.12 epsHOMO=-0.2668 epsLUMO=-0.1113 epsgap=0.1555 =266.8164 zpve=0.036943 U0=-227.798785 U=-227.79457 H=-227.793626 G=-227.825074 Cv=12.147 pbc="F F F" -O 0.00337122 -0.03146586 0.00157206 -C -0.01713174 1.17081409 0.00676444 -C 1.26555783 1.99719410 -0.00007703 -O 1.24507069 3.19946589 0.00681275 -H -0.94690483 1.77678142 0.01852668 -H 2.19534410 1.39121374 -0.01006739 -10 -Properties=species:S:1:pos:R:3 A=80.28766 B=3.3688 C=3.3688 mu=0 alpha=42.32 epsHOMO=-0.2412 epsLUMO=0.0684 epsgap=0.3096 =400.2236 zpve=0.083896 U0=-155.908941 U=-155.90318 H=-155.902236 G=-155.937641 Cv=17.447 pbc="F F F" -C -0.01787959 1.46676893 0.01011180 -C 0.00186644 0.01072913 0.00211109 -C 0.01819704 -1.19272861 -0.00450723 -C 0.03795170 -2.64876793 -0.01251193 -H 0.86460312 1.87589979 0.51479719 -H -0.03217624 1.87210581 -1.00791002 -H -0.90233115 1.85185524 0.53003953 -H -0.83477011 -3.06339007 0.50451436 -H 0.03276422 -3.04315564 -1.03491223 -H 0.93213486 -3.03931642 0.48626606 -10 -Properties=species:S:1:pos:R:3 A=27.39459 B=4.53005 C=4.08342 mu=0.7067 alpha=40.09 epsHOMO=-0.2592 epsLUMO=0.0566 epsgap=0.3157 =333.9589 zpve=0.084338 U0=-155.897345 U=-155.892291 H=-155.891347 G=-155.924226 Cv=17.13 pbc="F F F" -C -0.03095840 1.54775025 0.03167945 -C 0.01485375 0.00962471 -0.02082154 -C 0.69099073 -0.49954575 -1.20857624 -C 1.25222275 -0.89514467 -2.19527879 -H 0.97899300 1.96621609 0.03373834 -H -0.56082354 1.95002902 -0.83580745 -H -0.54576708 1.88225519 0.93709062 -H 0.51920135 -0.37425398 0.87496224 -H -1.00644802 -0.39030838 0.01340632 -H 1.74594541 -1.25604187 -3.06299415 -9 -Properties=species:S:1:pos:R:3 A=28.0016 B=4.67752 C=4.21765 mu=3.9233 alpha=35.38 epsHOMO=-0.3213 epsLUMO=0.034 epsgap=0.3553 =314.5335 zpve=0.07419 U0=-172.006141 U=-172.001467 H=-172.000523 G=-172.032826 Cv=14.988 pbc="F F F" -C -0.02522005 1.54773054 0.02133370 -C 0.01183939 0.00999278 -0.01556702 -C 0.68915114 -0.50836846 -1.20331488 -N 1.22565733 -0.90360846 -2.14737482 -H 0.98571620 1.96212990 0.02370153 -H -0.55522192 1.94599662 -0.84711771 -H -0.53962556 1.88294354 0.92536604 -H 0.52481910 -0.38226048 0.86933355 -H -1.00453542 -0.39832109 0.00530071 -8 -Properties=species:S:1:pos:R:3 A=32.19893 B=4.76299 C=4.30696 mu=4.4361 alpha=31.81 epsHOMO=-0.2683 epsLUMO=0.0173 epsgap=0.2855 =295.6635 zpve=0.063305 U0=-188.042067 U=-188.037478 H=-188.036534 G=-188.06863 Cv=14.488 pbc="F F F" -N -0.03608099 1.48762533 0.03678072 -C -0.01039471 0.02517605 -0.01506608 -C -1.37180956 -0.52108920 0.02739272 -N -2.44026695 -0.95956444 0.03370519 -H 0.91729752 1.83547547 0.05202682 -H -0.47162178 1.85677205 -0.80275569 -H 0.48946161 -0.40880401 -0.89715168 -H 0.52827150 -0.34533338 0.86469547 -8 -Properties=species:S:1:pos:R:3 A=35.11615 B=4.66117 C=4.22354 mu=1.7211 alpha=33.1 epsHOMO=-0.2595 epsLUMO=0.0277 epsgap=0.2872 =300.0993 zpve=0.060632 U0=-191.810916 U=-191.806025 H=-191.805081 G=-191.837634 Cv=15.855 pbc="F F F" -O -0.02013197 1.45738137 0.01010195 -C 0.00168478 0.03579980 0.00224736 -C -1.36366691 -0.47481101 0.01255056 -C -2.47902835 -0.91970202 0.02082451 -H 0.89446911 1.75736553 0.00327363 -H 0.51979028 -0.35175413 -0.88980302 -H 0.53670458 -0.36127661 0.88000894 -H -3.46773502 -1.30737009 0.02818120 -7 -Properties=species:S:1:pos:R:3 A=35.93172 B=4.83443 C=4.37827 mu=4.6788 alpha=28.56 epsHOMO=-0.3018 epsLUMO=0.0022 epsgap=0.3039 =280.6659 zpve=0.050262 U0=-207.916786 U=-207.912215 H=-207.911271 G=-207.943384 Cv=13.845 pbc="F F F" -O -0.03738221 1.44547841 0.01010769 -C 0.01292906 0.03441661 0.00209165 -C -1.35747452 -0.48874523 0.01239738 -N -2.42135277 -0.93685184 0.02016053 -H 0.86898057 1.76972949 0.00417577 -H 0.51692713 -0.36329790 -0.89215814 -H 0.53387616 -0.37278749 0.88223921 -10 -Properties=species:S:1:pos:R:3 A=26.14564 B=4.2893 C=4.12773 mu=2.6741 alpha=35.83 epsHOMO=-0.25 epsLUMO=-0.0205 epsgap=0.2295 =333.3276 zpve=0.084175 U0=-193.075202 U=-193.070116 H=-193.069171 G=-193.102798 Cv=15.954 pbc="F F F" -C 0.16879340 1.52513042 -0.15747056 -C -0.18737625 0.06194763 0.14679374 -C 0.50917645 -0.43996215 1.39125850 -O 1.18191079 -1.43565585 1.45816384 -H 1.23126511 1.63137254 -0.39632693 -H -0.40744668 1.89431681 -1.00969246 -H -0.04931034 2.17266260 0.69857432 -H -1.26905160 -0.01668067 0.32854433 -H 0.06279062 -0.60250479 -0.68474036 -H 0.35384841 0.20663370 2.28871052 -9 -Properties=species:S:1:pos:R:3 A=19.98749 B=6.11589 C=4.82584 mu=3.7071 alpha=32.78 epsHOMO=-0.2516 epsLUMO=0.0335 epsgap=0.2851 =279.7863 zpve=0.074166 U0=-209.144909 U=-209.139976 H=-209.139032 G=-209.172305 Cv=15.058 pbc="F F F" -C 0.01474916 1.47888256 -0.03509326 -N 0.18708077 0.03831317 -0.02906851 -C 0.45703661 -0.66858895 1.10069001 -O 0.58457996 -0.19876202 2.21119705 -H 0.76025289 1.96736104 -0.67198126 -H -0.98462423 1.75984483 -0.38485103 -H 0.14269849 1.82387718 0.99149397 -H 0.10386349 -0.46937726 -0.89461379 -H 0.55047568 -1.75253452 0.88868242 -8 -Properties=species:S:1:pos:R:3 A=48.62351 B=4.67298 C=4.38187 mu=3.92 alpha=29.47 epsHOMO=-0.2814 epsLUMO=0.0074 epsgap=0.2888 =293.917 zpve=0.061327 U0=-229.013797 U=-229.009003 H=-229.008059 G=-229.041086 Cv=13.885 pbc="F F F" -C 0.10653781 1.43512049 0.03336100 -O 0.42686564 0.04716929 0.09309945 -C 0.50714862 -0.63652665 -1.06318297 -O 0.76927014 -1.79955017 -1.11010475 -H 0.92349251 2.00385139 0.48465728 -H -0.03757815 1.76927183 -1.00057425 -H -0.81269569 1.60801916 0.59873287 -H 0.30458460 -0.00231902 -1.94965553 -8 -Properties=species:S:1:pos:R:3 A=36.49282 B=4.31175 C=4.04493 mu=1.7341 alpha=28.53 epsHOMO=-0.2537 epsLUMO=-0.0341 epsgap=0.2196 =303.8129 zpve=0.060508 U0=-228.992613 U=-228.987769 H=-228.986825 G=-229.019918 Cv=14.78 pbc="F F F" -O 0.19375856 1.37170995 0.31398297 -C 0.04992637 0.01405481 -0.04526239 -C -1.40888025 -0.39635680 -0.20601574 -O -1.77729462 -1.37233860 -0.80361689 -H -0.01723186 1.91269132 -0.45337758 -H 0.60440478 -0.26519766 -0.95205725 -H 0.46740406 -0.57521679 0.78176270 -H -2.11835055 0.30075962 0.29870515 -14 -Properties=species:S:1:pos:R:3 A=23.44662 B=3.60588 C=3.38683 mu=0 alpha=45.71 epsHOMO=-0.317 epsLUMO=0.0937 epsgap=0.4107 =426.2996 zpve=0.131708 U0=-158.340943 U=-158.33517 H=-158.334226 G=-158.36894 Cv=19.668 pbc="F F F" -C -0.03007624 1.55804912 0.02980774 -C 0.00707277 0.02834877 -0.00712529 -C 0.71320792 -0.52989340 -1.24765581 -C 0.75036895 -2.05959366 -1.28458957 -H 0.98195330 1.97845113 0.02800797 -H -0.55582433 1.96212343 -0.84270157 -H -0.54083762 1.92692755 0.92493697 -H 0.50928672 -0.34909049 0.89361319 -H -1.01733481 -0.36542299 0.03207393 -H 0.21098948 -0.15245770 -2.14839302 -H 1.73761331 -0.13611625 -1.28685672 -H 1.27616854 -2.46366410 -0.41210980 -H -0.26165699 -2.48000447 -1.28273552 -H 1.26108491 -2.42846444 -2.17974803 -12 -Properties=species:S:1:pos:R:3 A=26.59789 B=3.79186 C=3.53779 mu=1.3402 alpha=38.61 epsHOMO=-0.2619 epsLUMO=0.081 epsgap=0.3429 =382.8628 zpve=0.108241 U0=-194.261089 U=-194.255495 H=-194.254551 G=-194.28893 Cv=18.431 pbc="F F F" -C -0.00379012 1.52196790 0.01027996 -C -0.03589372 -0.00778254 0.00225671 -C -1.45219016 -0.56653711 0.01284386 -O -1.36799785 -1.98394604 0.00453606 -H 1.02477576 1.89411231 0.00239793 -H -0.51286674 1.93764614 -0.86653888 -H -0.49584080 1.92801344 0.90121721 -H 0.48140784 -0.39530552 -0.88316064 -H 0.49828589 -0.40486256 0.87330348 -H -1.98734220 -0.20399494 0.90666062 -H -2.00431485 -0.19459473 -0.86666460 -H -2.26171819 -2.33824298 0.01034748 -12 -Properties=species:S:1:pos:R:3 A=28.29567 B=4.14901 C=3.88349 mu=1.0363 alpha=39.34 epsHOMO=-0.2503 epsLUMO=0.0925 epsgap=0.3428 =368.9331 zpve=0.107895 U0=-194.254127 U=-194.248585 H=-194.247641 G=-194.281899 Cv=17.888 pbc="F F F" -C -0.00519551 1.50868224 0.03308760 -C 0.01316580 -0.01037819 0.01988643 -O 0.69758944 -0.46344140 1.16868979 -C 0.76688491 -1.86508183 1.24602669 -H 1.01445476 1.90423799 0.02996018 -H -0.52990866 1.89184059 -0.84726911 -H -0.51263765 1.87805843 0.92875424 -H -1.01599507 -0.40998729 0.00714282 -H 0.51093143 -0.38369435 -0.89219395 -H -0.23415841 -2.32451489 1.28977780 -H 1.30129276 -2.29853663 0.38486095 -H 1.30990287 -2.11811998 2.16013206 -10 -Properties=species:S:1:pos:R:3 A=14.79671 B=5.6956 C=4.58846 mu=0.0075 alpha=31.42 epsHOMO=-0.2594 epsLUMO=0.0584 epsgap=0.3179 =297.8398 zpve=0.085172 U0=-230.183076 U=-230.177723 H=-230.176779 G=-230.211195 Cv=16.837 pbc="F F F" -O -0.01418735 1.42642242 -0.05423651 -C -0.00391839 0.00717714 0.03496062 -C -1.42317019 -0.55560600 -0.01256639 -O -2.11229413 -0.10563875 -1.17228313 -H -0.18987882 1.64154705 -0.97555148 -H 0.59659504 -0.43315430 -0.77409095 -H 0.47311116 -0.24903733 0.98747216 -H -1.39494859 -1.64975279 -0.06283188 -H -1.96497417 -0.27216915 0.90131972 -H -2.35925991 0.80944396 -1.00614597 -12 -Properties=species:S:1:pos:R:3 A=15.61472 B=6.31363 C=5.54514 mu=0.1136 alpha=41.96 epsHOMO=-0.2727 epsLUMO=0.1012 epsgap=0.3738 =298.6061 zpve=0.109284 U0=-157.116735 U=-157.11209 H=-157.111146 G=-157.143262 Cv=16.49 pbc="F F F" -C -0.03602245 1.52092949 0.01569161 -C 0.01938487 0.01004403 0.04814953 -C 1.30891671 -0.71251995 -0.24920416 -C 0.75347423 -0.70267714 1.15564676 -H 0.90105691 1.95389291 0.38283338 -H -0.19640365 1.89437369 -1.00208569 -H -0.84685273 1.90588930 0.64451322 -H -0.88416809 -0.48001889 -0.30566335 -H 1.26900901 -1.63903068 -0.81150371 -H 2.19010738 -0.11131209 -0.44927513 -H 1.26248862 -0.09487455 1.89690028 -H 0.33540836 -1.62248918 1.54980695 -10 -Properties=species:S:1:pos:R:3 A=18.21931 B=6.63877 C=5.92459 mu=1.812 alpha=35.01 epsHOMO=-0.2633 epsLUMO=0.1052 epsgap=0.3685 =267.2979 zpve=0.085275 U0=-193.039603 U=-193.035186 H=-193.034242 G=-193.065979 Cv=14.764 pbc="F F F" -C -0.01646343 1.51830705 0.00771445 -C -0.02743227 0.01657694 -0.10889741 -C 0.77296553 -0.70131886 -1.10863115 -O 1.17212293 -0.66702969 0.26278829 -H 0.91865980 1.92914217 -0.38285880 -H -0.85158014 1.95456506 -0.55109460 -H -0.11487702 1.82596470 1.05397777 -H -0.93582598 -0.47350600 0.24682793 -H 0.44443922 -1.67201495 -1.47810622 -H 1.40538284 -0.13600339 -1.79271491 -11 -Properties=species:S:1:pos:R:3 A=16.58914 B=7.18798 C=6.11415 mu=1.1353 alpha=39.02 epsHOMO=-0.2304 epsLUMO=0.0968 epsgap=0.3271 =270.5508 zpve=0.097671 U0=-173.147782 U=-173.143343 H=-173.142399 G=-173.174073 Cv=15.298 pbc="F F F" -C -0.05275541 1.47419574 0.00999108 -N 0.01940516 0.02189947 0.06334866 -C 1.32511048 -0.56446850 -0.21420725 -C 0.78051759 -0.54057993 1.17242760 -H 0.86784202 1.97176076 0.36297285 -H -0.23845155 1.79095005 -1.02142866 -H -0.88683448 1.81938959 0.62950398 -H 1.31992909 -1.48186782 -0.79602038 -H 2.15202820 0.11020204 -0.43285248 -H 1.24563570 0.14996098 1.87499245 -H 0.37013358 -1.44020447 1.62233365 -10 -Properties=species:S:1:pos:R:3 A=16.79477 B=6.71015 C=5.86781 mu=1.3894 alpha=34.64 epsHOMO=-0.239 epsLUMO=0.0775 epsgap=0.3166 =263.966 zpve=0.085106 U0=-193.034988 U=-193.030356 H=-193.029411 G=-193.061689 Cv=15.679 pbc="F F F" -O 0.21577583 1.35386859 -0.10695107 -C 0.01628099 -0.02832718 -0.02915694 -C -0.39081465 -0.71497009 1.24315669 -C -1.35118138 -0.64280380 0.06273589 -H -0.43642338 1.79062849 0.45006761 -H 0.74981167 -0.52404050 -0.65624767 -H 0.05959375 -1.66840483 1.49729237 -H -0.61369353 -0.08848006 2.10170640 -H -2.19821932 0.03058430 0.15411451 -H -1.56395912 -1.54640426 -0.49826515 -12 -Properties=species:S:1:pos:R:3 A=10.66107 B=10.66107 C=6.30452 mu=0 alpha=41.83 epsHOMO=-0.2982 epsLUMO=0.0956 epsgap=0.3938 =268.4432 zpve=0.110511 U0=-157.115484 U=-157.111322 H=-157.110378 G=-157.141657 Cv=14.696 pbc="F F F" -C -0.02542957 1.54032578 -0.04215944 -C 1.52564399 1.50419989 0.04121276 -C 1.42146727 0.06146790 0.60844885 -C -0.02638833 -0.01070956 0.04935088 -H -0.46591372 1.99639894 0.84962894 -H -0.47900239 2.00028207 -0.92365389 -H 1.98248278 1.51375334 -0.95303183 -H 2.02191365 2.25986579 0.65522129 -H 2.14291679 -0.67956358 0.25531480 -H 1.43105853 0.05751479 1.70261879 -H -0.79053440 -0.48530296 0.66996573 -H -0.05233245 -0.47238446 -0.94236394 -10 -Properties=species:S:1:pos:R:3 A=12.06545 B=11.77731 C=6.71997 mu=1.7978 alpha=34.56 epsHOMO=-0.2424 epsLUMO=0.0859 epsgap=0.3283 =236.9759 zpve=0.086675 U0=-193.034094 U=-193.029968 H=-193.029024 G=-193.060777 Cv=12.915 pbc="F F F" -C -0.03489789 1.55259455 -0.08507691 -C 1.49349183 1.45459700 0.10114677 -O 1.42725884 0.02348990 0.27254886 -C -0.00634758 0.02236494 0.10944630 -H -0.56579210 2.11301721 0.68591527 -H -0.36628352 1.89393380 -1.06692327 -H 2.10031724 1.73525720 -0.76898349 -H 1.90023090 1.95506611 0.98896748 -H -0.51987849 -0.35595179 1.00235948 -H -0.31970062 -0.57567421 -0.75559275 -12 -Properties=species:S:1:pos:R:3 A=8.63483 B=4.11977 C=2.88825 mu=0.6875 alpha=45.37 epsHOMO=-0.2392 epsLUMO=0.0192 epsgap=0.2584 =452.5112 zpve=0.100501 U0=-248.375248 U=-248.368823 H=-248.367879 G=-248.405354 Cv=21.616 pbc="F F F" -C -0.10255370 1.49907127 -0.00133642 -C -0.00519774 -0.00343114 -0.02723470 -C -0.00688536 -0.76890740 1.26434744 -N 0.08186799 -0.69202791 -1.10087306 -O 0.07439067 0.13215394 -2.24195258 -H 0.78342093 1.94690862 -0.46354050 -H -0.96150430 1.83614082 -0.59021965 -H -0.19854173 1.87462989 1.01931607 -H -0.92620012 -0.57297946 1.82982479 -H 0.07256154 -1.84064782 1.07516863 -H 0.82937178 -0.45553340 1.90133089 -H 0.13975924 -0.51254299 -2.95389260 -10 -Properties=species:S:1:pos:R:3 A=9.17136 B=9.04195 C=4.5531 mu=1.8689 alpha=43.14 epsHOMO=-0.2029 epsLUMO=0.0499 epsgap=0.2528 =303.9808 zpve=0.082433 U0=-210.101789 U=-210.097816 H=-210.096872 G=-210.12818 Cv=14.821 pbc="F F F" -N -0.00829694 1.35362838 0.00995971 -C 1.28033254 1.82457421 -0.00022459 -C 2.12216639 0.73698898 -0.01445600 -C 1.30849904 -0.43109580 -0.01284154 -C -0.00342782 -0.01836884 0.00235736 -H -0.83243234 1.92772668 0.02122995 -H 1.48471820 2.88312630 0.00354313 -H 3.20066918 0.77343077 -0.02488416 -H 1.64810599 -1.45539683 -0.02180394 -H -0.92551647 -0.57697054 0.00839402 -9 -Properties=species:S:1:pos:R:3 A=9.77329 B=9.41192 C=4.7946 mu=3.6193 alpha=39.13 epsHOMO=-0.2253 epsLUMO=0.0332 epsgap=0.2585 =283.6817 zpve=0.071145 U0=-226.160842 U=-226.157088 H=-226.156144 G=-226.187104 Cv=13.371 pbc="F F F" -N -0.00996090 1.35636172 0.01000855 -C 1.29535422 1.79575578 -0.00047017 -C 2.05550519 0.65637438 -0.01422927 -N 1.25385745 -0.46275132 -0.01260894 -C 0.02301170 -0.00706382 0.00200146 -H -0.83195718 1.93618436 0.02132575 -H 1.54553961 2.84344998 0.00289054 -H 3.13177434 0.57569766 -0.02524155 -H -0.87667864 -0.60414531 0.00750794 -9 -Properties=species:S:1:pos:R:3 A=9.50228 B=9.29497 C=4.69874 mu=0.5571 alpha=39.2 epsHOMO=-0.2246 epsLUMO=0.0199 epsgap=0.2445 =289.005 zpve=0.069883 U0=-229.969129 U=-229.965414 H=-229.96447 G=-229.995393 Cv=13.358 pbc="F F F" -O -0.03255819 1.35065130 0.00967376 -C 1.25319237 1.79167710 -0.00030351 -C 2.11664681 0.74375623 -0.01441494 -C 1.30397796 -0.43852387 -0.01283316 -C 0.01629568 -0.00777185 0.00191294 -H 1.37951089 2.86161135 0.00427433 -H 3.19376406 0.79865501 -0.02458604 -H 1.63858893 -1.46383271 -0.02156949 -H -0.93736295 -0.50899925 0.00843891 -8 -Properties=species:S:1:pos:R:3 A=10.10926 B=9.69372 C=4.94857 mu=1.5081 alpha=35.17 epsHOMO=-0.2509 epsLUMO=0.001 epsgap=0.2519 =269.2444 zpve=0.058593 U0=-246.02915 U=-246.025614 H=-246.024669 G=-246.055309 Cv=12.04 pbc="F F F" -O -0.03808298 1.35643965 0.00975193 -C 1.26496982 1.76855319 -0.00061744 -C 2.04955750 0.66630033 -0.01406047 -N 1.24227075 -0.46732066 -0.01237541 -C 0.04019820 0.00561577 0.00183569 -H 1.43513907 2.83095265 0.00340823 -H 3.12532898 0.59783015 -0.02480757 -H -0.89387707 -0.53342154 0.00796935 -17 -Properties=species:S:1:pos:R:3 A=4.4178 B=4.41753 C=4.41717 mu=0.0003 alpha=56.01 epsHOMO=-0.3145 epsLUMO=0.0737 epsgap=0.3882 =486.2719 zpve=0.158836 U0=-197.632222 U=-197.625241 H=-197.624297 G=-197.661411 Cv=26.084 pbc="F F F" -C -0.01859029 1.53486266 0.01039904 -C 0.00210320 -0.00413672 0.00196980 -C 0.74643954 -0.51390760 1.24887638 -C -1.44190166 -0.53673615 0.01311457 -C 0.72234971 -0.50049606 -1.26445632 -H 0.99776928 1.94420529 0.00435474 -H -0.54181883 1.92841415 -0.86819911 -H -0.52758402 1.91888376 0.90148530 -H 0.25549576 -0.17602728 2.16833067 -H 0.77815389 -1.60892372 1.27088904 -H 1.77964786 -0.14975834 1.26944035 -H -1.99871895 -0.18888794 -0.86408066 -H -1.45956909 -1.63227235 0.00649916 -H -1.98088827 -0.19979639 0.90561580 -H 1.75482473 -0.13554018 -1.30118083 -H 0.75396989 -1.59524036 -1.29822280 -H 0.21358221 -0.15343900 -2.17067181 -15 -Properties=species:S:1:pos:R:3 A=4.69474 B=4.66938 C=4.47909 mu=1.384 alpha=49.04 epsHOMO=-0.2601 epsLUMO=0.0664 epsgap=0.3265 =449.0573 zpve=0.134977 U0=-233.560626 U=-233.553779 H=-233.552834 G=-233.589759 Cv=25.128 pbc="F F F" -C -0.00855999 1.54270147 0.00152717 -C 0.00506780 0.00702635 0.01856160 -C 0.76137731 -0.51839196 1.24766443 -C -1.42012774 -0.54933183 -0.02116008 -O 0.62590477 -0.48363745 -1.17657057 -H 1.01361406 1.94020776 0.00281128 -H -0.51459669 1.90702466 -0.89705781 -H -0.52214815 1.94882514 0.87918344 -H 0.28130613 -0.20165548 2.17943598 -H 0.79965506 -1.61131746 1.23022549 -H 1.79096916 -0.14072935 1.26083003 -H -1.93854829 -0.19910724 -0.91828281 -H -1.39937632 -1.64259062 -0.04563833 -H -1.98850725 -0.23059656 0.85726520 -H 1.53111188 -0.15488046 -1.19210222 -9 -Properties=species:S:1:pos:R:3 A=10.21876 B=4.04324 C=2.94978 mu=2.8579 alpha=42.02 epsHOMO=-0.2654 epsLUMO=-0.0575 epsgap=0.2079 =416.7799 zpve=0.065175 U0=-229.927277 U=-229.921622 H=-229.920677 G=-229.955843 Cv=18.723 pbc="F F F" -C -0.00693851 1.49559567 -0.01063358 -C -0.02986170 -0.01594623 0.06248673 -O -0.08815239 -0.63215062 1.10334812 -C 0.02336981 -0.70256747 -1.22062379 -C 0.06738132 -1.26058341 -2.28623129 -H 0.90429806 1.83281887 -0.51655384 -H -0.85338524 1.85556926 -0.60552819 -H -0.05237764 1.91099807 0.99695949 -H 0.10466868 -1.77036047 -3.21832617 -8 -Properties=species:S:1:pos:R:3 A=10.16063 B=4.15847 C=3.0056 mu=3.3351 alpha=37.08 epsHOMO=-0.3007 epsLUMO=-0.0889 epsgap=0.2118 =397.7757 zpve=0.05451 U0=-246.027383 U=-246.022024 H=-246.02108 G=-246.055769 Cv=16.963 pbc="F F F" -C -0.01171097 1.49973776 -0.01213268 -C -0.02919740 -0.00338745 0.07151218 -O -0.09164028 -0.64592927 1.08732241 -C 0.03831104 -0.69690258 -1.23748557 -N 0.09136286 -1.22982650 -2.26228068 -H 0.90093132 1.83370670 -0.51814928 -H -0.85663933 1.84864400 -0.61618135 -H -0.06416540 1.92131542 0.99203818 -8 -Properties=species:S:1:pos:R:3 A=10.68423 B=4.3198 C=3.07993 mu=5.1815 alpha=38.31 epsHOMO=-0.2739 epsLUMO=-0.0438 epsgap=0.2301 =384.5574 zpve=0.056619 U0=-242.19573 U=-242.190591 H=-242.189646 G=-242.223513 Cv=17.465 pbc="F F F" -N -0.05232108 1.34991769 0.15535538 -C 0.08549378 -0.01476951 0.02668890 -N 1.14555215 -0.71993906 -0.03966037 -C -1.19426556 -0.71152088 0.01802151 -N -2.22747610 -1.22790543 0.00132148 -H 0.75474469 1.91998737 -0.04411334 -H -0.94458527 1.76600473 -0.05932293 -H 1.98312429 -0.13415500 -0.02878551 -9 -Properties=species:S:1:pos:R:3 A=9.08854 B=4.42607 C=3.03169 mu=0.9461 alpha=36.51 epsHOMO=-0.2538 epsLUMO=-0.0964 epsgap=0.1574 =399.222 zpve=0.064992 U0=-267.10335 U=-267.097658 H=-267.096714 G=-267.132534 Cv=17.806 pbc="F F F" -C -0.43126066 1.30586664 -0.12758585 -C 0.25285715 -0.01026988 0.13138867 -O 0.69872099 -0.36890333 1.19604690 -C 0.38978943 -0.96773680 -1.06226208 -O -0.01611268 -0.71564293 -2.16583718 -H 0.10006565 1.85324490 -0.91332597 -H -1.44270433 1.13069361 -0.50923371 -H -0.46757554 1.89548158 0.78836479 -H 0.90329886 -1.91558845 -0.79531972 -9 -Properties=species:S:1:pos:R:3 A=10.08927 B=4.54115 C=3.13487 mu=2.7707 alpha=38.09 epsHOMO=-0.2561 epsLUMO=-0.064 epsgap=0.192 =384.4827 zpve=0.067668 U0=-263.278851 U=-263.273589 H=-263.272645 G=-263.306835 Cv=17.972 pbc="F F F" -N -0.16032929 1.08528962 -0.45398652 -C 0.18649127 -0.08762327 0.14758021 -N 1.28979627 -0.46427423 0.67927030 -C -0.94204384 -1.08440264 0.20423621 -O -2.01802205 -0.89007687 -0.30583149 -H 0.54574437 1.73837930 -0.74689246 -H -1.04684920 1.09570857 -0.93607796 -H 2.01738195 0.24991828 0.59881811 -H -0.67740148 -2.01003995 0.74746441 -13 -Properties=species:S:1:pos:R:3 A=7.92874 B=3.8272 C=2.82581 mu=0.6578 alpha=51.2 epsHOMO=-0.2589 epsLUMO=0.0571 epsgap=0.316 =481.9854 zpve=0.112471 U0=-195.186772 U=-195.180446 H=-195.179502 G=-195.215658 Cv=22.569 pbc="F F F" -C -0.01763434 1.54859468 -0.00253584 -C -0.01719180 0.00735789 0.03269604 -C 0.75818463 -0.52820155 1.25278862 -C -1.38213303 -0.51768438 0.00775516 -C -2.50796392 -0.94005629 0.00478552 -H 1.00944396 1.92639613 -0.02632830 -H -0.54514614 1.92217052 -0.88393459 -H -0.51243000 1.95339892 0.88560642 -H 0.49366179 -0.34904155 -0.87265890 -H 0.28968458 -0.19345372 2.18334679 -H 0.77833392 -1.62102506 1.25783727 -H 1.78952492 -0.16181727 1.23574954 -H -3.49998244 -1.31810165 -0.00723572 -12 -Properties=species:S:1:pos:R:3 A=7.90009 B=3.95466 C=2.88643 mu=3.9512 alpha=46.23 epsHOMO=-0.318 epsLUMO=0.0365 epsgap=0.3545 =460.694 zpve=0.102281 U0=-211.295796 U=-211.289821 H=-211.288877 G=-211.324525 Cv=20.467 pbc="F F F" -C -0.00242350 1.55035837 0.01056187 -C -0.01856600 0.01004352 -0.02261770 -C -1.45045318 -0.55766178 0.01293347 -C 0.70125749 -0.48583168 -1.20172764 -N 1.26190946 -0.87126766 -2.13619740 -H 1.01959496 1.93673215 0.00349668 -H -0.52972844 1.96141639 -0.85498386 -H -0.50134251 1.90346839 0.91755862 -H 0.52481877 -0.36222456 0.85525462 -H -2.02427903 -0.21428716 -0.85251869 -H -1.44438296 -1.65026927 0.00754107 -H -1.95805494 -0.21725882 0.91996760 -11 -Properties=species:S:1:pos:R:3 A=8.30095 B=3.97615 C=2.93764 mu=2.7429 alpha=42.87 epsHOMO=-0.2704 epsLUMO=0.0278 epsgap=0.2983 =440.0738 zpve=0.091554 U0=-227.338075 U=-227.332253 H=-227.331309 G=-227.366638 Cv=19.946 pbc="F F F" -C -0.01064295 1.54128691 0.00722087 -C -0.00203811 0.00756015 0.03863463 -N 0.76268677 -0.47101822 1.19250856 -C -1.38541954 -0.52099256 -0.00194097 -N -2.45680847 -0.95636072 0.00694969 -H 1.02043523 1.90106589 0.00392365 -H -0.52873161 1.91193452 -0.88031480 -H -0.51849536 1.94150431 0.89013197 -H 0.50418828 -0.35805500 -0.86276072 -H 0.30070681 -0.18364934 2.05155305 -H 0.78148992 -1.48670299 1.20428183 -11 -Properties=species:S:1:pos:R:3 A=8.55919 B=3.89952 C=2.91869 mu=1.3582 alpha=44.03 epsHOMO=-0.2665 epsLUMO=0.0336 epsgap=0.3001 =444.6452 zpve=0.088908 U0=-231.108368 U=-231.102292 H=-231.101348 G=-231.137061 Cv=21.306 pbc="F F F" -C -0.00754278 1.55566144 0.08650633 -C 0.01207541 0.02096553 0.04935916 -O -1.29150251 -0.52785128 0.22126386 -C 0.65093362 -0.47718651 -1.17430507 -C 1.13902673 -0.86577343 -2.20205518 -H 1.00824243 1.95969458 0.04929733 -H -0.56272245 1.95192010 -0.76978085 -H -0.49328161 1.88929438 1.00704123 -H 0.57811967 -0.34464907 0.91458864 -H -1.78516532 -0.35903262 -0.58909400 -H 1.58078877 -1.22365060 -3.09919447 -10 -Properties=species:S:1:pos:R:3 A=8.55908 B=4.01671 C=2.97882 mu=3.269 alpha=39.28 epsHOMO=-0.3051 epsLUMO=0.0115 epsgap=0.3166 =424.3395 zpve=0.078602 U0=-247.214861 U=-247.209162 H=-247.208218 G=-247.243338 Cv=19.252 pbc="F F F" -C -0.00992462 1.54951908 0.00732200 -C 0.00740485 0.01722020 0.05031270 -O 0.72085722 -0.49630479 1.15864583 -C -1.36995095 -0.52355929 0.00894432 -N -2.45081572 -0.93233138 0.00716912 -H 1.01892314 1.91630447 0.02724149 -H -0.50261490 1.90969362 -0.89997951 -H -0.54800426 1.95377611 0.87053664 -H 0.53602846 -0.36494195 -0.82970945 -H 0.27702643 -0.20644534 1.96421773 -13 -Properties=species:S:1:pos:R:3 A=7.66289 B=3.70925 C=2.79937 mu=2.6921 alpha=46.58 epsHOMO=-0.2469 epsLUMO=-0.0188 epsgap=0.2281 =482.0475 zpve=0.112328 U0=-232.364952 U=-232.358577 H=-232.357633 G=-232.394589 Cv=21.434 pbc="F F F" -C -0.02629647 1.53877116 -0.00783747 -C 0.01468904 0.00095310 0.00166037 -C 0.74737862 -0.53388859 1.24420471 -C -1.40211264 -0.53859257 -0.01557820 -O -1.87620455 -1.23582351 -0.87399559 -H 0.98823696 1.94625080 0.03705522 -H -0.50725090 1.92153422 -0.91285565 -H -0.57756514 1.92360883 0.85796386 -H 0.50584680 -0.36421091 -0.90619124 -H 0.22958380 -0.23909403 2.16431052 -H 0.81692736 -1.62559499 1.22973438 -H 1.76267153 -0.12846566 1.29050755 -H -2.00861510 -0.22792742 0.87121666 -11 -Properties=species:S:1:pos:R:3 A=8.31096 B=4.07963 C=2.92918 mu=2.8354 alpha=39.16 epsHOMO=-0.255 epsLUMO=-0.0279 epsgap=0.2271 =432.1489 zpve=0.088443 U0=-268.287661 U=-268.281505 H=-268.280561 G=-268.316982 Cv=20.344 pbc="F F F" -C -0.05483118 1.46032843 -0.17068647 -C 0.07105714 -0.03524485 0.11225841 -O 0.58204486 -0.33316656 1.40176256 -C -1.27639279 -0.74383027 0.03051185 -O -2.11855775 -0.49643175 -0.79163412 -H 0.93158072 1.93091398 -0.23353204 -H -0.57713995 1.61290939 -1.11835562 -H -0.62444315 1.94776119 0.62573670 -H 0.69687521 -0.50071620 -0.67159471 -H 1.45603316 0.06066659 1.47858584 -H -1.40723217 -1.53388627 0.80211181 -12 -Properties=species:S:1:pos:R:3 A=8.9331 B=4.19405 C=2.96046 mu=3.7163 alpha=44.42 epsHOMO=-0.2424 epsLUMO=0.0327 epsgap=0.2751 =441.85 zpve=0.10227 U0=-248.430371 U=-248.424309 H=-248.423365 G=-248.459383 Cv=19.918 pbc="F F F" -C -0.09776499 1.44917268 0.04561984 -N 0.01043053 0.00373818 -0.00070600 -C 0.05323028 -0.71432112 1.25264891 -C 0.06728716 -0.64067225 -1.20060778 -O 0.03636432 -0.11006515 -2.29109228 -H 0.75650660 1.88747109 0.57647421 -H -0.11648577 1.81632103 -0.98049172 -H -1.01641106 1.75521548 0.56151415 -H -0.85453556 -0.53657675 1.84390168 -H 0.13297722 -1.78696336 1.05851351 -H 0.91637348 -0.40737846 1.85761345 -H 0.14762240 -1.73801339 -1.06805139 -11 -Properties=species:S:1:pos:R:3 A=10.13982 B=3.81346 C=2.86965 mu=2.9514 alpha=39.33 epsHOMO=-0.2699 epsLUMO=-0.0262 epsgap=0.2437 =440.1727 zpve=0.088924 U0=-268.301176 U=-268.295084 H=-268.29414 G=-268.331307 Cv=19.824 pbc="F F F" -C -0.01708808 1.44281136 0.03225501 -C -0.05544542 -0.06560572 -0.02375999 -O -0.17379913 -0.76196233 0.96223129 -C 0.06068107 -0.73615179 -1.38825252 -O 0.00877771 -2.12574390 -1.27749486 -H 0.92062950 1.81463913 -0.39522215 -H -0.83050523 1.86542281 -0.56780350 -H -0.10911975 1.78049045 1.06493675 -H -0.75015067 -0.35235060 -2.03056074 -H 1.00312898 -0.40389029 -1.85592942 -H -0.09063255 -2.29450960 -0.32608019 -13 -Properties=species:S:1:pos:R:3 A=9.55216 B=3.57844 C=2.7341 mu=2.6168 alpha=46.19 epsHOMO=-0.2423 epsLUMO=-0.0072 epsgap=0.2351 =489.8518 zpve=0.112006 U0=-232.377706 U=-232.371073 H=-232.370129 G=-232.408256 Cv=21.716 pbc="F F F" -C -0.04384102 1.54099793 -0.03913814 -C 0.02765132 0.01885817 0.04778773 -C 0.76814644 -0.48842218 1.28000847 -C 0.87850208 -1.99746312 1.42393323 -O 1.24179142 0.26651340 2.09788702 -H 0.95796454 1.97690198 -0.06816012 -H -0.58447717 1.85356031 -0.93690369 -H -0.55173626 1.95737759 0.83440529 -H -0.97692824 -0.42709877 0.05353178 -H 0.51853486 -0.40806507 -0.83809409 -H 1.39924980 -2.42678188 0.56053177 -H 1.42001187 -2.24449367 2.33776426 -H -0.11750602 -2.45360651 1.45141965 -12 -Properties=species:S:1:pos:R:3 A=9.78802 B=3.78441 C=2.84416 mu=3.499 alpha=42.54 epsHOMO=-0.2438 epsLUMO=0.0355 epsgap=0.2793 =457.447 zpve=0.101847 U0=-248.448467 U=-248.441988 H=-248.441044 G=-248.478935 Cv=21.374 pbc="F F F" -C -0.00220144 1.53344329 -0.05960369 -C -0.03235837 0.01206167 0.06840265 -C 0.69666086 -0.48196462 1.31828322 -N 0.98104007 -1.82034162 1.31984698 -O 1.00392124 0.24606232 2.24136749 -H 1.02111190 1.89525169 -0.19182672 -H -0.59707204 1.86206427 -0.91645604 -H -0.39728839 1.99920712 0.84545201 -H -1.06936083 -0.34552935 0.13040806 -H 0.39947165 -0.46906818 -0.81777020 -H 0.63281951 -2.43917647 0.60874785 -H 1.35708014 -2.22217793 2.16303522 -12 -Properties=species:S:1:pos:R:3 A=10.01185 B=3.84195 C=2.87635 mu=3.5402 alpha=43.61 epsHOMO=-0.2418 epsLUMO=0.0387 epsgap=0.2805 =458.9803 zpve=0.101735 U0=-248.443503 U=-248.436899 H=-248.435955 G=-248.474272 Cv=20.967 pbc="F F F" -C 0.00619941 1.45933202 -0.03893859 -N -0.06411616 0.01422963 -0.12046901 -C -1.20595394 -0.68196796 0.16117120 -C -1.08982704 -2.19284899 0.02095775 -O -2.24482911 -0.14443077 0.49994418 -H 0.75053989 1.78372520 0.69715573 -H 0.25692001 1.90395263 -1.00876215 -H -0.97856026 1.81121438 0.27082194 -H 0.75820982 -0.49543660 -0.39464024 -H -0.09909703 -2.53543104 -0.28970003 -H -1.34097671 -2.65395851 0.97972885 -H -1.82903100 -2.53483935 -0.70813076 -11 -Properties=species:S:1:pos:R:3 A=10.44045 B=4.04692 C=2.98547 mu=3.5648 alpha=39.51 epsHOMO=-0.2436 epsLUMO=0.0599 epsgap=0.3034 =428.404 zpve=0.091713 U0=-264.504487 U=-264.498452 H=-264.497508 G=-264.533633 Cv=20.274 pbc="F F F" -C 0.00922630 1.45021814 -0.03194543 -N -0.01429488 -0.00054646 -0.01782667 -C -1.21576579 -0.67252403 0.04421896 -N -1.09733352 -2.04644898 -0.15997741 -O -2.28599299 -0.12893711 0.24196027 -H 0.89659038 1.81885022 0.49120949 -H 0.00233950 1.86388504 -1.04914155 -H -0.88562684 1.79943346 0.48353298 -H 0.75257305 -0.47506631 -0.46719265 -H -0.24882333 -2.48772194 0.16375654 -H -1.93419610 -2.53898393 0.11482880 -12 -Properties=species:S:1:pos:R:3 A=9.69683 B=4.1533 C=3.01703 mu=1.1876 alpha=43.57 epsHOMO=-0.2595 epsLUMO=0.0352 epsgap=0.2948 =443.1687 zpve=0.102062 U0=-248.416462 U=-248.410358 H=-248.409414 G=-248.445651 Cv=20.256 pbc="F F F" -C -0.09160695 1.37540046 -0.00729426 -O -0.06964534 -0.04831581 -0.11148812 -C 0.68114625 -0.56142673 -1.10732458 -C 0.60405298 -2.06701550 -1.08766892 -N 1.32645533 0.19266343 -1.89967731 -H 0.91463916 1.76843655 0.16257460 -H -0.48562826 1.82624615 -0.92235216 -H -0.73971430 1.60007423 0.84110605 -H -0.43430065 -2.39242162 -1.20325205 -H 1.20627110 -2.50049977 -1.88745640 -H 0.95651337 -2.44976670 -0.12501838 -H 1.83504115 -0.36104361 -2.58486055 -11 -Properties=species:S:1:pos:R:3 A=10.20392 B=4.16834 C=3.07281 mu=1.7569 alpha=39.33 epsHOMO=-0.2685 epsLUMO=0.0174 epsgap=0.2859 =427.6606 zpve=0.089436 U0=-268.32127 U=-268.315051 H=-268.314106 G=-268.351214 Cv=19.501 pbc="F F F" -C 0.07295254 1.38840919 0.06442046 -O 0.21049114 -0.03740028 0.04612736 -C 0.50102805 -0.57224288 -1.15975346 -C 0.62218715 -2.07378323 -1.06030684 -O 0.63922154 0.08224418 -2.16157325 -H 1.00029720 1.87062570 -0.25532064 -H -0.73338957 1.70782089 -0.60080454 -H -0.15751280 1.65154060 1.09692954 -H -0.31330001 -2.50202851 -0.68941906 -H 0.85646588 -2.48430275 -2.04157352 -H 1.40651685 -2.34055496 -0.34634301 -10 -Properties=species:S:1:pos:R:3 A=10.68182 B=4.39532 C=3.17969 mu=2.329 alpha=35.5 epsHOMO=-0.2669 epsLUMO=0.0575 epsgap=0.3244 =398.5908 zpve=0.079271 U0=-284.385189 U=-284.379361 H=-284.378417 G=-284.414085 Cv=19.084 pbc="F F F" -C -0.02645771 1.38305753 0.02085660 -O 0.04139566 -0.04439503 -0.00277452 -C 0.66775338 -0.56001625 -1.09000344 -N 0.72605063 -1.92219983 -0.98822954 -O 1.12952381 0.09648143 -1.99322180 -H 0.97529779 1.82044307 0.02387306 -H -0.57085698 1.76214621 -0.84790425 -H -0.55410722 1.63835731 0.94043594 -H 0.12662696 -2.38401590 -0.32566929 -H 0.97230070 -2.41798421 -1.82732573 -10 -Properties=species:S:1:pos:R:3 A=10.20696 B=3.93135 C=2.9219 mu=4.5676 alpha=35.87 epsHOMO=-0.246 epsLUMO=0.0273 epsgap=0.2733 =418.5967 zpve=0.078222 U0=-284.360325 U=-284.354148 H=-284.353204 G=-284.390143 Cv=20.089 pbc="F F F" -N -0.08115607 1.31807026 0.01276742 -C 0.05442259 -0.04477733 0.08155388 -O 1.12163074 -0.59783950 0.20940553 -C -1.28687786 -0.79011642 0.06178435 -O -1.04605114 -2.13954195 -0.23957158 -H 0.76623366 1.85991128 -0.04483425 -H -0.93021346 1.74567447 -0.31565615 -H -1.74559696 -0.65521334 1.05526700 -H -1.96026061 -0.31149222 -0.67026256 -H -1.85637296 -2.63017646 -0.07625478 -10 -Properties=species:S:1:pos:R:3 A=10.14389 B=4.0714 C=3.02223 mu=5.3004 alpha=35.19 epsHOMO=-0.2527 epsLUMO=0.0208 epsgap=0.2735 =408.0279 zpve=0.080317 U0=-284.372483 U=-284.367172 H=-284.366228 G=-284.40095 Cv=17.931 pbc="F F F" -N 0.01838100 0.93057357 0.04943058 -H -0.70674506 0.56116798 -0.55550134 -H 0.68248307 0.18430002 0.21487099 -H 0.21928084 2.79858558 -0.32670585 -C -0.54586914 1.44688585 1.30274586 -C -0.80466975 2.96030569 1.21052835 -O -0.21468822 3.53648884 0.15531687 -O -1.45295676 3.56338618 2.02143528 -H -1.47662579 0.96512183 1.62072169 -H 0.17781209 1.30324286 2.11157176 -13 -Properties=species:S:1:pos:R:3 A=6.6508 B=4.19317 C=3.39441 mu=2.2854 alpha=42.01 epsHOMO=-0.2597 epsLUMO=0.0631 epsgap=0.3228 =438.3028 zpve=0.11345 U0=-269.479234 U=-269.472993 H=-269.472049 G=-269.508213 Cv=22.072 pbc="F F F" -C -0.02775167 1.53421433 0.00651151 -C 0.08343246 0.01218277 0.05258986 -O 0.95856392 -0.48200710 -0.96694113 -C 0.69767574 -0.49516451 1.35948812 -O 2.02247062 -0.02842530 1.52222186 -H 0.94333763 1.98841434 0.21841589 -H -0.37320516 1.87354041 -0.97716908 -H -0.74773817 1.89256394 0.74926853 -H -0.91671848 -0.43697036 -0.06040528 -H 0.76726710 -0.01412177 -1.78502043 -H 0.11650002 -0.13351914 2.21392431 -H 0.65814685 -1.59563445 1.36274474 -H 2.46378566 -0.22762476 0.68698431 -17 -Properties=species:S:1:pos:R:3 A=7.26511 B=3.32874 C=2.54491 mu=0.0618 alpha=56.26 epsHOMO=-0.3085 epsLUMO=0.085 epsgap=0.3934 =565.8412 zpve=0.159632 U0=-197.629387 U=-197.622325 H=-197.621381 G=-197.659365 Cv=25.169 pbc="F F F" -C 0.05879035 1.56174169 -0.01945240 -C -0.01544024 0.03182306 0.00308332 -C -1.44103237 -0.54862028 0.05028629 -C -1.40777007 -2.07231030 -0.13012010 -C -2.18400529 -0.17659759 1.34114303 -H 1.09435638 1.90233607 -0.11900072 -H -0.50689566 1.97217319 -0.86388098 -H -0.34407951 2.00628082 0.89579522 -H 0.48926758 -0.36011214 -0.88953902 -H 0.55254938 -0.35139446 0.86269887 -H -2.00031575 -0.12153682 -0.79559684 -H -0.85501131 -2.55065810 0.68775438 -H -2.41787513 -2.49543628 -0.13720127 -H -0.91928762 -2.35363802 -1.06914014 -H -3.19100197 -0.60699559 1.35137051 -H -1.65197294 -0.56010584 2.22063481 -H -2.28793759 0.90575132 1.46079572 -15 -Properties=species:S:1:pos:R:3 A=8.11598 B=3.43678 C=2.66869 mu=1.3894 alpha=49.45 epsHOMO=-0.2617 epsLUMO=0.0733 epsgap=0.335 =521.8605 zpve=0.136091 U0=-233.555951 U=-233.549143 H=-233.548199 G=-233.585602 Cv=23.99 pbc="F F F" -C -0.03841227 1.55621396 0.00935837 -C 0.01907135 0.02716079 -0.02074203 -C 0.73141506 -0.52709328 -1.25454422 -C 0.83975868 -2.05206973 -1.22872655 -O -0.01226470 -0.08514394 -2.38973758 -H 0.96845664 1.98863424 0.03526234 -H -0.54377006 1.93824134 -0.88074407 -H -0.57652955 1.91383386 0.89256698 -H 0.53166867 -0.34991486 0.87240999 -H -0.99727186 -0.38638654 -0.00185289 -H 1.74909346 -0.09943025 -1.28826184 -H 1.34143968 -2.42487137 -2.12957624 -H 1.41822012 -2.39229108 -0.36340871 -H -0.15616765 -2.50443859 -1.18308875 -H 0.43090825 -0.40901616 -3.17979642 -15 -Properties=species:S:1:pos:R:3 A=7.55335 B=3.78323 C=2.80566 mu=1.0758 alpha=49.8 epsHOMO=-0.2476 epsLUMO=0.086 epsgap=0.3336 =507.9614 zpve=0.135681 U0=-233.545899 U=-233.539034 H=-233.53809 G=-233.57582 Cv=23.573 pbc="F F F" -C -0.00857605 1.38097833 0.09454670 -O -0.02255106 -0.00739461 -0.13062576 -C -1.30243341 -0.61997805 -0.02088309 -C -1.18687286 -1.97885751 -0.70303918 -C -1.75587824 -0.74662760 1.43714508 -H 1.00321550 1.73258087 -0.12443496 -H -0.71511468 1.90838275 -0.56674280 -H -0.24909065 1.64967237 1.13384681 -H -2.03737978 -0.00432241 -0.56884198 -H -0.44792779 -2.59945605 -0.18596870 -H -2.14751041 -2.50225525 -0.69102581 -H -0.86524212 -1.85860679 -1.74077836 -H -2.73321482 -1.23674908 1.49205117 -H -1.03621350 -1.34483828 2.00544671 -H -1.84898410 0.22935483 1.92198087 -15 -Properties=species:S:1:pos:R:3 A=6.14183 B=5.17679 C=3.79297 mu=0.1068 alpha=52.92 epsHOMO=-0.2633 epsLUMO=0.0893 epsgap=0.3526 =439.1643 zpve=0.137025 U0=-196.409349 U=-196.403398 H=-196.402453 G=-196.437676 Cv=22.258 pbc="F F F" -C -0.03913204 1.50456305 0.04978913 -C 0.00985856 -0.01113880 0.01248619 -C -1.24167981 -0.66663044 -0.53989979 -C 1.33803387 -0.66895748 -0.27400430 -C 0.75126442 -0.72221586 1.11862571 -H 0.88633040 1.92490040 0.45579358 -H -0.18183878 1.92045743 -0.95489291 -H -0.86832076 1.85815718 0.67439648 -H -1.43570890 -0.34344387 -1.56976411 -H -1.15326757 -1.75757800 -0.54433832 -H -2.12221416 -0.40570820 0.05951361 -H 1.34398374 -1.58235208 -0.86005084 -H 2.20196897 -0.03329084 -0.43929326 -H 1.21949576 -0.12246018 1.89246580 -H 0.36153319 -1.67153717 1.47171932 -13 -Properties=species:S:1:pos:R:3 A=6.49534 B=5.52329 C=3.9108 mu=1.8235 alpha=45.7 epsHOMO=-0.2596 epsLUMO=0.091 epsgap=0.3505 =405.521 zpve=0.112851 U0=-232.335768 U=-232.329981 H=-232.329037 G=-232.364091 Cv=20.719 pbc="F F F" -C -0.03970330 1.50954998 -0.04112233 -C -0.03023556 -0.00335329 -0.06498361 -C -1.24613189 -0.68356648 0.52531190 -C 0.78937201 -0.71129193 -1.06027559 -O 1.22546798 -0.60059036 0.29712283 -H 0.88992772 1.90948478 -0.45296558 -H -0.87952723 1.90350108 -0.62376642 -H -0.14448576 1.87590604 0.98604429 -H -2.15218727 -0.41012616 -0.02629495 -H -1.13412584 -1.77005431 0.49747032 -H -1.38586631 -0.38055453 1.56882757 -H 0.50712690 -1.71386874 -1.38085018 -H 1.37088104 -0.14369322 -1.78640853 -13 -Properties=species:S:1:pos:R:3 A=6.57648 B=5.31603 C=3.98453 mu=1.3822 alpha=45.85 epsHOMO=-0.2494 epsLUMO=0.0776 epsgap=0.327 =401.4888 zpve=0.113237 U0=-232.33341 U=-232.327734 H=-232.32679 G=-232.36147 Cv=21.17 pbc="F F F" -C -0.03714052 1.53215920 0.01332310 -C 0.01344335 0.02117019 0.01922005 -O -1.17476104 -0.59735284 -0.40931260 -C 1.30744798 -0.69902106 -0.27389650 -C 0.70844749 -0.71373504 1.12226318 -H 0.89026861 1.96240279 0.40276135 -H -0.17846151 1.91561262 -1.00518522 -H -0.87011232 1.88955201 0.62730953 -H -1.28352265 -0.40225391 -1.34690641 -H 1.23973813 -1.61642115 -0.84783774 -H 2.20543993 -0.11523820 -0.44744474 -H 1.19539159 -0.13889834 1.90240284 -H 0.24735439 -1.63670241 1.45378480 -10 -Properties=species:S:1:pos:R:3 A=12.18399 B=5.1384 C=3.78868 mu=2.5732 alpha=40.19 epsHOMO=-0.263 epsLUMO=0.0277 epsgap=0.2907 =352.0378 zpve=0.080558 U0=-247.201165 U=-247.196699 H=-247.195755 G=-247.22841 Cv=15.51 pbc="F F F" -N -0.04445152 1.25623533 0.31681915 -C -0.03914559 0.04913710 -0.01670558 -C -1.08749619 -1.04164446 -0.11488033 -C 0.09869688 -1.90754844 -0.58225894 -O 0.99647508 -0.76306629 -0.44028010 -H 0.88935980 1.66481350 0.24916893 -H -1.54324114 -1.32235812 0.83572172 -H -1.86910036 -0.85713839 -0.85325910 -H 0.39739753 -2.72227350 0.08158721 -H 0.06945080 -2.25418345 -1.61796084 -11 -Properties=species:S:1:pos:R:3 A=10.79571 B=4.78732 C=3.54292 mu=2.7119 alpha=42.77 epsHOMO=-0.2415 epsLUMO=-0.0194 epsgap=0.2222 =379.6371 zpve=0.090544 U0=-231.15578 U=-231.150804 H=-231.149859 G=-231.184401 Cv=17.076 pbc="F F F" -O -0.00101746 0.01846628 -0.20114742 -C -0.01405811 1.19658059 0.00205306 -C -1.13542911 2.22979148 0.21957980 -C -0.03500884 3.29097637 0.53052223 -C 1.08469664 2.25947795 0.18994331 -H -1.71642814 2.41772662 -0.68901078 -H -1.83392757 1.98415059 1.02444244 -H -0.05534741 4.17585264 -0.10670963 -H -0.02541477 3.61399048 1.57278917 -H 1.81074389 2.03288534 0.97578973 -H 1.63600801 2.46255380 -0.73376244 -10 -Properties=species:S:1:pos:R:3 A=12.23436 B=4.97895 C=3.70561 mu=3.6671 alpha=39.73 epsHOMO=-0.25 epsLUMO=0.038 epsgap=0.288 =355.0934 zpve=0.080442 U0=-247.225618 U=-247.220897 H=-247.219953 G=-247.253218 Cv=16.1 pbc="F F F" -O 0.03184134 -0.03461682 0.00849836 -C -0.02440334 1.16440533 0.02160811 -C -1.14541322 2.23712809 0.03012901 -C 0.00928805 3.27945878 0.04557758 -N 0.93843838 2.14652109 0.03554396 -H -1.77030529 2.23779730 -0.86428437 -H -1.77632809 2.21765386 0.92007934 -H 0.07170564 3.91863516 -0.84072193 -H 0.06622603 3.89826625 0.94657952 -H 1.94616840 2.09535713 0.03855945 -9 -Properties=species:S:1:pos:R:3 A=12.44643 B=5.23328 C=3.86599 mu=3.9339 alpha=35.4 epsHOMO=-0.2788 epsLUMO=0.0089 epsgap=0.2878 =336.0792 zpve=0.068574 U0=-267.106213 U=-267.101929 H=-267.100985 G=-267.13332 Cv=14.483 pbc="F F F" -O 0.04424004 0.03933243 -0.33071104 -C -0.04854479 1.17416230 0.00410076 -C -1.14211656 2.23611850 0.15338361 -C 0.02332538 3.12357010 0.61681625 -O 0.95663808 2.01190122 0.42603746 -H -1.62573755 2.51482212 -0.78401102 -H -1.90009213 1.99553386 0.90028663 -H 0.28368077 3.96172751 -0.03229287 -H 0.00782250 3.43942655 1.66162725 -10 -Properties=species:S:1:pos:R:3 A=11.54185 B=4.90368 C=3.63309 mu=2.5257 alpha=39.34 epsHOMO=-0.2437 epsLUMO=-0.0258 epsgap=0.2179 =359.152 zpve=0.07965 U0=-247.190194 U=-247.185543 H=-247.184599 G=-247.217688 Cv=16.083 pbc="F F F" -O 0.00053891 0.00665475 -0.09743049 -C -0.01371990 1.19213881 0.03139936 -C -1.10668681 2.26612284 0.17247913 -N -0.03543445 3.25123475 0.48533693 -C 1.05328615 2.29521506 0.14383904 -H -1.65047970 2.42796175 -0.76886054 -H -1.83606786 2.11370530 0.97567410 -H -0.05434600 4.07193609 -0.10727051 -H 1.80754206 2.16277968 0.92736250 -H 1.56737937 2.47130277 -0.81152688 -9 -Properties=species:S:1:pos:R:3 A=12.18264 B=4.94228 C=3.68162 mu=0.8477 alpha=35.94 epsHOMO=-0.2647 epsLUMO=-0.0352 epsgap=0.2295 =344.5473 zpve=0.066989 U0=-267.068488 U=-267.063976 H=-267.063032 G=-267.095887 Cv=15.115 pbc="F F F" -O -0.00031692 0.00676321 -0.17139774 -C -0.01417098 1.18672865 -0.00980279 -C -1.08283120 2.27653381 0.15574094 -O -0.03856115 3.26460423 0.27513986 -C 1.02857270 2.30515693 0.12774311 -H -1.72923855 2.43647507 -0.71671274 -H -1.70219898 2.19346725 1.05800754 -H 1.67366781 2.23923293 1.01324179 -H 1.64705068 2.48224548 -0.76148355 -15 -Properties=species:S:1:pos:R:3 A=9.63416 B=4.10254 C=3.28665 mu=0.095 alpha=52.94 epsHOMO=-0.2896 epsLUMO=0.0927 epsgap=0.3823 =455.0215 zpve=0.138424 U0=-196.407957 U=-196.40245 H=-196.401505 G=-196.436159 Cv=20.299 pbc="F F F" -C -0.47005934 1.44206547 -0.06684548 -C 0.14287306 0.05485144 0.03747984 -C 1.37254693 -0.30295273 -0.84679168 -C 1.95947994 -1.09364561 0.35326039 -C 0.99887433 -0.30768787 1.28563099 -H 0.29210839 2.21844829 0.06844100 -H -0.93614604 1.60094605 -1.04612799 -H -1.24148161 1.59707353 0.69627593 -H -0.63517341 -0.70560132 -0.10055095 -H 1.19338376 -0.84118177 -1.78129390 -H 1.96867777 0.58857708 -1.07004184 -H 1.69873597 -2.15500718 0.30521100 -H 3.03253510 -1.01077235 0.54148058 -H 1.48290386 0.58242428 1.70211026 -H 0.51304296 -0.84979686 2.10121062 -13 -Properties=species:S:1:pos:R:3 A=9.93129 B=4.56362 C=3.63704 mu=1.6826 alpha=45.72 epsHOMO=-0.241 epsLUMO=0.0915 epsgap=0.3325 =407.6909 zpve=0.11459 U0=-232.33123 U=-232.325877 H=-232.324933 G=-232.359589 Cv=18.582 pbc="F F F" -C -0.32308228 1.53751451 0.01947624 -C 0.11847214 0.08900172 -0.00523140 -C 0.87489284 -0.43763303 -1.24721738 -C 1.97841048 -0.80657483 -0.23740356 -O 1.27344926 -0.15419108 0.83794040 -H 0.49211985 2.19658795 -0.29527664 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- -.. only:: html - - .. image:: /examples/multi-gpu/images/thumb/sphx_glr_multi-gpu_thumb.png - :alt: - - :ref:`sphx_glr_examples_multi-gpu_multi-gpu.py` - -.. raw:: html - -
Multi-GPU training
-
- - -.. thumbnail-parent-div-close - -.. raw:: html - - - - -.. toctree:: - :hidden: - - /examples/multi-gpu/multi-gpu - - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-gallery - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download all examples in Python source code: multi-gpu_python.zip ` - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download all examples in Jupyter notebooks: multi-gpu_jupyter.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/multi-gpu/multi-gpu.ipynb b/docs/src/examples/multi-gpu/multi-gpu.ipynb deleted file mode 100644 index 6cc034d0f1..0000000000 --- a/docs/src/examples/multi-gpu/multi-gpu.ipynb +++ /dev/null @@ -1,32 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# Multi-GPU training\n\n``metatrain`` supports training a model with several GPUs, which can accelerate the\ntraining, especially when the training dataset is large / there are many training\nepochs. This feature is enabled by the :py:mod:`torch.distributed` module, and thus can\ndo multiprocess parallelism across several nodes.\n\nIn multi-GPU training, every batch of samples is split into smaller mini-batches and the\ncomputation is run for each of the smaller mini-batches in parallel on different GPUs.\nThe different gradients obtained on each device are then summed. This approach allows\nthe user to reduce the time it takes to train models.\n\nTo know if the model supports multi-GPU training, please check [Available Architectures](../../docs/src/architectures/index.rst) and see if the default hyperparameters have\nthe ``distributed`` option.\n\n## Input file\nTo do this, you only need to switch on the ``distributed`` option in the ``.yaml`` file\nfor the training. Let's take [this tutorial](../beginner_tutorials/train-from-scratch.rst) as an example. Now, the\n``options.yaml`` is\n\n.. literalinclude:: ./options-distributed.yaml\n :language: yaml\n :linenos:\n\n## Slurm script\n\nBelow is an example Slurm script for submitting the job. Please be aware that the actual\nconfigurations vary from clusters to clusters, so you have to modify it. Different\nscheduler will require similar options. ``metatrain`` will automatically use all the\nGPUs that you have asked for. You should make a single GPU visible for each process\n(setting `--gpus-per-node` equal to the number of GPUs, or setting `--gpus-per-task=1`,\ndepending on your cluster configuration).\n\n```bash\n#!/bin/bash\n#SBATCH --nodes 1\n#SBATCH --ntasks 2 # must equal to the number of GPUs\n#SBATCH --ntasks-per-node 2\n#SBATCH --gpus-per-node 2 # use 2 GPUs\n#SBATCH --cpus-per-task 8\n#SBATCH --exclusive\n#SBATCH --partition=h100 # adapt this to your cluster\n#SBATCH --time=1:00:00\n\n# load modules and/or virtual environments and/or containers here\n\nsrun mtt train options-distributed.yaml\n```\n## Performance\n\nIf the multi-GPU training runs successfully, you should see this in the training log:\n\n```bash\n[2025-10-08 11:34:22][INFO] - Distributed environment set up with MASTER_ADDR=kh080,\nMASTER_PORT=39591, WORLD_SIZE=2, RANK=0, LOCAL_RANK=0\n[2025-10-08 11:34:23][INFO] - Training on 2 devices with dtype torch.float32\n```\nThis 100-epoch training takes 23 seconds.\n\n```bash\n[2025-10-08 11:34:22][INFO] - Starting training from scratch\n...\n[2025-10-08 11:34:45][INFO] - Training finished!\n```\nNow let's switch off the multi-GPU training by writing ``distributed: false``, and\nsubmit this job again. The training takes 69 seconds.\n\n```bash\n[2025-10-08 11:37:38][INFO] - Setting up model\n...\n[2025-10-08 11:38:47][INFO] - Training finished!\n```\n## Multi-GPU fine-tuning\nYou can use multi-GPU for fine-tuning too, by writing ``distributed: True`` in the\n``.yaml`` input. For information about fine-tuning, please refer to [this tutorial on\nfine-tuning](../../getting-started/finetuning-example.rst).\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/src/examples/multi-gpu/multi-gpu.py b/docs/src/examples/multi-gpu/multi-gpu.py deleted file mode 100644 index 93031b659a..0000000000 --- a/docs/src/examples/multi-gpu/multi-gpu.py +++ /dev/null @@ -1,90 +0,0 @@ -""" -================== -Multi-GPU training -================== - -``metatrain`` supports training a model with several GPUs, which can accelerate the -training, especially when the training dataset is large / there are many training -epochs. This feature is enabled by the :py:mod:`torch.distributed` module, and thus can -do multiprocess parallelism across several nodes. - -In multi-GPU training, every batch of samples is split into smaller mini-batches and the -computation is run for each of the smaller mini-batches in parallel on different GPUs. -The different gradients obtained on each device are then summed. This approach allows -the user to reduce the time it takes to train models. - -To know if the model supports multi-GPU training, please check `Available Architectures -<../../docs/src/architectures/index.rst>`_ and see if the default hyperparameters have -the ``distributed`` option. - -Input file ----------- -To do this, you only need to switch on the ``distributed`` option in the ``.yaml`` file -for the training. Let's take `this tutorial -<../beginner_tutorials/train-from-scratch.rst>`_ as an example. Now, the -``options.yaml`` is - -.. literalinclude:: ./options-distributed.yaml - :language: yaml - :linenos: - -Slurm script ------------- - -Below is an example Slurm script for submitting the job. Please be aware that the actual -configurations vary from clusters to clusters, so you have to modify it. Different -scheduler will require similar options. ``metatrain`` will automatically use all the -GPUs that you have asked for. You should make a single GPU visible for each process -(setting `--gpus-per-node` equal to the number of GPUs, or setting `--gpus-per-task=1`, -depending on your cluster configuration). - -.. code-block:: bash - - #!/bin/bash - #SBATCH --nodes 1 - #SBATCH --ntasks 2 # must equal to the number of GPUs - #SBATCH --ntasks-per-node 2 - #SBATCH --gpus-per-node 2 # use 2 GPUs - #SBATCH --cpus-per-task 8 - #SBATCH --exclusive - #SBATCH --partition=h100 # adapt this to your cluster - #SBATCH --time=1:00:00 - - # load modules and/or virtual environments and/or containers here - - srun mtt train options-distributed.yaml - -Performance ------------ - -If the multi-GPU training runs successfully, you should see this in the training log: - -.. code-block:: bash - - [2025-10-08 11:34:22][INFO] - Distributed environment set up with MASTER_ADDR=kh080, - MASTER_PORT=39591, WORLD_SIZE=2, RANK=0, LOCAL_RANK=0 - [2025-10-08 11:34:23][INFO] - Training on 2 devices with dtype torch.float32 - -This 100-epoch training takes 23 seconds. - -.. code-block:: bash - - [2025-10-08 11:34:22][INFO] - Starting training from scratch - ... - [2025-10-08 11:34:45][INFO] - Training finished! - -Now let's switch off the multi-GPU training by writing ``distributed: false``, and -submit this job again. The training takes 69 seconds. - -.. code-block:: bash - - [2025-10-08 11:37:38][INFO] - Setting up model - ... - [2025-10-08 11:38:47][INFO] - Training finished! - -Multi-GPU fine-tuning ---------------------- -You can use multi-GPU for fine-tuning too, by writing ``distributed: True`` in the -``.yaml`` input. For information about fine-tuning, please refer to `this tutorial on -fine-tuning <../../getting-started/finetuning-example.rst>`_. -""" diff --git a/docs/src/examples/multi-gpu/multi-gpu.py.md5 b/docs/src/examples/multi-gpu/multi-gpu.py.md5 deleted file mode 100644 index 86e00a927d..0000000000 --- a/docs/src/examples/multi-gpu/multi-gpu.py.md5 +++ /dev/null @@ -1 +0,0 @@ -4666621a9eb6317d63ffb285c171dd9a \ No newline at end of file diff --git a/docs/src/examples/multi-gpu/multi-gpu.rst b/docs/src/examples/multi-gpu/multi-gpu.rst deleted file mode 100644 index e1d6b46bf7..0000000000 --- a/docs/src/examples/multi-gpu/multi-gpu.rst +++ /dev/null @@ -1,134 +0,0 @@ - -.. DO NOT EDIT. -.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. -.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: -.. "examples/multi-gpu/multi-gpu.py" -.. LINE NUMBERS ARE GIVEN BELOW. - -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - :ref:`Go to the end ` - to download the full example code. - -.. rst-class:: sphx-glr-example-title - -.. _sphx_glr_examples_multi-gpu_multi-gpu.py: - - -================== -Multi-GPU training -================== - -``metatrain`` supports training a model with several GPUs, which can accelerate the -training, especially when the training dataset is large / there are many training -epochs. This feature is enabled by the :py:mod:`torch.distributed` module, and thus can -do multiprocess parallelism across several nodes. - -In multi-GPU training, every batch of samples is split into smaller mini-batches and the -computation is run for each of the smaller mini-batches in parallel on different GPUs. -The different gradients obtained on each device are then summed. This approach allows -the user to reduce the time it takes to train models. - -To know if the model supports multi-GPU training, please check `Available Architectures -<../../docs/src/architectures/index.rst>`_ and see if the default hyperparameters have -the ``distributed`` option. - -Input file ----------- -To do this, you only need to switch on the ``distributed`` option in the ``.yaml`` file -for the training. Let's take `this tutorial -<../beginner_tutorials/train-from-scratch.rst>`_ as an example. Now, the -``options.yaml`` is - -.. literalinclude:: ./options-distributed.yaml - :language: yaml - :linenos: - -Slurm script ------------- - -Below is an example Slurm script for submitting the job. Please be aware that the actual -configurations vary from clusters to clusters, so you have to modify it. Different -scheduler will require similar options. ``metatrain`` will automatically use all the -GPUs that you have asked for. You should make a single GPU visible for each process -(setting `--gpus-per-node` equal to the number of GPUs, or setting `--gpus-per-task=1`, -depending on your cluster configuration). - -.. code-block:: bash - - #!/bin/bash - #SBATCH --nodes 1 - #SBATCH --ntasks 2 # must equal to the number of GPUs - #SBATCH --ntasks-per-node 2 - #SBATCH --gpus-per-node 2 # use 2 GPUs - #SBATCH --cpus-per-task 8 - #SBATCH --exclusive - #SBATCH --partition=h100 # adapt this to your cluster - #SBATCH --time=1:00:00 - - # load modules and/or virtual environments and/or containers here - - srun mtt train options-distributed.yaml - -Performance ------------ - -If the multi-GPU training runs successfully, you should see this in the training log: - -.. code-block:: bash - - [2025-10-08 11:34:22][INFO] - Distributed environment set up with MASTER_ADDR=kh080, - MASTER_PORT=39591, WORLD_SIZE=2, RANK=0, LOCAL_RANK=0 - [2025-10-08 11:34:23][INFO] - Training on 2 devices with dtype torch.float32 - -This 100-epoch training takes 23 seconds. - -.. code-block:: bash - - [2025-10-08 11:34:22][INFO] - Starting training from scratch - ... - [2025-10-08 11:34:45][INFO] - Training finished! - -Now let's switch off the multi-GPU training by writing ``distributed: false``, and -submit this job again. The training takes 69 seconds. - -.. code-block:: bash - - [2025-10-08 11:37:38][INFO] - Setting up model - ... - [2025-10-08 11:38:47][INFO] - Training finished! - -Multi-GPU fine-tuning ---------------------- -You can use multi-GPU for fine-tuning too, by writing ``distributed: True`` in the -``.yaml`` input. For information about fine-tuning, please refer to `this tutorial on -fine-tuning <../../getting-started/finetuning-example.rst>`_. - - -.. _sphx_glr_download_examples_multi-gpu_multi-gpu.py: - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-example - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: multi-gpu.ipynb ` - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: multi-gpu.py ` - - .. container:: sphx-glr-download sphx-glr-download-zip - - :download:`Download zipped: multi-gpu.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/multi-gpu/multi-gpu.zip b/docs/src/examples/multi-gpu/multi-gpu.zip deleted file mode 100644 index b4c6adaa55d42c51bb1e2789ea2fc089a57de639..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 7233 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zpve=0.045286 U0=-100.0 U=-132.714563 H=-132.713619 G=-132.742149 Cv=10.287 pbc="F F F" -C -0.01788629 1.46712787 0.01011311 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 0.00173819 0.01035260 0.00207588 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -N 0.01765235 -1.14452926 -0.00420445 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 1.00202941 1.86089927 0.00245401 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.54398848 1.84479901 -0.87075460 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.52707783 1.83518166 0.90486329 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -7 -Properties=species:S:1:pos:R:3:forces:R:3:my_tensor:R:9 A=57.22434 B=10.11122 C=9.07368 mu=2.5682 alpha=25.11 epsHOMO=-0.254 epsLUMO=-0.0198 epsgap=0.2342 =166.9728 zpve=0.055355 U0=-100.0 U=-153.783728 H=-153.782784 G=-153.812518 Cv=11.219 pbc="F F F" -C -0.00294482 1.50991366 0.00867278 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 0.02608284 0.00327563 -0.03745912 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -O 0.94228801 -0.65507035 -0.45682576 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 0.92278802 1.92634242 -0.39146557 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 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zpve=0.103182 U0=-100.0 U=-119.047927 H=-119.046983 G=-119.078157 Cv=14.84 pbc="F F F" -C -0.03113825 1.54081582 0.03192126 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 0.01215347 0.01092235 -0.01603259 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 0.72169129 -0.52583353 -1.26230570 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 0.97955987 1.96459116 0.03098367 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.55840223 1.94831192 -0.83816075 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.54252252 1.90153531 0.93005671 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 0.51522791 -0.36840234 0.88231134 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -1.01070641 -0.38456999 0.02051783 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 1.75851210 -0.17376585 -1.30871516 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 0.74087192 -1.62024959 -1.27516511 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 0.22023351 -0.19051179 -2.17729020 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -9 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0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.51674572 1.87611771 0.88710739 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 0.52377121 -0.38912295 0.88824082 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -1.02027544 -0.40507261 0.01690670 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 0.69529573 -1.40179568 -1.20148495 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -9 -Properties=species:S:1:pos:R:3:forces:R:3:my_tensor:R:9 A=39.37691 B=10.04033 C=8.90353 mu=1.1502 alpha=28.13 epsHOMO=-0.2525 epsLUMO=0.091 epsgap=0.3435 =187.1015 zpve=0.079534 U0=-100.0 U=-154.956045 H=-154.9551 G=-154.985747 Cv=12.934 pbc="F F F" -C -0.01482147 1.39241234 0.00567104 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -O -0.00471537 -0.01360660 0.01459658 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 0.63794893 -0.55329676 -1.11358232 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 1.00528358 1.81015807 0.00465631 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.54689560 1.79343530 -0.87251051 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.53002895 1.72291969 0.91101696 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 0.13993792 -0.25599333 -2.05098375 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 1.69265275 -0.23868382 -1.17477743 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 0.59959435 -1.64180161 -1.02407616 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -9 -Properties=species:S:1:pos:R:3:forces:R:3:my_tensor:R:9 A=20.15852 B=20.15302 C=12.5891 mu=0.0005 alpha=30.82 epsHOMO=-0.2888 epsLUMO=0.1042 epsgap=0.393 =155.8145 zpve=0.081231 U0=-100.0 U=-117.821426 H=-117.820482 G=-117.849087 Cv=11.041 pbc="F F F" -C -0.01193280 1.51433198 0.01031700 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 1.30299118 0.77886562 -0.00617842 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 0.00867174 0.00767042 0.00201032 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.30541464 2.01702117 0.92533239 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.32275498 2.02680156 -0.89347839 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 1.88493396 0.79145020 -0.92119393 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 1.90226631 0.78165391 0.89766373 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.27088043 -0.51289125 0.91137474 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.28821983 -0.50310409 -0.90740824 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -7 -Properties=species:S:1:pos:R:3:forces:R:3:my_tensor:R:9 A=25.77385 B=22.12109 C=14.18066 mu=1.7675 alpha=24.04 epsHOMO=-0.2682 epsLUMO=0.1042 epsgap=0.3724 =129.891 zpve=0.057289 U0=-100.0 U=-153.73941 H=-153.738466 G=-153.766642 Cv=9.176 pbc="F F F" -C 0.01537720 1.41764380 0.00956287 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 1.26478161 0.64923984 -0.00655044 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -O -0.00024018 -0.00771006 0.00204055 0.00000000 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1.91339997 0.00028781 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 2.23625322 2.57430970 -0.00529277 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.91670701 1.79467546 0.01781352 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 3.13429662 3.14370267 -0.01037656 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -5 -Properties=species:S:1:pos:R:3:forces:R:3:my_tensor:R:9 A=67.88408 B=5.00823 C=4.66413 mu=2.3112 alpha=26.25 epsHOMO=-0.3166 epsLUMO=-0.11 epsgap=0.2066 =251.0007 zpve=0.02654 U0=-100.0 U=-206.717875 H=-206.716931 G=-206.747625 Cv=11.329 pbc="F F F" -O -0.04055978 -0.03799144 0.00266513 0.00000000 0.00000000 0.00000000 0.00000000 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Cv=12.147 pbc="F F F" -O 0.00337122 -0.03146586 0.00157206 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C -0.01713174 1.17081409 0.00676444 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 1.26555783 1.99719410 -0.00007703 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -O 1.24507069 3.19946589 0.00681275 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.94690483 1.77678142 0.01852668 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 2.19534410 1.39121374 -0.01006739 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 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"is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray.flush" - } - ], - "e_path": [ - { - "is_class": false, - "is_explicit": false, - "module": "pathlib._local", - "module_short": "pathlib", - "name": "PosixPath" - }, - { - "is_class": false, - "is_explicit": false, - "module": "pathlib", - "module_short": "pathlib", - "name": "PosixPath" - } - ], - "energy": [ - { - "is_class": false, - "is_explicit": false, - "module": "torch", - "module_short": "torch", - "name": "ScriptObject" - } - ], - "f_mm": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "memmap" - } - ], - "f_mm.flush": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "memmap.flush" - }, - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray.flush" - } - ], - "f_path": [ - { - "is_class": false, - "is_explicit": false, - "module": "pathlib._local", - "module_short": "pathlib", - "name": "PosixPath" - }, - { - "is_class": false, - "is_explicit": false, - "module": "pathlib", - "module_short": "pathlib", - "name": "PosixPath" - } - ], - "filelist": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "list" - } - ], - "fname": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "str" - } - ], - "frames": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "list" - } - ], - "frames.append": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "builtin_function_or_method" - } - ], - "get_system_with_neighbor_lists": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": 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"is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "int64" - }, - { - "is_class": true, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "signedinteger" - }, - { - "is_class": true, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "integer" - }, - { - "is_class": true, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "number" - }, - { - "is_class": true, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "generic" - }, - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "int64" - } - ], - "np.memmap": [ - { - "is_class": true, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "memmap" - }, - { - "is_class": true, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray" - }, - { - "is_class": false, - 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"module": "pathlib", - "module_short": "pathlib", - "name": "PosixPath" - } - ], - "root": [ - { - "is_class": false, - "is_explicit": false, - "module": "pathlib._local", - "module_short": "pathlib", - "name": "PosixPath" - }, - { - "is_class": false, - "is_explicit": false, - "module": "pathlib", - "module_short": "pathlib", - "name": "PosixPath" - } - ], - "root.mkdir": [ - { - "is_class": false, - "is_explicit": false, - "module": "pathlib._local", - "module_short": "pathlib", - "name": "PosixPath.mkdir" - }, - { - "is_class": false, - "is_explicit": false, - "module": "pathlib", - "module_short": "pathlib", - "name": "PosixPath.mkdir" - }, - { - "is_class": false, - "is_explicit": false, - "module": "pathlib._local", - "module_short": "pathlib", - "name": "Path.mkdir" - }, - { - "is_class": false, - "is_explicit": false, - "module": "pathlib", - "module_short": "pathlib", - "name": "Path.mkdir" - }, - { - "is_class": false, - "is_explicit": false, - "module": "pathlib._local", - "module_short": "pathlib._local", - "name": "PurePosixPath.mkdir" - }, - { - "is_class": false, - "is_explicit": false, - "module": "pathlib", - "module_short": "pathlib", - "name": "PurePosixPath.mkdir" - }, - { - "is_class": false, - "is_explicit": false, - "module": "pathlib._abc", - "module_short": "pathlib._abc", - "name": "PathBase.mkdir" - }, - { - "is_class": false, - "is_explicit": false, - "module": "pathlib", - "module_short": "pathlib", - "name": "PathBase.mkdir" - }, - { - "is_class": false, - "is_explicit": false, - "module": "pathlib._local", - "module_short": "pathlib._local", - "name": "PurePath.mkdir" - }, - { - "is_class": false, - "is_explicit": false, - "module": "pathlib", - "module_short": "pathlib", - "name": "PurePath.mkdir" - }, - { - "is_class": false, - "is_explicit": false, - "module": "pathlib._abc", - "module_short": "pathlib._abc", - "name": "PurePathBase.mkdir" - }, - { - "is_class": false, - "is_explicit": false, - "module": "pathlib", - "module_short": "pathlib", - "name": "PurePathBase.mkdir" - } - ], - "s": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms" - } - ], - "s.arrays": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "dict" - } - ], - "s.get_cell": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.get_cell" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.get_cell" - } - ], - "s.get_positions": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.get_positions" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.get_positions" - } - ], - "s.get_potential_energy": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.get_potential_energy" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.get_potential_energy" - } - ], - "s.get_volume": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.get_volume" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.get_volume" - } - ], - "s.info": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "dict" - } - ], - "s.numbers": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.numbers" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.numbers" - } - ], - "s_mm": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "memmap" - } - ], - "s_mm.flush": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "memmap.flush" - }, - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray.flush" - } - ], - "s_path": [ - { - "is_class": false, - "is_explicit": false, - "module": "pathlib._local", - "module_short": "pathlib", - "name": "PosixPath" - }, - { - "is_class": false, - "is_explicit": false, - "module": "pathlib", - "module_short": "pathlib", - "name": "PosixPath" - } - ], - "structures": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "list" - } - ], - "subprocess.run": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "subprocess", - "module_short": "subprocess", - "name": "run" - } - ], - "system": [ - { - "is_class": false, - "is_explicit": false, - "module": "torch", - "module_short": "torch", - "name": "ScriptObject" - } - ], - "systems": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "list" - } - ], - "systems_to_torch": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "metatomic.torch", - "module_short": "metatomic.torch", - "name": "systems_to_torch" - } - ], - "torch.float64": [ - { - "is_class": false, - "is_explicit": false, - "module": "torch", - "module_short": "torch", - "name": "dtype" - }, - { - "is_class": false, - "is_explicit": false, - "module": "torch", - "module_short": "torch", - "name": "float64" - } - ], - "torch.tensor": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "builtin_function_or_method" - }, - { - "is_class": false, - "is_explicit": false, - "module": "torch", - "module_short": "torch", - "name": "tensor" - } - ], - "x_mm": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "memmap" - } - ], - "x_mm.flush": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "memmap.flush" - }, - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray.flush" - } - ], - "x_path": [ - { - "is_class": false, - "is_explicit": false, - "module": "pathlib._local", - "module_short": "pathlib", - "name": "PosixPath" - }, - { - "is_class": false, - "is_explicit": false, - "module": "pathlib", - "module_short": "pathlib", - "name": "PosixPath" - } - ] -} diff --git a/docs/src/examples/programmatic/data_preparation/data_preparation.ipynb b/docs/src/examples/programmatic/data_preparation/data_preparation.ipynb deleted file mode 100644 index bf87a8c055..0000000000 --- a/docs/src/examples/programmatic/data_preparation/data_preparation.ipynb +++ /dev/null @@ -1,169 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# How to prepare data for training\n\n.. attention::\n\n This tutorial is only relevant for users who need to prepare their data from scratch\n from several files or for big datasets. If you already have your data in a common\n file format (like XYZ or `ASE database`_), you can skip this tutorial and directly\n start training.\n\n\nXYZ, ASE databases, and also from metrain's\n:class:`metatrain.utils.data.dataset.DiskDataset ` file.\n\nFor the small datasets (<10k structures), you can simply provide an XYZ file or an ASE\ndatabase to ``metatrain``, and it will handle the data loading for you. Large datasets\n(>10k structures) may not fit into the GPU memory. In such cases, it is useful to\npre-process the dataset, save it to disk and load it on the fly during training.\n\nIn this tutorial, we will show how to prepare data for training using three different\nformats. You can choose the one that best fits your needs.\n\nWe start by importing the necessary packages.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import subprocess\nfrom pathlib import Path\n\nimport ase.io\nimport numpy as np\nimport torch\nfrom metatensor.torch import Labels, TensorBlock, TensorMap\nfrom metatomic.torch import NeighborListOptions, systems_to_torch\n\nfrom metatrain.utils.data.writers import DiskDatasetWriter\nfrom metatrain.utils.neighbor_lists import get_system_with_neighbor_lists" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create a XYZ training file (small datasets)\n\nFirst, we will show how to create a XYZ file with fields corresponding to the target\nproperties. On modern HPC systems, this format is suitable for datasets up to around\n1M structures. As an example, we will use 100 structures from a file read by ASE_.\nSince files from reference calculations may be located in different directories, we\nfirst create a list of all path that we want to read from. Here, for simplicity, we\nassume that all files are located in the same directory.\n\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "filelist = 100 * [\"qm9_reduced_100.xyz\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now read the structures using the ASE package. Check the ase documentation for\nmore details on how to read different file formats. Instead of creating the ``atoms``\nobject by reading from disk, you can also create an\n:class:`ase.Atoms` object containing the chemical ``symbols``, ``positions``, the\n``cell`` and the periodic boundary conditions (``pbc``) by hand using its constructor.\n\n.. hint::\n\n If a property is not read by the :func:`ase.io.read` function, you can add custom\n scalar properties to the ``info`` dictionary. Vector properties (e.g. forces) can be\n added to the ``arrays`` dictionary. Tensor properties (e.g. stress) must\n be flattened before adding them to the ``arrays`` dictionary.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "frames = []\nfor i, fname in enumerate(filelist):\n atoms = ase.io.read(fname, index=i)\n\n n_atoms = len(atoms)\n # scalar\n atoms.info[\"U0\"] = -100.0\n # vector\n atoms.arrays[\"forces\"] = np.zeros((n_atoms, 3))\n # tensor\n atoms.arrays[\"my_tensor\"] = np.zeros((n_atoms, 3, 3)).reshape(n_atoms, 9)\n\n frames.append(atoms)\n\nase.io.write(\"data.xyz\", frames)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Note

The names of the added properties (like, ``U0``, etc.) must be referenced correctly\n in the ``options.yaml`` file.

\n\n## Create a ``DiskDataset`` (large datasets)\n\nIn addition to the systems and targets (as above), we also save the neighbor\nlists that the model will use during training. We first create the writer object that\nwill write the data to a zip file.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "disk_dataset_writer = DiskDatasetWriter(\"qm9_reduced_100.zip\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then we loop over all structures, convert them to the internal torch format using\n:func:`metatomic.torch.systems_to_torch`, compute the neighbor lists using\n:func:`metatrain.utils.neighbor_lists.get_system_with_neighbor_lists` and write\neverything to disk using the writer's ``write()`` method.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "for i, fname in enumerate(filelist):\n atoms = ase.io.read(fname, index=i)\n\n system = systems_to_torch(atoms, dtype=torch.float64)\n system = get_system_with_neighbor_lists(\n system,\n [NeighborListOptions(cutoff=5.0, full_list=True, strict=True)],\n )\n energy = TensorMap(\n keys=Labels.single(),\n blocks=[\n TensorBlock(\n values=torch.tensor([[atoms.info[\"U0\"]]], dtype=torch.float64),\n samples=Labels(\n names=[\"system\"],\n values=torch.tensor([[i]]),\n ),\n components=[],\n properties=Labels(\"energy\", torch.tensor([[0]])),\n )\n ],\n )\n disk_dataset_writer.write([system], {\"energy\": energy})\n\ndisk_dataset_writer.finish()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively, you can also write the whole dataset at once, which might be more\nefficient (but also potentially run into memory issues). We use the same ``frames``\nthat we created above.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "disk_dataset_writer = DiskDatasetWriter(\"qm9_reduced_100_all_at_once.zip\")\n\nsystems = systems_to_torch(frames, dtype=torch.float64)\nsystems = [\n get_system_with_neighbor_lists(\n system,\n [NeighborListOptions(cutoff=5.0, full_list=True, strict=True)],\n )\n for system in systems\n]\nenergy = TensorMap(\n keys=Labels.single(),\n blocks=[\n TensorBlock(\n values=torch.tensor(\n [frame.info[\"U0\"] for frame in frames], dtype=torch.float64\n ).reshape(-1, 1),\n samples=Labels.range(\"system\", len(frames)),\n components=[],\n properties=Labels(\"energy\", torch.tensor([[0]])),\n )\n ],\n)\n\ndisk_dataset_writer.write(systems, {\"energy\": energy})\ndisk_dataset_writer.finish()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The dataset is saved to disk. You can now provide it to ``metatrain`` as a\ndataset to train from, simply by replacing your ``.xyz`` file with the newly created\nzip file (e.g. ``read_from: qm9_reduced_100.zip``).\n\n## Create a ``MemmapDataset`` (large datasets, parallel filesystems)\n\nIf your dataset is large and you are using a parallel filesystem (e.g. on an HPC\ncluster), it is recommended to use a ``MemmapDataset`` instead of a ``DiskDataset``.\nThe ``MemmapDataset`` stores the data inside memory-mapped numpy arrays instead of a\nzip file. Reading from this format avoids I/O bottlenecks, but it does not support\nspherical targets or storing neighbor lists.\n\nAs an example, we will use 100 structures from a dataset of carbon structures. The\nnumpy arrays must be saved inside a directory, using the following format.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "structures = ase.io.read(\"carbon_reduced_100.xyz\", index=\":\")\n\nroot = Path(\"carbon_reduced_100_memmap/\")\nroot.mkdir()\n\nns_path = root / \"ns.npy\"\nna_path = root / \"na.npy\"\na_path = root / \"a.bin\"\nx_path = root / \"x.bin\"\nc_path = root / \"c.bin\"\ne_path = root / \"e.bin\"\nf_path = root / \"f.bin\"\ns_path = root / \"s.bin\"\n\nns = len(structures)\nna = np.cumsum(np.array([0] + [len(s) for s in structures], dtype=np.int64))\nnp.save(ns_path, ns)\nnp.save(na_path, na)\n\na_mm = np.memmap(a_path, dtype=\"int32\", mode=\"w+\", shape=(na[-1],))\nx_mm = np.memmap(x_path, dtype=\"float32\", mode=\"w+\", shape=(na[-1], 3))\nc_mm = np.memmap(c_path, dtype=\"float32\", mode=\"w+\", shape=(ns, 3, 3))\ne_mm = np.memmap(e_path, dtype=\"float32\", mode=\"w+\", shape=(ns, 1))\nf_mm = np.memmap(f_path, dtype=\"float32\", mode=\"w+\", shape=(na[-1], 3))\ns_mm = np.memmap(s_path, dtype=\"float32\", mode=\"w+\", shape=(ns, 3, 3))\n\nfor i, s in enumerate(structures):\n a_mm[na[i] : na[i + 1]] = s.numbers\n x_mm[na[i] : na[i + 1]] = s.get_positions()\n c_mm[i] = s.get_cell()[:]\n e_mm[i] = s.get_potential_energy()\n f_mm[na[i] : na[i + 1]] = s.arrays[\"force\"]\n s_mm[i] = -s.info[\"virial\"] / s.get_volume()\n\na_mm.flush()\nx_mm.flush()\nc_mm.flush()\ne_mm.flush()\nf_mm.flush()\ns_mm.flush()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The dataset is saved to disk. You can now provide it to ``metatrain`` as a\ndataset to train from, simply by specifying the newly created\ndirectory as the path from which to read the systems\n(e.g. ``read_from: carbon_reduced_100_memmap/``).\n\nFor example, you can use the following options file:\n\n.. literalinclude:: options.yaml\n :language: yaml\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "subprocess.run([\"mtt\", \"train\", \"options.yaml\"])" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/src/examples/programmatic/data_preparation/data_preparation.py b/docs/src/examples/programmatic/data_preparation/data_preparation.py deleted file mode 100644 index 52a049bcdf..0000000000 --- a/docs/src/examples/programmatic/data_preparation/data_preparation.py +++ /dev/null @@ -1,242 +0,0 @@ -""" -How to prepare data for training -================================ - -.. attention:: - - This tutorial is only relevant for users who need to prepare their data from scratch - from several files or for big datasets. If you already have your data in a common - file format (like XYZ or `ASE database`_), you can skip this tutorial and directly - start training. - -.. _ASE database: https://ase-lib.org/ase/db/db.html - -XYZ, ASE databases, and also from metrain's -:class:`metatrain.utils.data.dataset.DiskDataset ` file. - -For the small datasets (<10k structures), you can simply provide an XYZ file or an ASE -database to ``metatrain``, and it will handle the data loading for you. Large datasets -(>10k structures) may not fit into the GPU memory. In such cases, it is useful to -pre-process the dataset, save it to disk and load it on the fly during training. - -In this tutorial, we will show how to prepare data for training using three different -formats. You can choose the one that best fits your needs. - -We start by importing the necessary packages. -""" - -# %% -# -import subprocess -from pathlib import Path - -import ase.io -import numpy as np -import torch -from metatensor.torch import Labels, TensorBlock, TensorMap -from metatomic.torch import NeighborListOptions, systems_to_torch - -from metatrain.utils.data.writers import DiskDatasetWriter -from metatrain.utils.neighbor_lists import get_system_with_neighbor_lists - - -# %% -# Create a XYZ training file (small datasets) -# ------------------------------------------- -# -# First, we will show how to create a XYZ file with fields corresponding to the target -# properties. On modern HPC systems, this format is suitable for datasets up to around -# 1M structures. As an example, we will use 100 structures from a file read by ASE_. -# Since files from reference calculations may be located in different directories, we -# first create a list of all path that we want to read from. Here, for simplicity, we -# assume that all files are located in the same directory. -# -# .. _ASE: https://ase-lib.org/ - -filelist = 100 * ["qm9_reduced_100.xyz"] - -# %% -# -# We will now read the structures using the ASE package. Check the ase documentation for -# more details on how to read different file formats. Instead of creating the ``atoms`` -# object by reading from disk, you can also create an -# :class:`ase.Atoms` object containing the chemical ``symbols``, ``positions``, the -# ``cell`` and the periodic boundary conditions (``pbc``) by hand using its constructor. -# -# .. hint:: -# -# If a property is not read by the :func:`ase.io.read` function, you can add custom -# scalar properties to the ``info`` dictionary. Vector properties (e.g. forces) can be -# added to the ``arrays`` dictionary. Tensor properties (e.g. stress) must -# be flattened before adding them to the ``arrays`` dictionary. - -frames = [] -for i, fname in enumerate(filelist): - atoms = ase.io.read(fname, index=i) - - n_atoms = len(atoms) - # scalar - atoms.info["U0"] = -100.0 - # vector - atoms.arrays["forces"] = np.zeros((n_atoms, 3)) - # tensor - atoms.arrays["my_tensor"] = np.zeros((n_atoms, 3, 3)).reshape(n_atoms, 9) - - frames.append(atoms) - -ase.io.write("data.xyz", frames) - -# %% -# -# .. note:: -# -# The names of the added properties (like, ``U0``, etc.) must be referenced correctly -# in the ``options.yaml`` file. -# -# Create a ``DiskDataset`` (large datasets) -# ----------------------------------------- -# -# In addition to the systems and targets (as above), we also save the neighbor -# lists that the model will use during training. We first create the writer object that -# will write the data to a zip file. - -disk_dataset_writer = DiskDatasetWriter("qm9_reduced_100.zip") - -# %% -# -# Then we loop over all structures, convert them to the internal torch format using -# :func:`metatomic.torch.systems_to_torch`, compute the neighbor lists using -# :func:`metatrain.utils.neighbor_lists.get_system_with_neighbor_lists` and write -# everything to disk using the writer's ``write()`` method. - -for i, fname in enumerate(filelist): - atoms = ase.io.read(fname, index=i) - - system = systems_to_torch(atoms, dtype=torch.float64) - system = get_system_with_neighbor_lists( - system, - [NeighborListOptions(cutoff=5.0, full_list=True, strict=True)], - ) - energy = TensorMap( - keys=Labels.single(), - blocks=[ - TensorBlock( - values=torch.tensor([[atoms.info["U0"]]], dtype=torch.float64), - samples=Labels( - names=["system"], - values=torch.tensor([[i]]), - ), - components=[], - properties=Labels("energy", torch.tensor([[0]])), - ) - ], - ) - disk_dataset_writer.write([system], {"energy": energy}) - -disk_dataset_writer.finish() - -# %% -# -# Alternatively, you can also write the whole dataset at once, which might be more -# efficient (but also potentially run into memory issues). We use the same ``frames`` -# that we created above. - -disk_dataset_writer = DiskDatasetWriter("qm9_reduced_100_all_at_once.zip") - -systems = systems_to_torch(frames, dtype=torch.float64) -systems = [ - get_system_with_neighbor_lists( - system, - [NeighborListOptions(cutoff=5.0, full_list=True, strict=True)], - ) - for system in systems -] -energy = TensorMap( - keys=Labels.single(), - blocks=[ - TensorBlock( - values=torch.tensor( - [frame.info["U0"] for frame in frames], dtype=torch.float64 - ).reshape(-1, 1), - samples=Labels.range("system", len(frames)), - components=[], - properties=Labels("energy", torch.tensor([[0]])), - ) - ], -) - -disk_dataset_writer.write(systems, {"energy": energy}) -disk_dataset_writer.finish() - -# %% -# -# The dataset is saved to disk. You can now provide it to ``metatrain`` as a -# dataset to train from, simply by replacing your ``.xyz`` file with the newly created -# zip file (e.g. ``read_from: qm9_reduced_100.zip``). -# -# Create a ``MemmapDataset`` (large datasets, parallel filesystems) -# ----------------------------------------------------------------- -# -# If your dataset is large and you are using a parallel filesystem (e.g. on an HPC -# cluster), it is recommended to use a ``MemmapDataset`` instead of a ``DiskDataset``. -# The ``MemmapDataset`` stores the data inside memory-mapped numpy arrays instead of a -# zip file. Reading from this format avoids I/O bottlenecks, but it does not support -# spherical targets or storing neighbor lists. -# -# As an example, we will use 100 structures from a dataset of carbon structures. The -# numpy arrays must be saved inside a directory, using the following format. - -structures = ase.io.read("carbon_reduced_100.xyz", index=":") - -root = Path("carbon_reduced_100_memmap/") -root.mkdir() - -ns_path = root / "ns.npy" -na_path = root / "na.npy" -a_path = root / "a.bin" -x_path = root / "x.bin" -c_path = root / "c.bin" -e_path = root / "e.bin" -f_path = root / "f.bin" -s_path = root / "s.bin" - -ns = len(structures) -na = np.cumsum(np.array([0] + [len(s) for s in structures], dtype=np.int64)) -np.save(ns_path, ns) -np.save(na_path, na) - -a_mm = np.memmap(a_path, dtype="int32", mode="w+", shape=(na[-1],)) -x_mm = np.memmap(x_path, dtype="float32", mode="w+", shape=(na[-1], 3)) -c_mm = np.memmap(c_path, dtype="float32", mode="w+", shape=(ns, 3, 3)) -e_mm = np.memmap(e_path, dtype="float32", mode="w+", shape=(ns, 1)) -f_mm = np.memmap(f_path, dtype="float32", mode="w+", shape=(na[-1], 3)) -s_mm = np.memmap(s_path, dtype="float32", mode="w+", shape=(ns, 3, 3)) - -for i, s in enumerate(structures): - a_mm[na[i] : na[i + 1]] = s.numbers - x_mm[na[i] : na[i + 1]] = s.get_positions() - c_mm[i] = s.get_cell()[:] - e_mm[i] = s.get_potential_energy() - f_mm[na[i] : na[i + 1]] = s.arrays["force"] - s_mm[i] = -s.info["virial"] / s.get_volume() - -a_mm.flush() -x_mm.flush() -c_mm.flush() -e_mm.flush() -f_mm.flush() -s_mm.flush() - -# %% -# -# The dataset is saved to disk. You can now provide it to ``metatrain`` as a -# dataset to train from, simply by specifying the newly created -# directory as the path from which to read the systems -# (e.g. ``read_from: carbon_reduced_100_memmap/``). -# -# For example, you can use the following options file: -# -# .. literalinclude:: options.yaml -# :language: yaml - -subprocess.run(["mtt", "train", "options.yaml"]) diff --git a/docs/src/examples/programmatic/data_preparation/data_preparation.py.md5 b/docs/src/examples/programmatic/data_preparation/data_preparation.py.md5 deleted file mode 100644 index ec11fcf764..0000000000 --- a/docs/src/examples/programmatic/data_preparation/data_preparation.py.md5 +++ /dev/null @@ -1 +0,0 @@ -54ca454338c67496640c3b734e586992 \ No newline at end of file diff --git a/docs/src/examples/programmatic/data_preparation/data_preparation.rst b/docs/src/examples/programmatic/data_preparation/data_preparation.rst deleted file mode 100644 index 6aa58fb836..0000000000 --- a/docs/src/examples/programmatic/data_preparation/data_preparation.rst +++ /dev/null @@ -1,390 +0,0 @@ - -.. DO NOT EDIT. -.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. -.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: -.. "examples/programmatic/data_preparation/data_preparation.py" -.. LINE NUMBERS ARE GIVEN BELOW. - -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - :ref:`Go to the end ` - to download the full example code. - -.. rst-class:: sphx-glr-example-title - -.. _sphx_glr_examples_programmatic_data_preparation_data_preparation.py: - - -How to prepare data for training -================================ - -.. attention:: - - This tutorial is only relevant for users who need to prepare their data from scratch - from several files or for big datasets. If you already have your data in a common - file format (like XYZ or `ASE database`_), you can skip this tutorial and directly - start training. - -.. _ASE database: https://ase-lib.org/ase/db/db.html - -XYZ, ASE databases, and also from metrain's -:class:`metatrain.utils.data.dataset.DiskDataset ` file. - -For the small datasets (<10k structures), you can simply provide an XYZ file or an ASE -database to ``metatrain``, and it will handle the data loading for you. Large datasets -(>10k structures) may not fit into the GPU memory. In such cases, it is useful to -pre-process the dataset, save it to disk and load it on the fly during training. - -In this tutorial, we will show how to prepare data for training using three different -formats. You can choose the one that best fits your needs. - -We start by importing the necessary packages. - -.. GENERATED FROM PYTHON SOURCE LINES 30-43 - -.. code-block:: Python - - import subprocess - from pathlib import Path - - import ase.io - import numpy as np - import torch - from metatensor.torch import Labels, TensorBlock, TensorMap - from metatomic.torch import NeighborListOptions, systems_to_torch - - from metatrain.utils.data.writers import DiskDatasetWriter - from metatrain.utils.neighbor_lists import get_system_with_neighbor_lists - - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 44-55 - -Create a XYZ training file (small datasets) -------------------------------------------- - -First, we will show how to create a XYZ file with fields corresponding to the target -properties. On modern HPC systems, this format is suitable for datasets up to around -1M structures. As an example, we will use 100 structures from a file read by ASE_. -Since files from reference calculations may be located in different directories, we -first create a list of all path that we want to read from. Here, for simplicity, we -assume that all files are located in the same directory. - -.. _ASE: https://ase-lib.org/ - -.. GENERATED FROM PYTHON SOURCE LINES 55-58 - -.. code-block:: Python - - - filelist = 100 * ["qm9_reduced_100.xyz"] - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 59-71 - -We will now read the structures using the ASE package. Check the ase documentation for -more details on how to read different file formats. Instead of creating the ``atoms`` -object by reading from disk, you can also create an -:class:`ase.Atoms` object containing the chemical ``symbols``, ``positions``, the -``cell`` and the periodic boundary conditions (``pbc``) by hand using its constructor. - -.. hint:: - - If a property is not read by the :func:`ase.io.read` function, you can add custom - scalar properties to the ``info`` dictionary. Vector properties (e.g. forces) can be - added to the ``arrays`` dictionary. Tensor properties (e.g. stress) must - be flattened before adding them to the ``arrays`` dictionary. - -.. GENERATED FROM PYTHON SOURCE LINES 72-89 - -.. code-block:: Python - - - frames = [] - for i, fname in enumerate(filelist): - atoms = ase.io.read(fname, index=i) - - n_atoms = len(atoms) - # scalar - atoms.info["U0"] = -100.0 - # vector - atoms.arrays["forces"] = np.zeros((n_atoms, 3)) - # tensor - atoms.arrays["my_tensor"] = np.zeros((n_atoms, 3, 3)).reshape(n_atoms, 9) - - frames.append(atoms) - - ase.io.write("data.xyz", frames) - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 90-101 - -.. note:: - - The names of the added properties (like, ``U0``, etc.) must be referenced correctly - in the ``options.yaml`` file. - -Create a ``DiskDataset`` (large datasets) ------------------------------------------ - -In addition to the systems and targets (as above), we also save the neighbor -lists that the model will use during training. We first create the writer object that -will write the data to a zip file. - -.. GENERATED FROM PYTHON SOURCE LINES 102-105 - -.. code-block:: Python - - - disk_dataset_writer = DiskDatasetWriter("qm9_reduced_100.zip") - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 106-110 - -Then we loop over all structures, convert them to the internal torch format using -:func:`metatomic.torch.systems_to_torch`, compute the neighbor lists using -:func:`metatrain.utils.neighbor_lists.get_system_with_neighbor_lists` and write -everything to disk using the writer's ``write()`` method. - -.. GENERATED FROM PYTHON SOURCE LINES 111-138 - -.. code-block:: Python - - - for i, fname in enumerate(filelist): - atoms = ase.io.read(fname, index=i) - - system = systems_to_torch(atoms, dtype=torch.float64) - system = get_system_with_neighbor_lists( - system, - [NeighborListOptions(cutoff=5.0, full_list=True, strict=True)], - ) - energy = TensorMap( - keys=Labels.single(), - blocks=[ - TensorBlock( - values=torch.tensor([[atoms.info["U0"]]], dtype=torch.float64), - samples=Labels( - names=["system"], - values=torch.tensor([[i]]), - ), - components=[], - properties=Labels("energy", torch.tensor([[0]])), - ) - ], - ) - disk_dataset_writer.write([system], {"energy": energy}) - - disk_dataset_writer.finish() - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 139-142 - -Alternatively, you can also write the whole dataset at once, which might be more -efficient (but also potentially run into memory issues). We use the same ``frames`` -that we created above. - -.. GENERATED FROM PYTHON SOURCE LINES 143-171 - -.. code-block:: Python - - - disk_dataset_writer = DiskDatasetWriter("qm9_reduced_100_all_at_once.zip") - - systems = systems_to_torch(frames, dtype=torch.float64) - systems = [ - get_system_with_neighbor_lists( - system, - [NeighborListOptions(cutoff=5.0, full_list=True, strict=True)], - ) - for system in systems - ] - energy = TensorMap( - keys=Labels.single(), - blocks=[ - TensorBlock( - values=torch.tensor( - [frame.info["U0"] for frame in frames], dtype=torch.float64 - ).reshape(-1, 1), - samples=Labels.range("system", len(frames)), - components=[], - properties=Labels("energy", torch.tensor([[0]])), - ) - ], - ) - - disk_dataset_writer.write(systems, {"energy": energy}) - disk_dataset_writer.finish() - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 172-187 - -The dataset is saved to disk. You can now provide it to ``metatrain`` as a -dataset to train from, simply by replacing your ``.xyz`` file with the newly created -zip file (e.g. ``read_from: qm9_reduced_100.zip``). - -Create a ``MemmapDataset`` (large datasets, parallel filesystems) ------------------------------------------------------------------ - -If your dataset is large and you are using a parallel filesystem (e.g. on an HPC -cluster), it is recommended to use a ``MemmapDataset`` instead of a ``DiskDataset``. -The ``MemmapDataset`` stores the data inside memory-mapped numpy arrays instead of a -zip file. Reading from this format avoids I/O bottlenecks, but it does not support -spherical targets or storing neighbor lists. - -As an example, we will use 100 structures from a dataset of carbon structures. The -numpy arrays must be saved inside a directory, using the following format. - -.. GENERATED FROM PYTHON SOURCE LINES 188-230 - -.. code-block:: Python - - - structures = ase.io.read("carbon_reduced_100.xyz", index=":") - - root = Path("carbon_reduced_100_memmap/") - root.mkdir() - - ns_path = root / "ns.npy" - na_path = root / "na.npy" - a_path = root / "a.bin" - x_path = root / "x.bin" - c_path = root / "c.bin" - e_path = root / "e.bin" - f_path = root / "f.bin" - s_path = root / "s.bin" - - ns = len(structures) - na = np.cumsum(np.array([0] + [len(s) for s in structures], dtype=np.int64)) - np.save(ns_path, ns) - np.save(na_path, na) - - a_mm = np.memmap(a_path, dtype="int32", mode="w+", shape=(na[-1],)) - x_mm = np.memmap(x_path, dtype="float32", mode="w+", shape=(na[-1], 3)) - c_mm = np.memmap(c_path, dtype="float32", mode="w+", shape=(ns, 3, 3)) - e_mm = np.memmap(e_path, dtype="float32", mode="w+", shape=(ns, 1)) - f_mm = np.memmap(f_path, dtype="float32", mode="w+", shape=(na[-1], 3)) - s_mm = np.memmap(s_path, dtype="float32", mode="w+", shape=(ns, 3, 3)) - - for i, s in enumerate(structures): - a_mm[na[i] : na[i + 1]] = s.numbers - x_mm[na[i] : na[i + 1]] = s.get_positions() - c_mm[i] = s.get_cell()[:] - e_mm[i] = s.get_potential_energy() - f_mm[na[i] : na[i + 1]] = s.arrays["force"] - s_mm[i] = -s.info["virial"] / s.get_volume() - - a_mm.flush() - x_mm.flush() - c_mm.flush() - e_mm.flush() - f_mm.flush() - s_mm.flush() - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 231-240 - -The dataset is saved to disk. You can now provide it to ``metatrain`` as a -dataset to train from, simply by specifying the newly created -directory as the path from which to read the systems -(e.g. ``read_from: carbon_reduced_100_memmap/``). - -For example, you can use the following options file: - -.. literalinclude:: options.yaml - :language: yaml - -.. GENERATED FROM PYTHON SOURCE LINES 241-243 - -.. code-block:: Python - - - subprocess.run(["mtt", "train", "options.yaml"]) - - - - -.. rst-class:: sphx-glr-script-out - - .. code-block:: none - - - CompletedProcess(args=['mtt', 'train', 'options.yaml'], returncode=0) - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** (0 minutes 10.476 seconds) - - -.. _sphx_glr_download_examples_programmatic_data_preparation_data_preparation.py: - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-example - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: data_preparation.ipynb ` - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: data_preparation.py ` - - .. container:: sphx-glr-download sphx-glr-download-zip - - :download:`Download zipped: data_preparation.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by 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- -.. only:: html - - .. image:: /examples/programmatic/data_preparation/images/thumb/sphx_glr_data_preparation_thumb.png - :alt: - - :ref:`sphx_glr_examples_programmatic_data_preparation_data_preparation.py` - -.. raw:: html - -
How to prepare data for training
-
- - -.. thumbnail-parent-div-close - -.. raw:: html - - - - -.. toctree:: - :hidden: - - /examples/programmatic/data_preparation/data_preparation - - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-gallery - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download all examples in Python source code: data_preparation_python.zip ` - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download all examples in Jupyter notebooks: data_preparation_jupyter.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/programmatic/data_preparation/options.yaml b/docs/src/examples/programmatic/data_preparation/options.yaml deleted file mode 100644 index f8de24c036..0000000000 --- a/docs/src/examples/programmatic/data_preparation/options.yaml +++ /dev/null @@ -1,38 +0,0 @@ -seed: 42 - -architecture: - name: pet - training: - num_epochs: 1 - batch_size: 8 - -training_set: - systems: - read_from: carbon_reduced_100_memmap/ - length_unit: angstrom - targets: - energy: - key: e - unit: eV - forces: - key: f - stress: - key: s - non_conservative_forces: - key: f - quantity: force - unit: eV/A - per_atom: true - type: - cartesian: - rank: 1 - non_conservative_stress: - key: s - quantity: pressure - unit: eV/A^3 - type: - cartesian: - rank: 2 - -test_set: 0.1 -validation_set: 0.1 diff --git a/docs/src/examples/programmatic/data_preparation/qm9_reduced_100.xyz b/docs/src/examples/programmatic/data_preparation/qm9_reduced_100.xyz deleted file mode 100644 index 14b2a0416e..0000000000 --- a/docs/src/examples/programmatic/data_preparation/qm9_reduced_100.xyz +++ /dev/null @@ -1,1201 +0,0 @@ -5 -Properties=species:S:1:pos:R:3 A=157.7118 B=157.70997 C=157.70699 mu=0 alpha=13.21 epsHOMO=-0.3877 epsLUMO=0.1171 epsgap=0.5048 =35.3641 zpve=0.044749 U0=-40.47893 U=-40.476062 H=-40.475117 G=-40.498597 Cv=6.469 pbc="F F F" -C -0.01269814 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zpve=0.015951 U0=-185.648533 U=-185.644825 H=-185.64388 G=-185.667652 Cv=10.398 pbc="F F F" -N 0.01745734 -1.16134217 -0.00415342 -C 0.00253212 -0.00344278 0.00179939 -C -0.01611419 1.37220987 0.00934873 -N -0.03264302 2.53008276 0.01609090 -6 -Properties=species:S:1:pos:R:3 A=68.5171 B=4.8345 C=4.51586 mu=2.7824 alpha=31.14 epsHOMO=-0.2777 epsLUMO=-0.0735 epsgap=0.2042 =268.3921 zpve=0.037208 U0=-190.624631 U=-190.620363 H=-190.619419 G=-190.650543 Cv=13.049 pbc="F F F" -O -0.06776120 -0.04233838 0.00282489 -C -0.00562945 1.16382879 0.00743862 -C 1.22985272 1.91339997 0.00028781 -C 2.23625322 2.57430970 -0.00529277 -H -0.91670701 1.79467546 0.01781352 -H 3.13429662 3.14370267 -0.01037656 -5 -Properties=species:S:1:pos:R:3 A=67.88408 B=5.00823 C=4.66413 mu=2.3112 alpha=26.25 epsHOMO=-0.3166 epsLUMO=-0.11 epsgap=0.2066 =251.0007 zpve=0.02654 U0=-206.721858 U=-206.717875 H=-206.716931 G=-206.747625 Cv=11.329 pbc="F F F" -O -0.04055978 -0.03799144 0.00266513 -C -0.01768870 1.16193251 0.00746008 -C 1.23315534 1.93381336 0.00043371 -N 2.19857378 2.57055097 -0.00483320 -H -0.92627534 1.78959196 0.01764859 -6 -Properties=species:S:1:pos:R:3 A=56.25048 B=4.77441 C=4.40088 mu=0.002 alpha=26.12 epsHOMO=-0.2668 epsLUMO=-0.1113 epsgap=0.1555 =266.8164 zpve=0.036943 U0=-227.798785 U=-227.79457 H=-227.793626 G=-227.825074 Cv=12.147 pbc="F F F" -O 0.00337122 -0.03146586 0.00157206 -C -0.01713174 1.17081409 0.00676444 -C 1.26555783 1.99719410 -0.00007703 -O 1.24507069 3.19946589 0.00681275 -H -0.94690483 1.77678142 0.01852668 -H 2.19534410 1.39121374 -0.01006739 -10 -Properties=species:S:1:pos:R:3 A=80.28766 B=3.3688 C=3.3688 mu=0 alpha=42.32 epsHOMO=-0.2412 epsLUMO=0.0684 epsgap=0.3096 =400.2236 zpve=0.083896 U0=-155.908941 U=-155.90318 H=-155.902236 G=-155.937641 Cv=17.447 pbc="F F F" -C -0.01787959 1.46676893 0.01011180 -C 0.00186644 0.01072913 0.00211109 -C 0.01819704 -1.19272861 -0.00450723 -C 0.03795170 -2.64876793 -0.01251193 -H 0.86460312 1.87589979 0.51479719 -H -0.03217624 1.87210581 -1.00791002 -H -0.90233115 1.85185524 0.53003953 -H -0.83477011 -3.06339007 0.50451436 -H 0.03276422 -3.04315564 -1.03491223 -H 0.93213486 -3.03931642 0.48626606 -10 -Properties=species:S:1:pos:R:3 A=27.39459 B=4.53005 C=4.08342 mu=0.7067 alpha=40.09 epsHOMO=-0.2592 epsLUMO=0.0566 epsgap=0.3157 =333.9589 zpve=0.084338 U0=-155.897345 U=-155.892291 H=-155.891347 G=-155.924226 Cv=17.13 pbc="F F F" -C -0.03095840 1.54775025 0.03167945 -C 0.01485375 0.00962471 -0.02082154 -C 0.69099073 -0.49954575 -1.20857624 -C 1.25222275 -0.89514467 -2.19527879 -H 0.97899300 1.96621609 0.03373834 -H -0.56082354 1.95002902 -0.83580745 -H -0.54576708 1.88225519 0.93709062 -H 0.51920135 -0.37425398 0.87496224 -H -1.00644802 -0.39030838 0.01340632 -H 1.74594541 -1.25604187 -3.06299415 -9 -Properties=species:S:1:pos:R:3 A=28.0016 B=4.67752 C=4.21765 mu=3.9233 alpha=35.38 epsHOMO=-0.3213 epsLUMO=0.034 epsgap=0.3553 =314.5335 zpve=0.07419 U0=-172.006141 U=-172.001467 H=-172.000523 G=-172.032826 Cv=14.988 pbc="F F F" -C -0.02522005 1.54773054 0.02133370 -C 0.01183939 0.00999278 -0.01556702 -C 0.68915114 -0.50836846 -1.20331488 -N 1.22565733 -0.90360846 -2.14737482 -H 0.98571620 1.96212990 0.02370153 -H -0.55522192 1.94599662 -0.84711771 -H -0.53962556 1.88294354 0.92536604 -H 0.52481910 -0.38226048 0.86933355 -H -1.00453542 -0.39832109 0.00530071 -8 -Properties=species:S:1:pos:R:3 A=32.19893 B=4.76299 C=4.30696 mu=4.4361 alpha=31.81 epsHOMO=-0.2683 epsLUMO=0.0173 epsgap=0.2855 =295.6635 zpve=0.063305 U0=-188.042067 U=-188.037478 H=-188.036534 G=-188.06863 Cv=14.488 pbc="F F F" -N -0.03608099 1.48762533 0.03678072 -C -0.01039471 0.02517605 -0.01506608 -C -1.37180956 -0.52108920 0.02739272 -N -2.44026695 -0.95956444 0.03370519 -H 0.91729752 1.83547547 0.05202682 -H -0.47162178 1.85677205 -0.80275569 -H 0.48946161 -0.40880401 -0.89715168 -H 0.52827150 -0.34533338 0.86469547 -8 -Properties=species:S:1:pos:R:3 A=35.11615 B=4.66117 C=4.22354 mu=1.7211 alpha=33.1 epsHOMO=-0.2595 epsLUMO=0.0277 epsgap=0.2872 =300.0993 zpve=0.060632 U0=-191.810916 U=-191.806025 H=-191.805081 G=-191.837634 Cv=15.855 pbc="F F F" -O -0.02013197 1.45738137 0.01010195 -C 0.00168478 0.03579980 0.00224736 -C -1.36366691 -0.47481101 0.01255056 -C -2.47902835 -0.91970202 0.02082451 -H 0.89446911 1.75736553 0.00327363 -H 0.51979028 -0.35175413 -0.88980302 -H 0.53670458 -0.36127661 0.88000894 -H -3.46773502 -1.30737009 0.02818120 -7 -Properties=species:S:1:pos:R:3 A=35.93172 B=4.83443 C=4.37827 mu=4.6788 alpha=28.56 epsHOMO=-0.3018 epsLUMO=0.0022 epsgap=0.3039 =280.6659 zpve=0.050262 U0=-207.916786 U=-207.912215 H=-207.911271 G=-207.943384 Cv=13.845 pbc="F F F" -O -0.03738221 1.44547841 0.01010769 -C 0.01292906 0.03441661 0.00209165 -C -1.35747452 -0.48874523 0.01239738 -N -2.42135277 -0.93685184 0.02016053 -H 0.86898057 1.76972949 0.00417577 -H 0.51692713 -0.36329790 -0.89215814 -H 0.53387616 -0.37278749 0.88223921 -10 -Properties=species:S:1:pos:R:3 A=26.14564 B=4.2893 C=4.12773 mu=2.6741 alpha=35.83 epsHOMO=-0.25 epsLUMO=-0.0205 epsgap=0.2295 =333.3276 zpve=0.084175 U0=-193.075202 U=-193.070116 H=-193.069171 G=-193.102798 Cv=15.954 pbc="F F F" -C 0.16879340 1.52513042 -0.15747056 -C -0.18737625 0.06194763 0.14679374 -C 0.50917645 -0.43996215 1.39125850 -O 1.18191079 -1.43565585 1.45816384 -H 1.23126511 1.63137254 -0.39632693 -H -0.40744668 1.89431681 -1.00969246 -H -0.04931034 2.17266260 0.69857432 -H -1.26905160 -0.01668067 0.32854433 -H 0.06279062 -0.60250479 -0.68474036 -H 0.35384841 0.20663370 2.28871052 -9 -Properties=species:S:1:pos:R:3 A=19.98749 B=6.11589 C=4.82584 mu=3.7071 alpha=32.78 epsHOMO=-0.2516 epsLUMO=0.0335 epsgap=0.2851 =279.7863 zpve=0.074166 U0=-209.144909 U=-209.139976 H=-209.139032 G=-209.172305 Cv=15.058 pbc="F F F" -C 0.01474916 1.47888256 -0.03509326 -N 0.18708077 0.03831317 -0.02906851 -C 0.45703661 -0.66858895 1.10069001 -O 0.58457996 -0.19876202 2.21119705 -H 0.76025289 1.96736104 -0.67198126 -H -0.98462423 1.75984483 -0.38485103 -H 0.14269849 1.82387718 0.99149397 -H 0.10386349 -0.46937726 -0.89461379 -H 0.55047568 -1.75253452 0.88868242 -8 -Properties=species:S:1:pos:R:3 A=48.62351 B=4.67298 C=4.38187 mu=3.92 alpha=29.47 epsHOMO=-0.2814 epsLUMO=0.0074 epsgap=0.2888 =293.917 zpve=0.061327 U0=-229.013797 U=-229.009003 H=-229.008059 G=-229.041086 Cv=13.885 pbc="F F F" -C 0.10653781 1.43512049 0.03336100 -O 0.42686564 0.04716929 0.09309945 -C 0.50714862 -0.63652665 -1.06318297 -O 0.76927014 -1.79955017 -1.11010475 -H 0.92349251 2.00385139 0.48465728 -H -0.03757815 1.76927183 -1.00057425 -H -0.81269569 1.60801916 0.59873287 -H 0.30458460 -0.00231902 -1.94965553 -8 -Properties=species:S:1:pos:R:3 A=36.49282 B=4.31175 C=4.04493 mu=1.7341 alpha=28.53 epsHOMO=-0.2537 epsLUMO=-0.0341 epsgap=0.2196 =303.8129 zpve=0.060508 U0=-228.992613 U=-228.987769 H=-228.986825 G=-229.019918 Cv=14.78 pbc="F F F" -O 0.19375856 1.37170995 0.31398297 -C 0.04992637 0.01405481 -0.04526239 -C -1.40888025 -0.39635680 -0.20601574 -O -1.77729462 -1.37233860 -0.80361689 -H -0.01723186 1.91269132 -0.45337758 -H 0.60440478 -0.26519766 -0.95205725 -H 0.46740406 -0.57521679 0.78176270 -H -2.11835055 0.30075962 0.29870515 -14 -Properties=species:S:1:pos:R:3 A=23.44662 B=3.60588 C=3.38683 mu=0 alpha=45.71 epsHOMO=-0.317 epsLUMO=0.0937 epsgap=0.4107 =426.2996 zpve=0.131708 U0=-158.340943 U=-158.33517 H=-158.334226 G=-158.36894 Cv=19.668 pbc="F F F" -C -0.03007624 1.55804912 0.02980774 -C 0.00707277 0.02834877 -0.00712529 -C 0.71320792 -0.52989340 -1.24765581 -C 0.75036895 -2.05959366 -1.28458957 -H 0.98195330 1.97845113 0.02800797 -H -0.55582433 1.96212343 -0.84270157 -H -0.54083762 1.92692755 0.92493697 -H 0.50928672 -0.34909049 0.89361319 -H -1.01733481 -0.36542299 0.03207393 -H 0.21098948 -0.15245770 -2.14839302 -H 1.73761331 -0.13611625 -1.28685672 -H 1.27616854 -2.46366410 -0.41210980 -H -0.26165699 -2.48000447 -1.28273552 -H 1.26108491 -2.42846444 -2.17974803 -12 -Properties=species:S:1:pos:R:3 A=26.59789 B=3.79186 C=3.53779 mu=1.3402 alpha=38.61 epsHOMO=-0.2619 epsLUMO=0.081 epsgap=0.3429 =382.8628 zpve=0.108241 U0=-194.261089 U=-194.255495 H=-194.254551 G=-194.28893 Cv=18.431 pbc="F F F" -C -0.00379012 1.52196790 0.01027996 -C -0.03589372 -0.00778254 0.00225671 -C -1.45219016 -0.56653711 0.01284386 -O -1.36799785 -1.98394604 0.00453606 -H 1.02477576 1.89411231 0.00239793 -H -0.51286674 1.93764614 -0.86653888 -H -0.49584080 1.92801344 0.90121721 -H 0.48140784 -0.39530552 -0.88316064 -H 0.49828589 -0.40486256 0.87330348 -H -1.98734220 -0.20399494 0.90666062 -H -2.00431485 -0.19459473 -0.86666460 -H -2.26171819 -2.33824298 0.01034748 -12 -Properties=species:S:1:pos:R:3 A=28.29567 B=4.14901 C=3.88349 mu=1.0363 alpha=39.34 epsHOMO=-0.2503 epsLUMO=0.0925 epsgap=0.3428 =368.9331 zpve=0.107895 U0=-194.254127 U=-194.248585 H=-194.247641 G=-194.281899 Cv=17.888 pbc="F F F" -C -0.00519551 1.50868224 0.03308760 -C 0.01316580 -0.01037819 0.01988643 -O 0.69758944 -0.46344140 1.16868979 -C 0.76688491 -1.86508183 1.24602669 -H 1.01445476 1.90423799 0.02996018 -H -0.52990866 1.89184059 -0.84726911 -H -0.51263765 1.87805843 0.92875424 -H -1.01599507 -0.40998729 0.00714282 -H 0.51093143 -0.38369435 -0.89219395 -H -0.23415841 -2.32451489 1.28977780 -H 1.30129276 -2.29853663 0.38486095 -H 1.30990287 -2.11811998 2.16013206 -10 -Properties=species:S:1:pos:R:3 A=14.79671 B=5.6956 C=4.58846 mu=0.0075 alpha=31.42 epsHOMO=-0.2594 epsLUMO=0.0584 epsgap=0.3179 =297.8398 zpve=0.085172 U0=-230.183076 U=-230.177723 H=-230.176779 G=-230.211195 Cv=16.837 pbc="F F F" -O -0.01418735 1.42642242 -0.05423651 -C -0.00391839 0.00717714 0.03496062 -C -1.42317019 -0.55560600 -0.01256639 -O -2.11229413 -0.10563875 -1.17228313 -H -0.18987882 1.64154705 -0.97555148 -H 0.59659504 -0.43315430 -0.77409095 -H 0.47311116 -0.24903733 0.98747216 -H -1.39494859 -1.64975279 -0.06283188 -H -1.96497417 -0.27216915 0.90131972 -H -2.35925991 0.80944396 -1.00614597 -12 -Properties=species:S:1:pos:R:3 A=15.61472 B=6.31363 C=5.54514 mu=0.1136 alpha=41.96 epsHOMO=-0.2727 epsLUMO=0.1012 epsgap=0.3738 =298.6061 zpve=0.109284 U0=-157.116735 U=-157.11209 H=-157.111146 G=-157.143262 Cv=16.49 pbc="F F F" -C -0.03602245 1.52092949 0.01569161 -C 0.01938487 0.01004403 0.04814953 -C 1.30891671 -0.71251995 -0.24920416 -C 0.75347423 -0.70267714 1.15564676 -H 0.90105691 1.95389291 0.38283338 -H -0.19640365 1.89437369 -1.00208569 -H -0.84685273 1.90588930 0.64451322 -H -0.88416809 -0.48001889 -0.30566335 -H 1.26900901 -1.63903068 -0.81150371 -H 2.19010738 -0.11131209 -0.44927513 -H 1.26248862 -0.09487455 1.89690028 -H 0.33540836 -1.62248918 1.54980695 -10 -Properties=species:S:1:pos:R:3 A=18.21931 B=6.63877 C=5.92459 mu=1.812 alpha=35.01 epsHOMO=-0.2633 epsLUMO=0.1052 epsgap=0.3685 =267.2979 zpve=0.085275 U0=-193.039603 U=-193.035186 H=-193.034242 G=-193.065979 Cv=14.764 pbc="F F F" -C -0.01646343 1.51830705 0.00771445 -C -0.02743227 0.01657694 -0.10889741 -C 0.77296553 -0.70131886 -1.10863115 -O 1.17212293 -0.66702969 0.26278829 -H 0.91865980 1.92914217 -0.38285880 -H -0.85158014 1.95456506 -0.55109460 -H -0.11487702 1.82596470 1.05397777 -H -0.93582598 -0.47350600 0.24682793 -H 0.44443922 -1.67201495 -1.47810622 -H 1.40538284 -0.13600339 -1.79271491 -11 -Properties=species:S:1:pos:R:3 A=16.58914 B=7.18798 C=6.11415 mu=1.1353 alpha=39.02 epsHOMO=-0.2304 epsLUMO=0.0968 epsgap=0.3271 =270.5508 zpve=0.097671 U0=-173.147782 U=-173.143343 H=-173.142399 G=-173.174073 Cv=15.298 pbc="F F F" -C -0.05275541 1.47419574 0.00999108 -N 0.01940516 0.02189947 0.06334866 -C 1.32511048 -0.56446850 -0.21420725 -C 0.78051759 -0.54057993 1.17242760 -H 0.86784202 1.97176076 0.36297285 -H -0.23845155 1.79095005 -1.02142866 -H -0.88683448 1.81938959 0.62950398 -H 1.31992909 -1.48186782 -0.79602038 -H 2.15202820 0.11020204 -0.43285248 -H 1.24563570 0.14996098 1.87499245 -H 0.37013358 -1.44020447 1.62233365 -10 -Properties=species:S:1:pos:R:3 A=16.79477 B=6.71015 C=5.86781 mu=1.3894 alpha=34.64 epsHOMO=-0.239 epsLUMO=0.0775 epsgap=0.3166 =263.966 zpve=0.085106 U0=-193.034988 U=-193.030356 H=-193.029411 G=-193.061689 Cv=15.679 pbc="F F F" -O 0.21577583 1.35386859 -0.10695107 -C 0.01628099 -0.02832718 -0.02915694 -C -0.39081465 -0.71497009 1.24315669 -C -1.35118138 -0.64280380 0.06273589 -H -0.43642338 1.79062849 0.45006761 -H 0.74981167 -0.52404050 -0.65624767 -H 0.05959375 -1.66840483 1.49729237 -H -0.61369353 -0.08848006 2.10170640 -H -2.19821932 0.03058430 0.15411451 -H -1.56395912 -1.54640426 -0.49826515 -12 -Properties=species:S:1:pos:R:3 A=10.66107 B=10.66107 C=6.30452 mu=0 alpha=41.83 epsHOMO=-0.2982 epsLUMO=0.0956 epsgap=0.3938 =268.4432 zpve=0.110511 U0=-157.115484 U=-157.111322 H=-157.110378 G=-157.141657 Cv=14.696 pbc="F F F" -C -0.02542957 1.54032578 -0.04215944 -C 1.52564399 1.50419989 0.04121276 -C 1.42146727 0.06146790 0.60844885 -C -0.02638833 -0.01070956 0.04935088 -H -0.46591372 1.99639894 0.84962894 -H -0.47900239 2.00028207 -0.92365389 -H 1.98248278 1.51375334 -0.95303183 -H 2.02191365 2.25986579 0.65522129 -H 2.14291679 -0.67956358 0.25531480 -H 1.43105853 0.05751479 1.70261879 -H -0.79053440 -0.48530296 0.66996573 -H -0.05233245 -0.47238446 -0.94236394 -10 -Properties=species:S:1:pos:R:3 A=12.06545 B=11.77731 C=6.71997 mu=1.7978 alpha=34.56 epsHOMO=-0.2424 epsLUMO=0.0859 epsgap=0.3283 =236.9759 zpve=0.086675 U0=-193.034094 U=-193.029968 H=-193.029024 G=-193.060777 Cv=12.915 pbc="F F F" -C -0.03489789 1.55259455 -0.08507691 -C 1.49349183 1.45459700 0.10114677 -O 1.42725884 0.02348990 0.27254886 -C -0.00634758 0.02236494 0.10944630 -H -0.56579210 2.11301721 0.68591527 -H -0.36628352 1.89393380 -1.06692327 -H 2.10031724 1.73525720 -0.76898349 -H 1.90023090 1.95506611 0.98896748 -H -0.51987849 -0.35595179 1.00235948 -H -0.31970062 -0.57567421 -0.75559275 -12 -Properties=species:S:1:pos:R:3 A=8.63483 B=4.11977 C=2.88825 mu=0.6875 alpha=45.37 epsHOMO=-0.2392 epsLUMO=0.0192 epsgap=0.2584 =452.5112 zpve=0.100501 U0=-248.375248 U=-248.368823 H=-248.367879 G=-248.405354 Cv=21.616 pbc="F F F" -C -0.10255370 1.49907127 -0.00133642 -C -0.00519774 -0.00343114 -0.02723470 -C -0.00688536 -0.76890740 1.26434744 -N 0.08186799 -0.69202791 -1.10087306 -O 0.07439067 0.13215394 -2.24195258 -H 0.78342093 1.94690862 -0.46354050 -H -0.96150430 1.83614082 -0.59021965 -H -0.19854173 1.87462989 1.01931607 -H -0.92620012 -0.57297946 1.82982479 -H 0.07256154 -1.84064782 1.07516863 -H 0.82937178 -0.45553340 1.90133089 -H 0.13975924 -0.51254299 -2.95389260 -10 -Properties=species:S:1:pos:R:3 A=9.17136 B=9.04195 C=4.5531 mu=1.8689 alpha=43.14 epsHOMO=-0.2029 epsLUMO=0.0499 epsgap=0.2528 =303.9808 zpve=0.082433 U0=-210.101789 U=-210.097816 H=-210.096872 G=-210.12818 Cv=14.821 pbc="F F F" -N -0.00829694 1.35362838 0.00995971 -C 1.28033254 1.82457421 -0.00022459 -C 2.12216639 0.73698898 -0.01445600 -C 1.30849904 -0.43109580 -0.01284154 -C -0.00342782 -0.01836884 0.00235736 -H -0.83243234 1.92772668 0.02122995 -H 1.48471820 2.88312630 0.00354313 -H 3.20066918 0.77343077 -0.02488416 -H 1.64810599 -1.45539683 -0.02180394 -H -0.92551647 -0.57697054 0.00839402 -9 -Properties=species:S:1:pos:R:3 A=9.77329 B=9.41192 C=4.7946 mu=3.6193 alpha=39.13 epsHOMO=-0.2253 epsLUMO=0.0332 epsgap=0.2585 =283.6817 zpve=0.071145 U0=-226.160842 U=-226.157088 H=-226.156144 G=-226.187104 Cv=13.371 pbc="F F F" -N -0.00996090 1.35636172 0.01000855 -C 1.29535422 1.79575578 -0.00047017 -C 2.05550519 0.65637438 -0.01422927 -N 1.25385745 -0.46275132 -0.01260894 -C 0.02301170 -0.00706382 0.00200146 -H -0.83195718 1.93618436 0.02132575 -H 1.54553961 2.84344998 0.00289054 -H 3.13177434 0.57569766 -0.02524155 -H -0.87667864 -0.60414531 0.00750794 -9 -Properties=species:S:1:pos:R:3 A=9.50228 B=9.29497 C=4.69874 mu=0.5571 alpha=39.2 epsHOMO=-0.2246 epsLUMO=0.0199 epsgap=0.2445 =289.005 zpve=0.069883 U0=-229.969129 U=-229.965414 H=-229.96447 G=-229.995393 Cv=13.358 pbc="F F F" -O -0.03255819 1.35065130 0.00967376 -C 1.25319237 1.79167710 -0.00030351 -C 2.11664681 0.74375623 -0.01441494 -C 1.30397796 -0.43852387 -0.01283316 -C 0.01629568 -0.00777185 0.00191294 -H 1.37951089 2.86161135 0.00427433 -H 3.19376406 0.79865501 -0.02458604 -H 1.63858893 -1.46383271 -0.02156949 -H -0.93736295 -0.50899925 0.00843891 -8 -Properties=species:S:1:pos:R:3 A=10.10926 B=9.69372 C=4.94857 mu=1.5081 alpha=35.17 epsHOMO=-0.2509 epsLUMO=0.001 epsgap=0.2519 =269.2444 zpve=0.058593 U0=-246.02915 U=-246.025614 H=-246.024669 G=-246.055309 Cv=12.04 pbc="F F F" -O -0.03808298 1.35643965 0.00975193 -C 1.26496982 1.76855319 -0.00061744 -C 2.04955750 0.66630033 -0.01406047 -N 1.24227075 -0.46732066 -0.01237541 -C 0.04019820 0.00561577 0.00183569 -H 1.43513907 2.83095265 0.00340823 -H 3.12532898 0.59783015 -0.02480757 -H -0.89387707 -0.53342154 0.00796935 -17 -Properties=species:S:1:pos:R:3 A=4.4178 B=4.41753 C=4.41717 mu=0.0003 alpha=56.01 epsHOMO=-0.3145 epsLUMO=0.0737 epsgap=0.3882 =486.2719 zpve=0.158836 U0=-197.632222 U=-197.625241 H=-197.624297 G=-197.661411 Cv=26.084 pbc="F F F" -C -0.01859029 1.53486266 0.01039904 -C 0.00210320 -0.00413672 0.00196980 -C 0.74643954 -0.51390760 1.24887638 -C -1.44190166 -0.53673615 0.01311457 -C 0.72234971 -0.50049606 -1.26445632 -H 0.99776928 1.94420529 0.00435474 -H -0.54181883 1.92841415 -0.86819911 -H -0.52758402 1.91888376 0.90148530 -H 0.25549576 -0.17602728 2.16833067 -H 0.77815389 -1.60892372 1.27088904 -H 1.77964786 -0.14975834 1.26944035 -H -1.99871895 -0.18888794 -0.86408066 -H -1.45956909 -1.63227235 0.00649916 -H -1.98088827 -0.19979639 0.90561580 -H 1.75482473 -0.13554018 -1.30118083 -H 0.75396989 -1.59524036 -1.29822280 -H 0.21358221 -0.15343900 -2.17067181 -15 -Properties=species:S:1:pos:R:3 A=4.69474 B=4.66938 C=4.47909 mu=1.384 alpha=49.04 epsHOMO=-0.2601 epsLUMO=0.0664 epsgap=0.3265 =449.0573 zpve=0.134977 U0=-233.560626 U=-233.553779 H=-233.552834 G=-233.589759 Cv=25.128 pbc="F F F" -C -0.00855999 1.54270147 0.00152717 -C 0.00506780 0.00702635 0.01856160 -C 0.76137731 -0.51839196 1.24766443 -C -1.42012774 -0.54933183 -0.02116008 -O 0.62590477 -0.48363745 -1.17657057 -H 1.01361406 1.94020776 0.00281128 -H -0.51459669 1.90702466 -0.89705781 -H -0.52214815 1.94882514 0.87918344 -H 0.28130613 -0.20165548 2.17943598 -H 0.79965506 -1.61131746 1.23022549 -H 1.79096916 -0.14072935 1.26083003 -H -1.93854829 -0.19910724 -0.91828281 -H -1.39937632 -1.64259062 -0.04563833 -H -1.98850725 -0.23059656 0.85726520 -H 1.53111188 -0.15488046 -1.19210222 -9 -Properties=species:S:1:pos:R:3 A=10.21876 B=4.04324 C=2.94978 mu=2.8579 alpha=42.02 epsHOMO=-0.2654 epsLUMO=-0.0575 epsgap=0.2079 =416.7799 zpve=0.065175 U0=-229.927277 U=-229.921622 H=-229.920677 G=-229.955843 Cv=18.723 pbc="F F F" -C -0.00693851 1.49559567 -0.01063358 -C -0.02986170 -0.01594623 0.06248673 -O -0.08815239 -0.63215062 1.10334812 -C 0.02336981 -0.70256747 -1.22062379 -C 0.06738132 -1.26058341 -2.28623129 -H 0.90429806 1.83281887 -0.51655384 -H -0.85338524 1.85556926 -0.60552819 -H -0.05237764 1.91099807 0.99695949 -H 0.10466868 -1.77036047 -3.21832617 -8 -Properties=species:S:1:pos:R:3 A=10.16063 B=4.15847 C=3.0056 mu=3.3351 alpha=37.08 epsHOMO=-0.3007 epsLUMO=-0.0889 epsgap=0.2118 =397.7757 zpve=0.05451 U0=-246.027383 U=-246.022024 H=-246.02108 G=-246.055769 Cv=16.963 pbc="F F F" -C -0.01171097 1.49973776 -0.01213268 -C -0.02919740 -0.00338745 0.07151218 -O -0.09164028 -0.64592927 1.08732241 -C 0.03831104 -0.69690258 -1.23748557 -N 0.09136286 -1.22982650 -2.26228068 -H 0.90093132 1.83370670 -0.51814928 -H -0.85663933 1.84864400 -0.61618135 -H -0.06416540 1.92131542 0.99203818 -8 -Properties=species:S:1:pos:R:3 A=10.68423 B=4.3198 C=3.07993 mu=5.1815 alpha=38.31 epsHOMO=-0.2739 epsLUMO=-0.0438 epsgap=0.2301 =384.5574 zpve=0.056619 U0=-242.19573 U=-242.190591 H=-242.189646 G=-242.223513 Cv=17.465 pbc="F F F" -N -0.05232108 1.34991769 0.15535538 -C 0.08549378 -0.01476951 0.02668890 -N 1.14555215 -0.71993906 -0.03966037 -C -1.19426556 -0.71152088 0.01802151 -N -2.22747610 -1.22790543 0.00132148 -H 0.75474469 1.91998737 -0.04411334 -H -0.94458527 1.76600473 -0.05932293 -H 1.98312429 -0.13415500 -0.02878551 -9 -Properties=species:S:1:pos:R:3 A=9.08854 B=4.42607 C=3.03169 mu=0.9461 alpha=36.51 epsHOMO=-0.2538 epsLUMO=-0.0964 epsgap=0.1574 =399.222 zpve=0.064992 U0=-267.10335 U=-267.097658 H=-267.096714 G=-267.132534 Cv=17.806 pbc="F F F" -C -0.43126066 1.30586664 -0.12758585 -C 0.25285715 -0.01026988 0.13138867 -O 0.69872099 -0.36890333 1.19604690 -C 0.38978943 -0.96773680 -1.06226208 -O -0.01611268 -0.71564293 -2.16583718 -H 0.10006565 1.85324490 -0.91332597 -H -1.44270433 1.13069361 -0.50923371 -H -0.46757554 1.89548158 0.78836479 -H 0.90329886 -1.91558845 -0.79531972 -9 -Properties=species:S:1:pos:R:3 A=10.08927 B=4.54115 C=3.13487 mu=2.7707 alpha=38.09 epsHOMO=-0.2561 epsLUMO=-0.064 epsgap=0.192 =384.4827 zpve=0.067668 U0=-263.278851 U=-263.273589 H=-263.272645 G=-263.306835 Cv=17.972 pbc="F F F" -N -0.16032929 1.08528962 -0.45398652 -C 0.18649127 -0.08762327 0.14758021 -N 1.28979627 -0.46427423 0.67927030 -C -0.94204384 -1.08440264 0.20423621 -O -2.01802205 -0.89007687 -0.30583149 -H 0.54574437 1.73837930 -0.74689246 -H -1.04684920 1.09570857 -0.93607796 -H 2.01738195 0.24991828 0.59881811 -H -0.67740148 -2.01003995 0.74746441 -13 -Properties=species:S:1:pos:R:3 A=7.92874 B=3.8272 C=2.82581 mu=0.6578 alpha=51.2 epsHOMO=-0.2589 epsLUMO=0.0571 epsgap=0.316 =481.9854 zpve=0.112471 U0=-195.186772 U=-195.180446 H=-195.179502 G=-195.215658 Cv=22.569 pbc="F F F" -C -0.01763434 1.54859468 -0.00253584 -C -0.01719180 0.00735789 0.03269604 -C 0.75818463 -0.52820155 1.25278862 -C -1.38213303 -0.51768438 0.00775516 -C -2.50796392 -0.94005629 0.00478552 -H 1.00944396 1.92639613 -0.02632830 -H -0.54514614 1.92217052 -0.88393459 -H -0.51243000 1.95339892 0.88560642 -H 0.49366179 -0.34904155 -0.87265890 -H 0.28968458 -0.19345372 2.18334679 -H 0.77833392 -1.62102506 1.25783727 -H 1.78952492 -0.16181727 1.23574954 -H -3.49998244 -1.31810165 -0.00723572 -12 -Properties=species:S:1:pos:R:3 A=7.90009 B=3.95466 C=2.88643 mu=3.9512 alpha=46.23 epsHOMO=-0.318 epsLUMO=0.0365 epsgap=0.3545 =460.694 zpve=0.102281 U0=-211.295796 U=-211.289821 H=-211.288877 G=-211.324525 Cv=20.467 pbc="F F F" -C -0.00242350 1.55035837 0.01056187 -C -0.01856600 0.01004352 -0.02261770 -C -1.45045318 -0.55766178 0.01293347 -C 0.70125749 -0.48583168 -1.20172764 -N 1.26190946 -0.87126766 -2.13619740 -H 1.01959496 1.93673215 0.00349668 -H -0.52972844 1.96141639 -0.85498386 -H -0.50134251 1.90346839 0.91755862 -H 0.52481877 -0.36222456 0.85525462 -H -2.02427903 -0.21428716 -0.85251869 -H -1.44438296 -1.65026927 0.00754107 -H -1.95805494 -0.21725882 0.91996760 -11 -Properties=species:S:1:pos:R:3 A=8.30095 B=3.97615 C=2.93764 mu=2.7429 alpha=42.87 epsHOMO=-0.2704 epsLUMO=0.0278 epsgap=0.2983 =440.0738 zpve=0.091554 U0=-227.338075 U=-227.332253 H=-227.331309 G=-227.366638 Cv=19.946 pbc="F F F" -C -0.01064295 1.54128691 0.00722087 -C -0.00203811 0.00756015 0.03863463 -N 0.76268677 -0.47101822 1.19250856 -C -1.38541954 -0.52099256 -0.00194097 -N -2.45680847 -0.95636072 0.00694969 -H 1.02043523 1.90106589 0.00392365 -H -0.52873161 1.91193452 -0.88031480 -H -0.51849536 1.94150431 0.89013197 -H 0.50418828 -0.35805500 -0.86276072 -H 0.30070681 -0.18364934 2.05155305 -H 0.78148992 -1.48670299 1.20428183 -11 -Properties=species:S:1:pos:R:3 A=8.55919 B=3.89952 C=2.91869 mu=1.3582 alpha=44.03 epsHOMO=-0.2665 epsLUMO=0.0336 epsgap=0.3001 =444.6452 zpve=0.088908 U0=-231.108368 U=-231.102292 H=-231.101348 G=-231.137061 Cv=21.306 pbc="F F F" -C -0.00754278 1.55566144 0.08650633 -C 0.01207541 0.02096553 0.04935916 -O -1.29150251 -0.52785128 0.22126386 -C 0.65093362 -0.47718651 -1.17430507 -C 1.13902673 -0.86577343 -2.20205518 -H 1.00824243 1.95969458 0.04929733 -H -0.56272245 1.95192010 -0.76978085 -H -0.49328161 1.88929438 1.00704123 -H 0.57811967 -0.34464907 0.91458864 -H -1.78516532 -0.35903262 -0.58909400 -H 1.58078877 -1.22365060 -3.09919447 -10 -Properties=species:S:1:pos:R:3 A=8.55908 B=4.01671 C=2.97882 mu=3.269 alpha=39.28 epsHOMO=-0.3051 epsLUMO=0.0115 epsgap=0.3166 =424.3395 zpve=0.078602 U0=-247.214861 U=-247.209162 H=-247.208218 G=-247.243338 Cv=19.252 pbc="F F F" -C -0.00992462 1.54951908 0.00732200 -C 0.00740485 0.01722020 0.05031270 -O 0.72085722 -0.49630479 1.15864583 -C -1.36995095 -0.52355929 0.00894432 -N -2.45081572 -0.93233138 0.00716912 -H 1.01892314 1.91630447 0.02724149 -H -0.50261490 1.90969362 -0.89997951 -H -0.54800426 1.95377611 0.87053664 -H 0.53602846 -0.36494195 -0.82970945 -H 0.27702643 -0.20644534 1.96421773 -13 -Properties=species:S:1:pos:R:3 A=7.66289 B=3.70925 C=2.79937 mu=2.6921 alpha=46.58 epsHOMO=-0.2469 epsLUMO=-0.0188 epsgap=0.2281 =482.0475 zpve=0.112328 U0=-232.364952 U=-232.358577 H=-232.357633 G=-232.394589 Cv=21.434 pbc="F F F" -C -0.02629647 1.53877116 -0.00783747 -C 0.01468904 0.00095310 0.00166037 -C 0.74737862 -0.53388859 1.24420471 -C -1.40211264 -0.53859257 -0.01557820 -O -1.87620455 -1.23582351 -0.87399559 -H 0.98823696 1.94625080 0.03705522 -H -0.50725090 1.92153422 -0.91285565 -H -0.57756514 1.92360883 0.85796386 -H 0.50584680 -0.36421091 -0.90619124 -H 0.22958380 -0.23909403 2.16431052 -H 0.81692736 -1.62559499 1.22973438 -H 1.76267153 -0.12846566 1.29050755 -H -2.00861510 -0.22792742 0.87121666 -11 -Properties=species:S:1:pos:R:3 A=8.31096 B=4.07963 C=2.92918 mu=2.8354 alpha=39.16 epsHOMO=-0.255 epsLUMO=-0.0279 epsgap=0.2271 =432.1489 zpve=0.088443 U0=-268.287661 U=-268.281505 H=-268.280561 G=-268.316982 Cv=20.344 pbc="F F F" -C -0.05483118 1.46032843 -0.17068647 -C 0.07105714 -0.03524485 0.11225841 -O 0.58204486 -0.33316656 1.40176256 -C -1.27639279 -0.74383027 0.03051185 -O -2.11855775 -0.49643175 -0.79163412 -H 0.93158072 1.93091398 -0.23353204 -H -0.57713995 1.61290939 -1.11835562 -H -0.62444315 1.94776119 0.62573670 -H 0.69687521 -0.50071620 -0.67159471 -H 1.45603316 0.06066659 1.47858584 -H -1.40723217 -1.53388627 0.80211181 -12 -Properties=species:S:1:pos:R:3 A=8.9331 B=4.19405 C=2.96046 mu=3.7163 alpha=44.42 epsHOMO=-0.2424 epsLUMO=0.0327 epsgap=0.2751 =441.85 zpve=0.10227 U0=-248.430371 U=-248.424309 H=-248.423365 G=-248.459383 Cv=19.918 pbc="F F F" -C -0.09776499 1.44917268 0.04561984 -N 0.01043053 0.00373818 -0.00070600 -C 0.05323028 -0.71432112 1.25264891 -C 0.06728716 -0.64067225 -1.20060778 -O 0.03636432 -0.11006515 -2.29109228 -H 0.75650660 1.88747109 0.57647421 -H -0.11648577 1.81632103 -0.98049172 -H -1.01641106 1.75521548 0.56151415 -H -0.85453556 -0.53657675 1.84390168 -H 0.13297722 -1.78696336 1.05851351 -H 0.91637348 -0.40737846 1.85761345 -H 0.14762240 -1.73801339 -1.06805139 -11 -Properties=species:S:1:pos:R:3 A=10.13982 B=3.81346 C=2.86965 mu=2.9514 alpha=39.33 epsHOMO=-0.2699 epsLUMO=-0.0262 epsgap=0.2437 =440.1727 zpve=0.088924 U0=-268.301176 U=-268.295084 H=-268.29414 G=-268.331307 Cv=19.824 pbc="F F F" -C -0.01708808 1.44281136 0.03225501 -C -0.05544542 -0.06560572 -0.02375999 -O -0.17379913 -0.76196233 0.96223129 -C 0.06068107 -0.73615179 -1.38825252 -O 0.00877771 -2.12574390 -1.27749486 -H 0.92062950 1.81463913 -0.39522215 -H -0.83050523 1.86542281 -0.56780350 -H -0.10911975 1.78049045 1.06493675 -H -0.75015067 -0.35235060 -2.03056074 -H 1.00312898 -0.40389029 -1.85592942 -H -0.09063255 -2.29450960 -0.32608019 -13 -Properties=species:S:1:pos:R:3 A=9.55216 B=3.57844 C=2.7341 mu=2.6168 alpha=46.19 epsHOMO=-0.2423 epsLUMO=-0.0072 epsgap=0.2351 =489.8518 zpve=0.112006 U0=-232.377706 U=-232.371073 H=-232.370129 G=-232.408256 Cv=21.716 pbc="F F F" -C -0.04384102 1.54099793 -0.03913814 -C 0.02765132 0.01885817 0.04778773 -C 0.76814644 -0.48842218 1.28000847 -C 0.87850208 -1.99746312 1.42393323 -O 1.24179142 0.26651340 2.09788702 -H 0.95796454 1.97690198 -0.06816012 -H -0.58447717 1.85356031 -0.93690369 -H -0.55173626 1.95737759 0.83440529 -H -0.97692824 -0.42709877 0.05353178 -H 0.51853486 -0.40806507 -0.83809409 -H 1.39924980 -2.42678188 0.56053177 -H 1.42001187 -2.24449367 2.33776426 -H -0.11750602 -2.45360651 1.45141965 -12 -Properties=species:S:1:pos:R:3 A=9.78802 B=3.78441 C=2.84416 mu=3.499 alpha=42.54 epsHOMO=-0.2438 epsLUMO=0.0355 epsgap=0.2793 =457.447 zpve=0.101847 U0=-248.448467 U=-248.441988 H=-248.441044 G=-248.478935 Cv=21.374 pbc="F F F" -C -0.00220144 1.53344329 -0.05960369 -C -0.03235837 0.01206167 0.06840265 -C 0.69666086 -0.48196462 1.31828322 -N 0.98104007 -1.82034162 1.31984698 -O 1.00392124 0.24606232 2.24136749 -H 1.02111190 1.89525169 -0.19182672 -H -0.59707204 1.86206427 -0.91645604 -H -0.39728839 1.99920712 0.84545201 -H -1.06936083 -0.34552935 0.13040806 -H 0.39947165 -0.46906818 -0.81777020 -H 0.63281951 -2.43917647 0.60874785 -H 1.35708014 -2.22217793 2.16303522 -12 -Properties=species:S:1:pos:R:3 A=10.01185 B=3.84195 C=2.87635 mu=3.5402 alpha=43.61 epsHOMO=-0.2418 epsLUMO=0.0387 epsgap=0.2805 =458.9803 zpve=0.101735 U0=-248.443503 U=-248.436899 H=-248.435955 G=-248.474272 Cv=20.967 pbc="F F F" -C 0.00619941 1.45933202 -0.03893859 -N -0.06411616 0.01422963 -0.12046901 -C -1.20595394 -0.68196796 0.16117120 -C -1.08982704 -2.19284899 0.02095775 -O -2.24482911 -0.14443077 0.49994418 -H 0.75053989 1.78372520 0.69715573 -H 0.25692001 1.90395263 -1.00876215 -H -0.97856026 1.81121438 0.27082194 -H 0.75820982 -0.49543660 -0.39464024 -H -0.09909703 -2.53543104 -0.28970003 -H -1.34097671 -2.65395851 0.97972885 -H -1.82903100 -2.53483935 -0.70813076 -11 -Properties=species:S:1:pos:R:3 A=10.44045 B=4.04692 C=2.98547 mu=3.5648 alpha=39.51 epsHOMO=-0.2436 epsLUMO=0.0599 epsgap=0.3034 =428.404 zpve=0.091713 U0=-264.504487 U=-264.498452 H=-264.497508 G=-264.533633 Cv=20.274 pbc="F F F" -C 0.00922630 1.45021814 -0.03194543 -N -0.01429488 -0.00054646 -0.01782667 -C -1.21576579 -0.67252403 0.04421896 -N -1.09733352 -2.04644898 -0.15997741 -O -2.28599299 -0.12893711 0.24196027 -H 0.89659038 1.81885022 0.49120949 -H 0.00233950 1.86388504 -1.04914155 -H -0.88562684 1.79943346 0.48353298 -H 0.75257305 -0.47506631 -0.46719265 -H -0.24882333 -2.48772194 0.16375654 -H -1.93419610 -2.53898393 0.11482880 -12 -Properties=species:S:1:pos:R:3 A=9.69683 B=4.1533 C=3.01703 mu=1.1876 alpha=43.57 epsHOMO=-0.2595 epsLUMO=0.0352 epsgap=0.2948 =443.1687 zpve=0.102062 U0=-248.416462 U=-248.410358 H=-248.409414 G=-248.445651 Cv=20.256 pbc="F F F" -C -0.09160695 1.37540046 -0.00729426 -O -0.06964534 -0.04831581 -0.11148812 -C 0.68114625 -0.56142673 -1.10732458 -C 0.60405298 -2.06701550 -1.08766892 -N 1.32645533 0.19266343 -1.89967731 -H 0.91463916 1.76843655 0.16257460 -H -0.48562826 1.82624615 -0.92235216 -H -0.73971430 1.60007423 0.84110605 -H -0.43430065 -2.39242162 -1.20325205 -H 1.20627110 -2.50049977 -1.88745640 -H 0.95651337 -2.44976670 -0.12501838 -H 1.83504115 -0.36104361 -2.58486055 -11 -Properties=species:S:1:pos:R:3 A=10.20392 B=4.16834 C=3.07281 mu=1.7569 alpha=39.33 epsHOMO=-0.2685 epsLUMO=0.0174 epsgap=0.2859 =427.6606 zpve=0.089436 U0=-268.32127 U=-268.315051 H=-268.314106 G=-268.351214 Cv=19.501 pbc="F F F" -C 0.07295254 1.38840919 0.06442046 -O 0.21049114 -0.03740028 0.04612736 -C 0.50102805 -0.57224288 -1.15975346 -C 0.62218715 -2.07378323 -1.06030684 -O 0.63922154 0.08224418 -2.16157325 -H 1.00029720 1.87062570 -0.25532064 -H -0.73338957 1.70782089 -0.60080454 -H -0.15751280 1.65154060 1.09692954 -H -0.31330001 -2.50202851 -0.68941906 -H 0.85646588 -2.48430275 -2.04157352 -H 1.40651685 -2.34055496 -0.34634301 -10 -Properties=species:S:1:pos:R:3 A=10.68182 B=4.39532 C=3.17969 mu=2.329 alpha=35.5 epsHOMO=-0.2669 epsLUMO=0.0575 epsgap=0.3244 =398.5908 zpve=0.079271 U0=-284.385189 U=-284.379361 H=-284.378417 G=-284.414085 Cv=19.084 pbc="F F F" -C -0.02645771 1.38305753 0.02085660 -O 0.04139566 -0.04439503 -0.00277452 -C 0.66775338 -0.56001625 -1.09000344 -N 0.72605063 -1.92219983 -0.98822954 -O 1.12952381 0.09648143 -1.99322180 -H 0.97529779 1.82044307 0.02387306 -H -0.57085698 1.76214621 -0.84790425 -H -0.55410722 1.63835731 0.94043594 -H 0.12662696 -2.38401590 -0.32566929 -H 0.97230070 -2.41798421 -1.82732573 -10 -Properties=species:S:1:pos:R:3 A=10.20696 B=3.93135 C=2.9219 mu=4.5676 alpha=35.87 epsHOMO=-0.246 epsLUMO=0.0273 epsgap=0.2733 =418.5967 zpve=0.078222 U0=-284.360325 U=-284.354148 H=-284.353204 G=-284.390143 Cv=20.089 pbc="F F F" -N -0.08115607 1.31807026 0.01276742 -C 0.05442259 -0.04477733 0.08155388 -O 1.12163074 -0.59783950 0.20940553 -C -1.28687786 -0.79011642 0.06178435 -O -1.04605114 -2.13954195 -0.23957158 -H 0.76623366 1.85991128 -0.04483425 -H -0.93021346 1.74567447 -0.31565615 -H -1.74559696 -0.65521334 1.05526700 -H -1.96026061 -0.31149222 -0.67026256 -H -1.85637296 -2.63017646 -0.07625478 -10 -Properties=species:S:1:pos:R:3 A=10.14389 B=4.0714 C=3.02223 mu=5.3004 alpha=35.19 epsHOMO=-0.2527 epsLUMO=0.0208 epsgap=0.2735 =408.0279 zpve=0.080317 U0=-284.372483 U=-284.367172 H=-284.366228 G=-284.40095 Cv=17.931 pbc="F F F" -N 0.01838100 0.93057357 0.04943058 -H -0.70674506 0.56116798 -0.55550134 -H 0.68248307 0.18430002 0.21487099 -H 0.21928084 2.79858558 -0.32670585 -C -0.54586914 1.44688585 1.30274586 -C -0.80466975 2.96030569 1.21052835 -O -0.21468822 3.53648884 0.15531687 -O -1.45295676 3.56338618 2.02143528 -H -1.47662579 0.96512183 1.62072169 -H 0.17781209 1.30324286 2.11157176 -13 -Properties=species:S:1:pos:R:3 A=6.6508 B=4.19317 C=3.39441 mu=2.2854 alpha=42.01 epsHOMO=-0.2597 epsLUMO=0.0631 epsgap=0.3228 =438.3028 zpve=0.11345 U0=-269.479234 U=-269.472993 H=-269.472049 G=-269.508213 Cv=22.072 pbc="F F F" -C -0.02775167 1.53421433 0.00651151 -C 0.08343246 0.01218277 0.05258986 -O 0.95856392 -0.48200710 -0.96694113 -C 0.69767574 -0.49516451 1.35948812 -O 2.02247062 -0.02842530 1.52222186 -H 0.94333763 1.98841434 0.21841589 -H -0.37320516 1.87354041 -0.97716908 -H -0.74773817 1.89256394 0.74926853 -H -0.91671848 -0.43697036 -0.06040528 -H 0.76726710 -0.01412177 -1.78502043 -H 0.11650002 -0.13351914 2.21392431 -H 0.65814685 -1.59563445 1.36274474 -H 2.46378566 -0.22762476 0.68698431 -17 -Properties=species:S:1:pos:R:3 A=7.26511 B=3.32874 C=2.54491 mu=0.0618 alpha=56.26 epsHOMO=-0.3085 epsLUMO=0.085 epsgap=0.3934 =565.8412 zpve=0.159632 U0=-197.629387 U=-197.622325 H=-197.621381 G=-197.659365 Cv=25.169 pbc="F F F" -C 0.05879035 1.56174169 -0.01945240 -C -0.01544024 0.03182306 0.00308332 -C -1.44103237 -0.54862028 0.05028629 -C -1.40777007 -2.07231030 -0.13012010 -C -2.18400529 -0.17659759 1.34114303 -H 1.09435638 1.90233607 -0.11900072 -H -0.50689566 1.97217319 -0.86388098 -H -0.34407951 2.00628082 0.89579522 -H 0.48926758 -0.36011214 -0.88953902 -H 0.55254938 -0.35139446 0.86269887 -H -2.00031575 -0.12153682 -0.79559684 -H -0.85501131 -2.55065810 0.68775438 -H -2.41787513 -2.49543628 -0.13720127 -H -0.91928762 -2.35363802 -1.06914014 -H -3.19100197 -0.60699559 1.35137051 -H -1.65197294 -0.56010584 2.22063481 -H -2.28793759 0.90575132 1.46079572 -15 -Properties=species:S:1:pos:R:3 A=8.11598 B=3.43678 C=2.66869 mu=1.3894 alpha=49.45 epsHOMO=-0.2617 epsLUMO=0.0733 epsgap=0.335 =521.8605 zpve=0.136091 U0=-233.555951 U=-233.549143 H=-233.548199 G=-233.585602 Cv=23.99 pbc="F F F" -C -0.03841227 1.55621396 0.00935837 -C 0.01907135 0.02716079 -0.02074203 -C 0.73141506 -0.52709328 -1.25454422 -C 0.83975868 -2.05206973 -1.22872655 -O -0.01226470 -0.08514394 -2.38973758 -H 0.96845664 1.98863424 0.03526234 -H -0.54377006 1.93824134 -0.88074407 -H -0.57652955 1.91383386 0.89256698 -H 0.53166867 -0.34991486 0.87240999 -H -0.99727186 -0.38638654 -0.00185289 -H 1.74909346 -0.09943025 -1.28826184 -H 1.34143968 -2.42487137 -2.12957624 -H 1.41822012 -2.39229108 -0.36340871 -H -0.15616765 -2.50443859 -1.18308875 -H 0.43090825 -0.40901616 -3.17979642 -15 -Properties=species:S:1:pos:R:3 A=7.55335 B=3.78323 C=2.80566 mu=1.0758 alpha=49.8 epsHOMO=-0.2476 epsLUMO=0.086 epsgap=0.3336 =507.9614 zpve=0.135681 U0=-233.545899 U=-233.539034 H=-233.53809 G=-233.57582 Cv=23.573 pbc="F F F" -C -0.00857605 1.38097833 0.09454670 -O -0.02255106 -0.00739461 -0.13062576 -C -1.30243341 -0.61997805 -0.02088309 -C -1.18687286 -1.97885751 -0.70303918 -C -1.75587824 -0.74662760 1.43714508 -H 1.00321550 1.73258087 -0.12443496 -H -0.71511468 1.90838275 -0.56674280 -H -0.24909065 1.64967237 1.13384681 -H -2.03737978 -0.00432241 -0.56884198 -H -0.44792779 -2.59945605 -0.18596870 -H -2.14751041 -2.50225525 -0.69102581 -H -0.86524212 -1.85860679 -1.74077836 -H -2.73321482 -1.23674908 1.49205117 -H -1.03621350 -1.34483828 2.00544671 -H -1.84898410 0.22935483 1.92198087 -15 -Properties=species:S:1:pos:R:3 A=6.14183 B=5.17679 C=3.79297 mu=0.1068 alpha=52.92 epsHOMO=-0.2633 epsLUMO=0.0893 epsgap=0.3526 =439.1643 zpve=0.137025 U0=-196.409349 U=-196.403398 H=-196.402453 G=-196.437676 Cv=22.258 pbc="F F F" -C -0.03913204 1.50456305 0.04978913 -C 0.00985856 -0.01113880 0.01248619 -C -1.24167981 -0.66663044 -0.53989979 -C 1.33803387 -0.66895748 -0.27400430 -C 0.75126442 -0.72221586 1.11862571 -H 0.88633040 1.92490040 0.45579358 -H -0.18183878 1.92045743 -0.95489291 -H -0.86832076 1.85815718 0.67439648 -H -1.43570890 -0.34344387 -1.56976411 -H -1.15326757 -1.75757800 -0.54433832 -H -2.12221416 -0.40570820 0.05951361 -H 1.34398374 -1.58235208 -0.86005084 -H 2.20196897 -0.03329084 -0.43929326 -H 1.21949576 -0.12246018 1.89246580 -H 0.36153319 -1.67153717 1.47171932 -13 -Properties=species:S:1:pos:R:3 A=6.49534 B=5.52329 C=3.9108 mu=1.8235 alpha=45.7 epsHOMO=-0.2596 epsLUMO=0.091 epsgap=0.3505 =405.521 zpve=0.112851 U0=-232.335768 U=-232.329981 H=-232.329037 G=-232.364091 Cv=20.719 pbc="F F F" -C -0.03970330 1.50954998 -0.04112233 -C -0.03023556 -0.00335329 -0.06498361 -C -1.24613189 -0.68356648 0.52531190 -C 0.78937201 -0.71129193 -1.06027559 -O 1.22546798 -0.60059036 0.29712283 -H 0.88992772 1.90948478 -0.45296558 -H -0.87952723 1.90350108 -0.62376642 -H -0.14448576 1.87590604 0.98604429 -H -2.15218727 -0.41012616 -0.02629495 -H -1.13412584 -1.77005431 0.49747032 -H -1.38586631 -0.38055453 1.56882757 -H 0.50712690 -1.71386874 -1.38085018 -H 1.37088104 -0.14369322 -1.78640853 -13 -Properties=species:S:1:pos:R:3 A=6.57648 B=5.31603 C=3.98453 mu=1.3822 alpha=45.85 epsHOMO=-0.2494 epsLUMO=0.0776 epsgap=0.327 =401.4888 zpve=0.113237 U0=-232.33341 U=-232.327734 H=-232.32679 G=-232.36147 Cv=21.17 pbc="F F F" -C -0.03714052 1.53215920 0.01332310 -C 0.01344335 0.02117019 0.01922005 -O -1.17476104 -0.59735284 -0.40931260 -C 1.30744798 -0.69902106 -0.27389650 -C 0.70844749 -0.71373504 1.12226318 -H 0.89026861 1.96240279 0.40276135 -H -0.17846151 1.91561262 -1.00518522 -H -0.87011232 1.88955201 0.62730953 -H -1.28352265 -0.40225391 -1.34690641 -H 1.23973813 -1.61642115 -0.84783774 -H 2.20543993 -0.11523820 -0.44744474 -H 1.19539159 -0.13889834 1.90240284 -H 0.24735439 -1.63670241 1.45378480 -10 -Properties=species:S:1:pos:R:3 A=12.18399 B=5.1384 C=3.78868 mu=2.5732 alpha=40.19 epsHOMO=-0.263 epsLUMO=0.0277 epsgap=0.2907 =352.0378 zpve=0.080558 U0=-247.201165 U=-247.196699 H=-247.195755 G=-247.22841 Cv=15.51 pbc="F F F" -N -0.04445152 1.25623533 0.31681915 -C -0.03914559 0.04913710 -0.01670558 -C -1.08749619 -1.04164446 -0.11488033 -C 0.09869688 -1.90754844 -0.58225894 -O 0.99647508 -0.76306629 -0.44028010 -H 0.88935980 1.66481350 0.24916893 -H -1.54324114 -1.32235812 0.83572172 -H -1.86910036 -0.85713839 -0.85325910 -H 0.39739753 -2.72227350 0.08158721 -H 0.06945080 -2.25418345 -1.61796084 -11 -Properties=species:S:1:pos:R:3 A=10.79571 B=4.78732 C=3.54292 mu=2.7119 alpha=42.77 epsHOMO=-0.2415 epsLUMO=-0.0194 epsgap=0.2222 =379.6371 zpve=0.090544 U0=-231.15578 U=-231.150804 H=-231.149859 G=-231.184401 Cv=17.076 pbc="F F F" -O -0.00101746 0.01846628 -0.20114742 -C -0.01405811 1.19658059 0.00205306 -C -1.13542911 2.22979148 0.21957980 -C -0.03500884 3.29097637 0.53052223 -C 1.08469664 2.25947795 0.18994331 -H -1.71642814 2.41772662 -0.68901078 -H -1.83392757 1.98415059 1.02444244 -H -0.05534741 4.17585264 -0.10670963 -H -0.02541477 3.61399048 1.57278917 -H 1.81074389 2.03288534 0.97578973 -H 1.63600801 2.46255380 -0.73376244 -10 -Properties=species:S:1:pos:R:3 A=12.23436 B=4.97895 C=3.70561 mu=3.6671 alpha=39.73 epsHOMO=-0.25 epsLUMO=0.038 epsgap=0.288 =355.0934 zpve=0.080442 U0=-247.225618 U=-247.220897 H=-247.219953 G=-247.253218 Cv=16.1 pbc="F F F" -O 0.03184134 -0.03461682 0.00849836 -C -0.02440334 1.16440533 0.02160811 -C -1.14541322 2.23712809 0.03012901 -C 0.00928805 3.27945878 0.04557758 -N 0.93843838 2.14652109 0.03554396 -H -1.77030529 2.23779730 -0.86428437 -H -1.77632809 2.21765386 0.92007934 -H 0.07170564 3.91863516 -0.84072193 -H 0.06622603 3.89826625 0.94657952 -H 1.94616840 2.09535713 0.03855945 -9 -Properties=species:S:1:pos:R:3 A=12.44643 B=5.23328 C=3.86599 mu=3.9339 alpha=35.4 epsHOMO=-0.2788 epsLUMO=0.0089 epsgap=0.2878 =336.0792 zpve=0.068574 U0=-267.106213 U=-267.101929 H=-267.100985 G=-267.13332 Cv=14.483 pbc="F F F" -O 0.04424004 0.03933243 -0.33071104 -C -0.04854479 1.17416230 0.00410076 -C -1.14211656 2.23611850 0.15338361 -C 0.02332538 3.12357010 0.61681625 -O 0.95663808 2.01190122 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"ase.units", - "name": "fs" - } - ], - "atoms": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms" - } - ], - "atoms.calc": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.calc" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.calc" - } - ], - "copy.deepcopy": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "copy", - "module_short": "copy", - "name": "deepcopy" - } - ], - "dyn": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.md.verlet", - "module_short": "ase.md", - "name": "VelocityVerlet" - }, - { - 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"is_explicit": false, - "module": "ase.md", - "module_short": "ase.md", - "name": "VelocityVerlet.run" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "VelocityVerlet.run" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.md.md", - "module_short": "ase.md.md", - "name": "MolecularDynamics.run" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.md", - "module_short": "ase.md", - "name": "MolecularDynamics.run" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "MolecularDynamics.run" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.optimize.optimize", - "module_short": "ase.optimize.optimize", - "name": "Dynamics.run" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.optimize", - "module_short": "ase.optimize", - "name": "Dynamics.run" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Dynamics.run" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.utils", - "module_short": "ase.utils", - "name": "IOContext.run" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "IOContext.run" - } - ], - "frame_ahead": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms" - } - ], - "frame_ahead.get_momenta": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.get_momenta" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.get_momenta" - } - ], - "frame_ahead.get_positions": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.get_positions" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.get_positions" - } - ], - "frame_ahead_trev": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms" - } - ], - "frame_ahead_trev.get_momenta": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.get_momenta" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.get_momenta" - } - ], - "frame_ahead_trev.set_momenta": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.set_momenta" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.set_momenta" - } - ], - "frame_now": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms" - } - ], - "frame_now_trev": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms" - } - ], - "frame_now_trev.get_momenta": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.get_momenta" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.get_momenta" - } - ], - "frame_now_trev.set_momenta": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.set_momenta" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.set_momenta" - } - ], - "get_structure_for_dataset": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - } - ], - "i": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "int" - } - ], - "s": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms" - } - ], - "s.arrays": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "dict" - } - ], - "spacing": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "int" - } - ], - "store_trajectory": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - } - ], - "structures_for_dataset": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "list" - } - ], - "structures_for_dataset.append": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "builtin_function_or_method" - } - ], - "subprocess.run": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "subprocess", - "module_short": "subprocess", - "name": "run" - } - ], - "time_lag": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "int" - } - ], - "trajectory": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "list" - } - ], - "trajectory.append": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "builtin_function_or_method" - } - ] -} diff --git a/docs/src/examples/programmatic/flashmd/flashmd.ipynb b/docs/src/examples/programmatic/flashmd/flashmd.ipynb deleted file mode 100644 index 6333cb2726..0000000000 --- a/docs/src/examples/programmatic/flashmd/flashmd.ipynb +++ /dev/null @@ -1,97 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# Training a FlashMD model\n\nThis tutorial demonstrates how to train a FlashMD model for the direct prediction\nof molecular dynamics. This type of model affords faster MD simulations compared to\nMLIPs by a factor between 10 and 30 (https://arxiv.org/abs/2505.19350).\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import copy\nimport subprocess\n\nimport ase\nimport ase.build\nimport ase.io\nimport ase.units\nfrom ase.calculators.emt import EMT\nfrom ase.md import VelocityVerlet\nfrom ase.md.langevin import Langevin\nfrom ase.md.velocitydistribution import MaxwellBoltzmannDistribution" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data generation\n\nFlashMD models train on molecular dynamics trajectories in the NVE ensemble (i.e.,\nmost often with the velocity Verlet integrator). These trajectories can be generated\nwith almost any MD code (i-PI, LAMMPS, etc.). Here, for simplicity, we will use ASE\nand its built-in EMT potential. In reality, you might want to use a more accurate\nbaseline such as ab initio MD or a machine-learned interatomic potential (MLIP).\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# We start by creating a simple system (a small box of aluminum).\natoms = ase.build.bulk(\"Al\", \"fcc\", cubic=True) * (2, 2, 2)\n\n# We first equilibrate the system at 300K using a Langevin thermostat.\nMaxwellBoltzmannDistribution(atoms, temperature_K=300)\natoms.calc = EMT()\ndyn = Langevin(\n atoms, 2 * ase.units.fs, temperature_K=300, friction=1 / (100 * ase.units.fs)\n)\ndyn.run(1000) # 2 ps equilibration (around 10 ps is better in practice)\n\n# Then, we run a production simulation in the NVE ensemble.\ntrajectory = []\n\n\ndef store_trajectory():\n trajectory.append(copy.deepcopy(atoms))\n\n\ndyn = VelocityVerlet(atoms, 1 * ase.units.fs)\ndyn.attach(store_trajectory, interval=1)\ndyn.run(2000) # 2 ps NVE run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data preparation\n\nNow, we need to generate the training data from the trajectory. FlashMD models\nrequire future positions and momenta as targets. We will save them in an `.xyz`\nfile under the `future_positions` and `future_momenta` keys.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# The FlashMD model will be trained to predict 30 steps into the future, i.e., 30 fs\n# since we ran the reference simulation with a time step of 1 fs. For this type\n# of system, FlashMD is expected to perform well up to around 60-80 fs.\ntime_lag = 30\n\n# We pick starting structures that are 200 steps apart. To avoid wasting training\n# structures, this should be set to be around the expected velocity-velocity\n# autocorrelation time for the system. This is the time scale that quantifies how long\n# it takes for the system to forget its original velocities.\nspacing = 200\n\n\ndef get_structure_for_dataset(frame_now, frame_ahead):\n s = copy.deepcopy(frame_now)\n s.arrays[\"future_positions\"] = frame_ahead.get_positions()\n s.arrays[\"future_momenta\"] = frame_ahead.get_momenta()\n return s\n\n\nstructures_for_dataset = []\nfor i in range(0, len(trajectory) - time_lag, spacing):\n frame_now = trajectory[i]\n frame_ahead = trajectory[i + time_lag]\n s = get_structure_for_dataset(frame_now, frame_ahead)\n structures_for_dataset.append(s)\n\n # Here, we also add the time-reversed pair (optional)\n frame_now_trev = copy.deepcopy(frame_now)\n frame_ahead_trev = copy.deepcopy(frame_ahead)\n frame_now_trev.set_momenta(-frame_now_trev.get_momenta())\n frame_ahead_trev.set_momenta(-frame_ahead_trev.get_momenta())\n s = get_structure_for_dataset(frame_ahead_trev, frame_now_trev)\n structures_for_dataset.append(s)\n\n# Write the structures to an xyz file\nase.io.write(\"flashmd.xyz\", structures_for_dataset)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Training the model\n\nThe dataset is now ready for training. You can now provide it to ``metatrain`` and\ntrain your FlashMD model!\n\nFor example, you can use the following options file:\n\n.. literalinclude:: options.yaml\n :language: yaml\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "subprocess.run([\"mtt\", \"train\", \"options.yaml\"])" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/src/examples/programmatic/flashmd/flashmd.py b/docs/src/examples/programmatic/flashmd/flashmd.py deleted file mode 100644 index 701716923b..0000000000 --- a/docs/src/examples/programmatic/flashmd/flashmd.py +++ /dev/null @@ -1,120 +0,0 @@ -""" -Training a FlashMD model -======================== - -This tutorial demonstrates how to train a FlashMD model for the direct prediction -of molecular dynamics. This type of model affords faster MD simulations compared to -MLIPs by a factor between 10 and 30 (https://arxiv.org/abs/2505.19350). -""" - -# %% -# - -import copy -import subprocess - -import ase -import ase.build -import ase.io -import ase.units -from ase.calculators.emt import EMT -from ase.md import VelocityVerlet -from ase.md.langevin import Langevin -from ase.md.velocitydistribution import MaxwellBoltzmannDistribution - - -# %% -# -# Data generation -# --------------- -# -# FlashMD models train on molecular dynamics trajectories in the NVE ensemble (i.e., -# most often with the velocity Verlet integrator). These trajectories can be generated -# with almost any MD code (i-PI, LAMMPS, etc.). Here, for simplicity, we will use ASE -# and its built-in EMT potential. In reality, you might want to use a more accurate -# baseline such as ab initio MD or a machine-learned interatomic potential (MLIP). - -# We start by creating a simple system (a small box of aluminum). -atoms = ase.build.bulk("Al", "fcc", cubic=True) * (2, 2, 2) - -# We first equilibrate the system at 300K using a Langevin thermostat. -MaxwellBoltzmannDistribution(atoms, temperature_K=300) -atoms.calc = EMT() -dyn = Langevin( - atoms, 2 * ase.units.fs, temperature_K=300, friction=1 / (100 * ase.units.fs) -) -dyn.run(1000) # 2 ps equilibration (around 10 ps is better in practice) - -# Then, we run a production simulation in the NVE ensemble. -trajectory = [] - - -def store_trajectory(): - trajectory.append(copy.deepcopy(atoms)) - - -dyn = VelocityVerlet(atoms, 1 * ase.units.fs) -dyn.attach(store_trajectory, interval=1) -dyn.run(2000) # 2 ps NVE run - - -# %% -# -# Data preparation -# ---------------- -# -# Now, we need to generate the training data from the trajectory. FlashMD models -# require future positions and momenta as targets. We will save them in an `.xyz` -# file under the `future_positions` and `future_momenta` keys. - -# The FlashMD model will be trained to predict 30 steps into the future, i.e., 30 fs -# since we ran the reference simulation with a time step of 1 fs. For this type -# of system, FlashMD is expected to perform well up to around 60-80 fs. -time_lag = 30 - -# We pick starting structures that are 200 steps apart. To avoid wasting training -# structures, this should be set to be around the expected velocity-velocity -# autocorrelation time for the system. This is the time scale that quantifies how long -# it takes for the system to forget its original velocities. -spacing = 200 - - -def get_structure_for_dataset(frame_now, frame_ahead): - s = copy.deepcopy(frame_now) - s.arrays["future_positions"] = frame_ahead.get_positions() - s.arrays["future_momenta"] = frame_ahead.get_momenta() - return s - - -structures_for_dataset = [] -for i in range(0, len(trajectory) - time_lag, spacing): - frame_now = trajectory[i] - frame_ahead = trajectory[i + time_lag] - s = get_structure_for_dataset(frame_now, frame_ahead) - structures_for_dataset.append(s) - - # Here, we also add the time-reversed pair (optional) - frame_now_trev = copy.deepcopy(frame_now) - frame_ahead_trev = copy.deepcopy(frame_ahead) - frame_now_trev.set_momenta(-frame_now_trev.get_momenta()) - frame_ahead_trev.set_momenta(-frame_ahead_trev.get_momenta()) - s = get_structure_for_dataset(frame_ahead_trev, frame_now_trev) - structures_for_dataset.append(s) - -# Write the structures to an xyz file -ase.io.write("flashmd.xyz", structures_for_dataset) - -# %% -# -# Training the model -# ------------------ -# -# The dataset is now ready for training. You can now provide it to ``metatrain`` and -# train your FlashMD model! -# -# For example, you can use the following options file: -# -# .. literalinclude:: options.yaml -# :language: yaml - -subprocess.run(["mtt", "train", "options.yaml"]) diff --git a/docs/src/examples/programmatic/flashmd/flashmd.py.md5 b/docs/src/examples/programmatic/flashmd/flashmd.py.md5 deleted file mode 100644 index 09379633c1..0000000000 --- a/docs/src/examples/programmatic/flashmd/flashmd.py.md5 +++ /dev/null @@ -1 +0,0 @@ -dc5c04be3dfe900edcf26c73a0ad6880 \ No newline at end of file diff --git a/docs/src/examples/programmatic/flashmd/flashmd.rst b/docs/src/examples/programmatic/flashmd/flashmd.rst deleted file mode 100644 index 1e80ddd319..0000000000 --- a/docs/src/examples/programmatic/flashmd/flashmd.rst +++ /dev/null @@ -1,225 +0,0 @@ - -.. DO NOT EDIT. -.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. -.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: -.. "examples/programmatic/flashmd/flashmd.py" -.. LINE NUMBERS ARE GIVEN BELOW. - -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - :ref:`Go to the end ` - to download the full example code. - -.. rst-class:: sphx-glr-example-title - -.. _sphx_glr_examples_programmatic_flashmd_flashmd.py: - - -Training a FlashMD model -======================== - -This tutorial demonstrates how to train a FlashMD model for the direct prediction -of molecular dynamics. This type of model affords faster MD simulations compared to -MLIPs by a factor between 10 and 30 (https://arxiv.org/abs/2505.19350). - -.. GENERATED FROM PYTHON SOURCE LINES 12-26 - -.. code-block:: Python - - - import copy - import subprocess - - import ase - import ase.build - import ase.io - import ase.units - from ase.calculators.emt import EMT - from ase.md import VelocityVerlet - from ase.md.langevin import Langevin - from ase.md.velocitydistribution import MaxwellBoltzmannDistribution - - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 27-35 - -Data generation ---------------- - -FlashMD models train on molecular dynamics trajectories in the NVE ensemble (i.e., -most often with the velocity Verlet integrator). These trajectories can be generated -with almost any MD code (i-PI, LAMMPS, etc.). Here, for simplicity, we will use ASE -and its built-in EMT potential. In reality, you might want to use a more accurate -baseline such as ab initio MD or a machine-learned interatomic potential (MLIP). - -.. GENERATED FROM PYTHON SOURCE LINES 36-61 - -.. code-block:: Python - - - # We start by creating a simple system (a small box of aluminum). - atoms = ase.build.bulk("Al", "fcc", cubic=True) * (2, 2, 2) - - # We first equilibrate the system at 300K using a Langevin thermostat. - MaxwellBoltzmannDistribution(atoms, temperature_K=300) - atoms.calc = EMT() - dyn = Langevin( - atoms, 2 * ase.units.fs, temperature_K=300, friction=1 / (100 * ase.units.fs) - ) - dyn.run(1000) # 2 ps equilibration (around 10 ps is better in practice) - - # Then, we run a production simulation in the NVE ensemble. - trajectory = [] - - - def store_trajectory(): - trajectory.append(copy.deepcopy(atoms)) - - - dyn = VelocityVerlet(atoms, 1 * ase.units.fs) - dyn.attach(store_trajectory, interval=1) - dyn.run(2000) # 2 ps NVE run - - - - - - -.. rst-class:: sphx-glr-script-out - - .. code-block:: none - - - True - - - -.. GENERATED FROM PYTHON SOURCE LINES 62-68 - -Data preparation ----------------- - -Now, we need to generate the training data from the trajectory. FlashMD models -require future positions and momenta as targets. We will save them in an `.xyz` -file under the `future_positions` and `future_momenta` keys. - -.. GENERATED FROM PYTHON SOURCE LINES 69-107 - -.. code-block:: Python - - - # The FlashMD model will be trained to predict 30 steps into the future, i.e., 30 fs - # since we ran the reference simulation with a time step of 1 fs. For this type - # of system, FlashMD is expected to perform well up to around 60-80 fs. - time_lag = 30 - - # We pick starting structures that are 200 steps apart. To avoid wasting training - # structures, this should be set to be around the expected velocity-velocity - # autocorrelation time for the system. This is the time scale that quantifies how long - # it takes for the system to forget its original velocities. - spacing = 200 - - - def get_structure_for_dataset(frame_now, frame_ahead): - s = copy.deepcopy(frame_now) - s.arrays["future_positions"] = frame_ahead.get_positions() - s.arrays["future_momenta"] = frame_ahead.get_momenta() - return s - - - structures_for_dataset = [] - for i in range(0, len(trajectory) - time_lag, spacing): - frame_now = trajectory[i] - frame_ahead = trajectory[i + time_lag] - s = get_structure_for_dataset(frame_now, frame_ahead) - structures_for_dataset.append(s) - - # Here, we also add the time-reversed pair (optional) - frame_now_trev = copy.deepcopy(frame_now) - frame_ahead_trev = copy.deepcopy(frame_ahead) - frame_now_trev.set_momenta(-frame_now_trev.get_momenta()) - frame_ahead_trev.set_momenta(-frame_ahead_trev.get_momenta()) - s = get_structure_for_dataset(frame_ahead_trev, frame_now_trev) - structures_for_dataset.append(s) - - # Write the structures to an xyz file - ase.io.write("flashmd.xyz", structures_for_dataset) - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 108-118 - -Training the model ------------------- - -The dataset is now ready for training. You can now provide it to ``metatrain`` and -train your FlashMD model! - -For example, you can use the following options file: - -.. literalinclude:: options.yaml - :language: yaml - -.. GENERATED FROM PYTHON SOURCE LINES 119-121 - -.. code-block:: Python - - - subprocess.run(["mtt", "train", "options.yaml"]) - - - - -.. rst-class:: sphx-glr-script-out - - .. code-block:: none - - - CompletedProcess(args=['mtt', 'train', 'options.yaml'], returncode=0) - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** (0 minutes 16.440 seconds) - - -.. _sphx_glr_download_examples_programmatic_flashmd_flashmd.py: - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-example - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: flashmd.ipynb ` - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: flashmd.py ` - - .. container:: sphx-glr-download sphx-glr-download-zip - - :download:`Download zipped: flashmd.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/programmatic/flashmd/flashmd.xyz b/docs/src/examples/programmatic/flashmd/flashmd.xyz deleted file mode 100644 index 7a9bb6e0ee..0000000000 --- a/docs/src/examples/programmatic/flashmd/flashmd.xyz +++ /dev/null @@ -1,680 +0,0 @@ -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=0.8948308788199846 free_energy=0.8948308788199846 stress="0.00582921114852916 0.000442369304973728 0.0011759981285591327 0.000442369304973728 0.004847050505392154 1.8183467986477127e-06 0.0011759981285591327 1.8183467986477127e-06 0.007675243103858247" pbc="T T T" -Al 0.16464029 0.00846807 0.08508833 -0.51435652 -1.29123021 -0.40577065 0.06123463 -0.13595049 0.07731856 -1.12368820 -1.14173022 0.32159375 0.02624888 -0.43303143 -0.16939770 0.17687361 -Al 0.04333576 2.12405991 2.14636843 0.32093878 -0.17299006 1.71382084 0.06562479 2.10857776 2.28688450 -0.03325533 -0.03348663 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-0.02406983 4.10656556 4.26687676 -1.00051381 0.85934586 -0.07518529 0.02755296 -0.10461418 0.07870897 -0.31515772 -Al 0.12009291 5.93594422 6.01046922 -1.18594847 0.30481321 -0.29021400 0.01409687 6.01670929 6.01936247 -0.62232620 1.02709759 0.47965827 0.02709685 0.02595310 0.35991925 0.20568057 -Al 2.17590269 3.96699525 6.18607872 1.82666693 0.88078720 0.01094781 2.31621025 4.10654665 6.18066021 0.51549710 1.49197521 -0.08331562 0.03066521 -0.16477845 0.36717608 -0.07313241 -Al 2.11661542 6.14055183 4.02959445 -1.21684111 0.18480678 0.01285619 1.99885366 6.16084043 4.05270339 -0.78737459 0.07326348 0.35179917 0.01482222 -0.05129393 0.08802409 0.16252990 -Al 4.08197493 0.10490606 0.17085796 1.18346222 0.39760916 -0.36612355 4.19092003 0.13248906 0.08093809 0.61699589 0.09336852 -1.26574518 0.01022865 0.01600799 -0.07344046 -0.29331826 -Al 3.93645088 2.18854557 2.09631276 1.82853584 -1.42156010 0.59584715 4.14907873 2.00771831 2.17584454 1.73625133 -1.65709058 0.77212338 0.05895968 0.28880840 -0.31079094 0.11048325 -Al 6.22340708 0.06013780 2.05279540 -1.24895018 0.55835249 -0.97816458 6.08747412 0.10501728 1.95480608 -0.94397278 0.14753550 -0.52447199 0.02477423 -0.20011305 -0.03687653 -0.14529374 -Al 6.02132034 2.07641943 0.14100884 0.65846298 0.04825727 0.02423917 6.14091227 2.06706350 0.11684310 1.45056452 -0.18007649 -0.52704098 0.02479758 0.29892593 -0.11730661 -0.11169449 -Al 4.18660823 -0.00242056 4.12764259 -0.01325988 -0.40875091 -0.61885377 4.14992585 -0.03770428 4.06126252 -0.56901563 -0.18660433 -0.46420485 0.02748321 -0.28348925 0.01573260 -0.08948531 -Al 4.09333604 1.99813612 6.08092681 0.40479253 1.09617456 -0.45695243 4.16603783 2.07017504 6.04884325 0.74864192 0.03050378 -0.02498149 0.00968249 0.27257342 -0.11621141 0.05646164 -Al 6.23948472 -0.22255063 6.05928214 1.59323678 -0.41493906 1.09744752 6.33990711 -0.19936221 6.21256097 -0.07209436 0.78147382 1.34706809 0.04886645 -0.21504796 0.45884785 0.45930897 -Al 6.09879426 1.89696639 4.07909200 -0.70948079 1.25639048 0.10789133 6.02790682 2.05765944 4.11081737 -0.39377453 1.39654577 0.50055116 0.02507895 -0.05684998 0.34628181 0.09833994 -Al 4.15353887 3.98323517 -0.00693228 0.07234339 -0.91889267 0.42736080 4.13333436 3.92650157 0.06146214 -0.32182670 -0.09386815 0.87097930 0.03166976 -0.23582388 0.21337205 0.08628749 -Al 4.13836479 6.07840665 1.93039247 -0.65108121 0.29673392 0.30647594 4.03241715 6.12659693 1.99383823 -1.21564110 0.47432752 0.76998567 0.02564584 -0.23677724 0.16515317 0.23204498 -Al 5.91356098 4.05332772 2.30407210 -0.11207184 -0.57732050 -0.43473224 6.00009513 3.99166004 2.18735879 1.52404557 -0.43452393 -1.67790947 0.04651277 0.65974572 -0.07548183 -0.39155185 -Al 6.04952230 6.03040117 0.00447251 -0.69510041 0.14983012 0.70220847 6.03184281 6.04602950 0.10751122 0.23359967 0.03041771 1.11755336 0.02607780 0.41601265 0.07073372 0.19255014 -Al 3.90492926 4.15165660 4.00534018 1.40576279 -0.78672061 0.03995367 4.05583797 4.06899126 4.06876345 1.18275263 -0.50712983 0.81502571 0.05153768 0.15003627 -0.14429056 0.51414408 -Al 4.07173410 6.18254887 6.19658769 -0.49226230 1.23361909 0.07352391 4.00532367 6.28118217 6.18870997 -0.47037602 0.49815600 -0.18710539 0.00176712 -0.22482859 -0.16464236 -0.11230117 -Al 6.02614131 4.07344579 5.98266586 -0.63584207 0.56407956 0.72323350 5.95129296 4.14512639 6.05337693 -0.64565669 0.77230350 0.62910140 -0.01167201 -0.08023362 0.05346028 -0.05969545 -Al 6.11856558 6.19884837 3.97257857 0.28150990 -0.47468254 -1.39776206 6.11209593 6.11582640 3.89617986 -0.39056651 -0.79178237 0.04705835 0.06848149 -0.20059260 -0.37868432 0.36157672 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=0.9775593220180188 free_energy=0.9775593220180188 stress="0.00561896524110683 0.00010955929300027058 0.0005917793502733804 0.00010955929300027058 0.004925564335948191 0.0001104654741423019 0.0005917793502733804 0.0001104654741423019 0.00593663920646172" pbc="T T T" -Al 0.06123463 -0.13595049 0.07731856 1.12368820 1.14173022 -0.32159375 0.16464029 0.00846807 0.08508833 0.51435652 1.29123021 0.40577065 0.02598423 0.06720061 0.25551433 0.28941604 -Al 0.06562479 2.10857776 2.28688450 0.03325533 0.03348663 -0.72445134 0.04333576 2.12405991 2.14636843 -0.32093878 0.17299006 -1.71382084 0.02256491 -0.22565110 0.11432577 -0.45364058 -Al 2.10411598 0.19875347 2.10572830 1.45828161 0.77836419 1.04256479 2.21922479 0.20052938 2.21911130 0.44642369 -0.68326702 0.86115870 0.03250304 -0.12232050 -0.53465264 0.10968291 -Al 2.07343613 2.03409454 0.21257465 -0.87860336 -0.18665112 -1.78096908 1.96155048 2.02748026 -0.04177685 -0.96270926 0.14683668 -2.36933351 0.03670059 -0.20199496 0.03991207 -0.65029019 -Al 0.06765827 0.10043852 4.11442519 0.00327411 0.12717917 1.13769447 0.05568839 0.05574736 4.18671791 -0.06245496 -0.81230955 0.20747444 0.03628151 -0.17114302 -0.41222499 -0.27086310 -Al -0.09891252 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-0.14281365 0.04460780 -Al 4.19092003 0.13248906 0.08093809 -0.61699589 -0.09336852 1.26574518 4.08197493 0.10490606 0.17085796 -1.18346222 -0.39760916 0.36612355 0.02757163 -0.36567294 -0.10165634 -0.24845931 -Al 4.14907873 2.00771831 2.17584454 -1.73625133 1.65709058 -0.77212338 3.93645088 2.18854557 2.09631276 -1.82853584 1.42156010 -0.59584715 0.05028353 -0.37036750 0.16837018 -0.06963989 -Al 6.08747412 0.10501728 1.95480608 0.94397278 -0.14753550 0.52447199 6.22340708 0.06013780 2.05279540 1.24895018 -0.55835249 0.97816458 0.03575680 0.38855109 -0.26863586 0.41829455 -Al 6.14091227 2.06706350 0.11684310 -1.45056452 0.18007649 0.52704098 6.02132034 2.07641943 0.14100884 -0.65846298 -0.04825727 -0.02423917 0.03669586 0.13526855 -0.05052286 -0.22414578 -Al 4.14992585 -0.03770428 4.06126252 0.56901563 0.18660433 0.46420485 4.18660823 -0.00242056 4.12764259 0.01325988 0.40875091 0.61885377 0.01061884 -0.10696225 0.11600464 0.18260704 -Al 4.16603783 2.07017504 6.04884325 -0.74864192 -0.03050378 0.02498149 4.09333604 1.99813612 6.08092681 -0.40479253 -1.09617456 0.45695243 0.02857475 -0.07814720 -0.49425034 0.26260027 -Al 6.33990711 -0.19936221 6.21256097 0.07209436 -0.78147382 -1.34706809 6.23948472 -0.22255063 6.05928214 -1.59323678 0.41493906 -1.09744752 0.07280774 -0.82921050 0.33606149 -0.26767690 -Al 6.02790682 2.05765944 4.11081737 0.39377453 -1.39654577 -0.50055116 6.09879426 1.89696639 4.07909200 0.70948079 -1.25639048 -0.10789133 0.04101935 0.32693833 -0.22536169 0.15011873 -Al 4.13333436 3.92650157 0.06146214 0.32182670 0.09386815 -0.87097930 4.15353887 3.98323517 -0.00693228 -0.07234339 0.91889267 -0.42736080 0.02816260 -0.00405970 0.27124079 0.16760924 -Al 4.03241715 6.12659693 1.99383823 1.21564110 -0.47432752 -0.76998567 4.13836479 6.07840665 1.93039247 0.65108121 -0.29673392 -0.30647594 0.00215586 -0.09098616 -0.05117162 0.05839465 -Al 6.00009513 3.99166004 2.18735879 -1.52404557 0.43452393 1.67790947 5.91356098 4.05332772 2.30407210 0.11207184 0.57732050 0.43473224 0.03399237 0.33102920 0.15723171 -0.33119674 -Al 6.03184281 6.04602950 0.10751122 -0.23359967 -0.03041771 -1.11755336 6.04952230 6.03040117 0.00447251 0.69510041 -0.14983012 -0.70220847 0.00997002 0.14757047 -0.13376238 0.05685800 -Al 4.05583797 4.06899126 4.06876345 -1.18275263 0.50712983 -0.81502571 3.90492926 4.15165660 4.00534018 -1.40576279 0.78672061 -0.03995367 0.02750048 -0.21361793 0.30143009 -0.08016059 -Al 4.00532367 6.28118217 6.18870997 0.47037602 -0.49815600 0.18710539 4.07173410 6.18254887 6.19658769 0.49226230 -1.23361909 -0.07352391 0.02729147 0.27109491 -0.31810102 -0.05656443 -Al 5.95129296 4.14512639 6.05337693 0.64565669 -0.77230350 -0.62910140 6.02614131 4.07344579 5.98266586 0.63584207 -0.56407956 -0.72323350 0.01327066 0.09816123 0.09290604 0.05317744 -Al 6.11209593 6.11582640 3.89617986 0.39056651 0.79178237 -0.04705835 6.11856558 6.19884837 3.97257857 -0.28150990 0.47468254 1.39776206 0.02946727 -0.18687223 0.12201556 0.46460257 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=0.8431874507022643 free_energy=0.8431874507022643 stress="0.006244031443369388 0.0007165788141183207 -0.0001627289267088679 0.0007165788141183207 0.005467715397826384 0.00047222160456977303 -0.0001627289267088679 0.00047222160456977303 0.0065578157468693605" pbc="T T T" -Al 0.00264000 0.07844353 -0.12467518 -0.24833940 -0.96183864 0.49485711 -0.00613836 -0.01402459 -0.02917609 0.11464281 -0.61415357 1.04937503 0.02354378 0.07510104 -0.00840324 0.37378042 -Al 0.01973625 1.94453284 2.07781540 -1.14858181 0.43868461 0.26323310 -0.06034934 1.99671851 2.07559403 -0.06503116 0.39052762 -0.24704382 0.01148113 0.05369455 0.09880419 -0.20381966 -Al 2.12606659 -0.00311886 2.19972208 0.41302172 -0.56223045 -0.78333704 2.16448074 -0.04161635 2.08799427 0.27800815 0.11297422 -1.05153068 0.01817628 -0.04086683 -0.06843728 -0.30666921 -Al 2.08365382 1.99498282 0.09681029 0.06882021 0.63561182 0.23612025 2.06579896 2.08443873 0.08890775 -0.32866700 0.89063479 -0.32704702 0.00011097 -0.18451322 0.18586768 -0.22949960 -Al 0.12624551 0.03061316 4.28957870 -0.65159606 0.30279168 -0.14250778 0.01688464 0.02731380 4.25777021 -1.11169736 -0.18773383 -0.34981465 0.02738317 -0.41340684 -0.31632109 -0.16491883 -Al -0.12317083 1.98438986 6.08615311 -0.68033828 -1.02831320 0.81904355 -0.13183977 1.92300711 6.19473968 0.38176425 0.04409683 1.08051663 0.07402757 0.51180957 0.15316222 0.16612647 -Al 2.21175225 -0.05970562 6.17702698 1.71592151 -0.01437753 0.71618332 2.33255833 -0.03585943 6.20959721 0.16274206 0.29935664 -0.22485442 0.01697825 -0.15226446 0.25002542 -0.16014115 -Al 1.89525038 2.00666748 4.05656774 -0.19076047 1.70309258 0.77182852 1.93014534 2.15094689 4.16742316 0.67201897 0.56694128 1.13024239 0.03364249 0.44639722 0.06190913 0.26114939 -Al 0.06324146 4.13002584 0.21584986 -2.29720152 -0.73838022 0.28813726 -0.15219333 4.02591028 0.20765365 -1.23671285 -1.01266081 -0.62514988 0.01526475 -0.04390983 -0.24796852 -0.14860606 -Al -0.03568176 6.33257459 2.11146462 -0.42398127 1.76229272 0.33760784 -0.07002781 6.45271076 2.14810086 -0.14533080 0.17006376 0.39452288 -0.00237100 0.05110291 -0.25026777 -0.04388833 -Al 2.10471123 4.07299382 2.11509901 0.47122512 0.43019454 0.05486540 2.11829867 4.14627541 2.10388427 -0.29998206 0.77682088 -0.38295811 0.01438037 -0.14629987 0.20813554 0.00577124 -Al 1.97633693 6.14574144 0.04358988 -1.05480691 0.28916459 1.55234288 1.90811728 6.14999876 0.22260918 -0.17152022 -0.35306556 1.41560132 0.01932936 0.25472308 -0.06090382 0.23382698 -Al 0.05576764 4.07998668 4.01400335 0.57940655 -0.69956036 -0.77452503 0.08668458 4.01869212 3.92835072 -0.17503553 -0.25442022 -0.69464186 0.01526375 -0.04964464 -0.03080400 -0.05355566 -Al 0.02355291 5.97583815 6.21389775 1.29498290 -1.18774745 -1.11923094 0.15227294 5.91293447 6.07765261 0.83971435 0.19996515 -1.17915732 0.03685764 0.07105344 0.27335028 -0.22054127 -Al 2.03002194 4.09589495 5.96428476 -1.40614172 0.07190351 0.68383253 1.89204490 4.05702037 6.10583172 -0.78217195 -0.76552063 1.83558429 0.02500748 -0.18326424 -0.28465803 0.43550242 -Al 2.10294141 6.13517588 4.16052368 0.37528798 0.06546951 -1.32171616 2.12907650 6.12170770 4.01699769 0.07558778 -0.25067742 -1.03910889 0.02080381 -0.08017251 -0.15558951 -0.18798561 -Al 4.05154633 0.04180795 0.09587695 0.65312298 -0.02520858 -1.44851649 4.11018101 -0.00892605 -0.00370261 0.47925477 -0.82387382 -0.06336589 0.03791563 -0.11142811 -0.33346680 0.10579317 -Al 3.99463169 2.11178591 2.08298854 -0.76972578 0.43303785 1.45166045 3.92031111 2.12118979 2.21012431 -0.46042768 -0.30497609 0.66557543 0.04547036 -0.02623818 -0.15264150 0.00397004 -Al 6.06752037 0.16677762 2.13683865 0.78676613 0.52003149 -0.55703345 6.12683194 0.12611308 2.05526672 0.04974244 -1.11123483 -0.86262203 0.05797723 0.02567701 -0.60535123 -0.15552347 -Al 5.86666543 1.98359002 0.13390663 0.14656181 0.34355113 1.00219990 5.94749354 2.06926988 0.20344323 1.06654883 1.03497871 0.17235924 0.03670554 0.54268980 0.34223721 -0.17161289 -Al 4.16577409 0.00121745 4.10194437 1.27315080 0.00074655 -0.12617867 4.26403075 -0.00106685 4.08963455 0.44919701 -0.14322398 -0.23043618 0.01149277 -0.17588287 0.06046008 0.09255009 -Al 4.07582143 2.12841289 6.08245812 1.38235068 -2.27850567 -0.84858267 4.19026486 1.88563097 6.03156973 0.57677380 -1.58734033 -0.07971653 0.02925887 -0.11131245 -0.33460233 0.22692870 -Al 5.98796219 0.06005076 6.14967933 -0.30947349 0.38719007 1.15895734 5.98875373 0.07194356 6.22854943 0.59269769 -0.40128101 0.30465130 0.02411334 0.04497396 -0.01868090 -0.27046677 -Al 6.06100384 2.05139366 3.94813839 -0.49745219 -0.04888184 -0.80333862 6.06592325 2.06154953 3.94251448 0.55104434 0.29164259 0.63815221 0.05209771 0.33987961 0.02698787 0.48412612 -Al 3.98653190 4.16133100 -0.02674004 0.73984761 0.38361114 -0.54089747 4.07643916 4.17447757 -0.05548456 0.74131189 -0.16140478 -0.03102748 0.01030857 0.16089545 -0.14489250 0.22158812 -Al 4.20417461 5.98448795 2.23233022 -0.20346446 -0.31251847 0.04086883 4.17099734 5.99067130 2.19698932 -0.23762055 0.42943188 -0.68889399 0.00680334 -0.18787453 0.23188721 -0.23712622 -Al 6.10462315 4.08037294 2.06163275 0.38894279 0.29950384 -1.10535613 6.12924789 4.11675117 1.96135721 -0.17956907 0.39845727 -0.28799727 0.01783917 0.05689690 -0.00826779 -0.15578421 -Al 6.09358588 6.26490740 -0.03882978 0.91781049 -1.07153163 0.44265129 6.17717319 6.10261802 0.04548933 0.55468604 -1.63616333 0.85314067 0.02017304 -0.05987567 -0.42047706 0.36560357 -Al 4.10680770 3.82744588 4.14546261 -0.81027804 -0.44715914 0.36296012 4.02050430 3.89795647 4.17643776 -0.67074170 1.63304057 0.32819991 0.06887453 -0.05253364 0.72242265 -0.15967413 -Al 4.11682929 5.93532890 6.08322235 0.81699115 0.44743862 -0.34601489 4.19368248 6.01081999 6.04143619 0.35039176 0.86499219 -0.32412669 0.02209869 0.09430284 0.22566179 -0.06844949 -Al 6.30009733 3.91190767 6.18755949 -1.39862320 0.96217439 -1.09305327 6.09853461 4.03300185 6.08106214 -1.90717822 0.93747738 -0.80260399 0.04093503 -0.55378817 0.34079773 0.04585371 -Al 6.19758956 5.96463840 4.22287004 0.06655417 -0.10023746 0.33293893 6.18804712 5.99231814 4.22843248 -0.16444078 0.56632849 -0.37582460 0.01124382 -0.15592133 0.26002436 -0.08430789 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.199462930854991 free_energy=1.199462930854991 stress="0.004767208885128757 0.0018557943876572078 0.0006883010676196985 0.0018557943876572078 0.0037451877241799847 0.00010112084390507357 0.0006883010676196985 0.00010112084390507357 0.005853968447125383" pbc="T T T" -Al -0.00613836 -0.01402459 -0.02917609 -0.11464281 0.61415357 -1.04937503 0.00264000 0.07844353 -0.12467518 0.24833940 0.96183864 -0.49485711 0.03349547 0.13631418 0.22371231 -0.02665473 -Al -0.06034934 1.99671851 2.07559403 0.06503116 -0.39052762 0.24704382 0.01973625 1.94453284 2.07781540 1.14858181 -0.43868461 -0.26323310 0.03069566 0.54953215 -0.15485292 -0.09515438 -Al 2.16448074 -0.04161635 2.08799427 -0.27800815 -0.11297422 1.05153068 2.12606659 -0.00311886 2.19972208 -0.41302172 0.56223045 0.78333704 0.02903386 -0.07183026 0.43753749 0.11822710 -Al 2.06579896 2.08443873 0.08890775 0.32866700 -0.89063479 0.32704702 2.08365382 1.99498282 0.09681029 -0.06882021 -0.63561182 -0.23612025 -0.01261383 -0.05041681 -0.04377495 -0.12021375 -Al 0.01688464 0.02731380 4.25777021 1.11169736 0.18773383 0.34981465 0.12624551 0.03061316 4.28957870 0.65159606 -0.30279168 0.14250778 0.00959253 0.06685513 0.02610194 0.01406195 -Al -0.13183977 1.92300711 6.19473968 -0.38176425 -0.04409683 -1.08051663 -0.12317083 1.98438986 6.08615311 0.68033828 1.02831320 -0.81904355 0.02594094 0.22847308 0.43171417 -0.01594792 -Al 2.33255833 -0.03585943 6.20959721 -0.16274206 -0.29935664 0.22485442 2.21175225 -0.05970562 6.17702698 -1.71592151 0.01437753 -0.71618332 0.04715044 -0.80048541 -0.04283806 -0.36059252 -Al 1.93014534 2.15094689 4.16742316 -0.67201897 -0.56694128 -1.13024239 1.89525038 2.00666748 4.05656774 0.19076047 -1.70309258 -0.77182852 0.04547979 0.13494897 -0.66677570 -0.00006346 -Al -0.15219333 4.02591028 0.20765365 1.23671285 1.01266081 0.62514988 0.06324146 4.13002584 0.21584986 2.29720152 0.73838022 -0.28813726 0.05785902 0.75702091 0.06974274 -0.48877325 -Al -0.07002781 6.45271076 2.14810086 0.14533080 -0.17006376 -0.39452288 -0.03568176 6.33257459 2.11146462 0.42398127 -1.76229272 -0.33760784 0.06474253 0.17077224 -0.77802528 0.07261592 -Al 2.11829867 4.14627541 2.10388427 0.29998206 -0.77682088 0.38295811 2.10471123 4.07299382 2.11509901 -0.47122512 -0.43019454 -0.05486540 0.04325769 -0.29954835 -0.06494235 -0.23070870 -Al 1.90811728 6.14999876 0.22260918 0.17152022 0.35306556 -1.41560132 1.97633693 6.14574144 0.04358988 1.05480691 -0.28916459 -1.55234288 0.03400526 0.30380031 -0.33563679 -0.38137659 -Al 0.08668458 4.01869212 3.92835072 0.17503553 0.25442022 0.69464186 0.05576764 4.07998668 4.01400335 -0.57940655 0.69956036 0.77452503 0.03395830 -0.35545700 0.29317011 0.14656180 -Al 0.15227294 5.91293447 6.07765261 -0.83971435 -0.19996515 1.17915732 0.02355291 5.97583815 6.21389775 -1.29498290 1.18774745 1.11923094 0.05380499 -0.35515339 0.58618643 0.16363494 -Al 1.89204490 4.05702037 6.10583172 0.78217195 0.76552063 -1.83558429 2.03002194 4.09589495 5.96428476 1.40614172 -0.07190351 -0.68383253 0.05655014 0.48087682 -0.23330014 0.28346975 -Al 2.12907650 6.12170770 4.01699769 -0.07558778 0.25067742 1.03910889 2.10294141 6.13517588 4.16052368 -0.37528798 -0.06546951 1.32171616 0.01383164 -0.13423586 -0.02847313 0.33897934 -Al 4.11018101 -0.00892605 -0.00370261 -0.47925477 0.82387382 0.06336589 4.05154633 0.04180795 0.09587695 -0.65312298 0.02520858 1.44851649 0.05512610 -0.01100678 -0.17680588 0.69708319 -Al 3.92031111 2.12118979 2.21012431 0.46042768 0.30497609 -0.66557543 3.99463169 2.11178591 2.08298854 0.76972578 -0.43303785 -1.45166045 0.03966986 0.23246647 -0.23272805 -0.41295966 -Al 6.12683194 0.12611308 2.05526672 -0.04974244 1.11123483 0.86262203 6.06752037 0.16677762 2.13683865 -0.78676613 -0.52003149 0.55703345 0.06854007 -0.45842272 -0.30669064 -0.01343308 -Al 5.94749354 2.06926988 0.20344323 -1.06654883 -1.03497871 -0.17235924 5.86666543 1.98359002 0.13390663 -0.14656181 -0.34355113 -1.00219990 0.02575345 0.02044889 -0.02948428 -0.36560040 -Al 4.26403075 -0.00106685 4.08963455 -0.44919701 0.14322398 0.23043618 4.16577409 0.00121745 4.10194437 -1.27315080 -0.00074655 0.12617867 0.02971316 -0.33221050 -0.12872405 -0.15561704 -Al 4.19026486 1.88563097 6.03156973 -0.57677380 1.58734033 0.07971653 4.07582143 2.12841289 6.08245812 -1.38235068 2.27850567 0.84858267 0.07937888 -0.38169238 0.78551687 0.25347125 -Al 5.98875373 0.07194356 6.22854943 -0.59269769 0.40128101 -0.30465130 5.98796219 0.06005076 6.14967933 0.30947349 -0.38719007 -1.15895734 0.08191988 0.55016627 -0.46022891 -0.23925496 -Al 6.06592325 2.06154953 3.94251448 -0.55104434 -0.29164259 -0.63815221 6.06100384 2.05139366 3.94813839 0.49745219 0.04888184 0.80333862 0.03121993 0.27105175 0.13738104 0.36978353 -Al 4.07643916 4.17447757 -0.05548456 -0.74131189 0.16140478 0.03102748 3.98653190 4.16133100 -0.02674004 -0.73984761 -0.38361114 0.54089747 0.00022805 -0.16847597 -0.18531003 0.13206767 -Al 4.17099734 5.99067130 2.19698932 0.23762055 -0.42943188 0.68889399 4.20417461 5.98448795 2.23233022 0.20346446 0.31251847 -0.04086883 0.01073897 0.13320509 0.24022310 -0.23658737 -Al 6.12924789 4.11675117 1.96135721 0.17956907 -0.39845727 0.28799727 6.10462315 4.08037294 2.06163275 -0.38894279 -0.29950384 1.10535613 0.07379302 -0.39731176 0.04396371 0.70195297 -Al 6.17717319 6.10261802 0.04548933 -0.55468604 1.63616333 -0.85314067 6.09358588 6.26490740 -0.03882978 -0.91781049 1.07153163 -0.44265129 0.01294625 -0.17323240 0.10112039 -0.12388794 -Al 4.02050430 3.89795647 4.17643776 0.67074170 -1.63304057 -0.32819991 4.10680770 3.82744588 4.14546261 0.81027804 0.44715914 -0.36296012 0.04311384 0.13335415 0.51719125 0.05994098 -Al 4.19368248 6.01081999 6.04143619 -0.35039176 -0.86499219 0.32412669 4.11682929 5.93532890 6.08322235 -0.81699115 -0.44743862 0.34601489 0.04040675 -0.38230370 0.08651462 0.12154124 -Al 6.09853461 4.03300185 6.08106214 1.90717822 -0.93747738 0.80260399 6.30009733 3.91190767 6.18755949 1.39862320 -0.96217439 1.09305327 0.01528697 0.21304517 -0.28664949 0.15362757 -Al 6.18804712 5.99231814 4.22843248 0.16444078 -0.56632849 0.37582460 6.19758956 5.96463840 4.22287004 -0.06655417 0.10023746 -0.33293893 0.02484329 -0.01054830 0.17516447 -0.36019346 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=0.9065094691624092 free_energy=0.9065094691624092 stress="0.006507009423339605 0.0024200002914994603 -0.00022075970102456615 0.0024200002914994603 0.003678820038033757 0.0003131131401096385 -0.00022075970102456615 0.0003131131401096385 0.007312699442367703" pbc="T T T" -Al -0.05687315 -0.03388876 0.16287718 -0.62415401 0.48575283 -0.55836291 -0.09751901 0.03599860 0.09321066 -0.12394847 0.73523479 -0.62019847 0.01112616 0.16295799 0.13968044 -0.12882471 -Al -0.12234110 2.19231320 2.15387766 1.00861367 -1.57941151 0.66276345 0.01139616 1.96556019 2.19882218 1.19138721 -2.21416778 -0.02033057 0.03917191 0.35088781 -0.57624461 -0.01860402 -Al 2.09633081 -0.05920609 2.03506729 -0.21253948 0.87178322 -1.02489539 2.03574680 0.05555292 1.96041217 -0.75263694 0.89100467 -0.28676913 0.02203356 -0.28844433 0.33445779 0.17295760 -Al 1.92195757 2.08566514 0.09981783 -0.92722952 1.07135520 0.46979408 1.92061339 2.10189245 0.14542741 0.70217813 -0.55943463 0.26313512 0.05592186 0.63441651 -0.62812231 0.01798893 -Al -0.00741120 -0.03077141 4.04497130 -0.44031493 -0.32289174 1.47291583 -0.03049963 -0.05056105 4.19982037 0.11424348 -0.12324487 1.23577439 -0.00389129 0.08429529 0.14616256 0.05357573 -Al 0.06992523 2.04324862 6.11008585 -0.26161740 -0.57231641 -0.29198561 0.03932700 1.98467390 6.03866854 -0.21793746 -0.35217998 -0.75598771 0.03158290 -0.06447048 -0.05904760 -0.35260513 -Al 1.96694875 0.08177180 6.08827943 1.53776900 -0.23958866 -0.09184192 2.11906023 0.03117454 6.13345218 1.10025815 -0.70347641 0.86064749 0.01614244 0.01636473 -0.13935295 0.32015827 -Al 2.08259697 2.05876291 3.99972874 -0.71842131 -0.82714834 0.00275744 2.00973480 1.97622512 4.02010251 -0.48526775 -0.46694493 0.35788291 0.01335781 -0.05040502 -0.13893131 0.16019511 -Al 0.20340214 3.89848819 -0.04075709 -0.25118538 0.02070701 -0.38765410 0.09294391 4.03739570 -0.04416142 -1.63262323 2.16328731 0.41349061 0.09654046 -0.50035612 0.94298176 0.16323541 -Al 0.07689771 5.95830737 2.05970779 0.11743480 1.13969252 1.04743038 0.10625812 6.11052427 2.14867226 0.36136407 1.64129812 0.42440703 0.01154623 0.12392107 0.15510911 -0.02683190 -Al 2.19147068 3.95328790 2.10796172 -0.00514515 1.84588262 -0.70971788 2.16131747 4.15904964 2.03922631 -0.50961952 1.63928809 -0.41946845 0.02905612 -0.22188394 0.22307029 -0.06689831 -Al 2.03339315 6.16149670 0.04281063 0.52403967 -0.58476155 0.40192600 2.11644421 6.07746229 0.05635386 0.90003300 -0.72050319 -0.08069261 0.00581559 0.21754649 -0.25349589 -0.22580373 -Al 0.10294054 4.02601996 4.12311921 -1.36287959 0.33238141 -1.97616984 -0.06872219 4.07655930 3.97710345 -1.49994028 0.48060013 -0.63148491 0.02093027 -0.31100270 0.15198661 0.28935017 -Al -0.10075637 6.07907040 6.25758904 -0.71819774 1.03251371 0.50502251 -0.16586567 6.17207637 6.29087578 -0.33258390 0.53377023 0.07704100 -0.01041834 0.00855072 -0.01225639 -0.11767073 -Al 2.18238812 4.10976764 6.06159169 -0.71634565 -0.34096018 1.71826176 2.08821832 4.07619989 6.22921551 -1.02299598 -0.27678490 1.08482611 0.01440522 -0.08110527 0.00289577 0.09799612 -Al 1.95471271 6.07099717 4.20016064 0.75539272 -1.48482873 0.13063489 2.05265912 5.92594709 4.20224038 0.91757975 -1.07261277 0.00018278 0.02004944 0.17276229 0.02533458 -0.12964673 -Al 4.14520964 0.09029539 0.03096883 0.29927440 -0.41128145 0.24919694 4.12053850 0.05927389 0.02285378 -0.65678862 -0.16807727 -0.34884643 0.02387441 -0.36354715 0.05905165 -0.19197670 -Al 4.19095976 1.82325787 2.09487272 -0.44549341 -0.59107529 -0.26642180 4.11967879 1.85218666 2.05326459 -0.79192471 1.11950872 -0.41421409 0.03170229 -0.16143128 0.52091574 -0.12674294 -Al 6.10398953 -0.00041724 2.11202646 -1.21671586 -0.70773067 0.45664155 6.00074920 -0.06527471 2.19013045 -0.49063111 -0.32781240 0.85265293 0.04838621 0.02988905 -0.02665872 0.19671820 -Al 6.28235358 2.07527407 -0.03202213 -0.36282641 1.18302989 0.09069705 6.17846980 2.17374566 0.02863224 -1.34417167 0.41372027 1.02766412 0.03038438 -0.49310962 -0.03787292 0.26705480 -Al 3.95149447 0.02423752 4.27168672 0.02698226 -0.88889052 -0.35920435 3.99165084 -0.06423077 4.17176729 0.70901358 -0.55830115 -1.24128662 0.03865897 0.17595498 -0.06817318 -0.46785640 -Al 4.02852893 2.12907013 6.09892033 -0.58939947 0.32605456 -0.05051467 3.96240436 2.12229634 6.11590360 -0.46660715 -0.46612718 0.21238287 0.02675293 -0.11126647 -0.19710196 0.24401900 -Al 6.04747231 0.11921411 6.12828967 0.11896527 -0.01256561 0.14624619 6.07684964 0.09564256 6.14706748 0.15923475 -0.26946916 0.11189882 0.02629113 0.26802832 -0.25192606 0.08901300 -Al 5.98145593 1.97409250 4.18293057 1.06899451 1.18135986 -1.09935286 6.15374823 2.07830632 4.06222184 1.79928918 0.49655379 -0.99841919 0.04114691 0.49166094 0.06508637 -0.11671418 -Al 4.12069553 4.01088427 0.09583877 -0.10679640 -0.08411900 -0.57593156 4.08884167 4.01005571 0.06664839 -0.45825209 0.11176405 0.05765539 0.00141192 -0.12361624 0.01410260 0.14955568 -Al 3.91385716 6.07753167 2.15237661 1.47169633 0.34783071 1.20608420 4.08951615 6.12905060 2.27741939 1.40640378 0.54343716 0.89411612 0.02683878 0.31858513 0.07829785 0.05659930 -Al 6.23662182 4.14496687 2.09678581 -0.61185756 1.69440233 -1.32144143 6.13771079 4.27103656 1.92537324 -0.91341194 0.34631586 -1.61980826 0.05937909 -0.43106324 -0.11741197 -0.21663190 -Al 5.90523809 6.19847867 0.25338484 -0.45080405 0.48458824 0.63436761 5.92213432 6.19010676 0.23478642 0.72199407 -0.46862759 -1.06309747 0.05178327 0.38838352 -0.40836912 -0.42988073 -Al 4.29741270 4.00473024 3.96935420 1.84337850 -0.79392536 -0.34554586 4.38331856 3.95080738 3.95984944 -0.37118994 -0.18458946 0.17716336 0.04375586 -0.55020238 0.17184611 0.17495914 -Al 4.20614660 6.12307351 6.04186950 0.67167299 -0.92593602 -0.32961301 4.21571980 6.01739599 6.05861261 -0.37489047 -0.93957132 0.52387035 0.03886869 -0.44187052 -0.07436257 0.36902865 -Al 5.97036097 4.09193150 6.18669339 -0.33576846 -0.97460702 0.34933105 6.02210565 3.99831477 6.20417538 1.21472514 -0.62595645 -0.09808774 0.02905907 0.51335127 -0.02178934 -0.07992779 -Al 6.06685097 6.13854115 4.10618547 0.91347767 -0.67529603 -0.15541771 6.18968122 6.06604804 4.08890238 1.14771696 -0.61790175 0.02390026 0.01484524 0.23621866 -0.01986234 -0.12578925 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=0.8936910426021347 free_energy=0.8936910426021347 stress="0.006456601417350244 0.001245362542856234 0.000314848374501027 0.001245362542856234 0.004203273121418416 0.0004897180596170072 0.000314848374501027 0.0004897180596170072 0.006303993845666421" pbc="T T T" -Al -0.09751901 0.03599860 0.09321066 0.12394847 -0.73523479 0.62019847 -0.05687315 -0.03388876 0.16287718 0.62415401 -0.48575283 0.55836291 0.00654396 0.15740654 0.01938202 0.05850025 -Al 0.01139616 1.96556019 2.19882218 -1.19138721 2.21416778 0.02033057 -0.12234110 2.19231320 2.15387766 -1.00861367 1.57941151 -0.66276345 0.03104177 -0.16760058 0.15216819 -0.37220376 -Al 2.03574680 0.05555292 1.96041217 0.75263694 -0.89100467 0.28676913 2.09633081 -0.05920609 2.03506729 0.21253948 -0.87178322 1.02489539 0.02657263 0.00907289 -0.33967957 0.29360387 -Al 1.92061339 2.10189245 0.14542741 -0.70217813 0.55943463 -0.26313512 1.92195757 2.08566514 0.09981783 0.92722952 -1.07135520 -0.46979408 0.02608954 0.27583605 -0.24057319 -0.18386389 -Al -0.03049963 -0.05056105 4.19982037 -0.11424348 0.12324487 -1.23577439 -0.00741120 -0.03077141 4.04497130 0.44031493 0.32289174 -1.47291583 0.02126788 0.27900379 -0.02032248 -0.20400825 -Al 0.03932700 1.98467390 6.03866854 0.21793746 0.35217998 0.75598771 0.06992523 2.04324862 6.11008585 0.26161740 0.57231641 0.29198561 0.00833124 0.09559553 0.22599691 0.14420947 -Al 2.11906023 0.03117454 6.13345218 -1.10025815 0.70347641 -0.86064749 1.96694875 0.08177180 6.08827943 -1.53776900 0.23958866 0.09184192 0.01002900 -0.28461997 -0.16785895 0.20314519 -Al 2.00973480 1.97622512 4.02010251 0.48526775 0.46694493 -0.35788291 2.08259697 2.05876291 3.99972874 0.71842131 0.82714834 -0.00275744 0.01197306 0.20906580 0.29939441 0.12684890 -Al 0.09294391 4.03739570 -0.04416142 1.63262323 -2.16328731 -0.41349061 0.20340214 3.89848819 -0.04075709 0.25118538 -0.02070701 0.38765410 0.04140552 -0.24556934 0.27879586 0.34258475 -Al 0.10625812 6.11052427 2.14867226 -0.36136407 -1.64129812 -0.42440703 0.07689771 5.95830737 2.05970779 -0.11743480 -1.13969252 -1.04743038 0.01396109 0.00777312 0.13408010 -0.33941082 -Al 2.16131747 4.15904964 2.03922631 0.50961952 -1.63928809 0.41946845 2.19147068 3.95328790 2.10796172 0.00514515 -1.84588262 0.70971788 0.01707042 -0.14382861 -0.34434364 0.20024871 -Al 2.11644421 6.07746229 0.05635386 -0.90003300 0.72050319 0.08069261 2.03339315 6.16149670 0.04281063 -0.52403967 0.58476155 -0.40192600 0.00206081 0.02033281 0.20432512 -0.08595024 -Al -0.06872219 4.07655930 3.97710345 1.49994028 -0.48060013 0.63148491 0.10294054 4.02601996 4.12311921 1.36287959 -0.33238141 1.97616984 0.02819301 0.26157916 -0.06778076 0.43655768 -Al -0.16586567 6.17207637 6.29087578 0.33258390 -0.53377023 -0.07704100 -0.10075637 6.07907040 6.25758904 0.71819774 -1.03251371 -0.50502251 0.01598050 0.28840170 -0.28415746 -0.17346984 -Al 2.08821832 4.07619989 6.22921551 1.02299598 0.27678490 -1.08482611 2.18238812 4.10976764 6.06159169 0.71634565 0.34096018 -1.71826176 0.04921525 -0.11593535 -0.00429203 -0.43390088 -Al 2.05265912 5.92594709 4.20224038 -0.91757975 1.07261277 -0.00018278 1.95471271 6.07099717 4.20016064 -0.75539272 1.48482873 -0.13063489 0.01529379 -0.06989421 0.23106615 0.05048688 -Al 4.12053850 0.05927389 0.02285378 0.65678862 0.16807727 0.34884643 4.14520964 0.09029539 0.03096883 -0.29927440 0.41128145 -0.24919694 0.02351633 -0.17773931 0.04432774 -0.09262356 -Al 4.11967879 1.85218666 2.05326459 0.79192471 -1.11950872 0.41421409 4.19095976 1.82325787 2.09487272 0.44549341 0.59107529 0.26642180 0.02433009 -0.03320538 0.51507295 0.03712893 -Al 6.00074920 -0.06527471 2.19013045 0.49063111 0.32781240 -0.85265293 6.10398953 -0.00041724 2.11202646 1.21671586 0.70773067 -0.45664155 0.04017997 0.40328961 0.27513973 -0.04573773 -Al 6.17846980 2.17374566 0.02863224 1.34417167 -0.41372027 -1.02766412 6.28235358 2.07527407 -0.03202213 0.36282641 -1.18302989 -0.09069705 0.02467001 -0.11429253 -0.44737685 0.27739501 -Al 3.99165084 -0.06423077 4.17176729 -0.70901358 0.55830115 1.24128662 3.95149447 0.02423752 4.27168672 -0.02698226 0.88889052 0.35920435 0.02679854 0.17328928 0.28407592 -0.00929847 -Al 3.96240436 2.12229634 6.11590360 0.46660715 0.46612718 -0.21238287 4.02852893 2.12907013 6.09892033 0.58939947 -0.32605456 0.05051467 0.01460498 0.19503240 -0.22878397 -0.04784979 -Al 6.07684964 0.09564256 6.14706748 -0.15923475 0.26946916 -0.11189882 6.04747231 0.11921411 6.12828967 -0.11896527 0.01256561 -0.14624619 0.01093029 -0.24023966 0.04731775 -0.08321038 -Al 6.15374823 2.07830632 4.06222184 -1.79928918 -0.49655379 0.99841919 5.98145593 1.97409250 4.18293057 -1.06899451 -1.18135986 1.09935286 0.05435442 -0.08874101 -0.39336106 0.11115771 -Al 4.08884167 4.01005571 0.06664839 0.45825209 -0.11176405 -0.05765539 4.12069553 4.01088427 0.09583877 0.10679640 0.08411900 0.57593156 0.02565535 -0.08767682 0.09731989 0.18006274 -Al 4.08951615 6.12905060 2.27741939 -1.40640378 -0.54343716 -0.89411612 3.91385716 6.07753167 2.15237661 -1.47169633 -0.34783071 -1.20608420 0.04858553 -0.34151032 -0.03621993 -0.32699512 -Al 6.13771079 4.27103656 1.92537324 0.91341194 -0.34631586 1.61980826 6.23662182 4.14496687 2.09678581 0.61185756 -1.69440233 1.32144143 0.06559281 0.14105015 -0.65322859 0.17886841 -Al 5.92213432 6.19010676 0.23478642 -0.72199407 0.46862759 1.06309747 5.90523809 6.19847867 0.25338484 0.45080405 -0.48458824 -0.63436761 0.06961904 0.31176115 -0.08418942 -0.59340663 -Al 4.38331856 3.95080738 3.95984944 0.37118994 0.18458946 -0.17716336 4.29741270 4.00473024 3.96935420 -1.84337850 0.79392536 0.34554586 0.05705253 -0.75167478 0.18459187 0.18259713 -Al 4.21571980 6.01739599 6.05861261 0.37489047 0.93957132 -0.52387035 4.20614660 6.12307351 6.04186950 -0.67167299 0.92593602 0.32961301 0.00309027 -0.20696170 0.06265517 0.15023070 -Al 6.02210565 3.99831477 6.20417538 -1.21472514 0.62595645 0.09808774 5.97036097 4.09193150 6.18669339 0.33576846 0.97460702 -0.34933105 0.03698178 0.38061707 0.19963221 -0.21003717 -Al 6.18968122 6.06604804 4.08890238 -1.14771696 0.61790175 -0.02390026 6.06685097 6.13854115 4.10618547 -0.91347767 0.67529603 0.15541771 0.04269961 -0.13961749 0.05682592 0.22834020 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.0496213996973287 free_energy=1.0496213996973287 stress="0.0050412540679878135 0.0006477853945826963 -0.0001668343085574835 0.0006477853945826963 0.004431297660261729 -8.201800653474678e-05 -0.0001668343085574835 -8.201800653474678e-05 0.006065264190204926" pbc="T T T" -Al 0.10122258 -0.10966878 0.11583238 -0.65183463 0.62379972 1.47616753 -0.02789025 -0.03552281 0.22769330 -1.61899170 0.61514262 0.44175987 0.01627154 -0.33604521 0.15207386 -0.15394549 -Al 0.04298388 1.98852636 2.08224377 -0.65436195 -0.13546458 0.57742725 -0.04970584 2.06319289 2.12731842 -0.90325891 1.27448603 0.38532747 0.04248188 -0.16366281 0.59843150 -0.19576280 -Al 1.99206376 0.14503676 1.98033833 -1.79781972 0.55562620 -1.56759129 1.88811699 0.11538020 1.91465887 -0.00780193 -1.00060361 0.43777414 0.06728315 0.40458477 -0.52792677 0.49227841 -Al 2.10797560 1.99011134 0.11372571 -0.44475192 0.44312265 0.92397889 2.05613089 2.09040771 0.15826877 -0.41214089 1.17508447 -0.20829008 0.03413359 -0.10775604 0.37858706 -0.21800418 -Al -0.15936233 0.17057456 3.91544150 -1.29978743 -0.97098952 0.00587215 -0.21763631 0.00103324 3.96971247 0.20235474 -1.72504926 0.78135607 0.06272531 0.49123211 -0.60877838 0.43075244 -Al -0.01152506 2.00306105 6.12939722 0.94784953 0.11075907 -1.37244704 0.08819301 1.99481137 6.01982464 0.68219599 -0.23021869 -0.46865056 0.03775713 0.12358988 -0.11514859 0.09023538 -Al 2.14323951 0.20075913 6.10010622 -1.73812154 -0.17182853 0.29106492 1.90025233 0.11955926 6.13488825 -2.24051188 -1.22601727 0.33663713 0.06683578 -0.58901543 -0.43573075 0.04714049 -Al 1.97865080 2.04835628 4.18844391 0.45187924 0.63826775 -0.80148921 2.07250112 2.11931772 4.06044556 1.20307312 0.43090982 -1.37451993 0.01568506 0.28505701 0.16048250 -0.32379161 -Al -0.04370556 3.90728441 -0.10008633 0.43830490 0.25321690 -1.23832876 0.03923277 3.99019439 -0.18228154 0.83114663 1.06259081 -0.33475925 0.04327799 0.35962944 0.46916804 0.36505477 -Al -0.09389839 5.86400230 2.19930966 0.51305389 -1.33457864 0.55029550 -0.00117448 5.78115198 2.20513526 1.07547071 -0.23326776 -0.41136707 0.03528062 0.27999378 0.39776872 -0.32603095 -Al 2.22516603 4.07412600 2.04313126 0.21175610 -0.54153479 -0.14682908 2.18346539 3.97293698 2.04838010 -0.79429155 -1.02113711 0.29409029 0.03528168 -0.52255050 -0.40714140 0.08135634 -Al 1.96041189 5.99331499 0.12839923 0.58861329 -0.89940228 -0.19320009 2.01824176 5.94296639 0.11195992 0.43202325 0.09997826 -0.04108933 0.02267279 0.02547308 0.17486406 -0.02555815 -Al 0.01622504 4.03305362 4.24629539 -0.20897970 -0.43290867 0.94771227 0.02499006 3.99292013 4.31635930 0.28724972 -0.11688449 0.25301718 0.03019158 0.24590168 -0.08028449 -0.13735206 -Al -0.04717474 6.10258323 5.98345334 -0.19924822 1.18923225 -0.23774787 0.02741756 6.21451803 5.99028232 1.35933240 0.67652992 0.45769964 0.03764803 0.63139411 0.02387530 0.14450647 -Al 2.19535093 4.07950163 6.27092103 0.49669069 0.42004002 0.21328905 2.20978472 4.11524578 6.23947720 -0.05515350 0.15201378 -0.84314754 0.02159794 -0.36514127 0.00680721 -0.28237673 -Al 2.05921180 6.16495709 3.90030620 -0.69666048 -0.11172841 0.05873806 1.98920727 6.16778992 3.96357739 -0.43021369 0.08401848 1.03229593 0.00713044 -0.06691243 0.11796941 0.38115234 -Al 4.05202003 0.01070115 0.03065185 0.31142016 0.04184735 -0.76860160 4.10296519 0.03301753 0.00685599 0.37715428 0.37813873 0.31260723 0.03359944 0.23804609 0.06016911 0.32284858 -Al 4.16048556 2.01738399 2.11378911 1.71041171 -0.56244486 -0.40338931 4.28023528 1.98793431 2.08875939 0.24151183 0.00146091 -0.01257181 0.00854906 -0.20256318 0.18174341 0.08334349 -Al 5.97946595 0.04290261 2.07413626 -0.63565391 0.16744851 -0.20527034 5.96914732 0.03056657 2.00681229 0.59948520 -0.41408151 -0.81513480 0.03257702 0.23362178 -0.13418913 -0.35317077 -Al 6.14869392 2.13655303 0.04954117 0.02697863 0.07880540 0.72690466 6.14798601 2.07663662 0.09817659 0.10826231 -0.98203882 -0.12943480 0.02941124 -0.14807618 -0.51257652 0.02897729 -Al 4.13968993 0.01500317 4.13107062 0.36686831 -0.52119845 -0.50714151 4.12849918 -0.01625099 4.07786007 -0.54926543 -0.02627973 -0.33540446 0.02332059 -0.28133810 0.13256547 -0.08767421 -Al 4.12198454 2.06695547 6.18224982 0.23483739 -0.77006126 -0.42204804 4.14691602 1.97643788 6.13939562 0.04527294 -0.78914839 -0.26333537 0.02368015 0.11217059 -0.10695550 -0.06611671 -Al 6.25566481 -0.10128551 6.15264624 0.07767739 -0.66391012 1.34869573 6.19542255 -0.10985806 6.31740764 -1.02804911 0.47648802 1.45681029 0.02828435 -0.47408145 0.33607811 0.20598649 -Al 6.06172674 2.04814788 4.26981803 0.10951947 0.65028507 0.52808798 6.06452125 2.11571020 4.29040165 0.02683886 0.42280976 -0.19900004 0.03186952 -0.09483808 0.08096578 -0.18784736 -Al 4.21267789 4.01513856 0.03586771 0.53490136 0.11884854 1.02157862 4.23023799 4.03581066 0.14419383 -0.29683180 0.22521400 0.78710524 0.04025142 -0.18199172 0.08392670 0.10520734 -Al 4.17385429 5.90062313 2.02517111 0.17128695 1.71280142 0.48869317 4.15723009 6.14546482 2.06602523 -0.61260436 2.36001851 0.26488937 0.04729958 -0.11317749 0.60326078 -0.07991139 -Al 6.13144139 3.99610944 2.30979861 0.23302038 -0.55368936 -1.89995381 6.14445941 3.92170496 2.08885168 0.04995955 -0.54317511 -1.76785371 0.03477328 -0.09796674 -0.23400137 -0.33491051 -Al 6.16283748 5.95400700 0.06897947 0.23148447 -1.03266710 1.40056725 6.19340006 5.90029562 0.21703943 0.34282833 0.12252987 1.11075021 0.01300785 -0.00206709 0.29220819 0.09141552 -Al 4.01950371 4.14201927 4.18919487 0.92682378 -0.14722422 -0.61737093 4.10340797 4.11578306 4.11517003 0.47645258 -0.23647008 -0.85203587 0.01185028 -0.00287716 -0.12312126 0.02622525 -Al 3.92146091 6.31423008 6.24958566 0.24791905 0.66040367 0.55511546 3.98540207 6.29439016 6.25071365 0.76382995 -0.87797052 -0.53694638 0.04517418 0.31196126 -0.60089521 -0.30642207 -Al 6.03248216 4.21619905 6.06675612 -1.07580833 1.32665565 -0.63066453 5.92275141 4.32792490 6.02127062 -0.73867048 0.61925818 0.06170182 0.02368320 -0.06660190 -0.14018440 -0.04115811 -Al 6.00340549 6.19022467 4.05053520 0.57173114 -0.14152942 -0.10211507 6.07052178 6.13902154 4.06241672 0.58334284 -0.75432982 0.17971912 0.04603572 0.07400719 -0.22401144 0.22355249 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.0598670279781714 free_energy=1.0598670279781714 stress="0.005052295521701254 0.0003623609712633322 -0.00036006216669003474 0.0003623609712633322 0.005464424852972196 2.0063803643432364e-05 -0.00036006216669003474 2.0063803643432364e-05 0.0069520428514399865" pbc="T T T" -Al -0.02789025 -0.03552281 0.22769330 1.61899170 -0.61514262 -0.44175987 0.10122258 -0.10966878 0.11583238 0.65183463 -0.62379972 -1.47616753 0.04677184 -0.11585444 -0.08582853 -0.40591217 -Al -0.04970584 2.06319289 2.12731842 0.90325891 -1.27448603 -0.38532747 0.04298388 1.98852636 2.08224377 0.65436195 0.13546458 -0.57742725 0.01754470 0.11219607 0.16306876 0.05525110 -Al 1.88811699 0.11538020 1.91465887 0.00780193 1.00060361 -0.43777414 1.99206376 0.14503676 1.98033833 1.79781972 -0.55562620 1.56759129 0.06239616 0.59048502 -0.34073568 0.63061900 -Al 2.05613089 2.09040771 0.15826877 0.41214089 -1.17508447 0.20829008 2.10797560 1.99011134 0.11372571 0.44475192 -0.44312265 -0.92397889 0.03123103 0.07552269 -0.04896383 -0.42003529 -Al -0.21763631 0.00103324 3.96971247 -0.20235474 1.72504926 -0.78135607 -0.15936233 0.17057456 3.91544150 1.29978743 0.97098952 -0.00587215 0.02442416 0.43939881 0.18014228 0.03613343 -Al 0.08819301 1.99481137 6.01982464 -0.68219599 0.23021869 0.46865056 -0.01152506 2.00306105 6.12939722 -0.94784953 -0.11075907 1.37244704 0.07245426 -0.26654639 -0.05319396 0.41796345 -Al 1.90025233 0.11955926 6.13488825 2.24051188 1.22601727 -0.33663713 2.14323951 0.20075913 6.10010622 1.73812154 0.17182853 -0.29106492 0.03011933 0.34609724 -0.24095767 0.02867786 -Al 2.07250112 2.11931772 4.06044556 -1.20307312 -0.43090982 1.37451993 1.97865080 2.04835628 4.18844391 -0.45187924 -0.63826775 0.80148921 0.00901304 0.14949738 -0.29855848 0.01534920 -Al 0.03923277 3.99019439 -0.18228154 -0.83114663 -1.06259081 0.33475925 -0.04370556 3.90728441 -0.10008633 -0.43830490 -0.25321690 1.23832876 0.01462821 -0.12137574 0.07376550 0.23510663 -Al -0.00117448 5.78115198 2.20513526 -1.07547071 0.23326776 0.41136707 -0.09389839 5.86400230 2.19930966 -0.51305389 1.33457864 -0.55029550 0.03359128 0.07004072 0.31193338 -0.26943981 -Al 2.18346539 3.97293698 2.04838010 0.79429155 1.02113711 -0.29409029 2.22516603 4.07412600 2.04313126 -0.21175610 0.54153479 0.14682908 0.03338994 -0.16830166 0.16270713 0.17446616 -Al 2.01824176 5.94296639 0.11195992 -0.43202325 -0.09997826 0.04108933 1.96041189 5.99331499 0.12839923 -0.58861329 0.89940228 0.19320009 0.01583102 -0.05773059 0.41657068 0.09985010 -Al 0.02499006 3.99292013 4.31635930 -0.28724972 0.11688449 -0.25301718 0.01622504 4.03305362 4.24629539 0.20897970 0.43290867 -0.94771227 0.03385578 0.08442802 0.27496233 -0.29517719 -Al 0.02741756 6.21451803 5.99028232 -1.35933240 -0.67652992 -0.45769964 -0.04717474 6.10258323 5.98345334 0.19924822 -1.18923225 0.23774787 0.03586727 0.21394511 -0.35792691 0.33497121 -Al 2.20978472 4.11524578 6.23947720 0.05515350 -0.15201378 0.84314754 2.19535093 4.07950163 6.27092103 -0.49669069 -0.42004002 -0.21328905 0.01842292 -0.01942167 -0.15205282 -0.38517509 -Al 1.98920727 6.16778992 3.96357739 0.43021369 -0.08401848 -1.03229593 2.05921180 6.16495709 3.90030620 0.69666048 0.11172841 -0.05873806 0.00685140 0.24241490 -0.03414395 0.24087567 -Al 4.10296519 0.03301753 0.00685599 -0.37715428 -0.37813873 -0.31260723 4.05202003 0.01070115 0.03065185 -0.31142016 -0.04184735 0.76860160 0.04250339 -0.24205869 0.08233928 0.29312272 -Al 4.28023528 1.98793431 2.08875939 -0.24151183 -0.00146091 0.01257181 4.16048556 2.01738399 2.11378911 -1.71041171 0.56244486 0.40338931 0.04993652 -0.66461868 0.13158732 0.15889151 -Al 5.96914732 0.03056657 2.00681229 -0.59948520 0.41408151 0.81513480 5.97946595 0.04290261 2.07413626 0.63565391 -0.16744851 0.20527034 0.07777627 0.51622871 -0.16776067 0.06714457 -Al 6.14798601 2.07663662 0.09817659 -0.10826231 0.98203882 0.12943480 6.14869392 2.13655303 0.04954117 -0.02697863 -0.07880540 -0.72690466 0.02947094 0.13317446 -0.12164259 -0.53733238 -Al 4.12849918 -0.01625099 4.07786007 0.54926543 0.02627973 0.33540446 4.13968993 0.01500317 4.13107062 -0.36686831 0.52119845 0.50714151 0.01151456 -0.24044548 0.18451663 0.17698936 -Al 4.14691602 1.97643788 6.13939562 -0.04527294 0.78914839 0.26333537 4.12198454 2.06695547 6.18224982 -0.23483739 0.77006126 0.42204804 0.00773501 -0.23033708 0.09615680 0.12908107 -Al 6.19542255 -0.10985806 6.31740764 1.02804911 -0.47648802 -1.45681029 6.25566481 -0.10128551 6.15264624 -0.07767739 0.66391012 -1.34869573 0.01888789 -0.16980109 0.27931138 -0.22127630 -Al 6.06452125 2.11571020 4.29040165 -0.02683886 -0.42280976 0.19900004 6.06172674 2.04814788 4.26981803 -0.10951947 -0.65028507 -0.52808798 0.03039213 0.06230484 -0.23591972 -0.27487674 -Al 4.23023799 4.03581066 0.14419383 0.29683180 -0.22521400 -0.78710524 4.21267789 4.01513856 0.03586771 -0.53490136 -0.11884854 -1.02157862 0.02983579 -0.34345818 0.01392646 -0.23961528 -Al 4.15723009 6.14546482 2.06602523 0.61260436 -2.36001851 -0.26488937 4.17385429 5.90062313 2.02517111 -0.17128695 -1.71280142 -0.48869317 0.02107550 -0.37514881 -0.20604012 -0.07170910 -Al 6.14445941 3.92170496 2.08885168 -0.04995955 0.54317511 1.76785371 6.13144139 3.99610944 2.30979861 -0.23302038 0.55368936 1.89995381 0.06509629 -0.02235608 0.28176745 0.40562405 -Al 6.19340006 5.90029562 0.21703943 -0.34282833 -0.12252987 -1.11075021 6.16283748 5.95400700 0.06897947 -0.23148447 1.03266710 -1.40056725 0.03240190 0.01905578 0.43541944 -0.30060952 -Al 4.10340797 4.11578306 4.11517003 -0.47645258 0.23647008 0.85203587 4.01950371 4.14201927 4.18919487 -0.92682378 0.14722422 0.61737093 0.02111889 -0.25861542 0.06941220 -0.18311221 -Al 3.98540207 6.29439016 6.25071365 -0.76382995 0.87797052 0.53694638 3.92146091 6.31423008 6.24958566 -0.24791905 -0.66040367 -0.55511546 0.03988236 -0.00157099 -0.31690941 -0.31468515 -Al 5.92275141 4.32792490 6.02127062 0.73867048 -0.61925818 -0.06170182 6.03248216 4.21619905 6.06675612 1.07580833 -1.32665565 0.63066453 0.04791296 0.31466609 -0.35458918 0.46986949 -Al 6.07052178 6.13902154 4.06241672 -0.58334284 0.75432982 -0.17971912 6.00340549 6.19022467 4.05053520 -0.57173114 0.14152942 0.10211507 0.04793427 -0.07181483 -0.14236349 -0.05103034 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.1701265082332784 free_energy=1.1701265082332784 stress="0.004240275766441019 0.0004635646804701818 -0.00019295021039451258 0.0004635646804701818 0.004553247416723432 -7.657078983153338e-05 -0.00019295021039451258 -7.657078983153338e-05 0.007360803371220558" pbc="T T T" -Al 0.02344403 0.01551248 -0.19761095 -1.16323505 -0.26122577 -0.29488629 -0.02535890 -0.01666860 -0.15289106 0.28826462 -0.30466071 1.07928487 0.03848448 0.38635724 -0.01650272 0.45105419 -Al 0.03869236 1.83830745 2.13618222 -0.19190768 -0.80007077 0.53464521 0.08203055 1.76798180 2.14508712 0.89406061 -0.34609683 -0.51131534 0.01523279 0.38861915 0.03657230 -0.17717062 -Al 2.21696033 -0.00369555 2.08103589 0.49172392 -0.98175087 -1.06769837 2.19078758 -0.10086233 1.96301195 -0.84339155 -0.62867711 -0.85437542 0.03245089 -0.51758763 -0.04627487 -0.13487795 -Al 2.16865957 2.03296493 0.08130823 0.73865548 -0.13990468 -0.19515489 2.19124544 2.02189874 0.05478480 -0.28379326 -0.00199163 -0.20331497 0.00608690 -0.34940230 -0.03453984 -0.09684311 -Al 0.11565556 -0.08703035 4.32584055 -0.60495818 0.49127841 -1.39549901 0.04339555 0.01238779 4.16767894 -0.66737671 1.12465011 -1.24941081 0.01355355 -0.06584591 0.38837878 -0.23459667 -Al -0.09739854 2.11083217 6.25209748 -0.29516044 0.35653077 1.04581886 -0.08667627 2.14215956 6.30564995 0.50258723 0.07103308 -0.19853601 0.04747541 0.23084929 0.03537826 -0.24619136 -Al 2.35267551 0.00857491 6.05157987 -0.46205813 0.04785286 0.21264578 2.23590422 0.01790017 6.07751606 -1.72194435 0.13108337 0.20121051 0.02223262 -0.36276836 0.02793866 0.09346508 -Al 1.97634963 2.18784736 4.17164038 0.77580769 1.02425092 -0.82884860 2.02584481 2.21163753 4.13023792 0.08198376 -0.57431653 0.12525941 0.02439692 -0.11994031 -0.47622501 0.20545082 -Al 0.01137334 4.01510313 0.24021383 -0.93415439 0.83732410 -0.98372568 -0.08986066 4.09259345 0.16573580 -0.67317829 0.43937864 -0.21180730 0.01860832 -0.09398153 0.00980540 0.02322964 -Al -0.10751780 6.36589664 2.03998191 0.11822679 -0.54233461 0.26748073 -0.03820982 6.26443372 2.07001341 1.02046144 -1.22516014 0.30771802 0.00482065 0.40180929 -0.31689531 -0.00398226 -Al 2.09552761 4.14451928 2.18418504 0.28280458 -0.14393301 0.00424007 2.09234396 4.13172013 2.10658804 -0.31946865 -0.16413754 -1.41682083 0.03780661 -0.19702601 0.03480980 -0.40777609 -Al 1.99087608 6.03041168 -0.04696864 -0.14353749 0.00033405 0.83188883 2.01064771 6.07441477 0.09136432 0.41685005 0.58729020 1.39480062 0.02136328 0.24767567 0.35521372 0.42009104 -Al -0.06811528 4.04554951 3.88373122 0.19556418 -0.00422045 -0.49531416 -0.02250295 4.08105928 3.87934750 0.51574885 0.51481387 0.42592389 0.03209531 0.21748865 0.27722318 0.27892949 -Al -0.06436346 5.98382832 6.16365794 0.54787878 -0.04163019 -0.40358502 -0.01197730 6.03704335 6.09441764 0.50293963 1.09095144 -0.90914127 0.07055460 -0.07208947 0.22177421 -0.08190182 -Al 1.75291701 4.11946475 6.27009913 -0.75441855 0.62759591 0.88376122 1.78535119 4.11234955 6.29094723 1.31931167 -0.62635373 -0.37150565 0.07954511 0.64657652 -0.45730187 -0.48359978 -Al 2.00672533 6.04974713 4.10309527 -0.18108971 -0.70710692 -0.49781637 1.99809219 5.98659917 4.06941863 -0.01399102 -0.24475885 -0.13255745 0.00993975 0.09944542 -0.06148956 0.11360764 -Al 4.11539389 0.03520825 0.06083098 -0.37689914 -0.24152312 -0.46323548 4.07469952 -0.03498634 0.02684264 -0.19923830 -0.99129311 -0.08715584 0.04132444 -0.15383560 -0.24851788 0.03160766 -Al 3.96405453 2.17752042 2.18998955 0.19189540 0.58832274 1.30730034 4.01802925 2.19751151 2.27659620 0.70331049 -0.25955173 0.13583508 0.04210457 0.22735563 -0.22016853 -0.19501995 -Al 6.02419456 0.06907121 2.15899948 -0.86179089 -0.52327712 -0.55594996 5.95351650 -0.02605980 2.10710666 -0.35401754 -1.01180705 -0.56216711 0.10819101 0.09030648 -0.34026152 0.16686401 -Al 6.06682015 1.83842144 0.35078828 0.90247749 0.13493551 -0.43509367 6.15717909 1.94699849 0.23962681 0.61351328 1.60199540 -1.27106336 0.06957469 0.07509888 0.68263391 -0.61665581 -Al 4.09559994 -0.01213247 3.92601624 -0.26958396 0.72276936 -0.29079217 4.06700132 0.08203661 4.00185596 -0.31957091 0.83035284 1.49004461 0.06181064 0.05031530 0.19264484 0.70677658 -Al 4.29055238 2.04739453 6.18569088 0.58759882 -1.28668758 0.36965449 4.27448707 1.96363150 6.20005144 -0.96264502 0.09207187 -0.21098962 0.05947481 -0.40519337 0.03382496 -0.04825640 -Al 6.12561752 0.17978212 6.10323681 -1.04249764 0.97887128 1.72843701 6.00728087 0.17225198 6.22503583 -0.77944975 -1.27841362 0.36626718 0.04775849 -0.28276633 -0.50715965 -0.23480464 -Al 6.15462339 2.15209799 3.95509619 0.62962767 -0.16602841 -0.68791306 6.19023259 2.09200001 3.94830015 0.00623352 -0.80143549 0.68591550 0.02957361 -0.13902931 -0.34116219 0.27727649 -Al 4.07354781 4.00321974 0.02550575 1.77424394 -0.37405529 0.38363590 4.25264567 3.99322319 0.05213031 1.25414992 0.19130653 -0.01109703 0.00777593 0.07227276 0.18198188 0.00223878 -Al 4.14674599 6.05104520 2.10483322 0.03280139 0.08676754 -0.19676071 4.12920284 6.03270448 2.07159733 -0.32141806 -0.39517706 -0.36038710 0.01713185 -0.15750992 -0.16402530 -0.08583804 -Al 6.08991039 4.12647927 1.95742699 -0.62354249 -0.68569643 0.37027988 6.01740939 4.05582249 2.03816183 -0.50607760 -0.53380887 1.15359371 0.03803245 -0.15271479 -0.02539625 0.22190570 -Al 6.06431252 6.22291666 0.03871599 2.18534921 -1.54573041 0.31498948 6.25920008 6.04374176 0.05037981 1.05099556 -1.43098646 -0.05659016 0.00767646 -0.00159240 -0.23646063 -0.13268791 -Al 4.01942104 3.87517101 4.25704919 -0.88586858 0.93575525 0.82201085 3.96334456 4.04024212 4.31720590 0.00146938 1.77670670 0.27764780 0.07506267 0.09139631 0.56879153 -0.22611728 -Al 4.13837525 5.87134393 6.04631195 -1.03231625 0.54871167 -0.61389713 4.01524921 6.00221715 6.02907801 -1.03591986 1.75098043 0.43472914 0.02877591 -0.19312635 0.47347531 0.17717706 -Al 6.15013780 4.04861391 6.05201872 0.69980634 1.03198283 0.66840442 6.21496319 4.13313340 6.15742268 0.27083775 0.42638431 0.94623041 0.05145999 0.10110606 -0.08241841 0.38824867 -Al 6.11246211 6.07650590 4.14447109 -0.33144313 0.03189243 -0.33902251 6.06873209 6.08937632 4.09675090 -0.44123689 0.18962766 -0.40622548 0.00975180 -0.06226305 0.05435279 -0.15160318 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.070238329593166 free_energy=1.070238329593166 stress="0.005001714831958823 0.0014787882206239893 -8.696280675829449e-05 0.0014787882206239893 0.003844643493447092 -0.0001922692050899891 -8.696280675829449e-05 -0.0001922692050899891 0.0070443138583341485" pbc="T T T" -Al -0.02535890 -0.01666860 -0.15289106 -0.28826462 0.30466071 -1.07928487 0.02344403 0.01551248 -0.19761095 1.16323505 0.26122577 0.29488629 0.03722304 0.42582734 0.02702365 0.38695244 -Al 0.08203055 1.76798180 2.14508712 -0.89406061 0.34609683 0.51131534 0.03869236 1.83830745 2.13618222 0.19190768 0.80007077 -0.53464521 0.02672090 0.21143978 0.32090730 -0.44958622 -Al 2.19078758 -0.10086233 1.96301195 0.84339155 0.62867711 0.85437542 2.21696033 -0.00369555 2.08103589 -0.49172392 0.98175087 1.06769837 0.03911533 -0.27001058 0.29666503 0.33427476 -Al 2.19124544 2.02189874 0.05478480 0.28379326 0.00199163 0.20331497 2.16865957 2.03296493 0.08130823 -0.73865548 0.13990468 0.19515489 -0.00314954 -0.27327489 0.08254668 0.07618516 -Al 0.04339555 0.01238779 4.16767894 0.66737671 -1.12465011 1.24941081 0.11565556 -0.08703035 4.32584055 0.60495818 -0.49127841 1.39549901 0.00963083 0.03281695 -0.01581709 0.26729276 -Al -0.08667627 2.14215956 6.30564995 -0.50258723 -0.07103308 0.19853601 -0.09739854 2.11083217 6.25209748 0.29516044 -0.35653077 -1.04581886 0.04710413 0.25331477 -0.23971365 -0.50879010 -Al 2.23590422 0.01790017 6.07751606 1.72194435 -0.13108337 -0.20121051 2.35267551 0.00857491 6.05157987 0.46205813 -0.04785286 -0.21264578 0.01508188 -0.44633611 0.04535953 -0.02872732 -Al 2.02584481 2.21163753 4.13023792 -0.08198376 0.57431653 -0.12525941 1.97634963 2.18784736 4.17164038 -0.77580769 -1.02425092 0.82884860 0.03426526 -0.22181921 -0.44990337 0.31539808 -Al -0.08986066 4.09259345 0.16573580 0.67317829 -0.43937864 0.21180730 0.01137334 4.01510313 0.24021383 0.93415439 -0.83732410 0.98372568 0.04397739 0.39526212 -0.28067905 0.36081251 -Al -0.03820982 6.26443372 2.07001341 -1.02046144 1.22516014 -0.30771802 -0.10751780 6.36589664 2.03998191 -0.11822679 0.54233461 -0.26748073 0.00806430 0.13589729 -0.13224951 0.04267469 -Al 2.09234396 4.13172013 2.10658804 0.31946865 0.16413754 1.41682083 2.09552761 4.14451928 2.18418504 -0.28280458 0.14393301 -0.00424007 0.03078722 -0.15619484 -0.10972569 -0.38485851 -Al 2.01064771 6.07441477 0.09136432 -0.41685005 -0.58729020 -1.39480062 1.99087608 6.03041168 -0.04696864 0.14353749 -0.00033405 -0.83188883 0.01603801 0.07817414 -0.06739502 -0.18467174 -Al -0.02250295 4.08105928 3.87934750 -0.51574885 -0.51481387 -0.42592389 -0.06811528 4.04554951 3.88373122 -0.19556418 0.00422045 0.49531416 0.02086440 -0.01636202 0.00576295 0.30074486 -Al -0.01197730 6.03704335 6.09441764 -0.50293963 -1.09095144 0.90914127 -0.06436346 5.98382832 6.16365794 -0.54787878 0.04163019 0.40358502 0.05282273 0.08804989 0.36704810 -0.16508080 -Al 1.78535119 4.11234955 6.29094723 -1.31931167 0.62635373 0.37150565 1.75291701 4.11946475 6.27009913 0.75441855 -0.62759591 -0.88376122 0.05243553 0.59585008 -0.22017073 -0.24897694 -Al 1.99809219 5.98659917 4.06941863 0.01399102 0.24475885 0.13255745 2.00672533 6.04974713 4.10309527 0.18108971 0.70710692 0.49781637 0.01532469 0.03231636 0.33995106 0.08668348 -Al 4.07469952 -0.03498634 0.02684264 0.19923830 0.99129311 0.08715584 4.11539389 0.03520825 0.06083098 0.37689914 0.24152312 0.46323548 0.04074881 0.18382248 -0.14097405 0.16679055 -Al 4.01802925 2.19751151 2.27659620 -0.70331049 0.25955173 -0.13583508 3.96405453 2.17752042 2.18998955 -0.19189540 -0.58832274 -1.30730034 0.04682243 0.04302862 -0.29901108 -0.48121469 -Al 5.95351650 -0.02605980 2.10710666 0.35401754 1.01180705 0.56216711 6.02419456 0.06907121 2.15899948 0.86179089 0.52327712 0.55594996 0.03783924 0.24606853 0.05579295 -0.14373582 -Al 6.15717909 1.94699849 0.23962681 -0.61351328 -1.60199540 1.27106336 6.06682015 1.83842144 0.35078828 -0.90247749 -0.13493551 0.43509367 0.03135380 -0.20568383 0.18195181 0.01840027 -Al 4.06700132 0.08203661 4.00185596 0.31957091 -0.83035284 -1.49004461 4.09559994 -0.01213247 3.92601624 0.26958396 -0.72276936 0.29079217 0.02636238 -0.07034558 -0.14278764 0.34231774 -Al 4.27448707 1.96363150 6.20005144 0.96264502 -0.09207187 0.21098962 4.29055238 2.04739453 6.18569088 -0.58759882 1.28668758 -0.36965449 0.07827745 -0.53037369 0.67598726 -0.24720494 -Al 6.00728087 0.17225198 6.22503583 0.77944975 1.27841362 -0.36626718 6.12561752 0.17978212 6.10323681 1.04249764 -0.97887128 -1.72843701 0.08523737 0.37537492 -0.79728642 -0.50933463 -Al 6.19023259 2.09200001 3.94830015 -0.00623352 0.80143549 -0.68591550 6.15462339 2.15209799 3.95509619 -0.62962767 0.16602841 0.68791306 0.02275020 -0.18558137 -0.07633978 0.52265087 -Al 4.25264567 3.99322319 0.05213031 -1.25414992 -0.19130653 0.01109703 4.07354781 4.00321974 0.02550575 -1.77424394 0.37405529 -0.38363590 0.01350932 -0.42846414 0.18846228 -0.22125765 -Al 4.12920284 6.03270448 2.07159733 0.32141806 0.39517706 0.36038710 4.14674599 6.05104520 2.10483322 -0.03280139 -0.08676754 0.19676071 0.01800742 -0.09019933 -0.11484602 0.01389448 -Al 6.01740939 4.05582249 2.03816183 0.50607760 0.53380887 -1.15359371 6.08991039 4.12647927 1.95742699 0.62354249 0.68569643 -0.37027988 0.06125176 0.23028801 0.12380966 0.30482279 -Al 6.25920008 6.04374176 0.05037981 -1.05099556 1.43098646 0.05659016 6.06431252 6.22291666 0.03871599 -2.18534921 1.54573041 -0.31498948 0.04723209 -0.66841658 0.37808134 -0.04875717 -Al 3.96334456 4.04024212 4.31720590 -0.00146938 -1.77670670 -0.27764780 4.01942104 3.87517101 4.25704919 0.88586858 -0.93575525 -0.82201085 0.05314517 0.38205832 -0.04498728 -0.22280964 -Al 4.01524921 6.00221715 6.02907801 1.03591986 -1.75098043 -0.43472914 4.13837525 5.87134393 6.04631195 1.03231625 -0.54871167 0.61389713 0.02835011 0.17479331 0.27056336 0.44538917 -Al 6.21496319 4.13313340 6.15742268 -0.27083775 -0.42638431 -0.94623041 6.15013780 4.04861391 6.05201872 -0.69980634 -1.03198283 -0.66840442 0.03431130 -0.31711057 -0.26362013 -0.24207206 -Al 6.06873209 6.08937632 4.09675090 0.44123689 -0.18962766 0.40622548 6.11246211 6.07650590 4.14447109 0.33144313 -0.03189243 0.33902251 -0.00126660 -0.00421014 0.03559352 0.10179363 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=0.9510780384805617 free_energy=0.9510780384805617 stress="0.006018694982681886 0.000707532545302181 -0.00027031016091648506 0.000707532545302181 0.004672717481672366 -0.00033102340476843346 -0.00027031016091648506 -0.00033102340476843346 0.005535259343197416" pbc="T T T" -Al -0.01275222 0.05726802 0.18005727 -0.19927745 0.57237382 -0.71890947 -0.02605177 0.10230075 0.07012401 0.05724316 0.26450821 -1.29509040 0.01715142 -0.00947614 -0.09650966 -0.17147379 -Al -0.04999668 2.09243871 2.18464611 1.16756593 -1.12969892 -0.21790407 0.07843493 1.98449425 2.15335321 1.05132111 -0.77155548 -0.19687641 0.01395655 0.09345415 0.04660318 -0.17104505 -Al 2.03841892 0.08400047 1.95797645 -0.21404749 0.13441634 -0.50957714 2.03483420 0.10028453 1.98389770 0.16796648 -0.10216174 0.79790502 0.03647746 0.09405088 0.15662974 0.57829811 -Al 1.88149100 2.16983810 0.17118949 -0.37348322 0.82040314 0.79865440 1.90338778 2.19789253 0.20698188 0.73824318 -0.36892834 -0.24218248 0.03894211 0.37671562 -0.31266842 -0.22837753 -Al 0.14528137 0.00609861 4.16828852 0.51651896 0.04198044 1.30642404 0.13990167 -0.00336235 4.22699801 -0.60545575 -0.26572591 -0.30847152 0.01662716 -0.34466581 -0.04509918 -0.38810039 -Al 0.04182469 1.96808443 6.11673011 0.95765160 -0.31122890 -0.76605778 0.13966297 1.96766674 6.06165521 0.68463553 0.33608300 -0.02917049 0.01239413 0.07942294 0.15698910 -0.01756646 -Al 2.14379409 -0.12620265 6.05723504 0.29418097 -0.70748394 -0.21503271 2.15377844 -0.18480510 6.10440512 -0.05892927 -0.28482844 1.00274337 0.03794733 -0.19517314 0.08730603 0.42342000 -Al 2.15660798 1.93854607 4.19392680 -0.18126174 -0.96469831 0.84297807 2.11891387 1.88693950 4.21250084 -0.39286002 0.15025753 -0.44609361 0.01900670 -0.19472662 0.19664517 -0.42360793 -Al -0.06455924 3.99585417 -0.07151346 -0.69408874 -0.10696503 2.18650854 -0.10422580 4.04038397 0.15494813 0.02422449 0.85208347 1.72868698 0.02889958 0.16905584 0.34485614 0.12291162 -Al 0.10204672 5.90301118 2.08022324 -0.36262648 0.97833494 -0.55983239 0.01170307 6.08867921 2.02963497 -1.02291003 2.29986604 -0.33505130 0.05030673 -0.42298381 0.50281089 0.03691161 -Al 2.05870312 4.15893716 2.17240187 -1.16319169 1.06499491 -1.14366143 1.97633583 4.19644025 2.07173789 -0.38942544 -0.34491464 -0.51929877 0.03741481 0.25165522 -0.43786106 -0.00881342 -Al 2.03093237 6.16616343 0.22636209 1.10853852 -0.60495543 0.06030238 2.13028979 6.05720479 0.12437434 0.56192064 -1.10937502 -1.72467316 0.05134746 -0.02221241 -0.42220860 -0.72697251 -Al -0.03248695 4.00614451 4.07624522 -2.09309665 0.34674387 -0.80620611 -0.19927375 4.05760085 4.03833007 -0.53714714 0.63756118 0.00335450 0.01991890 0.07730724 0.05282723 0.36410710 -Al -0.09799495 6.12242326 6.12879692 -0.47834541 1.61534925 -0.08451546 -0.09511775 6.22080798 6.14893328 0.50769178 0.03447663 0.43884577 0.01447713 0.32358306 -0.30293348 0.14506716 -Al 2.18056963 4.02380800 6.07306091 0.40203843 -0.98284014 1.18021244 2.16192487 3.96734595 6.20660576 -0.81339916 0.00838017 1.00797297 0.00791789 -0.31024128 0.23116548 0.18355736 -Al 2.01765645 6.04958972 4.15201148 -0.01861823 -0.58504763 -0.46188455 2.04342734 5.99328302 4.09422797 0.34837466 -0.40386889 -0.49054059 0.02178846 0.24261821 0.03990691 -0.11911530 -Al 4.05950027 -0.06948805 -0.04315484 0.58650611 -0.92751197 -0.32352926 4.12873504 -0.09385544 -0.04070397 0.53032298 0.48212852 0.47057884 0.03338857 0.12557091 0.41969896 0.17389399 -Al 4.19843764 2.12498899 2.10447206 -1.23468406 0.15096150 0.05290245 4.00730035 2.08501061 2.10790045 -1.94036692 -0.69849121 0.07967466 0.04405164 -0.53520078 -0.43452428 -0.10314871 -Al 6.05319828 0.12654426 2.12190414 -0.08523618 -0.98627825 -0.69445973 6.09741148 0.00653072 2.03616757 0.75210300 -1.00224225 -0.63806698 0.03369725 0.38187535 -0.23784589 -0.21805822 -Al 6.08315617 2.13848904 0.03278364 0.34836385 0.78684029 1.80041807 6.11034052 2.14825483 0.21182502 0.08442768 -0.68640145 1.37842183 0.01696111 -0.01858395 -0.34007695 0.00341296 -Al 4.20171060 -0.00534617 4.08410867 0.05322740 0.18446773 -0.22596081 4.18230453 0.01957983 4.08632220 -0.32423008 0.24543437 0.13977754 0.02462109 -0.23015003 0.03204022 0.22542410 -Al 4.02856458 1.93877892 6.15737613 0.25549493 0.15341887 -0.05673810 4.07999080 1.95201866 6.17330241 0.55505176 0.05601350 0.30160711 0.01943686 0.18372586 0.04424184 0.13678912 -Al 6.21393996 0.00944117 6.24427663 -1.40626442 -0.40271162 -0.73926252 6.04080561 0.00917481 6.13038896 -1.45023888 0.50548269 -1.28920177 0.02606800 -0.29677378 0.11749940 -0.24596496 -Al 6.09409643 2.06035727 4.10435555 2.00132806 1.00508627 -0.45776236 6.27316329 2.12537318 4.07608664 0.87434549 -0.05772887 -0.25061144 0.02706454 0.06834237 -0.08794993 0.27737462 -Al 3.97908084 3.96675084 0.12495806 -0.25445326 -0.00480584 -0.31471881 3.95079123 4.02823658 0.05441541 -0.04630141 1.00228514 -0.79129964 0.03631462 -0.09855059 0.42483074 -0.30497350 -Al 4.09072487 6.31549215 2.04941714 0.27041855 -0.22481211 0.29090850 4.08969105 6.21746492 2.11108375 -0.31599317 -1.37128517 0.71912391 0.04687197 -0.15187606 -0.54152674 0.22824196 -Al 5.91610022 4.17335776 1.94905240 -1.06862616 0.85962761 -0.97767055 5.87327491 4.22164516 1.92540329 0.18556879 -0.01720883 0.49616957 0.06920451 0.49634203 -0.23608660 0.48410634 -Al 6.11551220 6.04983000 0.17353957 0.57098905 0.83736373 -0.40771347 6.16862108 6.16298195 0.07892599 0.26759031 0.99142965 -1.25033343 0.03249291 0.01854674 0.26723564 -0.32819190 -Al 4.13322622 3.97642393 4.10132135 0.45184882 1.00528488 0.89563720 4.14516662 4.11240863 4.16214297 -0.21441665 1.18576487 0.17789512 0.03625823 -0.19862484 0.35325575 -0.13373786 -Al 4.26643379 6.07914675 6.09461896 0.28178320 0.06450424 -0.18289899 4.23721508 6.08072290 6.08307472 -0.68653876 -0.21080882 -0.04560572 0.03582803 -0.44375770 0.10709073 0.07250139 -Al 5.91104231 4.07169010 6.11094541 -0.13442338 -1.78041860 0.70994746 5.97789206 3.87813881 6.20956249 1.29526782 -1.44949375 1.04090804 0.04048538 0.50676704 -0.22659101 0.13008584 -Al 6.15996987 6.04803280 4.01943776 0.69527018 -0.90269514 -0.26059786 6.21360121 5.99364994 4.00244441 0.11191382 0.09326383 0.06890248 0.00375953 -0.01603652 0.14024865 0.00304423 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.0918324809063336 free_energy=1.0918324809063336 stress="0.004911883159152096 0.00013220167420827304 0.0002369478789135862 0.00013220167420827304 0.003970602953314662 -0.0003438719444869235 0.0002369478789135862 -0.0003438719444869235 0.0055649138761324265" pbc="T T T" -Al -0.02605177 0.10230075 0.07012401 -0.05724316 -0.26450821 1.29509040 -0.01275222 0.05726802 0.18005727 0.19927745 -0.57237382 0.71890947 0.01089759 0.18593413 -0.07582203 -0.16820964 -Al 0.07843493 1.98449425 2.15335321 -1.05132111 0.77155548 0.19687641 -0.04999668 2.09243871 2.18464611 -1.16756593 1.12969892 0.21790407 0.04567581 -0.17172117 0.19717195 0.13080363 -Al 2.03483420 0.10028453 1.98389770 -0.16796648 0.10216174 -0.79790502 2.03841892 0.08400047 1.95797645 0.21404749 -0.13441634 0.50957714 0.01691608 0.13022594 -0.35664845 0.22282597 -Al 1.90338778 2.19789253 0.20698188 -0.73824318 0.36892834 0.24218248 1.88149100 2.16983810 0.17118949 0.37348322 -0.82040314 -0.79865440 0.06815795 0.31039774 -0.43757469 -0.42454519 -Al 0.13990167 -0.00336235 4.22699801 0.60545575 0.26572591 0.30847152 0.14528137 0.00609861 4.16828852 -0.51651896 -0.04198044 -1.30642404 0.04009873 -0.32360977 -0.13723595 -0.53365538 -Al 0.13966297 1.96766674 6.06165521 -0.68463553 -0.33608300 0.02917049 0.04182469 1.96808443 6.11673011 -0.95765160 0.31122890 0.76605778 0.02510051 -0.22070553 0.21686273 0.39568814 -Al 2.15377844 -0.18480510 6.10440512 0.05892927 0.28482844 -1.00274337 2.14379409 -0.12620265 6.05723504 -0.29418097 0.70748394 0.21503271 0.03250977 -0.08766551 0.24981086 0.27030230 -Al 2.11891387 1.88693950 4.21250084 0.39286002 -0.15025753 0.44609361 2.15660798 1.93854607 4.19392680 0.18126174 0.96469831 -0.84297807 0.04096731 0.02458988 0.46219317 -0.31579068 -Al -0.10422580 4.04038397 0.15494813 -0.02422449 -0.85208347 -1.72868698 -0.06455924 3.99585417 -0.07151346 0.69408874 0.10696503 -2.18650854 0.03328949 0.27590974 0.20745221 -0.34762655 -Al 0.01170307 6.08867921 2.02963497 1.02291003 -2.29986604 0.33505130 0.10204672 5.90301118 2.08022324 0.36262648 -0.97833494 0.55983239 0.02244153 0.09457648 0.25475448 0.09846705 -Al 1.97633583 4.19644025 2.07173789 0.38942544 0.34491464 0.51929877 2.05870312 4.15893716 2.17240187 1.16319169 -1.06499491 1.14366143 0.04186118 0.18711983 -0.38195184 0.35018185 -Al 2.13028979 6.05720479 0.12437434 -0.56192064 1.10937502 1.72467316 2.03093237 6.16616343 0.22636209 -1.10853852 0.60495543 -0.06030238 0.02477497 -0.32445294 0.12500193 -0.32464125 -Al -0.19927375 4.05760085 4.03833007 0.53714714 -0.63756118 -0.00335450 -0.03248695 4.00614451 4.07624522 2.09309665 -0.34674387 0.80620611 0.09022963 0.89309837 0.11954330 0.16250083 -Al -0.09511775 6.22080798 6.14893328 -0.50769178 -0.03447663 -0.43884577 -0.09799495 6.12242326 6.12879692 0.47834541 -1.61534925 0.08451546 0.03257992 0.28207578 -0.61847458 0.11533067 -Al 2.16192487 3.96734595 6.20660576 0.81339916 -0.00838017 -1.00797297 2.18056963 4.02380800 6.07306091 -0.40203843 0.98284014 -1.18021244 0.03489733 -0.44904038 0.35302437 -0.32493968 -Al 2.04342734 5.99328302 4.09422797 -0.34837466 0.40386889 0.49054059 2.01765645 6.04958972 4.15201148 0.01861823 0.58504763 0.46188455 0.00966126 -0.03890277 0.12400499 0.09699627 -Al 4.12873504 -0.09385544 -0.04070397 -0.53032298 -0.48212852 -0.47057884 4.05950027 -0.06948805 -0.04315484 -0.58650611 0.92751197 0.32352926 0.03556891 -0.16783018 0.42389581 0.36210099 -Al 4.00730035 2.08501061 2.10790045 1.94036692 0.69849121 -0.07967466 4.19843764 2.12498899 2.10447206 1.23468406 -0.15096150 -0.05290245 -0.00293970 0.11086241 -0.07470684 0.04269246 -Al 6.09741148 0.00653072 2.03616757 -0.75210300 1.00224225 0.63806698 6.05319828 0.12654426 2.12190414 0.08523618 0.98627825 0.69445973 0.01104747 0.09768037 0.18464649 0.24915921 -Al 6.11034052 2.14825483 0.21182502 -0.08442768 0.68640145 -1.37842183 6.08315617 2.13848904 0.03278364 -0.34836385 -0.78684029 -1.80041807 0.03573598 -0.14190429 -0.49806651 -0.21100660 -Al 4.18230453 0.01957983 4.08632220 0.32423008 -0.24543437 -0.13977754 4.20171060 -0.00534617 4.08410867 -0.05322740 -0.18446773 0.22596081 0.01371445 -0.06840396 -0.02096407 -0.02025451 -Al 4.07999080 1.95201866 6.17330241 -0.55505176 -0.05601350 -0.30160711 4.02856458 1.93877892 6.15737613 -0.25549493 -0.15341887 0.05673810 0.02841402 -0.07095965 -0.01594452 0.05239176 -Al 6.04080561 0.00917481 6.13038896 1.45023888 -0.50548269 1.28920177 6.21393996 0.00944117 6.24427663 1.40626442 0.40271162 0.73926252 0.03629277 0.32068290 0.33369400 -0.11607776 -Al 6.27316329 2.12537318 4.07608664 -0.87434549 0.05772887 0.25061144 6.09409643 2.06035727 4.10435555 -2.00132806 -1.00508627 0.45776236 0.05742928 -0.71649904 -0.54630815 -0.09325273 -Al 3.95079123 4.02823658 0.05441541 0.04630141 -1.00228514 0.79129964 3.97908084 3.96675084 0.12495806 0.25445326 0.00480584 0.31471881 0.05548307 0.31981913 0.16868008 0.05237449 -Al 4.08969105 6.21746492 2.11108375 0.31599317 1.37128517 -0.71912391 4.09072487 6.31549215 2.04941714 -0.27041855 0.22481211 -0.29090850 0.02844169 -0.20167795 -0.15027434 -0.00927075 -Al 5.87327491 4.22164516 1.92540329 -0.18556879 0.01720883 -0.49616957 5.91610022 4.17335776 1.94905240 1.06862616 -0.85962761 0.97767055 0.06559015 0.31393846 -0.32464927 0.39609546 -Al 6.16862108 6.16298195 0.07892599 -0.26759031 -0.99142965 1.25033343 6.11551220 6.04983000 0.17353957 -0.57098905 -0.83736373 0.40771347 0.04572929 -0.24487065 -0.20481584 -0.18512544 -Al 4.14516662 4.11240863 4.16214297 0.21441665 -1.18576487 -0.17789512 4.13322622 3.97642393 4.10132135 -0.45184882 -1.00528488 -0.89563720 0.00884758 -0.17041663 -0.24813674 -0.22303960 -Al 4.23721508 6.08072290 6.08307472 0.68653876 0.21080882 0.04560572 4.26643379 6.07914675 6.09461896 -0.28178320 -0.06450424 0.18289899 0.02204928 -0.18264197 -0.22128409 0.03875775 -Al 5.97789206 3.87813881 6.20956249 -1.29526782 1.44949375 -1.04090804 5.91104231 4.07169010 6.11094541 0.13442338 1.78041860 -0.70994746 0.02835153 0.38848951 0.39279792 0.02366620 -Al 6.21360121 5.99364994 4.00244441 -0.11191382 -0.09326383 -0.06890248 6.15996987 6.04803280 4.01943776 -0.69527018 0.90269514 0.26059786 0.05201764 -0.35409825 0.49932362 0.23710075 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.0130284021902485 free_energy=1.0130284021902485 stress="0.005128678845193628 8.697537281318916e-07 -0.00029998580829961133 8.697537281318916e-07 0.0044229703237791355 6.279173330563964e-05 -0.00029998580829961133 6.279173330563964e-05 0.005507960838444401" pbc="T T T" -Al 0.02213502 -0.08451190 0.25018385 -0.26435718 0.55167561 0.67885138 -0.00601513 -0.01338803 0.26538651 -0.12007549 0.66756936 -0.40063079 0.02408400 -0.06484044 0.12179066 -0.34869014 -Al -0.11538440 1.96031939 2.04291776 -1.43214907 1.27051127 0.75093292 -0.20701340 2.14730757 2.12562758 0.04706424 1.92342147 0.65133519 0.03202621 0.15205486 0.42208068 0.08403587 -Al 2.03849331 0.18810670 2.08207549 0.59865585 -0.11796993 0.07979404 2.11775516 0.08872301 2.09423589 0.61551726 -1.41529797 0.18435427 0.04260248 0.23273160 -0.66690936 -0.00670461 -Al 1.99624644 2.10310818 0.14973596 -0.11425410 -0.48290690 0.22861129 1.99121706 2.05443881 0.16027995 -0.06084396 -0.25244343 0.01634178 0.02138092 0.12577098 -0.11886664 -0.12707024 -Al -0.05837425 0.06203386 4.04433271 -0.79962697 -0.75040507 -0.85240723 -0.07338641 -0.02033140 3.99608904 0.62142255 -0.46085540 0.02651968 0.02673161 0.31441920 -0.18786780 0.19621972 -Al -0.00770457 2.06909293 6.10104693 0.70817611 -0.29592739 -0.03445779 0.09317355 1.97944054 6.12719561 0.95505116 -1.21265131 0.50688098 0.06705886 0.23824199 -0.38420829 0.13357847 -Al 1.97896566 0.12917286 6.23590763 -0.80309374 -0.52241192 1.76837785 1.92181187 0.05904736 6.36191356 -0.07616280 -0.80955649 0.46944109 0.01683225 0.07847420 -0.05961487 -0.31959155 -Al 2.05369233 2.06438910 3.96006411 1.16961714 0.61354318 -0.65646365 2.17885121 2.12222930 3.95703070 0.92327454 0.22511332 0.53560529 0.01971213 0.11191042 0.09528210 0.42521272 -Al 0.00698362 3.98154618 -0.17370970 -0.69730309 1.47085102 -0.97100251 -0.05014748 4.17053588 -0.21631364 -0.16757894 1.42047130 0.17073287 0.03988046 0.00149831 0.52149023 0.40411433 -Al -0.05288955 5.95606644 2.22626273 0.52308388 0.54286053 0.91214082 0.02455706 6.00411830 2.26357548 0.76346422 0.18179056 -0.39144355 0.02830709 0.22229435 0.07992085 -0.25793799 -Al 2.11289958 4.12089312 2.01835908 -1.00165741 -0.25236311 -0.09011107 1.98148873 4.04688871 2.00932370 -1.26842272 -0.98754574 -0.02296711 0.03429157 -0.21815781 -0.31953105 -0.00845476 -Al 2.07385933 6.02691843 0.11851837 -0.51781348 -0.76700998 0.31669745 2.00474070 5.99913554 0.16090479 -0.65142880 0.30661728 0.36585202 0.01347043 -0.14407845 0.26818065 0.10918844 -Al 0.07379181 3.95074811 4.16066022 0.28274843 -0.63740086 1.66182124 0.09728314 3.95784625 4.32313585 0.09514929 0.79912115 1.20054960 0.01885460 -0.01987759 0.37385512 -0.01594232 -Al 0.01970170 6.31508873 6.13616062 0.66174879 -0.13277108 0.33549058 0.09583308 6.22386576 6.15136584 0.67824314 -1.40931939 -0.13831786 0.01521367 0.06967147 -0.52011188 -0.07661197 -Al 2.20594642 4.04358694 6.26889780 0.08089119 -0.05692082 -0.31614389 2.17778868 4.03407371 6.16573066 -0.53269470 -0.06858254 -1.36773904 0.02939705 -0.26239430 -0.04485116 -0.51278746 -Al 2.04939216 6.02783801 4.00101857 -0.07919856 -0.15244839 -0.85845379 2.03830579 6.07919793 3.98217142 -0.00570732 0.94869788 0.57250429 0.02402397 -0.07222048 0.45815395 0.38046490 -Al 4.04865600 0.03497252 -0.07876810 0.10580979 1.34718334 0.49769567 4.06059397 0.14738452 0.07982591 0.20250051 0.45891526 1.92089753 0.05650232 -0.06961689 0.00056470 0.86917019 -Al 4.22026774 1.99102600 2.05061721 0.60510986 -0.84381460 0.06119475 4.25729372 1.92260931 2.06541232 -0.05954731 -0.32524965 0.24966477 -0.00166435 -0.09625805 0.08753587 0.04403594 -Al 6.07549009 -0.08385214 2.14347932 1.30988168 0.84804195 -1.06791337 6.18056567 0.02814837 1.99756354 0.46774409 0.99668802 -1.38919434 0.03859889 -0.07418075 0.24944452 -0.26409109 -Al 6.19186074 2.09740161 -0.02857643 -0.06702953 -0.24934370 0.00668792 6.15579182 2.02780824 -0.04953139 -0.50020516 -0.84999854 -0.51819493 0.02756169 -0.22344633 -0.33945515 -0.00589771 -Al 4.14299385 0.08645619 4.14261476 1.17104305 -0.15778604 0.15932594 4.21210948 0.04321468 4.15160924 0.09843822 -0.56420467 -0.03427003 0.00692053 -0.32967189 -0.20223724 -0.00630615 -Al 4.16366296 1.99290003 6.16679844 -0.66254727 -1.02372937 0.09626487 4.05934754 1.93621067 6.14446934 -1.09792379 -0.05101719 -0.44714627 0.03185297 -0.29924423 0.30695412 -0.20001791 -Al 5.94480474 -0.10034035 6.23393033 -0.04907645 -1.75398260 -0.61710366 6.04243363 -0.23671976 6.16832261 1.67840732 -0.52946212 -0.36547481 0.05717584 0.67699772 0.16576331 -0.15697046 -Al 6.21648228 2.01870097 4.25738983 0.90066960 0.19860041 0.07877886 6.21560078 2.05080104 4.22636022 -0.88568653 0.33096460 -0.42276020 0.04531873 -0.54938602 0.09238392 -0.38424506 -Al 4.14912259 4.01367788 0.05212177 -0.92324580 1.05232549 -0.45720373 4.04592202 4.11349092 0.03147643 -0.97211388 0.62728330 0.13266298 0.02480920 -0.01688396 0.04148863 0.11571892 -Al 4.21417820 6.02712474 2.15191252 -0.70303143 1.65229380 -0.07107904 4.09333020 6.24255705 2.08300373 -1.25278141 1.96456036 -1.02883463 0.05230762 -0.42375731 0.38880200 -0.44569508 -Al 6.08982307 4.00502052 2.00375293 -1.44048406 -1.11928088 -1.31327079 5.96676282 3.90728881 1.94460099 -0.82399861 -0.33750090 0.25827774 0.03384097 0.18680633 -0.09610198 0.42573264 -Al 6.17182647 5.99789598 0.17494200 0.73652825 -1.35023526 0.41123487 6.22025715 5.90599463 0.17281582 -0.00098980 -0.05324400 -0.29314965 0.01796102 -0.11751999 0.13384365 -0.36771480 -Al 4.01232337 4.19970871 4.04024162 -0.39263944 0.14090196 -0.38674701 4.00711671 4.13838430 4.02226635 0.38052503 -0.96741127 -0.06566993 0.05085593 0.13910101 -0.62654015 0.21938361 -Al 4.01156082 6.12397827 6.26155146 0.96586937 0.07250967 -0.16429524 4.07155007 6.13058473 6.19820112 -0.03834417 0.12849413 -0.84500948 0.04665229 -0.10336532 -0.06773895 -0.38038120 -Al 5.93181598 4.19017322 5.97824763 -0.93957766 0.01456575 0.82293726 5.89401831 4.16565552 6.09467449 0.23795735 -0.48981186 1.05748443 0.02535221 0.37888875 -0.14028154 0.34871659 -Al 6.06160705 6.11125177 4.12436328 1.06725222 0.89084394 -1.01018489 6.17529306 6.16395072 4.03832704 0.74974647 -0.19555552 -0.58830190 0.04511526 0.15603860 -0.03321890 0.12953815 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.1047037076242896 free_energy=1.1047037076242896 stress="0.004138521127263458 0.0009391501441625187 0.00011793231796938067 0.0009391501441625187 0.004119625289886451 -0.00018275874172973586 0.00011793231796938067 -0.00018275874172973586 0.007073958518883559" pbc="T T T" -Al -0.00601513 -0.01338803 0.26538651 0.12007549 -0.66756936 0.40063079 0.02213502 -0.08451190 0.25018385 0.26435718 -0.55167561 -0.67885138 0.03076914 0.19096507 -0.04593207 -0.34803027 -Al -0.20701340 2.14730757 2.12562758 -0.04706424 -1.92342147 -0.65133519 -0.11538440 1.96031939 2.04291776 1.43214907 -1.27051127 -0.75093292 0.04393056 0.71520172 -0.03680347 -0.13705089 -Al 2.11775516 0.08872301 2.09423589 -0.61551726 1.41529797 -0.18435427 2.03849331 0.18810670 2.08207549 -0.59865585 0.11796993 -0.07979404 0.02049922 -0.25179988 -0.09134542 0.06993599 -Al 1.99121706 2.05443881 0.16027995 0.06084396 0.25244343 -0.01634178 1.99624644 2.10310818 0.14973596 0.11425410 0.48290690 -0.22861129 0.01246022 -0.03595561 0.18666288 -0.02643292 -Al -0.07338641 -0.02033140 3.99608904 -0.62142255 0.46085540 -0.02651968 -0.05837425 0.06203386 4.04433271 0.79962697 0.75040507 0.85240723 0.03892371 0.50396552 0.38058985 0.30839175 -Al 0.09317355 1.97944054 6.12719561 -0.95505116 1.21265131 -0.50688098 -0.00770457 2.06909293 6.10104693 -0.70817611 0.29592739 0.03445779 0.04634591 -0.12009872 -0.12086814 0.11597975 -Al 1.92181187 0.05904736 6.36191356 0.07616280 0.80955649 -0.46944109 1.97896566 0.12917286 6.23590763 0.80309374 0.52241192 -1.76837785 0.05695456 0.40878577 -0.14799086 -0.47405682 -Al 2.17885121 2.12222930 3.95703070 -0.92327454 -0.22511332 -0.53560529 2.05369233 2.06438910 3.96006411 -1.16961714 -0.61354318 0.65646365 0.02718331 -0.28795978 -0.34411634 0.30539513 -Al -0.05014748 4.17053588 -0.21631364 0.16757894 -1.42047130 -0.17073287 0.00698362 3.98154618 -0.17370970 0.69730309 -1.47085102 0.97100251 0.05741352 0.35071467 -0.58712393 0.37469906 -Al 0.02455706 6.00411830 2.26357548 -0.76346422 -0.18179056 0.39144355 -0.05288955 5.95606644 2.22626273 -0.52308388 -0.54286053 -0.91214082 0.05477183 -0.04443676 -0.23061949 -0.55987018 -Al 1.98148873 4.04688871 2.00932370 1.26842272 0.98754574 0.02296711 2.11289958 4.12089312 2.01835908 1.00165741 0.25236311 0.09011107 0.03509882 0.06153255 -0.08999473 0.09253752 -Al 2.00474070 5.99913554 0.16090479 0.65142880 -0.30661728 -0.36585202 2.07385933 6.02691843 0.11851837 0.51781348 0.76700998 -0.31669745 0.01364835 0.05071369 0.36745917 -0.07874840 -Al 0.09728314 3.95784625 4.32313585 -0.09514929 -0.79912115 -1.20054960 0.07379181 3.95074811 4.16066022 -0.28274843 0.63740086 -1.66182124 0.02306465 -0.13254854 0.43200105 -0.25592987 -Al 0.09583308 6.22386576 6.15136584 -0.67824314 1.40931939 0.13831786 0.01970170 6.31508873 6.13616062 -0.66174879 0.13277108 -0.33549058 0.03722723 -0.04317109 -0.25700677 -0.23399412 -Al 2.17778868 4.03407371 6.16573066 0.53269470 0.06858254 1.36773904 2.20594642 4.04358694 6.26889780 -0.08089119 0.05692082 0.31614389 0.01701042 -0.12802143 0.04953544 -0.10675663 -Al 2.03830579 6.07919793 3.98217142 0.00570732 -0.94869788 -0.57250429 2.04939216 6.02783801 4.00101857 0.07919856 0.15244839 0.85845379 0.02706303 0.15199849 0.17959259 0.49733150 -Al 4.06059397 0.14738452 0.07982591 -0.20250051 -0.45891526 -1.92089753 4.04865600 0.03497252 -0.07876810 -0.10580979 -1.34718334 -0.49769567 0.03940503 0.09237554 -0.49490403 -0.08433439 -Al 4.25729372 1.92260931 2.06541232 0.05954731 0.32524965 -0.24966477 4.22026774 1.99102600 2.05061721 -0.60510986 0.84381460 -0.06119475 0.05047209 -0.35149253 0.26052588 0.11741839 -Al 6.18056567 0.02814837 1.99756354 -0.46774409 -0.99668802 1.38919434 6.07549009 -0.08385214 2.14347932 -1.30988168 -0.84804195 1.06791337 0.06588470 -0.35899921 -0.15016671 0.16723139 -Al 6.15579182 2.02780824 -0.04953139 0.50020516 0.84999854 0.51819493 6.19186074 2.09740161 -0.02857643 0.06702953 0.24934370 -0.00668792 0.00663271 -0.04669239 0.00974961 -0.25286891 -Al 4.21210948 0.04321468 4.15160924 -0.09843822 0.56420467 0.03427003 4.14299385 0.08645619 4.14261476 -1.17104305 0.15778604 -0.15932594 0.02100275 -0.32405759 -0.06506621 -0.08030997 -Al 4.05934754 1.93621067 6.14446934 1.09792379 0.05101719 0.44714627 4.16366296 1.99290003 6.16679844 0.66254727 1.02372937 -0.09626487 0.01455896 0.00164852 0.24458914 -0.08242188 -Al 6.04243363 -0.23671976 6.16832261 -1.67840732 0.52946212 0.36547481 5.94480474 -0.10034035 6.23393033 0.04907645 1.75398260 0.61710366 0.04211444 0.37058568 0.57817759 0.26001291 -Al 6.21560078 2.05080104 4.22636022 0.88568653 -0.33096460 0.42276020 6.21648228 2.01870097 4.25738983 -0.90066960 -0.19860041 -0.07877886 0.02772826 -0.49207051 -0.02178290 0.04977811 -Al 4.04592202 4.11349092 0.03147643 0.97211388 -0.62728330 -0.13266298 4.14912259 4.01367788 0.05212177 0.92324580 -1.05232549 0.45720373 0.02983322 -0.02166875 -0.25777803 0.21986734 -Al 4.09333020 6.24255705 2.08300373 1.25278141 -1.96456036 1.02883463 4.21417820 6.02712474 2.15191252 0.70303143 -1.65229380 0.07107904 0.00366273 0.09250872 -0.27189183 -0.12916503 -Al 5.96676282 3.90728881 1.94460099 0.82399861 0.33750090 -0.25827774 6.08982307 4.00502052 2.00375293 1.44048406 1.11928088 1.31327079 0.08337120 0.18539275 0.55661727 0.48285839 -Al 6.22025715 5.90599463 0.17281582 0.00098980 0.05324400 0.29314965 6.17182647 5.99789598 0.17494200 -0.73652825 1.35023526 -0.41123487 0.05662362 -0.41202555 0.67932921 -0.07057314 -Al 4.00711671 4.13838430 4.02226635 -0.38052503 0.96741127 0.06566993 4.01232337 4.19970871 4.04024162 0.39263944 -0.14090196 0.38674701 0.01472974 0.30459472 -0.05071633 -0.01069060 -Al 4.07155007 6.13058473 6.19820112 0.03834417 -0.12849413 0.84500948 4.01156082 6.12397827 6.26155146 -0.96586937 -0.07250967 0.16429524 0.03660563 -0.43194861 0.08406947 -0.08097677 -Al 5.89401831 4.16565552 6.09467449 -0.23795735 0.48981186 -1.05748443 5.93181598 4.19017322 5.97824763 0.93957766 -0.01456575 -0.82293726 0.02227255 0.35322095 -0.18358794 -0.14640537 -Al 6.17529306 6.16395072 4.03832704 -0.74974647 0.19555552 0.58830190 6.06160705 6.11125177 4.12436328 -1.06725222 -0.89084394 1.01018489 0.04744160 -0.35125741 -0.56120394 0.09717892 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.1705957239373603 free_energy=1.1705957239373603 stress="0.003845133113172732 0.00044194282378009364 -0.0003317322932143324 0.00044194282378009364 0.004823164759565864 -0.00029437895860849356 -0.0003317322932143324 -0.00029437895860849356 0.00632812513627653" pbc="T T T" -Al -0.07821786 0.12108144 -0.16766018 -0.31105836 0.08388703 -0.63141108 -0.05270062 0.06097786 -0.17412374 0.70497751 -1.21496841 0.70935559 0.05109044 0.36299030 -0.33364679 0.23888644 -Al 0.09085967 1.71555159 2.07646922 0.95086557 -0.57698993 -0.08295244 0.19853509 1.69386181 2.06649659 0.90014887 0.23915207 -0.25077749 0.00038390 0.09802415 0.22986456 0.09326055 -Al 2.19426707 -0.15809549 2.02373705 1.03647976 -1.03793313 -0.04976920 2.23658087 -0.19380258 2.03850145 -0.21319336 0.49623588 0.43911276 0.05712845 -0.40657940 0.36796046 0.04493915 -Al 2.09224055 1.98196121 -0.05771524 -0.13416708 0.33750053 -0.29578459 2.07887852 2.01921285 -0.06688840 -0.14288731 0.29808352 0.12765216 -0.00460580 0.01594345 0.04017677 0.13878125 -Al -0.08478475 -0.12847821 4.22978432 0.04816760 1.42413180 0.02858846 -0.08829754 0.07314862 4.22138691 -0.00453537 1.72391754 -0.16935684 0.04496107 -0.06362365 0.61444069 -0.08989657 -Al -0.11142129 2.19243089 6.10770098 -0.29508494 -1.74031306 0.04224444 -0.09678244 1.99392413 6.09074772 0.45594717 -1.43132773 -0.32804129 0.03419664 0.35177307 -0.37446681 -0.10103132 -Al 1.96624328 0.03494261 6.02228034 -0.64379813 0.26059213 1.01041857 1.94546271 0.07343064 6.15728247 0.28186001 0.37447223 1.24972324 0.02227456 0.27337636 0.10202422 0.27831770 -Al 2.06870533 2.22704702 4.07403457 -0.06714251 0.61567618 -0.46811984 2.03518431 2.23346279 4.05781615 -0.47171450 -0.43154372 0.14868740 0.02538211 -0.19260893 -0.40686164 0.21489186 -Al 0.18967762 4.08336422 0.21743947 -0.56962473 1.38747015 -0.09640643 0.03782485 4.17525923 0.16782586 -1.89001058 0.15668653 -0.68728980 0.05252502 -0.64896938 -0.24446278 -0.26072600 -Al 0.01222183 6.25978028 2.03920652 0.80500617 0.40527830 0.35732957 0.09526192 6.28916164 2.07937186 0.56667804 0.12459322 0.38512026 0.01442015 0.07565116 -0.11384181 0.00532914 -Al 2.00164369 3.99107963 1.99727438 -0.12767300 -1.11536353 -2.17565712 2.06495718 3.91150630 1.83616264 1.05294354 -0.25973113 -0.66956097 0.06160533 0.55238040 0.21839811 0.33217773 -Al 2.15614552 6.00964837 0.04723443 -0.36000976 0.40697371 0.78991870 2.10781449 6.08554736 0.11366897 -0.51514776 0.77564353 0.28362216 0.02468155 -0.08517049 0.31151973 -0.00470665 -Al -0.02563888 4.10402757 3.89647265 0.47653601 1.32818343 0.76603495 0.03241275 4.21604793 4.00576873 0.58030786 0.46142675 1.11029174 0.02265039 0.03234221 0.00109147 0.23913714 -Al -0.01618421 6.05639081 6.24826744 0.72018435 -0.07301222 -0.08632860 0.05203349 6.06872737 6.22529035 0.43790944 0.33572790 -0.40137470 0.04648780 0.03031433 0.07432401 -0.05158156 -Al 1.94771696 4.13587640 6.16413404 -0.03963949 0.07061201 -0.48028331 1.97983812 4.10452836 6.11160637 0.57463206 -0.61689164 -0.28871923 0.02109376 0.24079029 -0.22729036 -0.13619823 -Al 1.86084557 6.09813012 4.16931790 0.03314649 -0.25956277 0.59576932 1.94573769 6.05014443 4.19139282 1.41026781 -0.37083548 -0.24236961 0.03404173 0.53960505 -0.28615455 -0.21038931 -Al 4.07882182 0.00677076 0.10893728 0.18806050 -0.25676481 0.97901444 4.07076995 -0.02019098 0.15472006 -0.27146475 -0.15993975 -0.19035691 0.04430474 -0.16355851 -0.05056738 -0.27788435 -Al 4.09698915 2.04241337 2.13967434 0.67562370 -0.07071069 0.19576034 4.11409461 2.04203469 2.13012541 -0.31081755 0.02257674 -0.39023380 0.02657714 -0.33227946 0.06828139 -0.14414582 -Al 5.93247690 -0.07858327 2.09892254 -0.89349890 -0.46046371 -0.84058752 5.93331369 -0.08370514 2.07968937 0.83191229 0.22731745 0.45711485 0.08222741 0.54708726 0.31663741 0.38228639 -Al 6.10748533 1.87416468 0.29330146 0.59206701 -0.84707729 -0.79441788 6.16150442 1.89159061 0.16804059 0.33829256 1.17602453 -1.23748826 0.06435485 -0.00997542 0.54848301 -0.43998100 -Al 3.92045497 0.06163327 4.07252328 -0.91223561 0.47767476 0.79626074 3.87722111 0.10313340 4.18873592 0.15217493 0.27151528 1.14257570 0.02482290 0.29862544 -0.04882112 0.27752877 -Al 4.21848770 2.13036532 6.14141824 -0.41114079 0.10240664 0.23353929 4.11356789 2.15132489 6.16104690 -1.24434655 0.21610760 -0.00815013 0.07155155 -0.51189100 0.06379645 0.07858018 -Al 5.83816546 0.19180441 6.18106669 -0.41228672 -0.38450265 1.11629972 5.88112726 0.07078982 6.23781975 1.00302767 -1.67088185 -0.14582247 0.06566150 0.64779487 -0.54878397 -0.29055007 -Al 6.13965224 2.17354209 3.95807566 0.59199442 -0.20549863 -1.31688692 6.20761334 2.07960042 3.88674503 0.51467342 -1.28628043 0.13875943 0.03296307 0.06960998 -0.52944362 0.28892537 -Al 4.21364971 4.02582056 0.04027400 0.63015129 0.22185216 0.01522223 4.23271569 4.05833408 0.04915904 -0.29191187 0.35947221 0.13961150 0.00859065 -0.28835755 0.05580523 0.04202125 -Al 4.22484888 6.13119549 2.12010265 0.09500864 -0.04043648 -0.08435994 4.15892190 6.01897958 2.13387411 -1.21149386 -1.69843909 0.27506941 0.05828532 -0.47979900 -0.73308901 0.13153562 -Al 6.09355870 4.00713122 1.99412437 -0.42926307 0.18877520 1.56403723 6.08490501 4.06392335 2.14781591 0.36585391 0.69472622 0.98088513 0.04450934 0.16143462 0.29058982 0.11129000 -Al 6.22427957 6.11304095 0.15661615 0.13062756 -1.01140828 0.52108241 6.18973320 6.01762233 0.15892707 -0.73797405 -0.52137499 -0.37754255 0.02995663 -0.33164617 -0.02589610 -0.36225408 -Al 4.17757328 4.00448317 4.26864029 0.21320757 0.73021792 -1.51694048 4.15864148 4.12972052 4.07699427 -0.64123928 1.20563015 -1.63272963 0.04009722 -0.21789368 0.47546617 -0.39690543 -Al 4.12732324 6.06330384 6.15537448 -1.55587482 1.46599085 -0.20539925 3.95865176 6.21074962 6.13660947 -1.37174879 0.99341933 -0.10773843 0.00159240 -0.09847691 0.08233686 0.01224113 -Al 6.25680380 4.06583361 6.25279967 0.05038495 0.04702882 0.57699152 6.22342951 4.07849676 6.29671196 -0.58779115 0.04362041 0.00625911 0.04900440 -0.29861961 0.14390298 0.01326880 -Al 6.12933970 6.08283506 4.22722171 -0.07501366 -1.47421443 -0.46320732 6.10527835 5.95295029 4.16772913 -0.26533035 -0.53413487 -0.46628833 0.01777950 -0.16829377 -0.08177340 -0.15714810 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.1319334923518758 free_energy=1.1319334923518758 stress="0.004724185153377603 7.628423560682366e-05 -0.0002545923994040662 7.628423560682366e-05 0.005218669922821519 -0.0007487346003771602 -0.0002545923994040662 -0.0007487346003771602 0.005293740127255423" pbc="T T T" -Al -0.05270062 0.06097786 -0.17412374 -0.70497751 1.21496841 -0.70935559 -0.07821786 0.12108144 -0.16766018 0.31105836 -0.08388703 0.63141108 0.07696546 0.21245686 -0.37979575 0.61031702 -Al 0.19853509 1.69386181 2.06649659 -0.90014887 -0.23915207 0.25077749 0.09085967 1.71555159 2.07646922 -0.95086557 0.57698993 0.08295244 0.02353866 -0.15155912 0.34707640 -0.19713105 -Al 2.23658087 -0.19380258 2.03850145 0.21319336 -0.49623588 -0.43911276 2.19426707 -0.15809549 2.02373705 -1.03647976 1.03793313 0.04976920 0.09072832 -0.31324147 0.58453916 0.26290110 -Al 2.07887852 2.01921285 -0.06688840 0.14288731 -0.29808352 -0.12765216 2.09224055 1.98196121 -0.05771524 0.13416708 -0.33750053 0.29578459 0.02375263 -0.04589477 -0.05241661 0.14080337 -Al -0.08829754 0.07314862 4.22138691 0.00453537 -1.72391754 0.16935684 -0.08478475 -0.12847821 4.22978432 -0.04816760 -1.42413180 -0.02858846 0.03241941 0.14300888 -0.46633462 -0.07246492 -Al -0.09678244 1.99392413 6.09074772 -0.45594717 1.43132773 0.32804129 -0.11142129 2.19243089 6.10770098 0.29508494 1.74031306 -0.04224444 0.04259439 0.14716773 0.52984534 -0.03711024 -Al 1.94546271 0.07343064 6.15728247 -0.28186001 -0.37447223 -1.24972324 1.96624328 0.03494261 6.02228034 0.64379813 -0.26059213 -1.01041857 0.03894151 0.30674188 -0.02846450 -0.13871674 -Al 2.03518431 2.23346279 4.05781615 0.47171450 0.43154372 -0.14868740 2.06870533 2.22704702 4.07403457 0.06714251 -0.61567618 0.46811984 0.01766624 -0.04441021 -0.27918251 0.17114627 -Al 0.03782485 4.17525923 0.16782586 1.89001058 -0.15668653 0.68728980 0.18967762 4.08336422 0.21743947 0.56962473 -1.38747015 0.09640643 0.04639039 -0.03141727 -0.52073599 -0.03208457 -Al 0.09526192 6.28916164 2.07937186 -0.56667804 -0.12459322 -0.38512026 0.01222183 6.25978028 2.03920652 -0.80500617 -0.40527830 -0.35732957 0.00748749 -0.21630445 -0.13170005 0.02047973 -Al 2.06495718 3.91150630 1.83616264 -1.05294354 0.25973113 0.66956097 2.00164369 3.99107963 1.99727438 0.12767300 1.11536353 2.17565712 0.05023971 0.09226620 0.38100607 0.56053859 -Al 2.10781449 6.08554736 0.11366897 0.51514776 -0.77564353 -0.28362216 2.15614552 6.00964837 0.04723443 0.36000976 -0.40697371 -0.78991870 0.02690310 -0.05944763 -0.09206261 -0.28167760 -Al 0.03241275 4.21604793 4.00576873 -0.58030786 -0.46142675 -1.11029174 -0.02563888 4.10402757 3.89647265 -0.47653601 -1.32818343 -0.76603495 0.04407131 0.01865042 -0.50807453 -0.00717840 -Al 0.05203349 6.06872737 6.22529035 -0.43790944 -0.33572790 0.40137470 -0.01618421 6.05639081 6.24826744 -0.72018435 0.07301222 0.08632860 0.03391349 -0.15246141 0.14326701 -0.17910228 -Al 1.97983812 4.10452836 6.11160637 -0.57463206 0.61689164 0.28871923 1.94771696 4.13587640 6.16413404 0.03963949 -0.07061201 0.48028331 0.01173459 0.13290160 -0.16687558 0.24062884 -Al 1.94573769 6.05014443 4.19139282 -1.41026781 0.37083548 0.24236961 1.86084557 6.09813012 4.16931790 -0.03314649 0.25956277 -0.59576932 0.03391715 0.31065990 0.18796839 -0.31160774 -Al 4.07076995 -0.02019098 0.15472006 0.27146475 0.15993975 0.19035691 4.07882182 0.00677076 0.10893728 -0.18806050 0.25676481 -0.97901444 0.05183028 -0.04269523 0.10126469 -0.37168808 -Al 4.11409461 2.04203469 2.13012541 0.31081755 -0.02257674 0.39023380 4.09698915 2.04241337 2.13967434 -0.67562370 0.07071069 -0.19576034 0.01849459 -0.23017099 -0.01318411 -0.18292420 -Al 5.93331369 -0.08370514 2.07968937 -0.83191229 -0.22731745 -0.45711485 5.93247690 -0.07858327 2.09892254 0.89349890 0.46046371 0.84058752 0.03547313 0.41018630 0.06012462 0.32619196 -Al 6.16150442 1.89159061 0.16804059 -0.33829256 -1.17602453 1.23748826 6.10748533 1.87416468 0.29330146 -0.59206701 0.84707729 0.79441788 0.02401281 -0.12497142 0.56777704 0.07678397 -Al 3.87722111 0.10313340 4.18873592 -0.15217493 -0.27151528 -1.14257570 3.92045497 0.06163327 4.07252328 0.91223561 -0.47767476 -0.79626074 0.02068391 0.36984144 -0.07058954 -0.09454685 -Al 4.11356789 2.15132489 6.16104690 1.24434655 -0.21610760 0.00815013 4.21848770 2.13036532 6.14141824 0.41114079 -0.10240664 -0.23353929 0.01320282 0.00686839 -0.04902407 -0.18646135 -Al 5.88112726 0.07078982 6.23781975 -1.00302767 1.67088185 0.14582247 5.83816546 0.19180441 6.18106669 0.41228672 0.38450265 -1.11629972 0.03781755 0.27659027 -0.23868751 -0.42541641 -Al 6.20761334 2.07960042 3.88674503 -0.51467342 1.28628043 -0.13875943 6.13965224 2.17354209 3.95807566 -0.59199442 0.20549863 1.31688692 0.04506028 -0.20682894 -0.07030927 0.54440327 -Al 4.23271569 4.05833408 0.04915904 0.29191187 -0.35947221 -0.13961150 4.21364971 4.02582056 0.04027400 -0.63015129 -0.22185216 -0.01522223 0.01553884 -0.30541546 0.02468783 0.01859276 -Al 4.15892190 6.01897958 2.13387411 1.21149386 1.69843909 -0.27506941 4.22484888 6.13119549 2.12010265 -0.09500864 0.04043648 0.08435994 0.04838400 -0.30999667 -0.11359591 0.00080967 -Al 6.08490501 4.06392335 2.14781591 -0.36585391 -0.69472622 -0.98088513 6.09355870 4.00713122 1.99412437 0.42926307 -0.18877520 -1.56403723 0.06658670 0.35172883 -0.02878338 -0.42193113 -Al 6.18973320 6.01762233 0.15892707 0.73797405 0.52137499 0.37754255 6.22427957 6.11304095 0.15661615 -0.13062756 1.01140828 -0.52108241 0.03404906 -0.27780295 0.39168594 -0.17430482 -Al 4.15864148 4.12972052 4.07699427 0.64123928 -1.20563015 1.63272963 4.17757328 4.00448317 4.26864029 -0.21320757 -0.73021792 1.51694048 0.03561012 -0.36476290 -0.23650006 0.32937773 -Al 3.95865176 6.21074962 6.13660947 1.37174879 -0.99341933 0.10773843 4.12732324 6.06330384 6.15537448 1.55587482 -1.46599085 0.20539925 0.01435656 0.22762121 -0.39890259 0.07316909 -Al 6.22342951 4.07849676 6.29671196 0.58779115 -0.04362041 -0.00625911 6.25680380 4.06583361 6.25279967 -0.05038495 -0.04702882 -0.57699152 0.03400607 -0.15967021 -0.12221934 -0.42208732 -Al 6.10527835 5.95295029 4.16772913 0.26533035 0.53413487 0.46628833 6.12933970 6.08283506 4.22722171 0.07501366 1.47421443 0.46320732 0.03556289 0.03036121 0.64819608 0.16029033 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=0.8951962562931346 free_energy=0.8951962562931346 stress="0.006762199627886309 0.0004169919220461864 -0.0008031643384668323 0.0004169919220461864 0.005747662582846801 -0.000428147773379447 -0.0008031643384668323 -0.000428147773379447 0.004994554919625807" pbc="T T T" -Al 0.04030615 0.09171277 0.07398912 0.38636985 -0.26785991 -1.18800449 0.06581519 0.04879040 -0.01555996 0.01961857 -0.34441577 -0.41055198 0.02629904 -0.04086984 -0.20727226 0.19639264 -Al -0.12171355 2.17383175 2.12351050 1.74966288 -0.20615385 -0.60071966 0.08038219 2.12948842 2.07436373 1.67722418 -0.80460017 -0.11102183 0.02141505 0.26516093 0.02322641 -0.07555855 -Al 2.03765560 -0.01002666 1.97078838 -0.62320780 -0.94937909 0.88574523 1.95879126 -0.08023619 2.10366340 -0.68772421 -0.33659151 1.23440452 0.02093106 -0.13340948 0.20230105 0.42010620 -Al 2.00171901 2.14455269 0.15523686 0.00321539 0.51057209 -0.48895757 2.04587205 2.12931995 0.08137624 0.87260899 -0.88441054 -0.86187432 0.02624709 0.19260231 -0.31137041 -0.12425624 -Al -0.09804956 0.06403908 3.98361968 -0.68929423 0.25759542 0.04306192 -0.12152908 0.05559522 4.00783576 0.10720760 -0.27312751 0.40484185 0.02128379 0.40046616 -0.30232589 0.12058408 -Al 0.15539836 1.97759483 6.24745943 0.87733745 0.17902510 -0.97164565 0.19943780 2.01780899 6.10701190 -0.20382960 0.54310498 -1.41613739 0.02505161 -0.18748584 0.14341215 -0.30518330 -Al 1.98624033 -0.15814989 6.19826227 -0.60773897 0.07461010 0.32927300 1.96694088 -0.10344309 6.19559125 0.40128717 0.84846884 -0.41404884 0.02540262 0.14631467 0.32192273 -0.18883617 -Al 2.04642751 1.89297675 4.15788683 -0.62869663 -0.13074701 -0.76423065 1.96736042 1.91065810 4.06338249 -0.63139454 0.52782311 -0.79878893 0.01199395 -0.15239640 0.13096791 -0.16119262 -Al -0.01747146 4.10007167 0.08539584 0.90325506 -1.61264617 1.64213071 0.12587224 3.97546132 0.23412357 1.39754803 -0.36322689 0.82907806 0.04763147 0.47137546 0.01176526 -0.01225831 -Al -0.10141215 5.99272522 2.07955022 0.61617945 1.31755784 -0.56483227 0.00340532 6.12824527 2.05016579 1.05375268 0.99523202 -0.05685841 0.01357684 0.37229650 0.05506781 0.22206913 -Al 2.12914650 3.94779452 2.14985644 0.86537330 -0.37586189 -0.99826326 2.20524110 3.96277242 2.08137541 0.63507760 0.61405245 -0.14791065 0.00930486 -0.18502388 0.32459501 0.12354929 -Al 2.10070277 6.13114012 0.16554730 0.10690922 -0.45122988 -0.00453352 2.06587039 6.02516082 0.11880134 -0.65364368 -1.17912109 -0.79129445 0.03197627 -0.32541784 -0.50132716 -0.30043325 -Al -0.00868798 3.98356535 4.22366706 -0.05772660 1.19852707 -0.83847878 -0.00235878 4.11774520 4.10616391 0.07879199 1.08551819 -1.21673047 0.02067019 0.14274806 0.13289078 -0.23181123 -Al -0.00632027 6.12440168 6.00449636 -0.28596208 1.22293318 0.12824325 -0.00767530 6.20129015 6.04712119 0.22000838 0.02427421 0.69871558 0.02763927 0.20998319 -0.17951572 0.13515265 -Al 2.16945475 3.98020268 6.04987816 0.57012647 0.10567414 -0.00825937 2.17804046 4.05003176 6.08039845 -0.50270234 1.13340743 0.36505972 0.02098777 -0.26108484 0.34398379 0.31495971 -Al 2.16814667 6.02303491 4.08164440 -1.14321789 -0.40270175 -0.79903788 2.01239751 6.01421621 3.99371947 -1.47880632 0.19508605 -0.58124010 0.00548880 -0.33213446 0.23488200 -0.14431356 -Al 4.08252758 -0.04479429 0.01262739 0.31174153 -0.51720945 0.16647834 4.11405658 -0.02918545 0.03730931 0.03406929 0.72011542 0.32169577 0.02708811 0.13870641 0.44236477 -0.00466326 -Al 4.11235350 1.97171785 2.18742279 -1.32958999 -1.52447359 0.18497931 3.96856818 1.83732052 2.16108047 -1.08780885 -0.76436510 -0.62167924 0.02836949 -0.14933564 0.02861238 -0.28262655 -Al 6.08381770 0.12544549 1.99268556 -0.44145584 -1.12110738 -0.60654371 6.05551529 -0.01294167 1.94669499 -0.02640194 -1.16919255 -0.05117513 0.02299619 0.06752537 -0.27914362 0.01791927 -Al 6.16610854 2.17394690 0.06744149 0.51495135 1.00235602 0.82543738 6.16218986 2.19148373 0.16019636 -0.53621336 -0.89440095 0.86565878 0.02532385 -0.35542614 -0.33718829 -0.00116998 -Al 4.07349123 0.06111168 4.08523602 -0.52503638 0.25400097 0.79263136 4.03559140 0.03489630 4.18004288 -0.24119216 -0.71501090 0.79505778 0.01948209 0.15015112 -0.28683814 0.13270393 -Al 4.04105891 1.96200610 6.13931792 -0.23991478 0.83969869 -0.56219122 4.03459946 2.03956322 6.10684676 0.01169608 0.46076989 0.00278711 -0.00287159 0.16588633 0.01577100 0.12521895 -Al 6.19833383 0.00456750 6.17281595 -1.73641053 -1.22728472 -0.44627987 6.02227943 -0.05614444 6.11849488 -1.14910064 0.42125459 -0.47054757 0.01187952 -0.15250100 0.17639050 -0.07632223 -Al 6.17546528 2.00658843 4.16939598 -0.89303301 0.41757900 0.13320522 6.04807583 2.08194629 4.18477719 -1.15524930 0.79987472 -0.03870507 0.02492388 -0.37832236 0.26108141 0.13908683 -Al 4.02300101 4.05460594 0.10407229 -0.08466468 0.88580504 -1.05547603 4.01649545 4.15694469 -0.05446419 0.09875659 0.87810922 -1.46360084 0.03982559 -0.07081709 0.10336648 -0.47897970 -Al 4.13349863 6.23926263 2.00253602 -0.32231191 -1.31722110 0.64706809 4.06992697 6.11066867 2.09916276 -0.71747734 -0.99336096 1.03186059 0.06579938 -0.22099494 0.01163989 0.19237468 -Al 5.67385897 4.18246137 2.07058895 0.51978493 -0.13990880 -0.51579553 5.87648240 4.10437225 2.02638211 2.79126132 -1.08325026 -0.21472952 0.12375417 1.11265999 -0.44058548 0.01588787 -Al 6.27963843 6.07074260 0.02249205 -0.14107743 0.95290867 0.00736737 6.20537212 6.16858980 0.02929378 -1.00616679 0.50341561 0.12020389 0.03831953 -0.51261400 0.21842855 0.03203579 -Al 4.07926200 4.08278615 4.13191192 1.64531526 1.38040234 1.10297444 4.24425510 4.18878201 4.27058686 1.15037549 0.54752722 1.18204432 0.05376507 0.06859666 -0.23184758 0.27856857 -Al 4.18633985 6.20421370 6.11489840 0.55329629 1.07419882 0.39805994 4.20374882 6.23617210 6.15914449 -0.48551820 -0.60066098 0.37044394 0.00953607 -0.07119094 -0.39193268 0.05899364 -Al 6.17803387 4.05672476 6.25146659 -0.38203238 -1.59931754 1.76792776 6.11338408 3.91725158 6.36368277 -0.45984604 -0.68749219 0.23439178 0.02796470 -0.36416804 -0.01186680 -0.40726781 -Al 6.07589854 6.00963866 4.02135256 0.50785270 0.16965766 1.35866614 6.12982596 6.06786846 4.17828535 0.47379136 0.79519342 1.21065105 0.02314055 -0.01128042 0.29854411 0.26926950 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=0.9510229816315583 free_energy=0.9510229816315583 stress="0.005355766511518642 -0.00015898575467537403 0.0002944926131876724 -0.00015898575467537403 0.005196658301976934 0.0003584193039226458 0.0002944926131876724 0.0003584193039226458 0.005599291602127005" pbc="T T T" -Al 0.06581519 0.04879040 -0.01555996 -0.01961857 0.34441577 0.41055198 0.04030615 0.09171277 0.07398912 -0.38636985 0.26785991 1.18800449 0.01576916 -0.15968619 0.13283827 0.27661730 -Al 0.08038219 2.12948842 2.07436373 -1.67722418 0.80460017 0.11102183 -0.12171355 2.17383175 2.12351050 -1.74966288 0.20615385 0.60071966 0.04460707 -0.28255525 -0.35259785 0.28011178 -Al 1.95879126 -0.08023619 2.10366340 0.68772421 0.33659151 -1.23440452 2.03765560 -0.01002666 1.97078838 0.62320780 0.94937909 -0.88574523 0.02359368 0.13992990 0.21201345 -0.19557014 -Al 2.04587205 2.12931995 0.08137624 -0.87260899 0.88441054 0.86187432 2.00171901 2.14455269 0.15523686 -0.00321539 -0.51057209 0.48895757 0.03279676 0.30898664 -0.48741908 -0.11215886 -Al -0.12152908 0.05559522 4.00783576 -0.10720760 0.27312751 -0.40484185 -0.09804956 0.06403908 3.98361968 0.68929423 -0.25759542 -0.04306192 0.01347739 0.10973139 -0.03677510 0.13421175 -Al 0.19943780 2.01780899 6.10701190 0.20382960 -0.54310498 1.41613739 0.15539836 1.97759483 6.24745943 -0.87733745 -0.17902510 0.97164565 0.01909485 -0.45547710 0.11054971 0.05347960 -Al 1.96694088 -0.10344309 6.19559125 -0.40128717 -0.84846884 0.41404884 1.98624033 -0.15814989 6.19826227 0.60773897 -0.07461010 -0.32927300 0.02848132 0.42244280 0.16448257 -0.25993558 -Al 1.96736042 1.91065810 4.06338249 0.63139454 -0.52782311 0.79878893 2.04642751 1.89297675 4.15788683 0.62869663 0.13074701 0.76423065 0.03727213 0.21379297 0.27802780 0.17032791 -Al 0.12587224 3.97546132 0.23412357 -1.39754803 0.36322689 -0.82907806 -0.01747146 4.10007167 0.08539584 -0.90325506 1.61264617 -1.64213071 0.06199020 -0.16357235 0.60377604 -0.50190637 -Al 0.00340532 6.12824527 2.05016579 -1.05375268 -0.99523202 0.05685841 -0.10141215 5.99272522 2.07955022 -0.61617945 -1.31755784 0.56483227 0.02054842 -0.12371944 -0.27899698 0.05954010 -Al 2.20524110 3.96277242 2.08137541 -0.63507760 -0.61405245 0.14791065 2.12914650 3.94779452 2.14985644 -0.86537330 0.37586189 0.99826326 0.02316608 0.01740835 0.24796754 0.33990223 -Al 2.06587039 6.02516082 0.11880134 0.65364368 1.17912109 0.79129445 2.10070277 6.13114012 0.16554730 -0.10690922 0.45122988 0.00453352 0.00840819 -0.14770860 0.10625320 -0.18062693 -Al -0.00235878 4.11774520 4.10616391 -0.07879199 -1.08551819 1.21673047 -0.00868798 3.98356535 4.22366706 0.05772660 -1.19852707 0.83847878 0.00081534 -0.04712535 -0.21330453 -0.03399417 -Al -0.00767530 6.20129015 6.04712119 -0.22000838 -0.02427421 -0.69871558 -0.00632027 6.12440168 6.00449636 0.28596208 -1.22293318 -0.12824325 0.03753049 0.12671702 -0.50446786 0.22636876 -Al 2.17804046 4.05003176 6.08039845 0.50270234 -1.13340743 -0.36505972 2.16945475 3.98020268 6.04987816 -0.57012647 -0.10567414 0.00825937 0.02336355 -0.43951671 0.25837907 -0.08081847 -Al 2.01239751 6.01421621 3.99371947 1.47880632 -0.19508605 0.58124010 2.16814667 6.02303491 4.08164440 1.14321789 0.40270175 0.79903788 0.01082547 0.12696809 0.14945724 0.31764973 -Al 4.11405658 -0.02918545 0.03730931 -0.03406929 -0.72011542 -0.32169577 4.08252758 -0.04479429 0.01262739 -0.31174153 0.51720945 -0.16647834 0.01906301 -0.30466960 0.27693186 0.05877250 -Al 3.96856818 1.83732052 2.16108047 1.08780885 0.76436510 0.62167924 4.11235350 1.97171785 2.18742279 1.32958999 1.52447359 -0.18497931 0.03263914 0.27954480 0.38528529 -0.18614564 -Al 6.05551529 -0.01294167 1.94669499 0.02640194 1.16919255 0.05117513 6.08381770 0.12544549 1.99268556 0.44145584 1.12110738 0.60654371 0.02332039 0.16683356 0.20183902 0.37917529 -Al 6.16218986 2.19148373 0.16019636 0.53621336 0.89440095 -0.86565878 6.16610854 2.17394690 0.06744149 -0.51495135 -1.00235602 -0.82543738 0.06315257 -0.25916765 -0.74877786 -0.03497495 -Al 4.03559140 0.03489630 4.18004288 0.24119216 0.71501090 -0.79505778 4.07349123 0.06111168 4.08523602 0.52503638 -0.25400097 -0.79263136 0.02223663 0.01150428 -0.25588228 -0.16690466 -Al 4.03459946 2.03956322 6.10684676 -0.01169608 -0.46076989 -0.00278711 4.04105891 1.96200610 6.13931792 0.23991478 -0.83969869 0.56219122 0.01165805 -0.04428177 -0.21964557 0.19817130 -Al 6.02227943 -0.05614444 6.11849488 1.14910064 -0.42125459 0.47054757 6.19833383 0.00456750 6.17281595 1.73641053 1.22728472 0.44627987 0.06512900 0.49711281 0.76615985 0.09028909 -Al 6.04807583 2.08194629 4.18477719 1.15524930 -0.79987472 0.03870507 6.17546528 2.00658843 4.16939598 0.89303301 -0.41757900 -0.13320522 0.00438215 0.18170041 -0.05711852 -0.23361038 -Al 4.01649545 4.15694469 -0.05446419 -0.09875659 -0.87810922 1.46360084 4.02300101 4.05460594 0.10407229 0.08466468 -0.88580504 1.05547603 0.04556192 0.18355018 -0.12843955 0.27279422 -Al 4.06992697 6.11066867 2.09916276 0.71747734 0.99336096 -1.03186059 4.13349863 6.23926263 2.00253602 0.32231191 1.31722110 -0.64706809 0.01208245 0.02307459 0.12557888 0.01444798 -Al 5.87648240 4.10437225 2.02638211 -2.79126132 1.08325026 0.21472952 5.67385897 4.18246137 2.07058895 -0.51978493 0.13990880 0.51579553 0.01720789 0.34241871 -0.05139472 0.17649261 -Al 6.20537212 6.16858980 0.02929378 1.00616679 -0.50341561 -0.12020389 6.27963843 6.07074260 0.02249205 0.14107743 -0.95290867 -0.00736737 0.02524995 -0.09127068 -0.43879120 0.03037808 -Al 4.24425510 4.18878201 4.27058686 -1.15037549 -0.54752722 -1.18204432 4.07926200 4.08278615 4.13191192 -1.64531526 -1.38040234 -1.10297444 0.04393131 -0.38433958 -0.26739960 -0.22946120 -Al 4.20374882 6.23617210 6.15914449 0.48551820 0.60066098 -0.37044394 4.18633985 6.20421370 6.11489840 -0.55329629 -1.07419882 -0.39805994 0.07028986 -0.53670625 -0.58233609 -0.02857253 -Al 6.11338408 3.91725158 6.36368277 0.45984604 0.68749219 -0.23439178 6.17803387 4.05672476 6.25146659 0.38203238 1.59931754 -1.76792776 0.05020167 0.31411432 0.50908613 -0.50588602 -Al 6.12982596 6.06786846 4.17828535 -0.47379136 -0.79519342 -1.21065105 6.07589854 6.00963866 4.02135256 -0.50785270 -0.16965766 -1.35866614 0.04317689 -0.02603431 0.09472086 -0.32816435 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.1675676068749237 free_energy=1.1675676068749237 stress="0.004908787662223326 -0.00033893401826187074 -0.00032429419235869704 -0.00033893401826187074 0.003913777206277226 -0.0007783761478527335 -0.00032429419235869704 -0.0007783761478527335 0.005075587681380582" pbc="T T T" -Al -0.00185302 0.02426664 0.16434278 0.12720369 -0.18378306 0.97776962 0.04317956 -0.00732802 0.21327814 0.53704185 -0.35326694 -0.12768044 0.02478353 0.23577860 -0.08203303 -0.27762373 -Al 0.01515044 2.07047049 2.12761419 -0.54305909 1.08927945 -0.02476894 -0.07414190 2.16810837 2.11370144 -0.92590854 0.60312353 -0.16969529 0.02646097 -0.24940757 -0.06215032 -0.10220150 -Al 2.12911798 0.06413381 2.11670377 -0.63193583 -0.88833950 -0.73418424 2.05568077 -0.06543782 2.01649404 -0.71430213 -1.06142925 -0.85744623 0.03271342 -0.03776546 -0.44659353 -0.28475965 -Al 2.04692238 2.04879653 0.23296122 0.82191423 -0.39734617 0.89358241 2.10467880 2.02652171 0.26734181 0.14311315 0.07710744 -0.32285886 0.03616315 -0.10735262 0.04534988 -0.31003431 -Al 0.07292698 0.00331094 4.01254402 -0.59531092 -0.61308493 -1.01977183 -0.00335281 -0.04100425 3.96620129 -0.66947288 -0.06458096 0.13750223 0.03282747 -0.14161936 0.01479325 0.37205277 -Al -0.05651734 1.90310706 6.22378422 0.46654326 -0.63291996 0.22779528 0.04412518 1.85090503 6.24204019 1.27846986 -0.29405123 0.12375454 0.01548789 0.32372064 0.09764366 -0.05639423 -Al 2.18769487 0.12011645 6.37437933 1.15993144 -0.78009484 0.96318082 2.25691278 0.00348286 6.41013064 -0.11834806 -1.25356898 -0.34525565 0.01688474 -0.17966655 -0.22714995 -0.40023312 -Al 2.00415971 1.90910936 4.04193885 -1.17122376 0.07582396 0.31190970 1.91023702 1.97021005 4.12805772 -0.49516514 0.73356827 1.00317123 0.03434012 0.14255335 0.51860156 0.46947175 -Al -0.25239450 4.10335980 0.02242564 -0.69683066 1.62570703 -0.32339798 -0.18710041 4.23784600 -0.01968754 1.85059825 0.64667348 -0.33199462 0.05784604 0.77381162 -0.08468665 -0.13149140 -Al -0.01215844 6.16217913 2.15758673 0.11013689 0.10018702 0.73447951 0.02853716 6.11675336 2.17225547 0.55742211 -0.97767944 -0.48468735 0.04879425 0.17788003 -0.27367699 -0.31312362 -Al 1.77529919 4.10550461 1.97620131 0.41382126 -0.67263535 0.28007888 1.88149467 4.03509934 2.04652599 1.43232492 -0.49658820 0.90008700 0.04690850 0.43369480 -0.07990822 0.31127974 -Al 2.02788973 5.98343987 0.07453357 -0.26863157 -0.55294163 -0.51026941 1.96741618 6.01044886 0.03400322 -0.73176476 0.76760756 -0.19601635 0.04568437 -0.18122048 0.63224725 0.04079831 -Al 0.03581419 4.00324193 4.22211570 0.27420213 0.71951715 1.26050839 0.08433936 4.08900385 4.35250833 0.42766594 0.60688305 0.84216730 0.00970041 0.19683984 0.18607845 0.09661851 -Al 0.10862960 6.18459276 6.09922979 1.58546667 -0.98017218 -1.38608442 0.26764130 6.08208888 5.96728535 1.12358099 -0.59757810 -0.83462249 -0.00100301 0.04895198 -0.15932505 -0.00060835 -Al 2.17990164 4.02680262 6.31606666 0.44723311 0.08849840 -0.26741732 2.13417363 3.98587652 6.18938034 -0.98552785 -0.77306952 -1.60734689 0.06492956 -0.68840023 -0.28622209 -0.78400965 -Al 2.07995963 6.02822313 4.03364142 0.57025551 -0.22930274 -0.65458184 2.13760259 6.04368598 4.00481849 0.47447187 0.48658019 0.09740398 0.01668477 -0.01513304 0.24503464 0.25468046 -Al 4.27054899 0.22143854 0.01660427 0.06118528 1.70818804 0.13362958 4.24464168 0.28650627 0.05614728 -0.40179527 -0.61986983 0.47823966 0.05659592 -0.30523991 -0.59143380 0.19369338 -Al 4.07664153 1.98663560 2.14441130 0.06417520 -0.76922333 -0.53474677 4.11325620 1.96361281 2.10187921 0.46184229 0.36275948 -0.04651572 0.02175900 0.25101132 0.33421525 -0.07072516 -Al 6.17171796 -0.01228142 1.97264767 -0.38881147 0.59572477 -0.81452230 6.08468826 0.06627537 1.96578331 -0.97797324 0.72849580 0.64438343 0.03532097 -0.35863836 0.14988671 0.50180839 -Al 6.15807277 2.09675177 0.05468414 0.40063793 0.73764000 -0.44812065 6.16710095 2.16376533 0.02441337 -0.18280135 0.46448263 -0.16731328 0.05807746 -0.23274447 -0.07558895 0.13259220 -Al 4.16097172 0.13497395 4.19020531 -0.02362303 -0.48826234 0.37275462 4.12378863 0.02963705 4.16655131 -0.61938066 -1.25530625 -0.76329818 0.04012127 -0.20597200 -0.41513788 -0.36259576 -Al 4.21794216 2.04681036 6.13715320 -1.16793728 -1.46010669 0.07390457 4.05627445 1.94029903 6.12659085 -1.50241383 -0.26633184 -0.27493638 0.06002539 -0.41023425 0.07874366 -0.09253041 -Al 5.99547231 -0.24515960 6.00903860 0.56026203 -0.68877577 -0.27697202 6.13481543 -0.24111003 6.02398672 1.78596149 0.69732803 0.53149961 0.07150722 0.55520675 0.52240333 0.25974533 -Al 6.23570967 2.11009799 4.24484450 -0.19907115 0.19874853 0.94092219 6.16936911 2.08792428 4.27794876 -0.88830542 -0.54928839 -0.21758544 0.03992958 -0.34185769 -0.29049410 -0.46468112 -Al 3.93007466 4.10046340 0.03181401 0.03098557 1.49654007 -0.50491144 4.04497018 4.19949967 0.07129031 1.72663821 0.25895382 0.99190441 0.07235608 0.79722560 -0.29198894 0.64980201 -Al 4.06922429 6.09154755 2.01019928 -1.54673877 1.27925131 -0.40728082 3.94069600 6.24228568 1.96361284 -0.55756108 1.18992474 -0.39526222 0.00296661 0.05598775 0.25053795 -0.04749189 -Al 6.06034779 4.09099328 2.11504827 0.20450501 -0.15112673 1.10530144 6.08149030 4.05429204 2.20193983 0.09331740 -0.40100700 0.42272420 0.01420419 0.07474584 -0.18139013 -0.13994017 -Al 6.17281603 6.07611340 -0.01198680 0.45516323 -0.25690963 -0.20399511 6.14803655 6.09298262 -0.00285753 -0.87385972 0.65027837 0.40420258 0.03562887 -0.40138833 0.13992465 0.16507872 -Al 4.02008785 4.12273212 3.94214357 -0.67106962 -0.77308791 0.43758277 3.94465049 3.99777711 4.02421544 -0.55242949 -1.33540912 0.96216734 0.02024145 -0.11776071 -0.34871473 0.28078714 -Al 4.08970161 6.00429343 6.11167176 -0.43365585 0.45554041 0.02111044 4.01401383 6.08616142 6.10372337 -0.73265454 0.85275676 -0.13980305 0.04281139 -0.31083308 0.28390454 -0.04738626 -Al 5.90646120 4.04704461 6.11929009 0.40417930 -0.09248174 0.71659416 5.99282082 4.07623325 6.16536055 0.94135989 0.57743653 -0.02512404 0.05472461 0.45436064 0.27419845 -0.06612105 -Al 6.16789697 6.00738288 4.01321235 0.18009723 0.43994835 -1.34007928 6.13219381 6.06809034 3.92213019 -0.90414425 0.59506537 -0.23176503 0.03209141 -0.23653464 0.12293115 0.22354268 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.0779681848667755 free_energy=1.0779681848667755 stress="0.005570476181737316 0.0008721194749470843 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0.00331094 4.01254402 0.59531092 0.61308493 1.01977183 0.03551378 0.11675540 0.26821401 0.30347234 -Al 0.04412518 1.85090503 6.24204019 -1.27846986 0.29405123 -0.12375454 -0.05651734 1.90310706 6.22378422 -0.46654326 0.63291996 -0.22779528 0.00589069 0.13162838 0.15419406 -0.01786246 -Al 2.25691278 0.00348286 6.41013064 0.11834806 1.25356898 0.34525565 2.18769487 0.12011645 6.37437933 -1.15993144 0.78009484 -0.96318082 0.06410727 -0.61097444 -0.00536474 -0.46713461 -Al 1.91023702 1.97021005 4.12805772 0.49516514 -0.73356827 -1.00317123 2.00415971 1.90910936 4.04193885 1.17122376 -0.07582396 -0.31190970 -0.00334055 0.25471921 -0.09553707 -0.05018883 -Al -0.18710041 4.23784600 -0.01968754 -1.85059825 -0.64667348 0.33199462 -0.25239450 4.10335980 0.02242564 0.69683066 -1.62570703 0.32339798 0.05740507 0.68343079 -0.45841369 0.08957556 -Al 0.02853716 6.11675336 2.17225547 -0.55742211 0.97767944 0.48468735 -0.01215844 6.16217913 2.15758673 -0.11013689 -0.10018702 -0.73447951 0.04668176 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- -.. only:: html - - .. image:: /examples/programmatic/flashmd/images/thumb/sphx_glr_flashmd_thumb.png - :alt: - - :ref:`sphx_glr_examples_programmatic_flashmd_flashmd.py` - -.. raw:: html - -
Training a FlashMD model
-
- - -.. thumbnail-parent-div-close - -.. raw:: html - - - - -.. toctree:: - :hidden: - - /examples/programmatic/flashmd/flashmd - - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-gallery - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download all examples in Python source code: flashmd_python.zip ` - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download all examples in Jupyter notebooks: flashmd_jupyter.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/programmatic/flashmd/options.yaml b/docs/src/examples/programmatic/flashmd/options.yaml deleted file mode 100644 index cc73011ea5..0000000000 --- a/docs/src/examples/programmatic/flashmd/options.yaml +++ /dev/null @@ -1,43 +0,0 @@ -seed: 42 - -architecture: - name: experimental.flashmd - training: - timestep: 30 # in this case 30 (time lag) * 1 fs (timestep of reference MD) - batch_size: 2 # to be increased in a production scenario - num_epochs: 5 # to be increased (at least 1000-10000) in a production scenario - log_interval: 1 - loss: - positions: - type: mse - weight: 1.0 - reduction: mean - momenta: - type: mse - weight: 1.0 - reduction: mean - -training_set: - systems: - read_from: flashmd.xyz - length_unit: A - targets: - positions: - key: future_positions - quantity: length - unit: A - type: - cartesian: - rank: 1 - per_atom: true - momenta: - key: future_momenta - quantity: momentum - unit: (eV*u)^1/2 - type: - cartesian: - rank: 1 - per_atom: true - -validation_set: 0.1 -test_set: 0.1 diff --git a/docs/src/examples/programmatic/use_architectures_outside/images/thumb/sphx_glr_use_outside_thumb.png b/docs/src/examples/programmatic/use_architectures_outside/images/thumb/sphx_glr_use_outside_thumb.png deleted file mode 100644 index 5f3d70d019fb5eec5b6eede318f689c9f410323b..0000000000000000000000000000000000000000 GIT binary patch literal 0 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- -.. only:: html - - .. image:: /examples/programmatic/use_architectures_outside/images/thumb/sphx_glr_use_outside_thumb.png - :alt: - - :ref:`sphx_glr_examples_programmatic_use_architectures_outside_use_outside.py` - -.. raw:: html - -
Using metatrain architectures outside of metatrain
-
- - -.. thumbnail-parent-div-close - -.. raw:: html - - - - -.. toctree:: - :hidden: - - /examples/programmatic/use_architectures_outside/use_outside - - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-gallery - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download all examples in Python source code: use_architectures_outside_python.zip ` - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download all examples in Jupyter notebooks: use_architectures_outside_jupyter.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/programmatic/use_architectures_outside/qm9_reduced_100.xyz b/docs/src/examples/programmatic/use_architectures_outside/qm9_reduced_100.xyz deleted file mode 100644 index 14b2a0416e..0000000000 --- a/docs/src/examples/programmatic/use_architectures_outside/qm9_reduced_100.xyz +++ /dev/null @@ -1,1201 +0,0 @@ -5 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F" -C -0.00828817 1.39046978 -0.00560069 -O -0.00797038 -0.02504537 0.02030606 -H 1.00658338 1.81556366 0.00348335 -H -0.54657475 1.79916975 -0.87390126 -H -0.52288871 1.72555240 0.89907326 -H 0.44142019 -0.33354425 -0.77152059 -7 -Properties=species:S:1:pos:R:3 A=160.28041 B=8.59323 C=8.59321 mu=0.7156 alpha=28.78 epsHOMO=-0.2609 epsLUMO=0.0613 epsgap=0.3222 =177.1963 zpve=0.05541 U0=-116.609549 U=-116.60555 H=-116.604606 G=-116.633775 Cv=12.482 pbc="F F F" -C -0.01782102 1.46435788 0.01009397 -C 0.00208816 0.00950777 0.00201200 -C 0.01834085 -1.19180518 -0.00450508 -H 0.99782210 1.87425349 0.00260606 -H -0.54220434 1.85801178 -0.86721192 -H -0.52533306 1.84834356 0.90148138 -H 0.03231658 -2.25314823 -0.01025999 -6 -Properties=species:S:1:pos:R:3 A=159.03567 B=9.22327 C=9.22324 mu=3.8266 alpha=24.45 epsHOMO=-0.3264 epsLUMO=0.0376 epsgap=0.364 =160.7223 zpve=0.045286 U0=-132.71815 U=-132.714563 H=-132.713619 G=-132.742149 Cv=10.287 pbc="F F F" -C -0.01788629 1.46712787 0.01011311 -C 0.00173819 0.01035260 0.00207588 -N 0.01765235 -1.14452926 -0.00420445 -H 1.00202941 1.86089927 0.00245401 -H -0.54398848 1.84479901 -0.87075460 -H -0.52707783 1.83518166 0.90486329 -7 -Properties=species:S:1:pos:R:3 A=57.22434 B=10.11122 C=9.07368 mu=2.5682 alpha=25.11 epsHOMO=-0.254 epsLUMO=-0.0198 epsgap=0.2342 =166.9728 zpve=0.055355 U0=-153.787612 U=-153.783728 H=-153.782784 G=-153.812518 Cv=11.219 pbc="F F F" -C -0.00294482 1.50991366 0.00867278 -C 0.02608284 0.00327563 -0.03745912 -O 0.94228801 -0.65507035 -0.45682576 -H 0.92278802 1.92634242 -0.39146557 -H -0.86201540 1.87852481 -0.56479538 -H -0.15050638 1.84393383 1.04289100 -H -0.89443009 -0.48643408 0.35774865 -6 -Properties=species:S:1:pos:R:3 A=73.8472 B=11.34793 C=9.83639 mu=3.7286 alpha=21.57 epsHOMO=-0.2543 epsLUMO=0.0302 epsgap=0.2845 =145.3078 zpve=0.045279 U0=-169.860788 U=-169.856903 H=-169.855958 G=-169.885594 Cv=10.89 pbc="F F F" -N -0.02589986 1.34614561 0.00889391 -C 0.04646728 -0.01174341 0.00120425 -O 1.07183520 -0.65258782 -0.01113313 -H 0.82535497 1.88504948 0.00373769 -H -0.90837680 1.82679638 0.01891992 -H -0.96144100 -0.47500376 0.00807391 -11 -Properties=species:S:1:pos:R:3 A=29.45018 B=8.37701 C=7.42076 mu=0.0597 alpha=34.75 epsHOMO=-0.323 epsLUMO=0.0949 epsgap=0.4179 =227.1361 zpve=0.103182 U0=-119.052475 U=-119.047927 H=-119.046983 G=-119.078157 Cv=14.84 pbc="F F F" -C -0.03113825 1.54081582 0.03192126 -C 0.01215347 0.01092235 -0.01603259 -C 0.72169129 -0.52583353 -1.26230570 -H 0.97955987 1.96459116 0.03098367 -H -0.55840223 1.94831192 -0.83816075 -H -0.54252252 1.90153531 0.93005671 -H 0.51522791 -0.36840234 0.88231134 -H -1.01070641 -0.38456999 0.02051783 -H 1.75851210 -0.17376585 -1.30871516 -H 0.74087192 -1.62024959 -1.27516511 -H 0.22023351 -0.19051179 -2.17729020 -9 -Properties=species:S:1:pos:R:3 A=35.09545 B=9.3686 C=8.1497 mu=1.4131 alpha=27.87 epsHOMO=-0.2619 epsLUMO=0.0798 epsgap=0.3417 =193.1659 zpve=0.079754 U0=-154.972731 U=-154.968412 H=-154.967467 G=-154.998148 Cv=13.546 pbc="F F F" -C -0.00860504 1.50203829 -0.00681217 -C 0.01099310 -0.01764877 -0.01377035 -O 0.68088841 -0.44041803 -1.19313210 -H 1.01153334 1.89662030 -0.01920155 -H -0.53159862 1.88076109 -0.88974659 -H -0.51674572 1.87611771 0.88710739 -H 0.52377121 -0.38912295 0.88824082 -H -1.02027544 -0.40507261 0.01690670 -H 0.69529573 -1.40179568 -1.20148495 -9 -Properties=species:S:1:pos:R:3 A=39.37691 B=10.04033 C=8.90353 mu=1.1502 alpha=28.13 epsHOMO=-0.2525 epsLUMO=0.091 epsgap=0.3435 =187.1015 zpve=0.079534 U0=-154.960361 U=-154.956045 H=-154.9551 G=-154.985747 Cv=12.934 pbc="F F F" -C -0.01482147 1.39241234 0.00567104 -O -0.00471537 -0.01360660 0.01459658 -C 0.63794893 -0.55329676 -1.11358232 -H 1.00528358 1.81015807 0.00465631 -H -0.54689560 1.79343530 -0.87251051 -H -0.53002895 1.72291969 0.91101696 -H 0.13993792 -0.25599333 -2.05098375 -H 1.69265275 -0.23868382 -1.17477743 -H 0.59959435 -1.64180161 -1.02407616 -9 -Properties=species:S:1:pos:R:3 A=20.15852 B=20.15302 C=12.5891 mu=0.0005 alpha=30.82 epsHOMO=-0.2888 epsLUMO=0.1042 epsgap=0.393 =155.8145 zpve=0.081231 U0=-117.824798 U=-117.821426 H=-117.820482 G=-117.849087 Cv=11.041 pbc="F F F" -C -0.01193280 1.51433198 0.01031700 -C 1.30299118 0.77886562 -0.00617842 -C 0.00867174 0.00767042 0.00201032 -H -0.30541464 2.01702117 0.92533239 -H -0.32275498 2.02680156 -0.89347839 -H 1.88493396 0.79145020 -0.92119393 -H 1.90226631 0.78165391 0.89766373 -H -0.27088043 -0.51289125 0.91137474 -H -0.28821983 -0.50310409 -0.90740824 -7 -Properties=species:S:1:pos:R:3 A=25.77385 B=22.12109 C=14.18066 mu=1.7675 alpha=24.04 epsHOMO=-0.2682 epsLUMO=0.1042 epsgap=0.3724 =129.891 zpve=0.057289 U0=-153.742562 U=-153.73941 H=-153.738466 G=-153.766642 Cv=9.176 pbc="F F F" -C 0.01537720 1.41764380 0.00956287 -C 1.26478161 0.64923984 -0.00655044 -O -0.00024018 -0.00771006 0.00204055 -H -0.31763326 1.88586598 0.93475751 -H -0.33527583 1.89576673 -0.90397578 -H 1.83246682 0.56256848 -0.93193290 -H 1.85008258 0.55267938 0.90680147 -10 -Properties=species:S:1:pos:R:3 A=10.12193 B=8.49011 C=4.89615 mu=2.7362 alpha=35.53 epsHOMO=-0.2431 epsLUMO=-0.0087 epsgap=0.2344 =292.4367 zpve=0.083382 U0=-193.08834 U=-193.082969 H=-193.082024 G=-193.116476 Cv=16.893 pbc="F F F" -C -0.00310189 1.48050328 -0.17259867 -C -0.04527202 -0.02887059 0.00139493 -C 1.29705800 -0.72194415 0.16889042 -O -1.08643172 -0.64273343 0.00630393 -H 0.59403529 1.74668579 -1.05225043 -H -1.01540407 1.87034023 -0.28382265 -H 0.48099600 1.95070605 0.69099376 -H 1.82512144 -0.32470731 1.04328658 -H 1.14892507 -1.79597689 0.28554393 -H 1.93682635 -0.52949692 -0.69995403 -9 -Properties=species:S:1:pos:R:3 A=10.78886 B=9.27509 C=5.14977 mu=3.6367 alpha=31.83 epsHOMO=-0.2436 epsLUMO=0.0347 epsgap=0.2783 =267.6148 zpve=0.07319 U0=-209.159302 U=-209.15402 H=-209.153076 G=-209.187468 Cv=16.561 pbc="F F F" -C 0.00679889 1.49969736 -0.02567030 -C -0.02740385 -0.01752051 -0.14894811 -N 1.17826667 -0.63324298 0.05691721 -O -1.03721965 -0.63980649 -0.40727572 -H 0.91390280 1.92923755 -0.46100067 -H -0.87080896 1.91124186 -0.52345176 -H -0.02826124 1.78517271 1.03149125 -H 1.98405139 -0.13175861 0.38674906 -H 1.19049421 -1.63919563 0.10006209 -8 -Properties=species:S:1:pos:R:3 A=11.1475 B=10.36388 C=5.42091 mu=3.4869 alpha=28.07 epsHOMO=-0.2495 epsLUMO=0.0556 epsgap=0.3051 =244.2308 zpve=0.063824 U0=-225.221461 U=-225.217075 H=-225.216131 G=-225.247724 Cv=15.292 pbc="F F F" -N 0.03605270 1.36077873 -0.12416403 -C -0.02591138 -0.02076560 0.00200645 -N 1.21968479 -0.62334220 0.11963222 -O -1.06822866 -0.64174597 0.00865573 -H 0.80749439 1.83455148 0.32144915 -H -0.85825775 1.80373500 0.02243770 -H 1.99927148 -0.17303054 -0.33602496 -H 1.18204327 -1.62147901 -0.02109234 -14 -Properties=species:S:1:pos:R:3 A=7.75166 B=7.74847 C=4.48668 mu=0.0897 alpha=45.46 epsHOMO=-0.3167 epsLUMO=0.0843 epsgap=0.401 =355.0621 zpve=0.131146 U0=-158.342346 U=-158.336603 H=-158.335658 G=-158.370016 Cv=20.273 pbc="F F F" -C -0.03215888 1.54021598 0.01074456 -C 0.03381741 0.00745852 0.00180716 -C 0.71375572 -0.50856404 -1.27296949 -C 0.73848999 -0.52220886 1.25750907 -H 0.97522736 1.97414341 0.00528362 -H -0.56196769 1.92232326 -0.86835418 -H -0.54817239 1.91268414 0.90208039 -H -0.99752525 -0.37310578 0.00977297 -H 1.75070021 -0.15586784 -1.33269631 -H 0.73446691 -1.60328288 -1.29939696 -H 0.19367011 -0.16085589 -2.17195508 -H 0.23611990 -0.18382017 2.17005260 -H 0.75964083 -1.61713153 1.27226101 -H 1.77635866 -0.16985318 1.30053019 -12 -Properties=species:S:1:pos:R:3 A=8.67553 B=8.00568 C=4.75542 mu=1.4259 alpha=38.58 epsHOMO=-0.2612 epsLUMO=0.074 epsgap=0.3351 =318.3721 zpve=0.107673 U0=-194.267232 U=-194.261748 H=-194.260804 G=-194.294663 Cv=19.052 pbc="F F F" -C -0.03315871 1.54782566 -0.00438815 -C -0.01108534 0.01859095 0.01676117 -C 0.70938384 -0.53596711 1.23978444 -O -1.33219841 -0.51712554 0.05236057 -H 0.98215692 1.95715900 -0.03182225 -H -0.56388100 1.92087623 -0.88845865 -H -0.54082548 1.93340945 0.88575489 -H 0.51042122 -0.33767241 -0.88819176 -H 0.22240492 -0.18785860 2.15650245 -H 0.68242024 -1.62887638 1.23305141 -H 1.75434301 -0.21246950 1.25524188 -H -1.81818681 -0.15577435 -0.69553962 -6 -Properties=species:S:1:pos:R:3 A=0 B=4.4259726 C=4.4259726 mu=0 alpha=38.52 epsHOMO=-0.2599 epsLUMO=-0.0214 epsgap=0.2386 =278.6264 zpve=0.037354 U0=-153.459846 U=-153.455442 H=-153.454498 G=-153.482621 Cv=15.312 pbc="F F F" -C 0.68098021 0.00000000 0.00000000 -C -0.68098021 0.00000000 0.00000000 -C -1.88766603 0.00000000 0.00000000 -C 1.88766603 0.00000000 0.00000000 -H -2.94960000 0.00000000 0.00000000 -H 2.94960000 0.00000000 0.00000000 -5 -Properties=species:S:1:pos:R:3 A=0 B=4.5793222 C=4.5793222 mu=3.792 alpha=32.66 epsHOMO=-0.3102 epsLUMO=-0.0543 epsgap=0.2559 =260.1896 zpve=0.027259 U0=-169.557758 U=-169.553764 H=-169.55282 G=-169.581024 Cv=12.93 pbc="F F F" -C 0.01510390 0.00000000 1.00000000 -C 1.38233149 0.00000000 1.00000000 -C -1.18862525 0.00000000 1.00000000 -H -2.25177201 0.00000000 1.00000000 -N 2.54296187 0.00000000 1.00000000 -4 -Properties=species:S:1:pos:R:3 A=0 B=4.73269 C=4.73269 mu=0.0023 alpha=27.7 epsHOMO=-0.3696 epsLUMO=-0.0926 epsgap=0.277 =242.9308 zpve=0.015951 U0=-185.648533 U=-185.644825 H=-185.64388 G=-185.667652 Cv=10.398 pbc="F F F" -N 0.01745734 -1.16134217 -0.00415342 -C 0.00253212 -0.00344278 0.00179939 -C -0.01611419 1.37220987 0.00934873 -N -0.03264302 2.53008276 0.01609090 -6 -Properties=species:S:1:pos:R:3 A=68.5171 B=4.8345 C=4.51586 mu=2.7824 alpha=31.14 epsHOMO=-0.2777 epsLUMO=-0.0735 epsgap=0.2042 =268.3921 zpve=0.037208 U0=-190.624631 U=-190.620363 H=-190.619419 G=-190.650543 Cv=13.049 pbc="F F F" -O -0.06776120 -0.04233838 0.00282489 -C -0.00562945 1.16382879 0.00743862 -C 1.22985272 1.91339997 0.00028781 -C 2.23625322 2.57430970 -0.00529277 -H -0.91670701 1.79467546 0.01781352 -H 3.13429662 3.14370267 -0.01037656 -5 -Properties=species:S:1:pos:R:3 A=67.88408 B=5.00823 C=4.66413 mu=2.3112 alpha=26.25 epsHOMO=-0.3166 epsLUMO=-0.11 epsgap=0.2066 =251.0007 zpve=0.02654 U0=-206.721858 U=-206.717875 H=-206.716931 G=-206.747625 Cv=11.329 pbc="F F F" -O -0.04055978 -0.03799144 0.00266513 -C -0.01768870 1.16193251 0.00746008 -C 1.23315534 1.93381336 0.00043371 -N 2.19857378 2.57055097 -0.00483320 -H -0.92627534 1.78959196 0.01764859 -6 -Properties=species:S:1:pos:R:3 A=56.25048 B=4.77441 C=4.40088 mu=0.002 alpha=26.12 epsHOMO=-0.2668 epsLUMO=-0.1113 epsgap=0.1555 =266.8164 zpve=0.036943 U0=-227.798785 U=-227.79457 H=-227.793626 G=-227.825074 Cv=12.147 pbc="F F F" -O 0.00337122 -0.03146586 0.00157206 -C -0.01713174 1.17081409 0.00676444 -C 1.26555783 1.99719410 -0.00007703 -O 1.24507069 3.19946589 0.00681275 -H -0.94690483 1.77678142 0.01852668 -H 2.19534410 1.39121374 -0.01006739 -10 -Properties=species:S:1:pos:R:3 A=80.28766 B=3.3688 C=3.3688 mu=0 alpha=42.32 epsHOMO=-0.2412 epsLUMO=0.0684 epsgap=0.3096 =400.2236 zpve=0.083896 U0=-155.908941 U=-155.90318 H=-155.902236 G=-155.937641 Cv=17.447 pbc="F F F" -C -0.01787959 1.46676893 0.01011180 -C 0.00186644 0.01072913 0.00211109 -C 0.01819704 -1.19272861 -0.00450723 -C 0.03795170 -2.64876793 -0.01251193 -H 0.86460312 1.87589979 0.51479719 -H -0.03217624 1.87210581 -1.00791002 -H -0.90233115 1.85185524 0.53003953 -H -0.83477011 -3.06339007 0.50451436 -H 0.03276422 -3.04315564 -1.03491223 -H 0.93213486 -3.03931642 0.48626606 -10 -Properties=species:S:1:pos:R:3 A=27.39459 B=4.53005 C=4.08342 mu=0.7067 alpha=40.09 epsHOMO=-0.2592 epsLUMO=0.0566 epsgap=0.3157 =333.9589 zpve=0.084338 U0=-155.897345 U=-155.892291 H=-155.891347 G=-155.924226 Cv=17.13 pbc="F F F" -C -0.03095840 1.54775025 0.03167945 -C 0.01485375 0.00962471 -0.02082154 -C 0.69099073 -0.49954575 -1.20857624 -C 1.25222275 -0.89514467 -2.19527879 -H 0.97899300 1.96621609 0.03373834 -H -0.56082354 1.95002902 -0.83580745 -H -0.54576708 1.88225519 0.93709062 -H 0.51920135 -0.37425398 0.87496224 -H -1.00644802 -0.39030838 0.01340632 -H 1.74594541 -1.25604187 -3.06299415 -9 -Properties=species:S:1:pos:R:3 A=28.0016 B=4.67752 C=4.21765 mu=3.9233 alpha=35.38 epsHOMO=-0.3213 epsLUMO=0.034 epsgap=0.3553 =314.5335 zpve=0.07419 U0=-172.006141 U=-172.001467 H=-172.000523 G=-172.032826 Cv=14.988 pbc="F F F" -C -0.02522005 1.54773054 0.02133370 -C 0.01183939 0.00999278 -0.01556702 -C 0.68915114 -0.50836846 -1.20331488 -N 1.22565733 -0.90360846 -2.14737482 -H 0.98571620 1.96212990 0.02370153 -H -0.55522192 1.94599662 -0.84711771 -H -0.53962556 1.88294354 0.92536604 -H 0.52481910 -0.38226048 0.86933355 -H -1.00453542 -0.39832109 0.00530071 -8 -Properties=species:S:1:pos:R:3 A=32.19893 B=4.76299 C=4.30696 mu=4.4361 alpha=31.81 epsHOMO=-0.2683 epsLUMO=0.0173 epsgap=0.2855 =295.6635 zpve=0.063305 U0=-188.042067 U=-188.037478 H=-188.036534 G=-188.06863 Cv=14.488 pbc="F F F" -N -0.03608099 1.48762533 0.03678072 -C -0.01039471 0.02517605 -0.01506608 -C -1.37180956 -0.52108920 0.02739272 -N -2.44026695 -0.95956444 0.03370519 -H 0.91729752 1.83547547 0.05202682 -H -0.47162178 1.85677205 -0.80275569 -H 0.48946161 -0.40880401 -0.89715168 -H 0.52827150 -0.34533338 0.86469547 -8 -Properties=species:S:1:pos:R:3 A=35.11615 B=4.66117 C=4.22354 mu=1.7211 alpha=33.1 epsHOMO=-0.2595 epsLUMO=0.0277 epsgap=0.2872 =300.0993 zpve=0.060632 U0=-191.810916 U=-191.806025 H=-191.805081 G=-191.837634 Cv=15.855 pbc="F F F" -O -0.02013197 1.45738137 0.01010195 -C 0.00168478 0.03579980 0.00224736 -C -1.36366691 -0.47481101 0.01255056 -C -2.47902835 -0.91970202 0.02082451 -H 0.89446911 1.75736553 0.00327363 -H 0.51979028 -0.35175413 -0.88980302 -H 0.53670458 -0.36127661 0.88000894 -H -3.46773502 -1.30737009 0.02818120 -7 -Properties=species:S:1:pos:R:3 A=35.93172 B=4.83443 C=4.37827 mu=4.6788 alpha=28.56 epsHOMO=-0.3018 epsLUMO=0.0022 epsgap=0.3039 =280.6659 zpve=0.050262 U0=-207.916786 U=-207.912215 H=-207.911271 G=-207.943384 Cv=13.845 pbc="F F F" -O -0.03738221 1.44547841 0.01010769 -C 0.01292906 0.03441661 0.00209165 -C -1.35747452 -0.48874523 0.01239738 -N -2.42135277 -0.93685184 0.02016053 -H 0.86898057 1.76972949 0.00417577 -H 0.51692713 -0.36329790 -0.89215814 -H 0.53387616 -0.37278749 0.88223921 -10 -Properties=species:S:1:pos:R:3 A=26.14564 B=4.2893 C=4.12773 mu=2.6741 alpha=35.83 epsHOMO=-0.25 epsLUMO=-0.0205 epsgap=0.2295 =333.3276 zpve=0.084175 U0=-193.075202 U=-193.070116 H=-193.069171 G=-193.102798 Cv=15.954 pbc="F F F" -C 0.16879340 1.52513042 -0.15747056 -C -0.18737625 0.06194763 0.14679374 -C 0.50917645 -0.43996215 1.39125850 -O 1.18191079 -1.43565585 1.45816384 -H 1.23126511 1.63137254 -0.39632693 -H -0.40744668 1.89431681 -1.00969246 -H -0.04931034 2.17266260 0.69857432 -H -1.26905160 -0.01668067 0.32854433 -H 0.06279062 -0.60250479 -0.68474036 -H 0.35384841 0.20663370 2.28871052 -9 -Properties=species:S:1:pos:R:3 A=19.98749 B=6.11589 C=4.82584 mu=3.7071 alpha=32.78 epsHOMO=-0.2516 epsLUMO=0.0335 epsgap=0.2851 =279.7863 zpve=0.074166 U0=-209.144909 U=-209.139976 H=-209.139032 G=-209.172305 Cv=15.058 pbc="F F F" -C 0.01474916 1.47888256 -0.03509326 -N 0.18708077 0.03831317 -0.02906851 -C 0.45703661 -0.66858895 1.10069001 -O 0.58457996 -0.19876202 2.21119705 -H 0.76025289 1.96736104 -0.67198126 -H -0.98462423 1.75984483 -0.38485103 -H 0.14269849 1.82387718 0.99149397 -H 0.10386349 -0.46937726 -0.89461379 -H 0.55047568 -1.75253452 0.88868242 -8 -Properties=species:S:1:pos:R:3 A=48.62351 B=4.67298 C=4.38187 mu=3.92 alpha=29.47 epsHOMO=-0.2814 epsLUMO=0.0074 epsgap=0.2888 =293.917 zpve=0.061327 U0=-229.013797 U=-229.009003 H=-229.008059 G=-229.041086 Cv=13.885 pbc="F F F" -C 0.10653781 1.43512049 0.03336100 -O 0.42686564 0.04716929 0.09309945 -C 0.50714862 -0.63652665 -1.06318297 -O 0.76927014 -1.79955017 -1.11010475 -H 0.92349251 2.00385139 0.48465728 -H -0.03757815 1.76927183 -1.00057425 -H -0.81269569 1.60801916 0.59873287 -H 0.30458460 -0.00231902 -1.94965553 -8 -Properties=species:S:1:pos:R:3 A=36.49282 B=4.31175 C=4.04493 mu=1.7341 alpha=28.53 epsHOMO=-0.2537 epsLUMO=-0.0341 epsgap=0.2196 =303.8129 zpve=0.060508 U0=-228.992613 U=-228.987769 H=-228.986825 G=-229.019918 Cv=14.78 pbc="F F F" -O 0.19375856 1.37170995 0.31398297 -C 0.04992637 0.01405481 -0.04526239 -C -1.40888025 -0.39635680 -0.20601574 -O -1.77729462 -1.37233860 -0.80361689 -H -0.01723186 1.91269132 -0.45337758 -H 0.60440478 -0.26519766 -0.95205725 -H 0.46740406 -0.57521679 0.78176270 -H -2.11835055 0.30075962 0.29870515 -14 -Properties=species:S:1:pos:R:3 A=23.44662 B=3.60588 C=3.38683 mu=0 alpha=45.71 epsHOMO=-0.317 epsLUMO=0.0937 epsgap=0.4107 =426.2996 zpve=0.131708 U0=-158.340943 U=-158.33517 H=-158.334226 G=-158.36894 Cv=19.668 pbc="F F F" -C -0.03007624 1.55804912 0.02980774 -C 0.00707277 0.02834877 -0.00712529 -C 0.71320792 -0.52989340 -1.24765581 -C 0.75036895 -2.05959366 -1.28458957 -H 0.98195330 1.97845113 0.02800797 -H -0.55582433 1.96212343 -0.84270157 -H -0.54083762 1.92692755 0.92493697 -H 0.50928672 -0.34909049 0.89361319 -H -1.01733481 -0.36542299 0.03207393 -H 0.21098948 -0.15245770 -2.14839302 -H 1.73761331 -0.13611625 -1.28685672 -H 1.27616854 -2.46366410 -0.41210980 -H -0.26165699 -2.48000447 -1.28273552 -H 1.26108491 -2.42846444 -2.17974803 -12 -Properties=species:S:1:pos:R:3 A=26.59789 B=3.79186 C=3.53779 mu=1.3402 alpha=38.61 epsHOMO=-0.2619 epsLUMO=0.081 epsgap=0.3429 =382.8628 zpve=0.108241 U0=-194.261089 U=-194.255495 H=-194.254551 G=-194.28893 Cv=18.431 pbc="F F F" -C -0.00379012 1.52196790 0.01027996 -C -0.03589372 -0.00778254 0.00225671 -C -1.45219016 -0.56653711 0.01284386 -O -1.36799785 -1.98394604 0.00453606 -H 1.02477576 1.89411231 0.00239793 -H -0.51286674 1.93764614 -0.86653888 -H -0.49584080 1.92801344 0.90121721 -H 0.48140784 -0.39530552 -0.88316064 -H 0.49828589 -0.40486256 0.87330348 -H -1.98734220 -0.20399494 0.90666062 -H -2.00431485 -0.19459473 -0.86666460 -H -2.26171819 -2.33824298 0.01034748 -12 -Properties=species:S:1:pos:R:3 A=28.29567 B=4.14901 C=3.88349 mu=1.0363 alpha=39.34 epsHOMO=-0.2503 epsLUMO=0.0925 epsgap=0.3428 =368.9331 zpve=0.107895 U0=-194.254127 U=-194.248585 H=-194.247641 G=-194.281899 Cv=17.888 pbc="F F F" -C -0.00519551 1.50868224 0.03308760 -C 0.01316580 -0.01037819 0.01988643 -O 0.69758944 -0.46344140 1.16868979 -C 0.76688491 -1.86508183 1.24602669 -H 1.01445476 1.90423799 0.02996018 -H -0.52990866 1.89184059 -0.84726911 -H -0.51263765 1.87805843 0.92875424 -H -1.01599507 -0.40998729 0.00714282 -H 0.51093143 -0.38369435 -0.89219395 -H -0.23415841 -2.32451489 1.28977780 -H 1.30129276 -2.29853663 0.38486095 -H 1.30990287 -2.11811998 2.16013206 -10 -Properties=species:S:1:pos:R:3 A=14.79671 B=5.6956 C=4.58846 mu=0.0075 alpha=31.42 epsHOMO=-0.2594 epsLUMO=0.0584 epsgap=0.3179 =297.8398 zpve=0.085172 U0=-230.183076 U=-230.177723 H=-230.176779 G=-230.211195 Cv=16.837 pbc="F F F" -O -0.01418735 1.42642242 -0.05423651 -C -0.00391839 0.00717714 0.03496062 -C -1.42317019 -0.55560600 -0.01256639 -O -2.11229413 -0.10563875 -1.17228313 -H -0.18987882 1.64154705 -0.97555148 -H 0.59659504 -0.43315430 -0.77409095 -H 0.47311116 -0.24903733 0.98747216 -H -1.39494859 -1.64975279 -0.06283188 -H -1.96497417 -0.27216915 0.90131972 -H -2.35925991 0.80944396 -1.00614597 -12 -Properties=species:S:1:pos:R:3 A=15.61472 B=6.31363 C=5.54514 mu=0.1136 alpha=41.96 epsHOMO=-0.2727 epsLUMO=0.1012 epsgap=0.3738 =298.6061 zpve=0.109284 U0=-157.116735 U=-157.11209 H=-157.111146 G=-157.143262 Cv=16.49 pbc="F F F" -C -0.03602245 1.52092949 0.01569161 -C 0.01938487 0.01004403 0.04814953 -C 1.30891671 -0.71251995 -0.24920416 -C 0.75347423 -0.70267714 1.15564676 -H 0.90105691 1.95389291 0.38283338 -H -0.19640365 1.89437369 -1.00208569 -H -0.84685273 1.90588930 0.64451322 -H -0.88416809 -0.48001889 -0.30566335 -H 1.26900901 -1.63903068 -0.81150371 -H 2.19010738 -0.11131209 -0.44927513 -H 1.26248862 -0.09487455 1.89690028 -H 0.33540836 -1.62248918 1.54980695 -10 -Properties=species:S:1:pos:R:3 A=18.21931 B=6.63877 C=5.92459 mu=1.812 alpha=35.01 epsHOMO=-0.2633 epsLUMO=0.1052 epsgap=0.3685 =267.2979 zpve=0.085275 U0=-193.039603 U=-193.035186 H=-193.034242 G=-193.065979 Cv=14.764 pbc="F F F" -C -0.01646343 1.51830705 0.00771445 -C -0.02743227 0.01657694 -0.10889741 -C 0.77296553 -0.70131886 -1.10863115 -O 1.17212293 -0.66702969 0.26278829 -H 0.91865980 1.92914217 -0.38285880 -H -0.85158014 1.95456506 -0.55109460 -H -0.11487702 1.82596470 1.05397777 -H -0.93582598 -0.47350600 0.24682793 -H 0.44443922 -1.67201495 -1.47810622 -H 1.40538284 -0.13600339 -1.79271491 -11 -Properties=species:S:1:pos:R:3 A=16.58914 B=7.18798 C=6.11415 mu=1.1353 alpha=39.02 epsHOMO=-0.2304 epsLUMO=0.0968 epsgap=0.3271 =270.5508 zpve=0.097671 U0=-173.147782 U=-173.143343 H=-173.142399 G=-173.174073 Cv=15.298 pbc="F F F" -C -0.05275541 1.47419574 0.00999108 -N 0.01940516 0.02189947 0.06334866 -C 1.32511048 -0.56446850 -0.21420725 -C 0.78051759 -0.54057993 1.17242760 -H 0.86784202 1.97176076 0.36297285 -H -0.23845155 1.79095005 -1.02142866 -H -0.88683448 1.81938959 0.62950398 -H 1.31992909 -1.48186782 -0.79602038 -H 2.15202820 0.11020204 -0.43285248 -H 1.24563570 0.14996098 1.87499245 -H 0.37013358 -1.44020447 1.62233365 -10 -Properties=species:S:1:pos:R:3 A=16.79477 B=6.71015 C=5.86781 mu=1.3894 alpha=34.64 epsHOMO=-0.239 epsLUMO=0.0775 epsgap=0.3166 =263.966 zpve=0.085106 U0=-193.034988 U=-193.030356 H=-193.029411 G=-193.061689 Cv=15.679 pbc="F F F" -O 0.21577583 1.35386859 -0.10695107 -C 0.01628099 -0.02832718 -0.02915694 -C -0.39081465 -0.71497009 1.24315669 -C -1.35118138 -0.64280380 0.06273589 -H -0.43642338 1.79062849 0.45006761 -H 0.74981167 -0.52404050 -0.65624767 -H 0.05959375 -1.66840483 1.49729237 -H -0.61369353 -0.08848006 2.10170640 -H -2.19821932 0.03058430 0.15411451 -H -1.56395912 -1.54640426 -0.49826515 -12 -Properties=species:S:1:pos:R:3 A=10.66107 B=10.66107 C=6.30452 mu=0 alpha=41.83 epsHOMO=-0.2982 epsLUMO=0.0956 epsgap=0.3938 =268.4432 zpve=0.110511 U0=-157.115484 U=-157.111322 H=-157.110378 G=-157.141657 Cv=14.696 pbc="F F F" -C -0.02542957 1.54032578 -0.04215944 -C 1.52564399 1.50419989 0.04121276 -C 1.42146727 0.06146790 0.60844885 -C -0.02638833 -0.01070956 0.04935088 -H -0.46591372 1.99639894 0.84962894 -H -0.47900239 2.00028207 -0.92365389 -H 1.98248278 1.51375334 -0.95303183 -H 2.02191365 2.25986579 0.65522129 -H 2.14291679 -0.67956358 0.25531480 -H 1.43105853 0.05751479 1.70261879 -H -0.79053440 -0.48530296 0.66996573 -H -0.05233245 -0.47238446 -0.94236394 -10 -Properties=species:S:1:pos:R:3 A=12.06545 B=11.77731 C=6.71997 mu=1.7978 alpha=34.56 epsHOMO=-0.2424 epsLUMO=0.0859 epsgap=0.3283 =236.9759 zpve=0.086675 U0=-193.034094 U=-193.029968 H=-193.029024 G=-193.060777 Cv=12.915 pbc="F F F" -C -0.03489789 1.55259455 -0.08507691 -C 1.49349183 1.45459700 0.10114677 -O 1.42725884 0.02348990 0.27254886 -C -0.00634758 0.02236494 0.10944630 -H -0.56579210 2.11301721 0.68591527 -H -0.36628352 1.89393380 -1.06692327 -H 2.10031724 1.73525720 -0.76898349 -H 1.90023090 1.95506611 0.98896748 -H -0.51987849 -0.35595179 1.00235948 -H -0.31970062 -0.57567421 -0.75559275 -12 -Properties=species:S:1:pos:R:3 A=8.63483 B=4.11977 C=2.88825 mu=0.6875 alpha=45.37 epsHOMO=-0.2392 epsLUMO=0.0192 epsgap=0.2584 =452.5112 zpve=0.100501 U0=-248.375248 U=-248.368823 H=-248.367879 G=-248.405354 Cv=21.616 pbc="F F F" -C -0.10255370 1.49907127 -0.00133642 -C -0.00519774 -0.00343114 -0.02723470 -C -0.00688536 -0.76890740 1.26434744 -N 0.08186799 -0.69202791 -1.10087306 -O 0.07439067 0.13215394 -2.24195258 -H 0.78342093 1.94690862 -0.46354050 -H -0.96150430 1.83614082 -0.59021965 -H -0.19854173 1.87462989 1.01931607 -H -0.92620012 -0.57297946 1.82982479 -H 0.07256154 -1.84064782 1.07516863 -H 0.82937178 -0.45553340 1.90133089 -H 0.13975924 -0.51254299 -2.95389260 -10 -Properties=species:S:1:pos:R:3 A=9.17136 B=9.04195 C=4.5531 mu=1.8689 alpha=43.14 epsHOMO=-0.2029 epsLUMO=0.0499 epsgap=0.2528 =303.9808 zpve=0.082433 U0=-210.101789 U=-210.097816 H=-210.096872 G=-210.12818 Cv=14.821 pbc="F F F" -N -0.00829694 1.35362838 0.00995971 -C 1.28033254 1.82457421 -0.00022459 -C 2.12216639 0.73698898 -0.01445600 -C 1.30849904 -0.43109580 -0.01284154 -C -0.00342782 -0.01836884 0.00235736 -H -0.83243234 1.92772668 0.02122995 -H 1.48471820 2.88312630 0.00354313 -H 3.20066918 0.77343077 -0.02488416 -H 1.64810599 -1.45539683 -0.02180394 -H -0.92551647 -0.57697054 0.00839402 -9 -Properties=species:S:1:pos:R:3 A=9.77329 B=9.41192 C=4.7946 mu=3.6193 alpha=39.13 epsHOMO=-0.2253 epsLUMO=0.0332 epsgap=0.2585 =283.6817 zpve=0.071145 U0=-226.160842 U=-226.157088 H=-226.156144 G=-226.187104 Cv=13.371 pbc="F F F" -N -0.00996090 1.35636172 0.01000855 -C 1.29535422 1.79575578 -0.00047017 -C 2.05550519 0.65637438 -0.01422927 -N 1.25385745 -0.46275132 -0.01260894 -C 0.02301170 -0.00706382 0.00200146 -H -0.83195718 1.93618436 0.02132575 -H 1.54553961 2.84344998 0.00289054 -H 3.13177434 0.57569766 -0.02524155 -H -0.87667864 -0.60414531 0.00750794 -9 -Properties=species:S:1:pos:R:3 A=9.50228 B=9.29497 C=4.69874 mu=0.5571 alpha=39.2 epsHOMO=-0.2246 epsLUMO=0.0199 epsgap=0.2445 =289.005 zpve=0.069883 U0=-229.969129 U=-229.965414 H=-229.96447 G=-229.995393 Cv=13.358 pbc="F F F" -O -0.03255819 1.35065130 0.00967376 -C 1.25319237 1.79167710 -0.00030351 -C 2.11664681 0.74375623 -0.01441494 -C 1.30397796 -0.43852387 -0.01283316 -C 0.01629568 -0.00777185 0.00191294 -H 1.37951089 2.86161135 0.00427433 -H 3.19376406 0.79865501 -0.02458604 -H 1.63858893 -1.46383271 -0.02156949 -H -0.93736295 -0.50899925 0.00843891 -8 -Properties=species:S:1:pos:R:3 A=10.10926 B=9.69372 C=4.94857 mu=1.5081 alpha=35.17 epsHOMO=-0.2509 epsLUMO=0.001 epsgap=0.2519 =269.2444 zpve=0.058593 U0=-246.02915 U=-246.025614 H=-246.024669 G=-246.055309 Cv=12.04 pbc="F F F" -O -0.03808298 1.35643965 0.00975193 -C 1.26496982 1.76855319 -0.00061744 -C 2.04955750 0.66630033 -0.01406047 -N 1.24227075 -0.46732066 -0.01237541 -C 0.04019820 0.00561577 0.00183569 -H 1.43513907 2.83095265 0.00340823 -H 3.12532898 0.59783015 -0.02480757 -H -0.89387707 -0.53342154 0.00796935 -17 -Properties=species:S:1:pos:R:3 A=4.4178 B=4.41753 C=4.41717 mu=0.0003 alpha=56.01 epsHOMO=-0.3145 epsLUMO=0.0737 epsgap=0.3882 =486.2719 zpve=0.158836 U0=-197.632222 U=-197.625241 H=-197.624297 G=-197.661411 Cv=26.084 pbc="F F F" -C -0.01859029 1.53486266 0.01039904 -C 0.00210320 -0.00413672 0.00196980 -C 0.74643954 -0.51390760 1.24887638 -C -1.44190166 -0.53673615 0.01311457 -C 0.72234971 -0.50049606 -1.26445632 -H 0.99776928 1.94420529 0.00435474 -H -0.54181883 1.92841415 -0.86819911 -H -0.52758402 1.91888376 0.90148530 -H 0.25549576 -0.17602728 2.16833067 -H 0.77815389 -1.60892372 1.27088904 -H 1.77964786 -0.14975834 1.26944035 -H -1.99871895 -0.18888794 -0.86408066 -H -1.45956909 -1.63227235 0.00649916 -H -1.98088827 -0.19979639 0.90561580 -H 1.75482473 -0.13554018 -1.30118083 -H 0.75396989 -1.59524036 -1.29822280 -H 0.21358221 -0.15343900 -2.17067181 -15 -Properties=species:S:1:pos:R:3 A=4.69474 B=4.66938 C=4.47909 mu=1.384 alpha=49.04 epsHOMO=-0.2601 epsLUMO=0.0664 epsgap=0.3265 =449.0573 zpve=0.134977 U0=-233.560626 U=-233.553779 H=-233.552834 G=-233.589759 Cv=25.128 pbc="F F F" -C -0.00855999 1.54270147 0.00152717 -C 0.00506780 0.00702635 0.01856160 -C 0.76137731 -0.51839196 1.24766443 -C -1.42012774 -0.54933183 -0.02116008 -O 0.62590477 -0.48363745 -1.17657057 -H 1.01361406 1.94020776 0.00281128 -H -0.51459669 1.90702466 -0.89705781 -H -0.52214815 1.94882514 0.87918344 -H 0.28130613 -0.20165548 2.17943598 -H 0.79965506 -1.61131746 1.23022549 -H 1.79096916 -0.14072935 1.26083003 -H -1.93854829 -0.19910724 -0.91828281 -H -1.39937632 -1.64259062 -0.04563833 -H -1.98850725 -0.23059656 0.85726520 -H 1.53111188 -0.15488046 -1.19210222 -9 -Properties=species:S:1:pos:R:3 A=10.21876 B=4.04324 C=2.94978 mu=2.8579 alpha=42.02 epsHOMO=-0.2654 epsLUMO=-0.0575 epsgap=0.2079 =416.7799 zpve=0.065175 U0=-229.927277 U=-229.921622 H=-229.920677 G=-229.955843 Cv=18.723 pbc="F F F" -C -0.00693851 1.49559567 -0.01063358 -C -0.02986170 -0.01594623 0.06248673 -O -0.08815239 -0.63215062 1.10334812 -C 0.02336981 -0.70256747 -1.22062379 -C 0.06738132 -1.26058341 -2.28623129 -H 0.90429806 1.83281887 -0.51655384 -H -0.85338524 1.85556926 -0.60552819 -H -0.05237764 1.91099807 0.99695949 -H 0.10466868 -1.77036047 -3.21832617 -8 -Properties=species:S:1:pos:R:3 A=10.16063 B=4.15847 C=3.0056 mu=3.3351 alpha=37.08 epsHOMO=-0.3007 epsLUMO=-0.0889 epsgap=0.2118 =397.7757 zpve=0.05451 U0=-246.027383 U=-246.022024 H=-246.02108 G=-246.055769 Cv=16.963 pbc="F F F" -C -0.01171097 1.49973776 -0.01213268 -C -0.02919740 -0.00338745 0.07151218 -O -0.09164028 -0.64592927 1.08732241 -C 0.03831104 -0.69690258 -1.23748557 -N 0.09136286 -1.22982650 -2.26228068 -H 0.90093132 1.83370670 -0.51814928 -H -0.85663933 1.84864400 -0.61618135 -H -0.06416540 1.92131542 0.99203818 -8 -Properties=species:S:1:pos:R:3 A=10.68423 B=4.3198 C=3.07993 mu=5.1815 alpha=38.31 epsHOMO=-0.2739 epsLUMO=-0.0438 epsgap=0.2301 =384.5574 zpve=0.056619 U0=-242.19573 U=-242.190591 H=-242.189646 G=-242.223513 Cv=17.465 pbc="F F F" -N -0.05232108 1.34991769 0.15535538 -C 0.08549378 -0.01476951 0.02668890 -N 1.14555215 -0.71993906 -0.03966037 -C -1.19426556 -0.71152088 0.01802151 -N -2.22747610 -1.22790543 0.00132148 -H 0.75474469 1.91998737 -0.04411334 -H -0.94458527 1.76600473 -0.05932293 -H 1.98312429 -0.13415500 -0.02878551 -9 -Properties=species:S:1:pos:R:3 A=9.08854 B=4.42607 C=3.03169 mu=0.9461 alpha=36.51 epsHOMO=-0.2538 epsLUMO=-0.0964 epsgap=0.1574 =399.222 zpve=0.064992 U0=-267.10335 U=-267.097658 H=-267.096714 G=-267.132534 Cv=17.806 pbc="F F F" -C -0.43126066 1.30586664 -0.12758585 -C 0.25285715 -0.01026988 0.13138867 -O 0.69872099 -0.36890333 1.19604690 -C 0.38978943 -0.96773680 -1.06226208 -O -0.01611268 -0.71564293 -2.16583718 -H 0.10006565 1.85324490 -0.91332597 -H -1.44270433 1.13069361 -0.50923371 -H -0.46757554 1.89548158 0.78836479 -H 0.90329886 -1.91558845 -0.79531972 -9 -Properties=species:S:1:pos:R:3 A=10.08927 B=4.54115 C=3.13487 mu=2.7707 alpha=38.09 epsHOMO=-0.2561 epsLUMO=-0.064 epsgap=0.192 =384.4827 zpve=0.067668 U0=-263.278851 U=-263.273589 H=-263.272645 G=-263.306835 Cv=17.972 pbc="F F F" -N -0.16032929 1.08528962 -0.45398652 -C 0.18649127 -0.08762327 0.14758021 -N 1.28979627 -0.46427423 0.67927030 -C -0.94204384 -1.08440264 0.20423621 -O -2.01802205 -0.89007687 -0.30583149 -H 0.54574437 1.73837930 -0.74689246 -H -1.04684920 1.09570857 -0.93607796 -H 2.01738195 0.24991828 0.59881811 -H -0.67740148 -2.01003995 0.74746441 -13 -Properties=species:S:1:pos:R:3 A=7.92874 B=3.8272 C=2.82581 mu=0.6578 alpha=51.2 epsHOMO=-0.2589 epsLUMO=0.0571 epsgap=0.316 =481.9854 zpve=0.112471 U0=-195.186772 U=-195.180446 H=-195.179502 G=-195.215658 Cv=22.569 pbc="F F F" -C -0.01763434 1.54859468 -0.00253584 -C -0.01719180 0.00735789 0.03269604 -C 0.75818463 -0.52820155 1.25278862 -C -1.38213303 -0.51768438 0.00775516 -C -2.50796392 -0.94005629 0.00478552 -H 1.00944396 1.92639613 -0.02632830 -H -0.54514614 1.92217052 -0.88393459 -H -0.51243000 1.95339892 0.88560642 -H 0.49366179 -0.34904155 -0.87265890 -H 0.28968458 -0.19345372 2.18334679 -H 0.77833392 -1.62102506 1.25783727 -H 1.78952492 -0.16181727 1.23574954 -H -3.49998244 -1.31810165 -0.00723572 -12 -Properties=species:S:1:pos:R:3 A=7.90009 B=3.95466 C=2.88643 mu=3.9512 alpha=46.23 epsHOMO=-0.318 epsLUMO=0.0365 epsgap=0.3545 =460.694 zpve=0.102281 U0=-211.295796 U=-211.289821 H=-211.288877 G=-211.324525 Cv=20.467 pbc="F F F" -C -0.00242350 1.55035837 0.01056187 -C -0.01856600 0.01004352 -0.02261770 -C -1.45045318 -0.55766178 0.01293347 -C 0.70125749 -0.48583168 -1.20172764 -N 1.26190946 -0.87126766 -2.13619740 -H 1.01959496 1.93673215 0.00349668 -H -0.52972844 1.96141639 -0.85498386 -H -0.50134251 1.90346839 0.91755862 -H 0.52481877 -0.36222456 0.85525462 -H -2.02427903 -0.21428716 -0.85251869 -H -1.44438296 -1.65026927 0.00754107 -H -1.95805494 -0.21725882 0.91996760 -11 -Properties=species:S:1:pos:R:3 A=8.30095 B=3.97615 C=2.93764 mu=2.7429 alpha=42.87 epsHOMO=-0.2704 epsLUMO=0.0278 epsgap=0.2983 =440.0738 zpve=0.091554 U0=-227.338075 U=-227.332253 H=-227.331309 G=-227.366638 Cv=19.946 pbc="F F F" -C -0.01064295 1.54128691 0.00722087 -C -0.00203811 0.00756015 0.03863463 -N 0.76268677 -0.47101822 1.19250856 -C -1.38541954 -0.52099256 -0.00194097 -N -2.45680847 -0.95636072 0.00694969 -H 1.02043523 1.90106589 0.00392365 -H -0.52873161 1.91193452 -0.88031480 -H -0.51849536 1.94150431 0.89013197 -H 0.50418828 -0.35805500 -0.86276072 -H 0.30070681 -0.18364934 2.05155305 -H 0.78148992 -1.48670299 1.20428183 -11 -Properties=species:S:1:pos:R:3 A=8.55919 B=3.89952 C=2.91869 mu=1.3582 alpha=44.03 epsHOMO=-0.2665 epsLUMO=0.0336 epsgap=0.3001 =444.6452 zpve=0.088908 U0=-231.108368 U=-231.102292 H=-231.101348 G=-231.137061 Cv=21.306 pbc="F F F" -C -0.00754278 1.55566144 0.08650633 -C 0.01207541 0.02096553 0.04935916 -O -1.29150251 -0.52785128 0.22126386 -C 0.65093362 -0.47718651 -1.17430507 -C 1.13902673 -0.86577343 -2.20205518 -H 1.00824243 1.95969458 0.04929733 -H -0.56272245 1.95192010 -0.76978085 -H -0.49328161 1.88929438 1.00704123 -H 0.57811967 -0.34464907 0.91458864 -H -1.78516532 -0.35903262 -0.58909400 -H 1.58078877 -1.22365060 -3.09919447 -10 -Properties=species:S:1:pos:R:3 A=8.55908 B=4.01671 C=2.97882 mu=3.269 alpha=39.28 epsHOMO=-0.3051 epsLUMO=0.0115 epsgap=0.3166 =424.3395 zpve=0.078602 U0=-247.214861 U=-247.209162 H=-247.208218 G=-247.243338 Cv=19.252 pbc="F F F" -C -0.00992462 1.54951908 0.00732200 -C 0.00740485 0.01722020 0.05031270 -O 0.72085722 -0.49630479 1.15864583 -C -1.36995095 -0.52355929 0.00894432 -N -2.45081572 -0.93233138 0.00716912 -H 1.01892314 1.91630447 0.02724149 -H -0.50261490 1.90969362 -0.89997951 -H -0.54800426 1.95377611 0.87053664 -H 0.53602846 -0.36494195 -0.82970945 -H 0.27702643 -0.20644534 1.96421773 -13 -Properties=species:S:1:pos:R:3 A=7.66289 B=3.70925 C=2.79937 mu=2.6921 alpha=46.58 epsHOMO=-0.2469 epsLUMO=-0.0188 epsgap=0.2281 =482.0475 zpve=0.112328 U0=-232.364952 U=-232.358577 H=-232.357633 G=-232.394589 Cv=21.434 pbc="F F F" -C -0.02629647 1.53877116 -0.00783747 -C 0.01468904 0.00095310 0.00166037 -C 0.74737862 -0.53388859 1.24420471 -C -1.40211264 -0.53859257 -0.01557820 -O -1.87620455 -1.23582351 -0.87399559 -H 0.98823696 1.94625080 0.03705522 -H -0.50725090 1.92153422 -0.91285565 -H -0.57756514 1.92360883 0.85796386 -H 0.50584680 -0.36421091 -0.90619124 -H 0.22958380 -0.23909403 2.16431052 -H 0.81692736 -1.62559499 1.22973438 -H 1.76267153 -0.12846566 1.29050755 -H -2.00861510 -0.22792742 0.87121666 -11 -Properties=species:S:1:pos:R:3 A=8.31096 B=4.07963 C=2.92918 mu=2.8354 alpha=39.16 epsHOMO=-0.255 epsLUMO=-0.0279 epsgap=0.2271 =432.1489 zpve=0.088443 U0=-268.287661 U=-268.281505 H=-268.280561 G=-268.316982 Cv=20.344 pbc="F F F" -C -0.05483118 1.46032843 -0.17068647 -C 0.07105714 -0.03524485 0.11225841 -O 0.58204486 -0.33316656 1.40176256 -C -1.27639279 -0.74383027 0.03051185 -O -2.11855775 -0.49643175 -0.79163412 -H 0.93158072 1.93091398 -0.23353204 -H -0.57713995 1.61290939 -1.11835562 -H -0.62444315 1.94776119 0.62573670 -H 0.69687521 -0.50071620 -0.67159471 -H 1.45603316 0.06066659 1.47858584 -H -1.40723217 -1.53388627 0.80211181 -12 -Properties=species:S:1:pos:R:3 A=8.9331 B=4.19405 C=2.96046 mu=3.7163 alpha=44.42 epsHOMO=-0.2424 epsLUMO=0.0327 epsgap=0.2751 =441.85 zpve=0.10227 U0=-248.430371 U=-248.424309 H=-248.423365 G=-248.459383 Cv=19.918 pbc="F F F" -C -0.09776499 1.44917268 0.04561984 -N 0.01043053 0.00373818 -0.00070600 -C 0.05323028 -0.71432112 1.25264891 -C 0.06728716 -0.64067225 -1.20060778 -O 0.03636432 -0.11006515 -2.29109228 -H 0.75650660 1.88747109 0.57647421 -H -0.11648577 1.81632103 -0.98049172 -H -1.01641106 1.75521548 0.56151415 -H -0.85453556 -0.53657675 1.84390168 -H 0.13297722 -1.78696336 1.05851351 -H 0.91637348 -0.40737846 1.85761345 -H 0.14762240 -1.73801339 -1.06805139 -11 -Properties=species:S:1:pos:R:3 A=10.13982 B=3.81346 C=2.86965 mu=2.9514 alpha=39.33 epsHOMO=-0.2699 epsLUMO=-0.0262 epsgap=0.2437 =440.1727 zpve=0.088924 U0=-268.301176 U=-268.295084 H=-268.29414 G=-268.331307 Cv=19.824 pbc="F F F" -C -0.01708808 1.44281136 0.03225501 -C -0.05544542 -0.06560572 -0.02375999 -O -0.17379913 -0.76196233 0.96223129 -C 0.06068107 -0.73615179 -1.38825252 -O 0.00877771 -2.12574390 -1.27749486 -H 0.92062950 1.81463913 -0.39522215 -H -0.83050523 1.86542281 -0.56780350 -H -0.10911975 1.78049045 1.06493675 -H -0.75015067 -0.35235060 -2.03056074 -H 1.00312898 -0.40389029 -1.85592942 -H -0.09063255 -2.29450960 -0.32608019 -13 -Properties=species:S:1:pos:R:3 A=9.55216 B=3.57844 C=2.7341 mu=2.6168 alpha=46.19 epsHOMO=-0.2423 epsLUMO=-0.0072 epsgap=0.2351 =489.8518 zpve=0.112006 U0=-232.377706 U=-232.371073 H=-232.370129 G=-232.408256 Cv=21.716 pbc="F F F" -C -0.04384102 1.54099793 -0.03913814 -C 0.02765132 0.01885817 0.04778773 -C 0.76814644 -0.48842218 1.28000847 -C 0.87850208 -1.99746312 1.42393323 -O 1.24179142 0.26651340 2.09788702 -H 0.95796454 1.97690198 -0.06816012 -H -0.58447717 1.85356031 -0.93690369 -H -0.55173626 1.95737759 0.83440529 -H -0.97692824 -0.42709877 0.05353178 -H 0.51853486 -0.40806507 -0.83809409 -H 1.39924980 -2.42678188 0.56053177 -H 1.42001187 -2.24449367 2.33776426 -H -0.11750602 -2.45360651 1.45141965 -12 -Properties=species:S:1:pos:R:3 A=9.78802 B=3.78441 C=2.84416 mu=3.499 alpha=42.54 epsHOMO=-0.2438 epsLUMO=0.0355 epsgap=0.2793 =457.447 zpve=0.101847 U0=-248.448467 U=-248.441988 H=-248.441044 G=-248.478935 Cv=21.374 pbc="F F F" -C -0.00220144 1.53344329 -0.05960369 -C -0.03235837 0.01206167 0.06840265 -C 0.69666086 -0.48196462 1.31828322 -N 0.98104007 -1.82034162 1.31984698 -O 1.00392124 0.24606232 2.24136749 -H 1.02111190 1.89525169 -0.19182672 -H -0.59707204 1.86206427 -0.91645604 -H -0.39728839 1.99920712 0.84545201 -H -1.06936083 -0.34552935 0.13040806 -H 0.39947165 -0.46906818 -0.81777020 -H 0.63281951 -2.43917647 0.60874785 -H 1.35708014 -2.22217793 2.16303522 -12 -Properties=species:S:1:pos:R:3 A=10.01185 B=3.84195 C=2.87635 mu=3.5402 alpha=43.61 epsHOMO=-0.2418 epsLUMO=0.0387 epsgap=0.2805 =458.9803 zpve=0.101735 U0=-248.443503 U=-248.436899 H=-248.435955 G=-248.474272 Cv=20.967 pbc="F F F" -C 0.00619941 1.45933202 -0.03893859 -N -0.06411616 0.01422963 -0.12046901 -C -1.20595394 -0.68196796 0.16117120 -C -1.08982704 -2.19284899 0.02095775 -O -2.24482911 -0.14443077 0.49994418 -H 0.75053989 1.78372520 0.69715573 -H 0.25692001 1.90395263 -1.00876215 -H -0.97856026 1.81121438 0.27082194 -H 0.75820982 -0.49543660 -0.39464024 -H -0.09909703 -2.53543104 -0.28970003 -H -1.34097671 -2.65395851 0.97972885 -H -1.82903100 -2.53483935 -0.70813076 -11 -Properties=species:S:1:pos:R:3 A=10.44045 B=4.04692 C=2.98547 mu=3.5648 alpha=39.51 epsHOMO=-0.2436 epsLUMO=0.0599 epsgap=0.3034 =428.404 zpve=0.091713 U0=-264.504487 U=-264.498452 H=-264.497508 G=-264.533633 Cv=20.274 pbc="F F F" -C 0.00922630 1.45021814 -0.03194543 -N -0.01429488 -0.00054646 -0.01782667 -C -1.21576579 -0.67252403 0.04421896 -N -1.09733352 -2.04644898 -0.15997741 -O -2.28599299 -0.12893711 0.24196027 -H 0.89659038 1.81885022 0.49120949 -H 0.00233950 1.86388504 -1.04914155 -H -0.88562684 1.79943346 0.48353298 -H 0.75257305 -0.47506631 -0.46719265 -H -0.24882333 -2.48772194 0.16375654 -H -1.93419610 -2.53898393 0.11482880 -12 -Properties=species:S:1:pos:R:3 A=9.69683 B=4.1533 C=3.01703 mu=1.1876 alpha=43.57 epsHOMO=-0.2595 epsLUMO=0.0352 epsgap=0.2948 =443.1687 zpve=0.102062 U0=-248.416462 U=-248.410358 H=-248.409414 G=-248.445651 Cv=20.256 pbc="F F F" -C -0.09160695 1.37540046 -0.00729426 -O -0.06964534 -0.04831581 -0.11148812 -C 0.68114625 -0.56142673 -1.10732458 -C 0.60405298 -2.06701550 -1.08766892 -N 1.32645533 0.19266343 -1.89967731 -H 0.91463916 1.76843655 0.16257460 -H -0.48562826 1.82624615 -0.92235216 -H -0.73971430 1.60007423 0.84110605 -H -0.43430065 -2.39242162 -1.20325205 -H 1.20627110 -2.50049977 -1.88745640 -H 0.95651337 -2.44976670 -0.12501838 -H 1.83504115 -0.36104361 -2.58486055 -11 -Properties=species:S:1:pos:R:3 A=10.20392 B=4.16834 C=3.07281 mu=1.7569 alpha=39.33 epsHOMO=-0.2685 epsLUMO=0.0174 epsgap=0.2859 =427.6606 zpve=0.089436 U0=-268.32127 U=-268.315051 H=-268.314106 G=-268.351214 Cv=19.501 pbc="F F F" -C 0.07295254 1.38840919 0.06442046 -O 0.21049114 -0.03740028 0.04612736 -C 0.50102805 -0.57224288 -1.15975346 -C 0.62218715 -2.07378323 -1.06030684 -O 0.63922154 0.08224418 -2.16157325 -H 1.00029720 1.87062570 -0.25532064 -H -0.73338957 1.70782089 -0.60080454 -H -0.15751280 1.65154060 1.09692954 -H -0.31330001 -2.50202851 -0.68941906 -H 0.85646588 -2.48430275 -2.04157352 -H 1.40651685 -2.34055496 -0.34634301 -10 -Properties=species:S:1:pos:R:3 A=10.68182 B=4.39532 C=3.17969 mu=2.329 alpha=35.5 epsHOMO=-0.2669 epsLUMO=0.0575 epsgap=0.3244 =398.5908 zpve=0.079271 U0=-284.385189 U=-284.379361 H=-284.378417 G=-284.414085 Cv=19.084 pbc="F F F" -C -0.02645771 1.38305753 0.02085660 -O 0.04139566 -0.04439503 -0.00277452 -C 0.66775338 -0.56001625 -1.09000344 -N 0.72605063 -1.92219983 -0.98822954 -O 1.12952381 0.09648143 -1.99322180 -H 0.97529779 1.82044307 0.02387306 -H -0.57085698 1.76214621 -0.84790425 -H -0.55410722 1.63835731 0.94043594 -H 0.12662696 -2.38401590 -0.32566929 -H 0.97230070 -2.41798421 -1.82732573 -10 -Properties=species:S:1:pos:R:3 A=10.20696 B=3.93135 C=2.9219 mu=4.5676 alpha=35.87 epsHOMO=-0.246 epsLUMO=0.0273 epsgap=0.2733 =418.5967 zpve=0.078222 U0=-284.360325 U=-284.354148 H=-284.353204 G=-284.390143 Cv=20.089 pbc="F F F" -N -0.08115607 1.31807026 0.01276742 -C 0.05442259 -0.04477733 0.08155388 -O 1.12163074 -0.59783950 0.20940553 -C -1.28687786 -0.79011642 0.06178435 -O -1.04605114 -2.13954195 -0.23957158 -H 0.76623366 1.85991128 -0.04483425 -H -0.93021346 1.74567447 -0.31565615 -H -1.74559696 -0.65521334 1.05526700 -H -1.96026061 -0.31149222 -0.67026256 -H -1.85637296 -2.63017646 -0.07625478 -10 -Properties=species:S:1:pos:R:3 A=10.14389 B=4.0714 C=3.02223 mu=5.3004 alpha=35.19 epsHOMO=-0.2527 epsLUMO=0.0208 epsgap=0.2735 =408.0279 zpve=0.080317 U0=-284.372483 U=-284.367172 H=-284.366228 G=-284.40095 Cv=17.931 pbc="F F F" -N 0.01838100 0.93057357 0.04943058 -H -0.70674506 0.56116798 -0.55550134 -H 0.68248307 0.18430002 0.21487099 -H 0.21928084 2.79858558 -0.32670585 -C -0.54586914 1.44688585 1.30274586 -C -0.80466975 2.96030569 1.21052835 -O -0.21468822 3.53648884 0.15531687 -O -1.45295676 3.56338618 2.02143528 -H -1.47662579 0.96512183 1.62072169 -H 0.17781209 1.30324286 2.11157176 -13 -Properties=species:S:1:pos:R:3 A=6.6508 B=4.19317 C=3.39441 mu=2.2854 alpha=42.01 epsHOMO=-0.2597 epsLUMO=0.0631 epsgap=0.3228 =438.3028 zpve=0.11345 U0=-269.479234 U=-269.472993 H=-269.472049 G=-269.508213 Cv=22.072 pbc="F F F" -C -0.02775167 1.53421433 0.00651151 -C 0.08343246 0.01218277 0.05258986 -O 0.95856392 -0.48200710 -0.96694113 -C 0.69767574 -0.49516451 1.35948812 -O 2.02247062 -0.02842530 1.52222186 -H 0.94333763 1.98841434 0.21841589 -H -0.37320516 1.87354041 -0.97716908 -H -0.74773817 1.89256394 0.74926853 -H -0.91671848 -0.43697036 -0.06040528 -H 0.76726710 -0.01412177 -1.78502043 -H 0.11650002 -0.13351914 2.21392431 -H 0.65814685 -1.59563445 1.36274474 -H 2.46378566 -0.22762476 0.68698431 -17 -Properties=species:S:1:pos:R:3 A=7.26511 B=3.32874 C=2.54491 mu=0.0618 alpha=56.26 epsHOMO=-0.3085 epsLUMO=0.085 epsgap=0.3934 =565.8412 zpve=0.159632 U0=-197.629387 U=-197.622325 H=-197.621381 G=-197.659365 Cv=25.169 pbc="F F F" -C 0.05879035 1.56174169 -0.01945240 -C -0.01544024 0.03182306 0.00308332 -C -1.44103237 -0.54862028 0.05028629 -C -1.40777007 -2.07231030 -0.13012010 -C -2.18400529 -0.17659759 1.34114303 -H 1.09435638 1.90233607 -0.11900072 -H -0.50689566 1.97217319 -0.86388098 -H -0.34407951 2.00628082 0.89579522 -H 0.48926758 -0.36011214 -0.88953902 -H 0.55254938 -0.35139446 0.86269887 -H -2.00031575 -0.12153682 -0.79559684 -H -0.85501131 -2.55065810 0.68775438 -H -2.41787513 -2.49543628 -0.13720127 -H -0.91928762 -2.35363802 -1.06914014 -H -3.19100197 -0.60699559 1.35137051 -H -1.65197294 -0.56010584 2.22063481 -H -2.28793759 0.90575132 1.46079572 -15 -Properties=species:S:1:pos:R:3 A=8.11598 B=3.43678 C=2.66869 mu=1.3894 alpha=49.45 epsHOMO=-0.2617 epsLUMO=0.0733 epsgap=0.335 =521.8605 zpve=0.136091 U0=-233.555951 U=-233.549143 H=-233.548199 G=-233.585602 Cv=23.99 pbc="F F F" -C -0.03841227 1.55621396 0.00935837 -C 0.01907135 0.02716079 -0.02074203 -C 0.73141506 -0.52709328 -1.25454422 -C 0.83975868 -2.05206973 -1.22872655 -O -0.01226470 -0.08514394 -2.38973758 -H 0.96845664 1.98863424 0.03526234 -H -0.54377006 1.93824134 -0.88074407 -H -0.57652955 1.91383386 0.89256698 -H 0.53166867 -0.34991486 0.87240999 -H -0.99727186 -0.38638654 -0.00185289 -H 1.74909346 -0.09943025 -1.28826184 -H 1.34143968 -2.42487137 -2.12957624 -H 1.41822012 -2.39229108 -0.36340871 -H -0.15616765 -2.50443859 -1.18308875 -H 0.43090825 -0.40901616 -3.17979642 -15 -Properties=species:S:1:pos:R:3 A=7.55335 B=3.78323 C=2.80566 mu=1.0758 alpha=49.8 epsHOMO=-0.2476 epsLUMO=0.086 epsgap=0.3336 =507.9614 zpve=0.135681 U0=-233.545899 U=-233.539034 H=-233.53809 G=-233.57582 Cv=23.573 pbc="F F F" -C -0.00857605 1.38097833 0.09454670 -O -0.02255106 -0.00739461 -0.13062576 -C -1.30243341 -0.61997805 -0.02088309 -C -1.18687286 -1.97885751 -0.70303918 -C -1.75587824 -0.74662760 1.43714508 -H 1.00321550 1.73258087 -0.12443496 -H -0.71511468 1.90838275 -0.56674280 -H -0.24909065 1.64967237 1.13384681 -H -2.03737978 -0.00432241 -0.56884198 -H -0.44792779 -2.59945605 -0.18596870 -H -2.14751041 -2.50225525 -0.69102581 -H -0.86524212 -1.85860679 -1.74077836 -H -2.73321482 -1.23674908 1.49205117 -H -1.03621350 -1.34483828 2.00544671 -H -1.84898410 0.22935483 1.92198087 -15 -Properties=species:S:1:pos:R:3 A=6.14183 B=5.17679 C=3.79297 mu=0.1068 alpha=52.92 epsHOMO=-0.2633 epsLUMO=0.0893 epsgap=0.3526 =439.1643 zpve=0.137025 U0=-196.409349 U=-196.403398 H=-196.402453 G=-196.437676 Cv=22.258 pbc="F F F" -C -0.03913204 1.50456305 0.04978913 -C 0.00985856 -0.01113880 0.01248619 -C -1.24167981 -0.66663044 -0.53989979 -C 1.33803387 -0.66895748 -0.27400430 -C 0.75126442 -0.72221586 1.11862571 -H 0.88633040 1.92490040 0.45579358 -H -0.18183878 1.92045743 -0.95489291 -H -0.86832076 1.85815718 0.67439648 -H -1.43570890 -0.34344387 -1.56976411 -H -1.15326757 -1.75757800 -0.54433832 -H -2.12221416 -0.40570820 0.05951361 -H 1.34398374 -1.58235208 -0.86005084 -H 2.20196897 -0.03329084 -0.43929326 -H 1.21949576 -0.12246018 1.89246580 -H 0.36153319 -1.67153717 1.47171932 -13 -Properties=species:S:1:pos:R:3 A=6.49534 B=5.52329 C=3.9108 mu=1.8235 alpha=45.7 epsHOMO=-0.2596 epsLUMO=0.091 epsgap=0.3505 =405.521 zpve=0.112851 U0=-232.335768 U=-232.329981 H=-232.329037 G=-232.364091 Cv=20.719 pbc="F F F" -C -0.03970330 1.50954998 -0.04112233 -C -0.03023556 -0.00335329 -0.06498361 -C -1.24613189 -0.68356648 0.52531190 -C 0.78937201 -0.71129193 -1.06027559 -O 1.22546798 -0.60059036 0.29712283 -H 0.88992772 1.90948478 -0.45296558 -H -0.87952723 1.90350108 -0.62376642 -H -0.14448576 1.87590604 0.98604429 -H -2.15218727 -0.41012616 -0.02629495 -H -1.13412584 -1.77005431 0.49747032 -H -1.38586631 -0.38055453 1.56882757 -H 0.50712690 -1.71386874 -1.38085018 -H 1.37088104 -0.14369322 -1.78640853 -13 -Properties=species:S:1:pos:R:3 A=6.57648 B=5.31603 C=3.98453 mu=1.3822 alpha=45.85 epsHOMO=-0.2494 epsLUMO=0.0776 epsgap=0.327 =401.4888 zpve=0.113237 U0=-232.33341 U=-232.327734 H=-232.32679 G=-232.36147 Cv=21.17 pbc="F F F" -C -0.03714052 1.53215920 0.01332310 -C 0.01344335 0.02117019 0.01922005 -O -1.17476104 -0.59735284 -0.40931260 -C 1.30744798 -0.69902106 -0.27389650 -C 0.70844749 -0.71373504 1.12226318 -H 0.89026861 1.96240279 0.40276135 -H -0.17846151 1.91561262 -1.00518522 -H -0.87011232 1.88955201 0.62730953 -H -1.28352265 -0.40225391 -1.34690641 -H 1.23973813 -1.61642115 -0.84783774 -H 2.20543993 -0.11523820 -0.44744474 -H 1.19539159 -0.13889834 1.90240284 -H 0.24735439 -1.63670241 1.45378480 -10 -Properties=species:S:1:pos:R:3 A=12.18399 B=5.1384 C=3.78868 mu=2.5732 alpha=40.19 epsHOMO=-0.263 epsLUMO=0.0277 epsgap=0.2907 =352.0378 zpve=0.080558 U0=-247.201165 U=-247.196699 H=-247.195755 G=-247.22841 Cv=15.51 pbc="F F F" -N -0.04445152 1.25623533 0.31681915 -C -0.03914559 0.04913710 -0.01670558 -C -1.08749619 -1.04164446 -0.11488033 -C 0.09869688 -1.90754844 -0.58225894 -O 0.99647508 -0.76306629 -0.44028010 -H 0.88935980 1.66481350 0.24916893 -H -1.54324114 -1.32235812 0.83572172 -H -1.86910036 -0.85713839 -0.85325910 -H 0.39739753 -2.72227350 0.08158721 -H 0.06945080 -2.25418345 -1.61796084 -11 -Properties=species:S:1:pos:R:3 A=10.79571 B=4.78732 C=3.54292 mu=2.7119 alpha=42.77 epsHOMO=-0.2415 epsLUMO=-0.0194 epsgap=0.2222 =379.6371 zpve=0.090544 U0=-231.15578 U=-231.150804 H=-231.149859 G=-231.184401 Cv=17.076 pbc="F F F" -O -0.00101746 0.01846628 -0.20114742 -C -0.01405811 1.19658059 0.00205306 -C -1.13542911 2.22979148 0.21957980 -C -0.03500884 3.29097637 0.53052223 -C 1.08469664 2.25947795 0.18994331 -H -1.71642814 2.41772662 -0.68901078 -H -1.83392757 1.98415059 1.02444244 -H -0.05534741 4.17585264 -0.10670963 -H -0.02541477 3.61399048 1.57278917 -H 1.81074389 2.03288534 0.97578973 -H 1.63600801 2.46255380 -0.73376244 -10 -Properties=species:S:1:pos:R:3 A=12.23436 B=4.97895 C=3.70561 mu=3.6671 alpha=39.73 epsHOMO=-0.25 epsLUMO=0.038 epsgap=0.288 =355.0934 zpve=0.080442 U0=-247.225618 U=-247.220897 H=-247.219953 G=-247.253218 Cv=16.1 pbc="F F F" -O 0.03184134 -0.03461682 0.00849836 -C -0.02440334 1.16440533 0.02160811 -C -1.14541322 2.23712809 0.03012901 -C 0.00928805 3.27945878 0.04557758 -N 0.93843838 2.14652109 0.03554396 -H -1.77030529 2.23779730 -0.86428437 -H -1.77632809 2.21765386 0.92007934 -H 0.07170564 3.91863516 -0.84072193 -H 0.06622603 3.89826625 0.94657952 -H 1.94616840 2.09535713 0.03855945 -9 -Properties=species:S:1:pos:R:3 A=12.44643 B=5.23328 C=3.86599 mu=3.9339 alpha=35.4 epsHOMO=-0.2788 epsLUMO=0.0089 epsgap=0.2878 =336.0792 zpve=0.068574 U0=-267.106213 U=-267.101929 H=-267.100985 G=-267.13332 Cv=14.483 pbc="F F F" -O 0.04424004 0.03933243 -0.33071104 -C -0.04854479 1.17416230 0.00410076 -C -1.14211656 2.23611850 0.15338361 -C 0.02332538 3.12357010 0.61681625 -O 0.95663808 2.01190122 0.42603746 -H -1.62573755 2.51482212 -0.78401102 -H -1.90009213 1.99553386 0.90028663 -H 0.28368077 3.96172751 -0.03229287 -H 0.00782250 3.43942655 1.66162725 -10 -Properties=species:S:1:pos:R:3 A=11.54185 B=4.90368 C=3.63309 mu=2.5257 alpha=39.34 epsHOMO=-0.2437 epsLUMO=-0.0258 epsgap=0.2179 =359.152 zpve=0.07965 U0=-247.190194 U=-247.185543 H=-247.184599 G=-247.217688 Cv=16.083 pbc="F F F" -O 0.00053891 0.00665475 -0.09743049 -C -0.01371990 1.19213881 0.03139936 -C -1.10668681 2.26612284 0.17247913 -N -0.03543445 3.25123475 0.48533693 -C 1.05328615 2.29521506 0.14383904 -H -1.65047970 2.42796175 -0.76886054 -H -1.83606786 2.11370530 0.97567410 -H -0.05434600 4.07193609 -0.10727051 -H 1.80754206 2.16277968 0.92736250 -H 1.56737937 2.47130277 -0.81152688 -9 -Properties=species:S:1:pos:R:3 A=12.18264 B=4.94228 C=3.68162 mu=0.8477 alpha=35.94 epsHOMO=-0.2647 epsLUMO=-0.0352 epsgap=0.2295 =344.5473 zpve=0.066989 U0=-267.068488 U=-267.063976 H=-267.063032 G=-267.095887 Cv=15.115 pbc="F F F" -O -0.00031692 0.00676321 -0.17139774 -C -0.01417098 1.18672865 -0.00980279 -C -1.08283120 2.27653381 0.15574094 -O -0.03856115 3.26460423 0.27513986 -C 1.02857270 2.30515693 0.12774311 -H -1.72923855 2.43647507 -0.71671274 -H -1.70219898 2.19346725 1.05800754 -H 1.67366781 2.23923293 1.01324179 -H 1.64705068 2.48224548 -0.76148355 -15 -Properties=species:S:1:pos:R:3 A=9.63416 B=4.10254 C=3.28665 mu=0.095 alpha=52.94 epsHOMO=-0.2896 epsLUMO=0.0927 epsgap=0.3823 =455.0215 zpve=0.138424 U0=-196.407957 U=-196.40245 H=-196.401505 G=-196.436159 Cv=20.299 pbc="F F F" -C -0.47005934 1.44206547 -0.06684548 -C 0.14287306 0.05485144 0.03747984 -C 1.37254693 -0.30295273 -0.84679168 -C 1.95947994 -1.09364561 0.35326039 -C 0.99887433 -0.30768787 1.28563099 -H 0.29210839 2.21844829 0.06844100 -H -0.93614604 1.60094605 -1.04612799 -H -1.24148161 1.59707353 0.69627593 -H -0.63517341 -0.70560132 -0.10055095 -H 1.19338376 -0.84118177 -1.78129390 -H 1.96867777 0.58857708 -1.07004184 -H 1.69873597 -2.15500718 0.30521100 -H 3.03253510 -1.01077235 0.54148058 -H 1.48290386 0.58242428 1.70211026 -H 0.51304296 -0.84979686 2.10121062 -13 -Properties=species:S:1:pos:R:3 A=9.93129 B=4.56362 C=3.63704 mu=1.6826 alpha=45.72 epsHOMO=-0.241 epsLUMO=0.0915 epsgap=0.3325 =407.6909 zpve=0.11459 U0=-232.33123 U=-232.325877 H=-232.324933 G=-232.359589 Cv=18.582 pbc="F F F" -C -0.32308228 1.53751451 0.01947624 -C 0.11847214 0.08900172 -0.00523140 -C 0.87489284 -0.43763303 -1.24721738 -C 1.97841048 -0.80657483 -0.23740356 -O 1.27344926 -0.15419108 0.83794040 -H 0.49211985 2.19658795 -0.29527664 -H -1.17615835 1.68923490 -0.65134808 -H -0.62776963 1.82747912 1.03013205 -H -0.71410200 -0.56389187 0.29597305 -H 0.41033314 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zpve=0.115063 U0=-232.333258 U=-232.328097 H=-232.327153 G=-232.361103 Cv=19.023 pbc="F F F" -O 0.54622847 1.03270424 -0.58398083 -C -0.00112269 -0.11036621 0.01898238 -C 0.13945950 -0.34310429 1.54777117 -C -1.26863784 -0.99876517 1.51632654 -C -1.53844651 -0.26418236 0.17423510 -H 0.20332379 1.80433874 -0.12071219 -H 0.41296647 -0.95857955 -0.53560402 -H 0.98890738 -0.92719626 1.90921867 -H 0.11786549 0.61323034 2.08356053 -H -1.20828813 -2.08191064 1.38036699 -H -1.94143614 -0.79119746 2.35014116 -H -2.00495682 0.71307933 0.34581493 -H -2.09160311 -0.78230202 -0.61249171 -11 -Properties=species:S:1:pos:R:3 A=10.85394 B=4.52867 C=3.62328 mu=2.4158 alpha=38.58 epsHOMO=-0.2465 epsLUMO=0.0604 epsgap=0.3069 =374.4087 zpve=0.091005 U0=-268.248371 U=-268.243234 H=-268.24229 G=-268.276572 Cv=17.344 pbc="F F F" -O 0.07721714 1.41738724 -0.14439016 -C 0.01675156 0.02484791 0.03233817 -C 0.11193823 -0.54392541 1.47000744 -O -1.16387804 -1.20093191 1.32441605 -C -1.41930178 -0.52040930 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zr?z=ukpy>#kHM?W!^t35adcHaL+nM9Bl3eskJ7j6cTv)_K69vl94L@>-V&PGnvlb4 zbJTf1{G&kbfFy66XDS7SY}G)lnm!MRwL{ZJn}P*1H8#IYLCsSHQ#55c6a+0)4J8!? zZ!*x4tjWl=s)Obv#9J*$=3ZY1wbHDq@WWPcXu*HGWchNQw&F#4L~f*W{OAqpdkPm_ z%vF`P>P(&oCLr^aWxCQn%VMX@tb5Kpat){(C+T)Q30uMvEQ3BKd`3$>I7J$N3Z;q7H>^X=dbXhe5EY-z z83*B3vMxfx#~ar5G9U89lM3z8(S*1#obF+MIt}bFv-|hEp81?m>P>fxlN+&)2CJxa z#yOmv)(zVI$~LB;xxQ9}m3>~JO)uV(U>Ede2~Y>n&vszW)1U*;=~^1JrmdG+p~FLZ z93tl}BqeS09d8^bgO2nHBXgy~2x+G_)4wmylzw1QVKndr3^uGp^ur}an#vm9l~Lbwr|L0(c10v*^ zNfa6)`uVOU3iQMga~G`V7X!QWy43GfEw*>tOob=uuiqcFKGUVFB_hWdjFT~$dUBxl z4?|AE^uDc10I6ph?C=cA3eB#_e*4WyTsRDfASqhS?`td` - to download the full example code. - -.. rst-class:: sphx-glr-example-title - -.. _sphx_glr_examples_programmatic_use_architectures_outside_use_outside.py: - - -Using metatrain architectures outside of metatrain -================================================== - -This tutorial demonstrates how to use one of metatrain's implemented architectures -outside of metatrain. This will be done by taking internal representations of a -NanoPET model (as an example) and using them inside a user-defined torch ``Module``. - -Only architectures which can output internal representations ("features" output) can -be used in this way. - -.. GENERATED FROM PYTHON SOURCE LINES 15-28 - -.. code-block:: Python - - - import torch - from metatomic.torch import ModelOutput - - from metatrain.pet import PET - from metatrain.utils.architectures import get_default_hypers - from metatrain.utils.data import DatasetInfo, read_systems - from metatrain.utils.neighbor_lists import ( - get_requested_neighbor_lists, - get_system_with_neighbor_lists, - ) - - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 29-31 - -Read some sample systems. Metatrain always reads systems in float64, while torch -uses float32 by default. We will convert the systems to float32. - -.. GENERATED FROM PYTHON SOURCE LINES 32-37 - -.. code-block:: Python - - - systems = read_systems("qm9_reduced_100.xyz") - systems = [s.to(torch.float32) for s in systems] - - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 38-41 - -Define the custom model using the PET architecture as a building block. -The dummy architecture here adds a linear layer and a tanh activation function -on top of the PET model. - -.. GENERATED FROM PYTHON SOURCE LINES 42-68 - -.. code-block:: Python - - - - class PETWithTanh(torch.nn.Module): - def __init__(self): - super().__init__() - self.pet = PET( - get_default_hypers("pet")["model"], - DatasetInfo( - length_unit="angstrom", - atomic_types=[1, 6, 7, 8, 9], - targets={}, - ), - ) - self.linear = torch.nn.Linear(384, 1) - self.tanh = torch.nn.Tanh() - - def forward(self, systems): - model_outputs = self.pet( - systems, - {"features": ModelOutput()}, - # ModelOutput(per_atom=True) would give per-atom features - ) - features = model_outputs["features"].block().values - return self.tanh(self.linear(features)) - - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 69-71 - -Now we can train the custom model. Here is one training step executed with -some random targets. - -.. GENERATED FROM PYTHON SOURCE LINES 72-91 - -.. code-block:: Python - - my_targets = torch.randn(100, 1) - - # instantiate the model - model = PETWithTanh() - - # all metatrain models require neighbor lists to be present in the input systems - systems = [ - get_system_with_neighbor_lists(sys, get_requested_neighbor_lists(model)) - for sys in systems - ] - - # define an optimizer - optimizer = torch.optim.SGD(model.parameters(), lr=1e-3) - - # this is one training step - predictions = model(systems) - loss = torch.nn.functional.mse_loss(predictions, my_targets) - loss.backward() - optimizer.step() - - - - - - - - -.. _sphx_glr_download_examples_programmatic_use_architectures_outside_use_outside.py: - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-example - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: use_outside.ipynb ` - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: use_outside.py ` - - .. container:: sphx-glr-download sphx-glr-download-zip - - :download:`Download zipped: use_outside.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/programmatic/use_architectures_outside/use_outside.zip b/docs/src/examples/programmatic/use_architectures_outside/use_outside.zip deleted file mode 100644 index 6a075f505bc2699e4a479fa393b86291801c082d..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 7022 zcmdT}+iv8<77dpJl93QcLP9!EB}WKb>M;`6-#@B#bE zvbkUhF`RFh;TNQ33ND4h`Gr_PJ22%Yg&=e$cRuGTKmPE9WqBeb^SEYQG4Yg>06sn^ z5Rtf;rO3dFY{)o?UEE2;B2^F&>cva;=+S3+QcCgYQQ$aVD7hI5&eqEmR57?ig;il(KvosDW zDQgOdD&!Yd4yQ**u@I~TWUCm#FyqzIq*4dNc&Vw+g^6GjUP==!H!D%--J26`cyq!7 zeAdEzq89lC4&q6qH`<77&sHVU^W{7*BAIGq!-Vwp9EOXIFT~?gV1E(~DJLyO#Xef6 zX1PVyTQ}V zcdp-~KGpEQXG0*Ux%q677YsI3%~5B4^&i;awvxo9HWW%WL)vU$lr{Z0V3a#-UZygk 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- -.. only:: html - - .. image:: /examples/train_from_scratch/images/thumb/sphx_glr_train_from_scratch_thumb.png - :alt: - - :ref:`sphx_glr_examples_train_from_scratch_train_from_scratch.py` - -.. raw:: html - -
Training a model from scratch
-
- - -.. thumbnail-parent-div-close - -.. raw:: html - - - - -.. toctree:: - :hidden: - - /examples/train_from_scratch/train_from_scratch - - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-gallery - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download all examples in Python source code: train_from_scratch_python.zip ` - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download all examples in Jupyter notebooks: train_from_scratch_jupyter.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/train_from_scratch/options.yaml b/docs/src/examples/train_from_scratch/options.yaml deleted file mode 100644 index ceef2551ed..0000000000 --- a/docs/src/examples/train_from_scratch/options.yaml +++ /dev/null @@ -1,22 +0,0 @@ -architecture: - name: pet # the architecture name for PET, or the name for other architectures, if you want to train other models - model: - cutoff: 4.5 # the cutoff - training: - num_epochs: 10 # this is for a reasonable time of a tutorial, for a good model, consider increasing the number - batch_size: 10 # the size of the training data feed to the model per batch, determining the GPU memory usage during the training - log_interval: 1 - checkpoint_interval: 10 # it saves checkpoints of the model every 10 epochs - -# this needs specifying based on the specific dataset -training_set: - systems: - read_from: ./ethanol_reduced_100.xyz - length_unit: Angstrom - targets: - energy: - key: energy # name of the target value - unit: eV # unit of the target value - -test_set: 0.1 # 10 % of the training_set are randomly split and taken for test set -validation_set: 0.1 # 10 % of the training_set are randomly split and for validation set diff --git a/docs/src/examples/train_from_scratch/train_from_scratch.ipynb b/docs/src/examples/train_from_scratch/train_from_scratch.ipynb deleted file mode 100644 index d0f0dadee6..0000000000 --- a/docs/src/examples/train_from_scratch/train_from_scratch.ipynb +++ /dev/null @@ -1,32 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n\n# Training a model from scratch\nThis tutorial explains how to train a model with ``metatrain`` from scratch and evaluate\nit. [This dataset](./ethanol_reduced_100.xyz) is used here as an example of the\npreferred dataset format. If you have your own dataset, you can simply replace the\ndataset file name with yours.\n\n## Train the model\n\n### Configure the ``options.yaml`` and run the training\nBelow is an example ``options.yaml`` for training a PET model. In order to train other\nmodels, simply replace the architecture name with other models' architecture name. For\nthe supported models, please check `Available Architectures`_ .\n\n\n.. literalinclude:: ./options.yaml\n :language: yaml\n :linenos:\n\nStart the training by running\n\n```bash\nmtt train options.yaml\n```\nIt will start training. ``metatrain`` will automatically read the atomic forces from the\ntraining set, if they are stored in it and named as \"forces\". The model can also be\ntrained to learn other properties through transfer learning. For this, please refer to\nthis `transfer learning tutorial`_.\n\n\nOnce the training is started, a folder named ``outputs`` will be created automatically\nunder the folder where you run the command. Under this ``outputs`` folder, there is a\nfolder with the timestamp. Below is a normal structure of that folder of a successful\ntraining run.\n\n```bash\noutputs/2025-10-07/17-08-25/\n\u251c\u2500\u2500 indices # the results of dataset-spliting\n\u2502 \u251c\u2500\u2500 test.txt\n\u2502 \u251c\u2500\u2500 training.txt\n\u2502 \u2514\u2500\u2500 validation.txt\n\u251c\u2500\u2500 model_0.ckpt # the intermediate model saved at the 0th training step\n\u251c\u2500\u2500 model.ckpt # the final model\n\u251c\u2500\u2500 model.pt # the final model , usable directly by ASE and LAMMPS\n\u251c\u2500\u2500 options_restart.yaml # an expanded options file\n\u251c\u2500\u2500 train.csv # structured log of training metrics (loss, MAE, RMSE,...)\n\u2514\u2500\u2500 train.log # a human-friendly output\n```\nThe ``train.log`` provides information of the training procedure. For example, by\nchecking the following:\n\n```bash\n[2025-10-07 17:08:25][INFO] - Setting up training set [2025-10-07 17:08:25][INFO] -\nForces found in section 'energy', we will use this gradient to train the model\n[2025-10-07 17:08:25][WARNING] - No stress found in section 'energy'.\n```\nYou can know that the forces are identified by ``metatrain`` and are used during the\ntraining, and it fails to find stress. The following provides some statistical of the\ntraining, validation, and the test set\n\n```bash\n[2025-10-07 17:08:25][INFO] - Training dataset:\n Dataset containing 80 structures Mean and standard deviation of targets: -\n energy:\n - mean -9.708e+04 eV\n - std 3.97 eV\n[2025-10-07 17:08:25][INFO] - Validation dataset:\n Dataset containing 10 structures Mean and standard deviation of targets: -\n energy:\n - mean -9.708e+04 eV\n - std 3.73 eV\n[2025-10-07 17:08:25][INFO] - Test dataset:\n Dataset containing 10 structures Mean and standard deviation of targets: -\n energy:\n - mean -9.708e+04 eV\n - std 3.535 eV\n```\nThe training metrics are outputted every epoch, like\n\n```bash\n[2025-10-07 17:08:28][INFO] - Epoch: 0 | learning rate: 0.000e+00 | training\nloss: 6.305e+03 | training energy RMSE (per atom): 884.08 meV | training energy MAE\n(per atom): 773.44 meV | training forces RMSE: 28059.9 meV/A | training forces MAE:\n20581.1 meV/A | validation loss: 7.725e+02 | validation energy RMSE (per atom):\n877.08 meV | validation energy MAE (per atom): 772.04 meV | validation forces RMSE:\n27779.2 meV/A | validation forces MAE: 20201.9 meV/A\n```\nThese metrics are also outputted into ``train.csv`` in a formatted way, which can be\nused for plotting graph like loss curve.\n\nIt is easy to restart the training from the last step, by running\n\n```bash\nmtt train options.yaml --restart model.ckpt\n```\n## Evaluate the trained model\nIn order to evaluate the model on the test set, we can use the mtt eval sub-command.\nFirst, create the input file ``eval.yaml`` with the following options:\n\n.. literalinclude:: ./eval.yaml\n :language: yaml\n :linenos:\n\nand run\n\n```bash\nmtt eval PATH_TO_YOUR_MODEL/model.pt eval.yaml # be sure to replace the path\n```\nAfter this, a file named ``output.xyz`` will be created, with the atom positions and the\npredicted forces recorded in it. Also, you should see these statistical on your screen\n\n```bash\n[2025-10-07 17:11:47][INFO] - energy RMSE (per atom): 436.50 meV | energy MAE (per\natom): 341.32 meV | forces RMSE: 27823.1 meV/A | forces MAE: 20392.7 meV/A\n[2025-10-07 17:11:47][INFO] - Evaluation time: 1.10 s [1.2185 \u00b1 1.2768 ms per atom]\n```\nFurther analysis can be performed now that the model is trained. We provide a `Python\nscript`_ that can be used to generate a parity plot of the target vs predicted energies,\nbut otherwise leave this open-ended.\n\n\nTo run the script, download it from the repository, modify the paths as necessary\n(indicated with a #TODO), and run. This will generate a plot saved at parity_plot.png.\n\n\n## Use the model\nWith the trained model, you can run molecular dynamics. Please refer to these two\ntutorials for `ASE`_ and `LAMMPS`_ to see how to do that.\n\n\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/src/examples/train_from_scratch/train_from_scratch.py b/docs/src/examples/train_from_scratch/train_from_scratch.py deleted file mode 100644 index 3cf4c432c1..0000000000 --- a/docs/src/examples/train_from_scratch/train_from_scratch.py +++ /dev/null @@ -1,151 +0,0 @@ -""" -.. _train-from-scratch: - -Training a model from scratch -############################# -This tutorial explains how to train a model with ``metatrain`` from scratch and evaluate -it. :download:`This dataset ` is used here as an example of -the preferred dataset format. If you have your own dataset, you can simply replace the -dataset file name with yours. - -Train the model ---------------- - -Configure the ``options.yaml`` and run the training -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -Below is an example ``options.yaml`` for training a PET model. In order to train other -models, simply replace the architecture name with other models' architecture name. For -the supported models, please check `Available Architectures`_ . - -.. _`Available Architectures`: https://metatensor.github.io/metatrain/latest/architectures/index.html - -.. literalinclude:: ./options.yaml - :language: yaml - :linenos: - -Start the training by running - -.. code-block:: bash - - mtt train options.yaml - -It will start training. ``metatrain`` will automatically read the atomic forces from the -training set, if they are stored in it and named as "forces". The model can also be -trained to learn other properties through transfer learning. For this, please refer to -this `transfer learning tutorial`_. - -.. _`transfer learning tutorial`: https://metatensor.github.io/metatrain/latest/advanced-concepts/transfer-learning.html - -Once the training is started, a folder named ``outputs`` will be created automatically -under the folder where you run the command. Under this ``outputs`` folder, there is a -folder with the timestamp. Below is a normal structure of that folder of a successful -training run. - -.. code-block:: bash - - outputs/2025-10-07/17-08-25/ - ├── indices # the results of dataset-spliting - │ ├── test.txt - │ ├── training.txt - │ └── validation.txt - ├── model_0.ckpt # the intermediate model saved at the 0th training step - ├── model.ckpt # the final model - ├── model.pt # the final model , usable directly by ASE and LAMMPS - ├── options_restart.yaml # an expanded options file - ├── train.csv # structured log of training metrics (loss, MAE, RMSE,...) - └── train.log # a human-friendly output - -The ``train.log`` provides information of the training procedure. For example, by -checking the following: - -.. code-block:: bash - - [2025-10-07 17:08:25][INFO] - Setting up training set [2025-10-07 17:08:25][INFO] - - Forces found in section 'energy', we will use this gradient to train the model - [2025-10-07 17:08:25][WARNING] - No stress found in section 'energy'. - -You can know that the forces are identified by ``metatrain`` and are used during the -training, and it fails to find stress. The following provides some statistical of the -training, validation, and the test set - -.. code-block:: bash - - [2025-10-07 17:08:25][INFO] - Training dataset: - Dataset containing 80 structures Mean and standard deviation of targets: - - energy: - - mean -9.708e+04 eV - - std 3.97 eV - [2025-10-07 17:08:25][INFO] - Validation dataset: - Dataset containing 10 structures Mean and standard deviation of targets: - - energy: - - mean -9.708e+04 eV - - std 3.73 eV - [2025-10-07 17:08:25][INFO] - Test dataset: - Dataset containing 10 structures Mean and standard deviation of targets: - - energy: - - mean -9.708e+04 eV - - std 3.535 eV - -The training metrics are outputted every epoch, like - -.. code-block:: bash - - [2025-10-07 17:08:28][INFO] - Epoch: 0 | learning rate: 0.000e+00 | training - loss: 6.305e+03 | training energy RMSE (per atom): 884.08 meV | training energy MAE - (per atom): 773.44 meV | training forces RMSE: 28059.9 meV/A | training forces MAE: - 20581.1 meV/A | validation loss: 7.725e+02 | validation energy RMSE (per atom): - 877.08 meV | validation energy MAE (per atom): 772.04 meV | validation forces RMSE: - 27779.2 meV/A | validation forces MAE: 20201.9 meV/A - -These metrics are also outputted into ``train.csv`` in a formatted way, which can be -used for plotting graph like loss curve. - -It is easy to restart the training from the last step, by running - -.. code-block:: bash - - mtt train options.yaml --restart model.ckpt - -Evaluate the trained model --------------------------- -In order to evaluate the model on the test set, we can use the mtt eval sub-command. -First, create the input file ``eval.yaml`` with the following options: - -.. literalinclude:: ./eval.yaml - :language: yaml - :linenos: - -and run - -.. code-block:: bash - - mtt eval PATH_TO_YOUR_MODEL/model.pt eval.yaml # be sure to replace the path - -After this, a file named ``output.xyz`` will be created, with the atom positions and the -predicted forces recorded in it. Also, you should see these statistical on your screen - -.. code-block:: bash - - [2025-10-07 17:11:47][INFO] - energy RMSE (per atom): 436.50 meV | energy MAE (per - atom): 341.32 meV | forces RMSE: 27823.1 meV/A | forces MAE: 20392.7 meV/A - [2025-10-07 17:11:47][INFO] - Evaluation time: 1.10 s [1.2185 ± 1.2768 ms per atom] - -Further analysis can be performed now that the model is trained. We provide a `Python -script`_ that can be used to generate a parity plot of the target vs predicted energies, -but otherwise leave this open-ended. - -.. _`Python script`: https://raw.githubusercontent.com/metatensor/Workshop-spring-2025/refs/heads/main/training-custom-models/part-1-gap/parity_plot.py - -To run the script, download it from the repository, modify the paths as necessary -(indicated with a #TODO), and run. This will generate a plot saved at parity_plot.png. - - -Use the model -------------------------- -With the trained model, you can run molecular dynamics. Please refer to these two -tutorials for `ASE`_ and `LAMMPS`_ to see how to do that. - -.. _`ASE`: https://docs.metatensor.org/metatomic/latest/examples/2-running-ase-md.html - -.. _`LAMMPS`: https://atomistic-cookbook.org/examples/pet-mad-nc/pet-mad-nc.html#running-lammps-on-gpus-with-kokkos -""" diff --git a/docs/src/examples/train_from_scratch/train_from_scratch.py.md5 b/docs/src/examples/train_from_scratch/train_from_scratch.py.md5 deleted file mode 100644 index d77b561efc..0000000000 --- a/docs/src/examples/train_from_scratch/train_from_scratch.py.md5 +++ /dev/null @@ -1 +0,0 @@ -6dabc07a5102263b98df5d6e7b15c10a \ No newline at end of file diff --git a/docs/src/examples/train_from_scratch/train_from_scratch.rst b/docs/src/examples/train_from_scratch/train_from_scratch.rst deleted file mode 100644 index c57f518ceb..0000000000 --- a/docs/src/examples/train_from_scratch/train_from_scratch.rst +++ /dev/null @@ -1,195 +0,0 @@ - -.. DO NOT EDIT. -.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. -.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: -.. "examples/train_from_scratch/train_from_scratch.py" -.. LINE NUMBERS ARE GIVEN BELOW. - -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - :ref:`Go to the end ` - to download the full example code. - -.. rst-class:: sphx-glr-example-title - -.. _sphx_glr_examples_train_from_scratch_train_from_scratch.py: - - -.. _train-from-scratch: - -Training a model from scratch -############################# -This tutorial explains how to train a model with ``metatrain`` from scratch and evaluate -it. `This dataset <./ethanol_reduced_100.xyz>`_ is used here as an example of the -preferred dataset format. If you have your own dataset, you can simply replace the -dataset file name with yours. - -Train the model ---------------- - -Configure the ``options.yaml`` and run the training -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -Below is an example ``options.yaml`` for training a PET model. In order to train other -models, simply replace the architecture name with other models' architecture name. For -the supported models, please check `Available Architectures`_ . - -.. _`Available Architectures`: https://metatensor.github.io/metatrain/latest/architectures/index.html - -.. literalinclude:: ./options.yaml - :language: yaml - :linenos: - -Start the training by running - -.. code-block:: bash - - mtt train options.yaml - -It will start training. ``metatrain`` will automatically read the atomic forces from the -training set, if they are stored in it and named as "forces". The model can also be -trained to learn other properties through transfer learning. For this, please refer to -this `transfer learning tutorial`_. - -.. _`transfer learning tutorial`: https://metatensor.github.io/metatrain/latest/advanced-concepts/transfer-learning.html - -Once the training is started, a folder named ``outputs`` will be created automatically -under the folder where you run the command. Under this ``outputs`` folder, there is a -folder with the timestamp. Below is a normal structure of that folder of a successful -training run. - -.. code-block:: bash - - outputs/2025-10-07/17-08-25/ - ├── indices # the results of dataset-spliting - │ ├── test.txt - │ ├── training.txt - │ └── validation.txt - ├── model_0.ckpt # the intermediate model saved at the 0th training step - ├── model.ckpt # the final model - ├── model.pt # the final model , usable directly by ASE and LAMMPS - ├── options_restart.yaml # an expanded options file - ├── train.csv # structured log of training metrics (loss, MAE, RMSE,...) - └── train.log # a human-friendly output - -The ``train.log`` provides information of the training procedure. For example, by -checking the following: - -.. code-block:: bash - - [2025-10-07 17:08:25][INFO] - Setting up training set [2025-10-07 17:08:25][INFO] - - Forces found in section 'energy', we will use this gradient to train the model - [2025-10-07 17:08:25][WARNING] - No stress found in section 'energy'. - -You can know that the forces are identified by ``metatrain`` and are used during the -training, and it fails to find stress. The following provides some statistical of the -training, validation, and the test set - -.. code-block:: bash - - [2025-10-07 17:08:25][INFO] - Training dataset: - Dataset containing 80 structures Mean and standard deviation of targets: - - energy: - - mean -9.708e+04 eV - - std 3.97 eV - [2025-10-07 17:08:25][INFO] - Validation dataset: - Dataset containing 10 structures Mean and standard deviation of targets: - - energy: - - mean -9.708e+04 eV - - std 3.73 eV - [2025-10-07 17:08:25][INFO] - Test dataset: - Dataset containing 10 structures Mean and standard deviation of targets: - - energy: - - mean -9.708e+04 eV - - std 3.535 eV - -The training metrics are outputted every epoch, like - -.. code-block:: bash - - [2025-10-07 17:08:28][INFO] - Epoch: 0 | learning rate: 0.000e+00 | training - loss: 6.305e+03 | training energy RMSE (per atom): 884.08 meV | training energy MAE - (per atom): 773.44 meV | training forces RMSE: 28059.9 meV/A | training forces MAE: - 20581.1 meV/A | validation loss: 7.725e+02 | validation energy RMSE (per atom): - 877.08 meV | validation energy MAE (per atom): 772.04 meV | validation forces RMSE: - 27779.2 meV/A | validation forces MAE: 20201.9 meV/A - -These metrics are also outputted into ``train.csv`` in a formatted way, which can be -used for plotting graph like loss curve. - -It is easy to restart the training from the last step, by running - -.. code-block:: bash - - mtt train options.yaml --restart model.ckpt - -Evaluate the trained model --------------------------- -In order to evaluate the model on the test set, we can use the mtt eval sub-command. -First, create the input file ``eval.yaml`` with the following options: - -.. literalinclude:: ./eval.yaml - :language: yaml - :linenos: - -and run - -.. code-block:: bash - - mtt eval PATH_TO_YOUR_MODEL/model.pt eval.yaml # be sure to replace the path - -After this, a file named ``output.xyz`` will be created, with the atom positions and the -predicted forces recorded in it. Also, you should see these statistical on your screen - -.. code-block:: bash - - [2025-10-07 17:11:47][INFO] - energy RMSE (per atom): 436.50 meV | energy MAE (per - atom): 341.32 meV | forces RMSE: 27823.1 meV/A | forces MAE: 20392.7 meV/A - [2025-10-07 17:11:47][INFO] - Evaluation time: 1.10 s [1.2185 ± 1.2768 ms per atom] - -Further analysis can be performed now that the model is trained. We provide a `Python -script`_ that can be used to generate a parity plot of the target vs predicted energies, -but otherwise leave this open-ended. - -.. _`Python script`: https://raw.githubusercontent.com/metatensor/Workshop-spring-2025/refs/heads/main/training-custom-models/part-1-gap/parity_plot.py - -To run the script, download it from the repository, modify the paths as necessary -(indicated with a #TODO), and run. This will generate a plot saved at parity_plot.png. - - -Use the model -------------------------- -With the trained model, you can run molecular dynamics. Please refer to these two -tutorials for `ASE`_ and `LAMMPS`_ to see how to do that. - -.. _`ASE`: https://docs.metatensor.org/metatomic/latest/examples/2-running-ase-md.html - -.. _`LAMMPS`: https://atomistic-cookbook.org/examples/pet-mad-nc/pet-mad-nc.html#running-lammps-on-gpus-with-kokkos - - -.. _sphx_glr_download_examples_train_from_scratch_train_from_scratch.py: - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-example - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: train_from_scratch.ipynb ` - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: train_from_scratch.py ` - - .. container:: sphx-glr-download sphx-glr-download-zip - - :download:`Download zipped: train_from_scratch.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git 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- -.. only:: html - - .. image:: /examples/validation/images/thumb/sphx_glr_parity_plot_thumb.png - :alt: - - :ref:`sphx_glr_examples_validation_parity_plot.py` - -.. raw:: html - -
Model validation with parity plots for energies and forces
-
- - -.. thumbnail-parent-div-close - -.. raw:: html - - - - -.. toctree:: - :hidden: - - /examples/validation/parity_plot - - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-gallery - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download all examples in Python source code: validation_python.zip ` - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download all examples in Jupyter notebooks: validation_jupyter.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/validation/output.xyz b/docs/src/examples/validation/output.xyz deleted file mode 100644 index a07f3fddcc..0000000000 --- a/docs/src/examples/validation/output.xyz +++ /dev/null @@ -1,1100 +0,0 @@ -9 -Properties=species:S:1:pos:R:3:forces:R:3 energy=-97079.8125 pbc="F F F" -H 0.16712676 -1.19985032 0.84958249 -7.49372673 12.43364716 -17.29284668 -H -0.93496192 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+0,0 @@ -{ - "ase.io.read": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.io", - "module_short": "ase.io", - "name": "read" - } - ], - "axs": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray" - } - ], - "chemiscope.ase_vectors_to_arrows": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "chemiscope", - "module_short": "chemiscope", - "name": "ase_vectors_to_arrows" - } - ], - "chemiscope.quick_settings": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "chemiscope", - "module_short": "chemiscope", - "name": "quick_settings" - } - ], - "chemiscope.show": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "chemiscope", - "module_short": "chemiscope", - "name": "show" - } - ], - "cs": [ - { - "is_class": false, - "is_explicit": false, - "module": "chemiscope.jupyter", - "module_short": "chemiscope.jupyter", - "name": "ChemiscopeWidget" - }, - { - "is_class": false, - "is_explicit": false, - "module": "chemiscope", - "module_short": "chemiscope", - "name": "ChemiscopeWidget" - } - ], - "e_predictions": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray" - } - ], - "e_targets": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray" - } - ], - "f_predictions": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray" - } - ], - "f_targets": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray" - } - ], - "fig": [ - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.figure", - "module_short": "matplotlib.figure", - "name": "Figure" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib", - "module_short": "matplotlib", - "name": "Figure" - } - ], - "frame": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms" - } - ], - "frame.arrays": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "dict" - } - ], - "frame.get_forces": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.get_forces" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.get_forces" - } - ], - "frame.get_total_energy": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.get_total_energy" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.get_total_energy" - } - ], - "max_e": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "float64" - } - ], - "max_f": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "float64" - } - ], - "min_e": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "float64" - } - ], - "min_f": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "float64" - } - ], - "np.array": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "builtin_function_or_method" - }, - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "array" - } - ], - "np.max": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "_ArrayFunctionDispatcher" - }, - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "max" - } - ], - "np.mean": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "_ArrayFunctionDispatcher" - }, - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "mean" - } - ], - "np.min": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "_ArrayFunctionDispatcher" - }, - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "min" - } - ], - "np.sqrt": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ufunc" - }, - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "sqrt" - } - ], - "plt.show": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot", - "module_short": "matplotlib.pyplot", - "name": "show" - } - ], - "plt.subplots": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot", - "module_short": "matplotlib.pyplot", - "name": "subplots" - } - ], - "plt.tight_layout": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot", - "module_short": "matplotlib.pyplot", - "name": "tight_layout" - } - ], - "predictions": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "list" - } - ], - "targets": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "list" - } - ] -} diff --git a/docs/src/examples/validation/parity_plot.ipynb b/docs/src/examples/validation/parity_plot.ipynb deleted file mode 100644 index 7b3b971834..0000000000 --- a/docs/src/examples/validation/parity_plot.ipynb +++ /dev/null @@ -1,154 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# Model validation with parity plots for energies and forces\n\nThis tutorial shows how to visualise your model output using parity plots. In the\n`train-from-scratch` we learned how to evaluate a trained model on a test set and\nsave the results to an output file. Here we will show how to create parity plots from\nthese results.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Import necessary libraries\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import ase.io\nimport chemiscope\nimport matplotlib.pyplot as plt\nimport numpy as np" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load the target and predicted data\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "targets = ase.io.read(\n \"../train_from_scratch/ethanol_reduced_100.xyz\", \":\"\n) # reference data (ground truth)\npredictions = ase.io.read(\"output.xyz\", \":\") # predicted data from the model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Extract the energies from the loaded frames\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "e_targets = np.array(\n [frame.get_total_energy() / len(frame) for frame in targets]\n) # target energies\ne_predictions = np.array(\n [frame.get_total_energy() / len(frame) for frame in predictions]\n) # predicted energies\nf_targets = np.array(\n [frame.get_forces().flatten() for frame in targets]\n).flatten() # target forces\nf_predictions = np.array(\n [frame.get_forces().flatten() for frame in predictions]\n).flatten() # predicted forces" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create parity plots to compare predicted vs. target energies and forces\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(1, 2, figsize=(12, 5))\n\n# Parity plot for energies\naxs[0].scatter(e_targets, e_predictions)\naxs[0].axline((np.min(e_targets), np.min(e_targets)), slope=1, ls=\"--\", color=\"red\")\naxs[0].set_xlabel(\"Target energy / kcal\")\naxs[0].set_ylabel(\"Predicted energy / kcal\")\nmin_e = np.min(np.array([e_targets, e_predictions])) - 2\nmax_e = np.max(np.array([e_targets, e_predictions])) + 2\naxs[0].set_xlim([min_e, max_e])\naxs[0].set_ylim([min_e, max_e])\naxs[0].set_title(\"Energy Parity Plot\")\n\n# Parity plot for forces\naxs[1].scatter(f_targets, f_predictions, alpha=0.5)\naxs[1].axline((np.min(f_targets), np.min(f_targets)), slope=1, ls=\"--\", color=\"red\")\naxs[1].set_xlabel(\"Target force / kcal/\u00c5\")\naxs[1].set_ylabel(\"Predicted force / kcal/\u00c5\")\nmin_f = np.min(np.array([f_targets, f_predictions])) - 2\nmax_f = np.max(np.array([f_targets, f_predictions])) + 2\naxs[1].set_xlim([min_f, max_f])\naxs[1].set_ylim([min_f, max_f])\naxs[1].set_title(\"Force Parity Plot\")\n\nplt.tight_layout()\nplt.show()\n\nprint(\n \"RMSE energy (per atom):\",\n np.sqrt(np.mean((e_targets - e_predictions) ** 2)),\n \"kcal\",\n)\nprint(\"RMSE forces:\", np.sqrt(np.mean((f_targets - f_predictions) ** 2)), \"kcal/\u00c5 \")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The results are a bit poor here because the model was not trained well enough and\nwas created only for demonstration purposes. In the case of a well-trained model, the\npoints should be closer to the diagonal line.\n\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Check outliers with ``Chemiscope``\nWith the approach above, you can inspect the whole dataset, but it might be difficult\nto identify outliers. `Chemiscope ` is a\nvisualisation tool, allowing you to explore the dataset interactively. The following\nexample shows how to use it to check the structure of probable outliers and the atomic\nforces.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "for frame in targets + predictions:\n frame.arrays[\"forces\"] = frame.get_forces()\n# a workaround, because the chemiscope interface for getting forces is broken with ASE\n# 3.23" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot the energy parity plot with Chemiscope. This can be rendered as a widget in a\nJupyter notebook.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "cs = chemiscope.show(\n targets, # reading structures from the dataset\n properties={\n \"Target energy\": {\"values\": e_targets, \"target\": \"structure\", \"units\": \"kcal\"},\n \"Predicted energy\": {\n \"values\": e_predictions,\n \"target\": \"structure\",\n \"units\": \"kcal\",\n },\n }, # plotting the energy parity plot\n mode=\"default\",\n shapes={\n \"target_forces\": chemiscope.ase_vectors_to_arrows(\n targets, key=\"forces\", scale=0.05, radius=0.15\n ),\n \"predicted_forces\": chemiscope.ase_vectors_to_arrows(\n predictions, key=\"forces\", scale=0.05, radius=0.15\n ),\n }, # plotting the atomic forces\n settings=chemiscope.quick_settings(\n trajectory=True,\n map_settings={\"joinPoints\": False},\n structure_settings={\n \"unitCell\": True,\n \"environments\": {\"activated\": False},\n \"shape\": \"predicted_forces\", # show predicted forces by defalut\n },\n ),\n)\ncs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can check the structures by clicking the red dots on the parity plot, or\ndragging the bar in the bottom right corner. Currently, plotting the diagonal line is\nnot supported in chemiscope, so please check the parity plot above to see the\noutliers.\n\nThe atomic forces are shown in arrows. The predicted forces are shown here. The target\nforces can be toggled by clicking the \"target_forces\" option in the menu in the upper\nright corner of the right panel.\n\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/src/examples/validation/parity_plot.py b/docs/src/examples/validation/parity_plot.py deleted file mode 100644 index e806c51c35..0000000000 --- a/docs/src/examples/validation/parity_plot.py +++ /dev/null @@ -1,142 +0,0 @@ -""" -Model validation with parity plots for energies and forces -========================================================== - -This tutorial shows how to visualise your model output using parity plots. In the -:ref:`train-from-scratch` we learned how to evaluate a trained model on a test set and -save the results to an output file. Here we will show how to create parity plots from -these results. - -""" - -# %% -# Import necessary libraries -import ase.io -import chemiscope -import matplotlib.pyplot as plt -import numpy as np - - -# %% -# Load the target and predicted data -targets = ase.io.read( - "../train_from_scratch/ethanol_reduced_100.xyz", ":" -) # reference data (ground truth) -predictions = ase.io.read("output.xyz", ":") # predicted data from the model - - -# %% -# Extract the energies from the loaded frames -e_targets = np.array( - [frame.get_total_energy() / len(frame) for frame in targets] -) # target energies -e_predictions = np.array( - [frame.get_total_energy() / len(frame) for frame in predictions] -) # predicted energies -f_targets = np.array( - [frame.get_forces().flatten() for frame in targets] -).flatten() # target forces -f_predictions = np.array( - [frame.get_forces().flatten() for frame in predictions] -).flatten() # predicted forces - - -# %% -# Create parity plots to compare predicted vs. target energies and forces -# ----------------------------------------------------------------------- - -fig, axs = plt.subplots(1, 2, figsize=(12, 5)) - -# Parity plot for energies -axs[0].scatter(e_targets, e_predictions) -axs[0].axline((np.min(e_targets), np.min(e_targets)), slope=1, ls="--", color="red") -axs[0].set_xlabel("Target energy / kcal") -axs[0].set_ylabel("Predicted energy / kcal") -min_e = np.min(np.array([e_targets, e_predictions])) - 2 -max_e = np.max(np.array([e_targets, e_predictions])) + 2 -axs[0].set_xlim([min_e, max_e]) -axs[0].set_ylim([min_e, max_e]) -axs[0].set_title("Energy Parity Plot") - -# Parity plot for forces -axs[1].scatter(f_targets, f_predictions, alpha=0.5) -axs[1].axline((np.min(f_targets), np.min(f_targets)), slope=1, ls="--", color="red") -axs[1].set_xlabel("Target force / kcal/Ã…") -axs[1].set_ylabel("Predicted force / kcal/Ã…") -min_f = np.min(np.array([f_targets, f_predictions])) - 2 -max_f = np.max(np.array([f_targets, f_predictions])) + 2 -axs[1].set_xlim([min_f, max_f]) -axs[1].set_ylim([min_f, max_f]) -axs[1].set_title("Force Parity Plot") - -plt.tight_layout() -plt.show() - -print( - "RMSE energy (per atom):", - np.sqrt(np.mean((e_targets - e_predictions) ** 2)), - "kcal", -) -print("RMSE forces:", np.sqrt(np.mean((f_targets - f_predictions) ** 2)), "kcal/Ã… ") -# %% -# The results are a bit poor here because the model was not trained well enough and -# was created only for demonstration purposes. In the case of a well-trained model, the -# points should be closer to the diagonal line. - -# %% -# Check outliers with ``Chemiscope`` -# ---------------------------------- -# With the approach above, you can inspect the whole dataset, but it might be difficult -# to identify outliers. `Chemiscope ` is a -# visualisation tool, allowing you to explore the dataset interactively. The following -# example shows how to use it to check the structure of probable outliers and the atomic -# forces. - -for frame in targets + predictions: - frame.arrays["forces"] = frame.get_forces() -# a workaround, because the chemiscope interface for getting forces is broken with ASE -# 3.23 - -# %% -# Plot the energy parity plot with Chemiscope. This can be rendered as a widget in a -# Jupyter notebook. -cs = chemiscope.show( - targets, # reading structures from the dataset - properties={ - "Target energy": {"values": e_targets, "target": "structure", "units": "kcal"}, - "Predicted energy": { - "values": e_predictions, - "target": "structure", - "units": "kcal", - }, - }, # plotting the energy parity plot - mode="default", - shapes={ - "target_forces": chemiscope.ase_vectors_to_arrows( - targets, key="forces", scale=0.05, radius=0.15 - ), - "predicted_forces": chemiscope.ase_vectors_to_arrows( - predictions, key="forces", scale=0.05, radius=0.15 - ), - }, # plotting the atomic forces - settings=chemiscope.quick_settings( - trajectory=True, - map_settings={"joinPoints": False}, - structure_settings={ - "unitCell": True, - "environments": {"activated": False}, - "shape": "predicted_forces", # show predicted forces by defalut - }, - ), -) -cs - -# %% -# You can check the structures by clicking the red dots on the parity plot, or -# dragging the bar in the bottom right corner. Currently, plotting the diagonal line is -# not supported in chemiscope, so please check the parity plot above to see the -# outliers. -# -# The atomic forces are shown in arrows. The predicted forces are shown here. The target -# forces can be toggled by clicking the "target_forces" option in the menu in the upper -# right corner of the right panel. diff --git a/docs/src/examples/validation/parity_plot.py.md5 b/docs/src/examples/validation/parity_plot.py.md5 deleted file mode 100644 index c1a3ea37a7..0000000000 --- a/docs/src/examples/validation/parity_plot.py.md5 +++ /dev/null @@ -1 +0,0 @@ -4381fe7e0c0bc39cf97ff93007ea6114 \ No newline at end of file diff --git a/docs/src/examples/validation/parity_plot.rst b/docs/src/examples/validation/parity_plot.rst deleted file mode 100644 index de747cd147..0000000000 --- a/docs/src/examples/validation/parity_plot.rst +++ /dev/null @@ -1,281 +0,0 @@ - -.. DO NOT EDIT. -.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. -.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: -.. "examples/validation/parity_plot.py" -.. LINE NUMBERS ARE GIVEN BELOW. - -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - :ref:`Go to the end ` - to download the full example code. - -.. rst-class:: sphx-glr-example-title - -.. _sphx_glr_examples_validation_parity_plot.py: - - -Model validation with parity plots for energies and forces -========================================================== - -This tutorial shows how to visualise your model output using parity plots. In the -:ref:`train-from-scratch` we learned how to evaluate a trained model on a test set and -save the results to an output file. Here we will show how to create parity plots from -these results. - -.. GENERATED FROM PYTHON SOURCE LINES 13-14 - -Import necessary libraries - -.. GENERATED FROM PYTHON SOURCE LINES 14-20 - -.. code-block:: Python - - import ase.io - import chemiscope - import matplotlib.pyplot as plt - import numpy as np - - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 21-22 - -Load the target and predicted data - -.. GENERATED FROM PYTHON SOURCE LINES 22-28 - -.. code-block:: Python - - targets = ase.io.read( - "../train_from_scratch/ethanol_reduced_100.xyz", ":" - ) # reference data (ground truth) - predictions = ase.io.read("output.xyz", ":") # predicted data from the model - - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 29-30 - -Extract the energies from the loaded frames - -.. GENERATED FROM PYTHON SOURCE LINES 30-44 - -.. code-block:: Python - - e_targets = np.array( - [frame.get_total_energy() / len(frame) for frame in targets] - ) # target energies - e_predictions = np.array( - [frame.get_total_energy() / len(frame) for frame in predictions] - ) # predicted energies - f_targets = np.array( - [frame.get_forces().flatten() for frame in targets] - ).flatten() # target forces - f_predictions = np.array( - [frame.get_forces().flatten() for frame in predictions] - ).flatten() # predicted forces - - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 45-47 - -Create parity plots to compare predicted vs. target energies and forces ------------------------------------------------------------------------ - -.. GENERATED FROM PYTHON SOURCE LINES 47-81 - -.. code-block:: Python - - - fig, axs = plt.subplots(1, 2, figsize=(12, 5)) - - # Parity plot for energies - axs[0].scatter(e_targets, e_predictions) - axs[0].axline((np.min(e_targets), np.min(e_targets)), slope=1, ls="--", color="red") - axs[0].set_xlabel("Target energy / kcal") - axs[0].set_ylabel("Predicted energy / kcal") - min_e = np.min(np.array([e_targets, e_predictions])) - 2 - max_e = np.max(np.array([e_targets, e_predictions])) + 2 - axs[0].set_xlim([min_e, max_e]) - axs[0].set_ylim([min_e, max_e]) - axs[0].set_title("Energy Parity Plot") - - # Parity plot for forces - axs[1].scatter(f_targets, f_predictions, alpha=0.5) - axs[1].axline((np.min(f_targets), np.min(f_targets)), slope=1, ls="--", color="red") - axs[1].set_xlabel("Target force / kcal/Ã…") - axs[1].set_ylabel("Predicted force / kcal/Ã…") - min_f = np.min(np.array([f_targets, f_predictions])) - 2 - max_f = np.max(np.array([f_targets, f_predictions])) + 2 - axs[1].set_xlim([min_f, max_f]) - axs[1].set_ylim([min_f, max_f]) - axs[1].set_title("Force Parity Plot") - - plt.tight_layout() - plt.show() - - print( - "RMSE energy (per atom):", - np.sqrt(np.mean((e_targets - e_predictions) ** 2)), - "kcal", - ) - print("RMSE forces:", np.sqrt(np.mean((f_targets - f_predictions) ** 2)), "kcal/Ã… ") - - - - -.. rst-class:: sphx-glr-script-out - - .. code-block:: none - - RMSE energy (per atom): 0.3662913918764794 kcal - RMSE forces: 15.042111936421486 kcal/Ã… - - - - -.. GENERATED FROM PYTHON SOURCE LINES 82-85 - -The results are a bit poor here because the model was not trained well enough and -was created only for demonstration purposes. In the case of a well-trained model, the -points should be closer to the diagonal line. - -.. GENERATED FROM PYTHON SOURCE LINES 87-94 - -Check outliers with ``Chemiscope`` ----------------------------------- -With the approach above, you can inspect the whole dataset, but it might be difficult -to identify outliers. `Chemiscope ` is a -visualisation tool, allowing you to explore the dataset interactively. The following -example shows how to use it to check the structure of probable outliers and the atomic -forces. - -.. GENERATED FROM PYTHON SOURCE LINES 94-100 - -.. code-block:: Python - - - for frame in targets + predictions: - frame.arrays["forces"] = frame.get_forces() - # a workaround, because the chemiscope interface for getting forces is broken with ASE - # 3.23 - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 101-103 - -Plot the energy parity plot with Chemiscope. This can be rendered as a widget in a -Jupyter notebook. - -.. GENERATED FROM PYTHON SOURCE LINES 103-134 - -.. code-block:: Python - - cs = chemiscope.show( - targets, # reading structures from the dataset - properties={ - "Target energy": {"values": e_targets, "target": "structure", "units": "kcal"}, - "Predicted energy": { - "values": e_predictions, - "target": "structure", - "units": "kcal", - }, - }, # plotting the energy parity plot - mode="default", - shapes={ - "target_forces": chemiscope.ase_vectors_to_arrows( - targets, key="forces", scale=0.05, radius=0.15 - ), - "predicted_forces": chemiscope.ase_vectors_to_arrows( - predictions, key="forces", scale=0.05, radius=0.15 - ), - }, # plotting the atomic forces - settings=chemiscope.quick_settings( - trajectory=True, - map_settings={"joinPoints": False}, - structure_settings={ - "unitCell": True, - "environments": {"activated": False}, - "shape": "predicted_forces", # show predicted forces by defalut - }, - ), - ) - cs - - - -.. chemiscope:: _datasets/fig_parity_plot_001.json.gz - :mode: default - :warning_timeout: 2000 - - - -.. raw:: html - -
- -
-
-
- -.. GENERATED FROM PYTHON SOURCE LINES 135-143 - -You can check the structures by clicking the red dots on the parity plot, or -dragging the bar in the bottom right corner. Currently, plotting the diagonal line is -not supported in chemiscope, so please check the parity plot above to see the -outliers. - -The atomic forces are shown in arrows. The predicted forces are shown here. The target -forces can be toggled by clicking the "target_forces" option in the menu in the upper -right corner of the right panel. - - -.. _sphx_glr_download_examples_validation_parity_plot.py: - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-example - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: parity_plot.ipynb ` - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: parity_plot.py ` - - .. container:: sphx-glr-download sphx-glr-download-zip - - :download:`Download zipped: parity_plot.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/validation/parity_plot.zip b/docs/src/examples/validation/parity_plot.zip deleted file mode 100644 index f3d9dbd83d2f478cceb628d85b1850ddb7b94f50..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 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"metatomic.torch.ase_calculator", - "name": "MetatomicCalculator" - }, - { - "is_class": false, - "is_explicit": false, - "module": "metatomic.torch", - "module_short": "metatomic.torch", - "name": "MetatomicCalculator" - }, - { - "is_class": false, - "is_explicit": false, - "module": "metatomic", - "module_short": "metatomic", - "name": "MetatomicCalculator" - } - ], - "calc_zbl": [ - { - "is_class": false, - "is_explicit": false, - "module": "metatomic.torch.ase_calculator", - "module_short": "metatomic.torch.ase_calculator", - "name": "MetatomicCalculator" - }, - { - "is_class": false, - "is_explicit": false, - "module": "metatomic.torch", - "module_short": "metatomic.torch", - "name": "MetatomicCalculator" - }, - { - "is_class": false, - "is_explicit": false, - "module": "metatomic", - "module_short": "metatomic", - "name": "MetatomicCalculator" - } - ], - "distance": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": 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"module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot", - "module_short": "matplotlib.pyplot", - "name": "plot" - } - ], - "plt.show": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot", - "module_short": "matplotlib.pyplot", - "name": "show" - } - ], - "plt.tight_layout": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot", - "module_short": "matplotlib.pyplot", - "name": "tight_layout" - } - ], - "plt.title": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot", - "module_short": "matplotlib.pyplot", - "name": "title" - } - ], - "plt.xlabel": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot", - "module_short": "matplotlib.pyplot", - "name": "xlabel" - } - ], - "plt.ylabel": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot", - "module_short": "matplotlib.pyplot", - "name": "ylabel" - } - ], - "structures": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "list" - } - ], - "structures.append": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "builtin_function_or_method" - } - ], - "structures_for_pair": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "list" - } - ], - "torch.jit.optimized_execution": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "torch.jit", - "module_short": "torch.jit", - "name": "optimized_execution" - } - ] -} diff --git a/docs/src/examples/zbl/dimers.ipynb b/docs/src/examples/zbl/dimers.ipynb deleted file mode 100644 index b241500ab9..0000000000 --- a/docs/src/examples/zbl/dimers.ipynb +++ /dev/null @@ -1,140 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# Training a model with ZBL corrections\n\nThis tutorial demonstrates how to train a model with ZBL corrections.\n\nThe training set for this example consists of a\nsubset of the ethanol moleculs from the [rMD17 dataset](https://iopscience.iop.org/article/10.1088/2632-2153/abba6f/meta).\n\nThe models are trained using the following training options, respectively:\n\n.. literalinclude:: options_no_zbl.yaml\n :language: yaml\n\n.. literalinclude:: options_zbl.yaml\n :language: yaml\n\nAs you can see, they are identical, except for the ``zbl`` key in the\n``model`` section.\nYou can train the same models yourself with\n\n.. literalinclude:: train.sh\n :language: bash\n\nA detailed step-by-step introduction on how to train a model is provided in\nthe `label_basic_usage` tutorial.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, we start by importing the necessary libraries, including the integration of ASE\ncalculators for metatensor atomistic models.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import ase\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\nfrom metatomic.torch.ase_calculator import MetatomicCalculator" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setting up the dimers\n\nWe set up a series of dimers with different atom pairs and distances. We will\ncalculate the energies of these dimers using the models trained with and without ZBL\ncorrections.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "distances = np.linspace(0.5, 6.0, 200)\npairs = {}\nfor pair in [(\"H\", \"H\"), (\"H\", \"C\"), (\"C\", \"C\"), (\"C\", \"O\"), (\"O\", \"O\"), (\"H\", \"O\")]:\n structures = []\n for distance in distances:\n atoms = ase.Atoms(\n symbols=[pair[0], pair[1]],\n positions=[[0, 0, 0], [0, 0, distance]],\n )\n structures.append(atoms)\n pairs[pair] = structures" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now load the two exported models, one with and one without ZBL corrections\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "calc_no_zbl = MetatomicCalculator(\"model_no_zbl.pt\", extensions_directory=\"extensions/\")\ncalc_zbl = MetatomicCalculator(\"model_zbl.pt\", extensions_directory=\"extensions/\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Calculate and plot energies without ZBL\n\nWe calculate the energies of the dimer curves for each pair of atoms and\nplot the results, using the non-ZBL-corrected model.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "for pair, structures_for_pair in pairs.items():\n energies = []\n for atoms in structures_for_pair:\n atoms.set_calculator(calc_no_zbl)\n with torch.jit.optimized_execution(False):\n energies.append(atoms.get_potential_energy())\n energies = np.array(energies) - energies[-1]\n plt.plot(distances, energies, label=f\"{pair[0]}-{pair[1]}\")\nplt.title(\"Dimer curves - no ZBL\")\nplt.xlabel(\"Distance (\u00c5)\")\nplt.ylabel(\"Energy (eV)\")\nplt.legend()\nplt.tight_layout()\nplt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Calculate and plot energies from the ZBL-corrected model\n\nWe repeat the same procedure as above, but this time with the ZBL-corrected model.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "for pair, structures_for_pair in pairs.items():\n energies = []\n for atoms in structures_for_pair:\n atoms.set_calculator(calc_zbl)\n with torch.jit.optimized_execution(False):\n energies.append(atoms.get_potential_energy())\n energies = np.array(energies) - energies[-1]\n plt.plot(distances, energies, label=f\"{pair[0]}-{pair[1]}\")\nplt.title(\"Dimer curves - with ZBL\")\nplt.xlabel(\"Distance (\u00c5)\")\nplt.ylabel(\"Energy (eV)\")\nplt.legend()\nplt.tight_layout()\nplt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It can be seen that all the dimer curves include a strong repulsion\nat short distances, which is due to the ZBL contribution. Even the H-H dimer,\nwhose ZBL correction is very weak due to the small covalent radii of hydrogen,\nwould show a strong repulsion closer to the origin (here, we only plotted\nstarting from a distance of 0.5 \u00c5). Let's zoom in on the H-H dimer to see\nthis effect more clearly.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "new_distances = np.linspace(0.1, 2.0, 200)\n\nstructures = []\nfor distance in new_distances:\n atoms = ase.Atoms(\n symbols=[\"H\", \"H\"],\n positions=[[0, 0, 0], [0, 0, distance]],\n )\n structures.append(atoms)\n\nfor atoms in structures:\n atoms.set_calculator(calc_zbl)\nwith torch.jit.optimized_execution(False):\n energies = [atoms.get_potential_energy() for atoms in structures]\nenergies = np.array(energies) - energies[-1]\nplt.plot(new_distances, energies, label=\"H-H\")\nplt.title(\"Dimer curve - H-H with ZBL\")\nplt.xlabel(\"Distance (\u00c5)\")\nplt.ylabel(\"Energy (eV)\")\nplt.legend()\nplt.tight_layout()\nplt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/src/examples/zbl/dimers.py b/docs/src/examples/zbl/dimers.py deleted file mode 100644 index 59c11a88bc..0000000000 --- a/docs/src/examples/zbl/dimers.py +++ /dev/null @@ -1,146 +0,0 @@ -""" -Training a model with ZBL corrections -===================================== - -This tutorial demonstrates how to train a model with ZBL corrections. - -The training set for this example consists of a -subset of the ethanol moleculs from the `rMD17 dataset -`_. - -The models are trained using the following training options, respectively: - -.. literalinclude:: options_no_zbl.yaml - :language: yaml - -.. literalinclude:: options_zbl.yaml - :language: yaml - -As you can see, they are identical, except for the ``zbl`` key in the -``model`` section. -You can train the same models yourself with - -.. literalinclude:: train.sh - :language: bash - -A detailed step-by-step introduction on how to train a model is provided in -the :ref:`label_basic_usage` tutorial. -""" - -# %% -# -# First, we start by importing the necessary libraries, including the integration of ASE -# calculators for metatensor atomistic models. - -import ase -import matplotlib.pyplot as plt -import numpy as np -import torch -from metatomic.torch.ase_calculator import MetatomicCalculator - - -# %% -# -# Setting up the dimers -# --------------------- -# -# We set up a series of dimers with different atom pairs and distances. We will -# calculate the energies of these dimers using the models trained with and without ZBL -# corrections. - -distances = np.linspace(0.5, 6.0, 200) -pairs = {} -for pair in [("H", "H"), ("H", "C"), ("C", "C"), ("C", "O"), ("O", "O"), ("H", "O")]: - structures = [] - for distance in distances: - atoms = ase.Atoms( - symbols=[pair[0], pair[1]], - positions=[[0, 0, 0], [0, 0, distance]], - ) - structures.append(atoms) - pairs[pair] = structures - -# %% -# -# We now load the two exported models, one with and one without ZBL corrections - -calc_no_zbl = MetatomicCalculator("model_no_zbl.pt", extensions_directory="extensions/") -calc_zbl = MetatomicCalculator("model_zbl.pt", extensions_directory="extensions/") - - -# %% -# -# Calculate and plot energies without ZBL -# --------------------------------------- -# -# We calculate the energies of the dimer curves for each pair of atoms and -# plot the results, using the non-ZBL-corrected model. - -for pair, structures_for_pair in pairs.items(): - energies = [] - for atoms in structures_for_pair: - atoms.set_calculator(calc_no_zbl) - with torch.jit.optimized_execution(False): - energies.append(atoms.get_potential_energy()) - energies = np.array(energies) - energies[-1] - plt.plot(distances, energies, label=f"{pair[0]}-{pair[1]}") -plt.title("Dimer curves - no ZBL") -plt.xlabel("Distance (Ã…)") -plt.ylabel("Energy (eV)") -plt.legend() -plt.tight_layout() -plt.show() - -# %% -# -# Calculate and plot energies from the ZBL-corrected model -# -------------------------------------------------------- -# -# We repeat the same procedure as above, but this time with the ZBL-corrected model. - -for pair, structures_for_pair in pairs.items(): - energies = [] - for atoms in structures_for_pair: - atoms.set_calculator(calc_zbl) - with torch.jit.optimized_execution(False): - energies.append(atoms.get_potential_energy()) - energies = np.array(energies) - energies[-1] - plt.plot(distances, energies, label=f"{pair[0]}-{pair[1]}") -plt.title("Dimer curves - with ZBL") -plt.xlabel("Distance (Ã…)") -plt.ylabel("Energy (eV)") -plt.legend() -plt.tight_layout() -plt.show() - -# %% -# -# It can be seen that all the dimer curves include a strong repulsion -# at short distances, which is due to the ZBL contribution. Even the H-H dimer, -# whose ZBL correction is very weak due to the small covalent radii of hydrogen, -# would show a strong repulsion closer to the origin (here, we only plotted -# starting from a distance of 0.5 Ã…). Let's zoom in on the H-H dimer to see -# this effect more clearly. - -new_distances = np.linspace(0.1, 2.0, 200) - -structures = [] -for distance in new_distances: - atoms = ase.Atoms( - symbols=["H", "H"], - positions=[[0, 0, 0], [0, 0, distance]], - ) - structures.append(atoms) - -for atoms in structures: - atoms.set_calculator(calc_zbl) -with torch.jit.optimized_execution(False): - energies = [atoms.get_potential_energy() for atoms in structures] -energies = np.array(energies) - energies[-1] -plt.plot(new_distances, energies, label="H-H") -plt.title("Dimer curve - H-H with ZBL") -plt.xlabel("Distance (Ã…)") -plt.ylabel("Energy (eV)") -plt.legend() -plt.tight_layout() -plt.show() diff --git a/docs/src/examples/zbl/dimers.py.md5 b/docs/src/examples/zbl/dimers.py.md5 deleted file mode 100644 index 2d0c746956..0000000000 --- a/docs/src/examples/zbl/dimers.py.md5 +++ /dev/null @@ -1 +0,0 @@ -fc55a8030c89ecfae84a5d1c4dce758f \ No newline at end of file diff --git a/docs/src/examples/zbl/dimers.rst b/docs/src/examples/zbl/dimers.rst deleted file mode 100644 index c529edc435..0000000000 --- a/docs/src/examples/zbl/dimers.rst +++ /dev/null @@ -1,287 +0,0 @@ - -.. DO NOT EDIT. -.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. -.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: -.. "examples/zbl/dimers.py" -.. LINE NUMBERS ARE GIVEN BELOW. - -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - :ref:`Go to the end ` - to download the full example code. - -.. rst-class:: sphx-glr-example-title - -.. _sphx_glr_examples_zbl_dimers.py: - - -Training a model with ZBL corrections -===================================== - -This tutorial demonstrates how to train a model with ZBL corrections. - -The training set for this example consists of a -subset of the ethanol moleculs from the `rMD17 dataset -`_. - -The models are trained using the following training options, respectively: - -.. literalinclude:: options_no_zbl.yaml - :language: yaml - -.. literalinclude:: options_zbl.yaml - :language: yaml - -As you can see, they are identical, except for the ``zbl`` key in the -``model`` section. -You can train the same models yourself with - -.. literalinclude:: train.sh - :language: bash - -A detailed step-by-step introduction on how to train a model is provided in -the :ref:`label_basic_usage` tutorial. - -.. GENERATED FROM PYTHON SOURCE LINES 31-33 - -First, we start by importing the necessary libraries, including the integration of ASE -calculators for metatensor atomistic models. - -.. GENERATED FROM PYTHON SOURCE LINES 34-42 - -.. code-block:: Python - - - import ase - import matplotlib.pyplot as plt - import numpy as np - import torch - from metatomic.torch.ase_calculator import MetatomicCalculator - - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 43-49 - -Setting up the dimers ---------------------- - -We set up a series of dimers with different atom pairs and distances. We will -calculate the energies of these dimers using the models trained with and without ZBL -corrections. - -.. GENERATED FROM PYTHON SOURCE LINES 50-63 - -.. code-block:: Python - - - distances = np.linspace(0.5, 6.0, 200) - pairs = {} - for pair in [("H", "H"), ("H", "C"), ("C", "C"), ("C", "O"), ("O", "O"), ("H", "O")]: - structures = [] - for distance in distances: - atoms = ase.Atoms( - symbols=[pair[0], pair[1]], - positions=[[0, 0, 0], [0, 0, distance]], - ) - structures.append(atoms) - pairs[pair] = structures - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 64-65 - -We now load the two exported models, one with and one without ZBL corrections - -.. GENERATED FROM PYTHON SOURCE LINES 66-71 - -.. code-block:: Python - - - calc_no_zbl = MetatomicCalculator("model_no_zbl.pt", extensions_directory="extensions/") - calc_zbl = MetatomicCalculator("model_zbl.pt", extensions_directory="extensions/") - - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 72-77 - -Calculate and plot energies without ZBL ---------------------------------------- - -We calculate the energies of the dimer curves for each pair of atoms and -plot the results, using the non-ZBL-corrected model. - -.. GENERATED FROM PYTHON SOURCE LINES 78-94 - -.. code-block:: Python - - - for pair, structures_for_pair in pairs.items(): - energies = [] - for atoms in structures_for_pair: - atoms.set_calculator(calc_no_zbl) - with torch.jit.optimized_execution(False): - energies.append(atoms.get_potential_energy()) - energies = np.array(energies) - energies[-1] - plt.plot(distances, energies, label=f"{pair[0]}-{pair[1]}") - plt.title("Dimer curves - no ZBL") - plt.xlabel("Distance (Ã…)") - plt.ylabel("Energy (eV)") - plt.legend() - plt.tight_layout() - plt.show() - - - - - -.. rst-class:: sphx-glr-script-out - - .. code-block:: none - - /Users/philiploche/repos/lab-cosmo/metatrain/examples/zbl/dimers.py:82: FutureWarning: Please use atoms.calc = calc - atoms.set_calculator(calc_no_zbl) - - - - -.. GENERATED FROM PYTHON SOURCE LINES 95-99 - -Calculate and plot energies from the ZBL-corrected model --------------------------------------------------------- - -We repeat the same procedure as above, but this time with the ZBL-corrected model. - -.. GENERATED FROM PYTHON SOURCE LINES 100-116 - -.. code-block:: Python - - - for pair, structures_for_pair in pairs.items(): - energies = [] - for atoms in structures_for_pair: - atoms.set_calculator(calc_zbl) - with torch.jit.optimized_execution(False): - energies.append(atoms.get_potential_energy()) - energies = np.array(energies) - energies[-1] - plt.plot(distances, energies, label=f"{pair[0]}-{pair[1]}") - plt.title("Dimer curves - with ZBL") - plt.xlabel("Distance (Ã…)") - plt.ylabel("Energy (eV)") - plt.legend() - plt.tight_layout() - plt.show() - - - - - -.. rst-class:: sphx-glr-script-out - - .. code-block:: none - - /Users/philiploche/repos/lab-cosmo/metatrain/examples/zbl/dimers.py:104: FutureWarning: Please use atoms.calc = calc - atoms.set_calculator(calc_zbl) - - - - -.. GENERATED FROM PYTHON SOURCE LINES 117-123 - -It can be seen that all the dimer curves include a strong repulsion -at short distances, which is due to the ZBL contribution. Even the H-H dimer, -whose ZBL correction is very weak due to the small covalent radii of hydrogen, -would show a strong repulsion closer to the origin (here, we only plotted -starting from a distance of 0.5 Ã…). Let's zoom in on the H-H dimer to see -this effect more clearly. - -.. GENERATED FROM PYTHON SOURCE LINES 124-147 - -.. code-block:: Python - - - new_distances = np.linspace(0.1, 2.0, 200) - - structures = [] - for distance in new_distances: - atoms = ase.Atoms( - symbols=["H", "H"], - positions=[[0, 0, 0], [0, 0, distance]], - ) - structures.append(atoms) - - for atoms in structures: - atoms.set_calculator(calc_zbl) - with torch.jit.optimized_execution(False): - energies = [atoms.get_potential_energy() for atoms in structures] - energies = np.array(energies) - energies[-1] - plt.plot(new_distances, energies, label="H-H") - plt.title("Dimer curve - H-H with ZBL") - plt.xlabel("Distance (Ã…)") - plt.ylabel("Energy (eV)") - plt.legend() - plt.tight_layout() - plt.show() - - - - -.. rst-class:: sphx-glr-script-out - - .. code-block:: none - - /Users/philiploche/repos/lab-cosmo/metatrain/examples/zbl/dimers.py:136: FutureWarning: Please use atoms.calc = calc - atoms.set_calculator(calc_zbl) - - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** (0 minutes 15.025 seconds) - - -.. _sphx_glr_download_examples_zbl_dimers.py: - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-example - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: dimers.ipynb ` - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: dimers.py ` - - .. container:: sphx-glr-download sphx-glr-download-zip - - :download:`Download zipped: dimers.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/zbl/dimers.zip b/docs/src/examples/zbl/dimers.zip deleted file mode 100644 index 6fc0c82974246cb822013bf756b95c1fb876a0d3..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 11424 zcmeHNTW{OQ6;3y8fwJvGfqiKoiUEVg%78@Mag%O^xH{CL^J)Fy#GiT0izTwkHH^2M-4SH_= z{*Qwne;$1Jx1Zd&K|eo0-6WlhqKxuY7>2>Kf~P7~r;M|CHW897Q$1sU{_SIyWJMto 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- -.. only:: html - - .. image:: /examples/zbl/images/thumb/sphx_glr_dimers_thumb.png - :alt: - - :ref:`sphx_glr_examples_zbl_dimers.py` - -.. raw:: html - -
Training a model with ZBL corrections
-
- - -.. thumbnail-parent-div-close - -.. raw:: html - - - - -.. toctree:: - :hidden: - - /examples/zbl/dimers - - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-gallery - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download all examples as source code: zbl.zip ` - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download all examples in Jupyter notebooks: zbl_jupyter.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/zbl/options_no_zbl.yaml b/docs/src/examples/zbl/options_no_zbl.yaml deleted file mode 100644 index efc0bf57ce..0000000000 --- a/docs/src/examples/zbl/options_no_zbl.yaml +++ /dev/null @@ -1,22 +0,0 @@ -seed: 42 - -architecture: - name: soap_bpnn - model: - zbl: false - training: - num_epochs: 10 - batch_size: 10 - -# training set section -training_set: - systems: - read_from: ethanol_reduced_100.xyz - length_unit: angstrom - targets: - energy: - key: energy - unit: eV # very important to run simulations - -validation_set: 0.1 -test_set: 0.0 diff --git a/docs/src/examples/zbl/options_zbl.yaml b/docs/src/examples/zbl/options_zbl.yaml deleted file mode 100644 index f7969f9d90..0000000000 --- a/docs/src/examples/zbl/options_zbl.yaml +++ /dev/null @@ -1,22 +0,0 @@ -seed: 42 - -architecture: - name: soap_bpnn - model: - zbl: true - training: - num_epochs: 10 - batch_size: 10 - -# training set section -training_set: - systems: - read_from: ethanol_reduced_100.xyz - length_unit: angstrom - targets: - energy: - key: energy - unit: eV # very important to run simulations - -validation_set: 0.1 -test_set: 0.0 diff --git a/docs/src/examples/zbl/train.sh b/docs/src/examples/zbl/train.sh deleted file mode 100644 index 03b6baab24..0000000000 --- a/docs/src/examples/zbl/train.sh +++ /dev/null @@ -1,4 +0,0 @@ -#!/bin/bash - -mtt train options_no_zbl.yaml -o model_no_zbl.pt -mtt train options_zbl.yaml -o model_zbl.pt diff --git a/docs/src/examples/zbl/zbl.zip b/docs/src/examples/zbl/zbl.zip deleted file mode 100644 index 1e804b005e3441c902c5f04b4b933aeec40abd00..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 4789 zcmd^D-;WbF5WbeSYPaeG73#Aagh-@hb9V@eqF$vdKSTwHt3c2;Iwj7olel=-tF`xX zf%3pZpZibnpY$K=H}>vk!z~nEP^AWPalH1-c;@?N#yPrs?vo2U^!oDWU&c?)e{)G< zOz+R}wmFw+XqFeh9nNGroN==Zg2YFT3qZNiN^@>?tp7?FLLJ}kVLbA&U%jP;~mSl@l zo@7eEw$w;t4byYR15?as6~-1jg<{+$u3j1P*^FPC4-;Ye57S9$ zt&5dmJTDCwF)IvNPX^7kQhMdSRn9I$-eBUQv=G-`uK zz!)1To-PW$5F$Bg!zK)VEv0&#lP?TUga?|_nH!=W3mNDYRVOn`hDm0kn@y-o&9SHpzcOlb?HU^$KO zkY&nN&(k8w5Mjg8teS$ZXc;)FIBn1pg>Ev0;K|0K@`&s{*+i>u)W!x4-#ir7xuM7$ z2Yk-K(FScV=glW@BUp?L90T$dImB=DSt*wDxyS)Y=LME=_@D7K#v%j&^fe)al$BJf z;Y3i9vO+5IMJW^Cji@AVcv_O!VGgKpiIAkzKo^#hB8lCkMfkqf%nsnp5VSQJk3@Gj zyxM2i!reaG-`(v6-qZv3{6#?Vq_0Gqakq1)(`We8>$7rv!;f#!d~^K3&p)8~a_shE zd^~czMydtq3#1enGd^~6q^+`%G*)Bdw@^BuDAEpuIXt9s*X^W7^fK#Yrj{vm`j55cuu|LH)T_5|3=eshi8SsyoBRUjI;Z>?MsAii zu!fkYC@)I$*u~neH1ZV@5n=RE?IRHoGEmznq}7))tj(YX0b#wYyU^Hz^KQop-PC5p zM4S>qT^)}l)lHqR4?3IKVW;N>BX7Q4s;}YiD6-*&iig6T(6PP-{WgT#DA+Prv9jWR zE0H{nSXAUEz#YL&@M!4~PQ}@Q3oZw9oJqt*Ahb{xsc^%+RHp;*Yf!SQiW?YK@ulC0 z{RESfibM_|A*v>j+w-ul&%MP1ZwpAU0Z}dJH9CaIM2(!+Z9rlJTZbl}B7c`Qq?(?{ zXCj`6Q#2mLh3;*xj3|xrbYgWT+QNFcfWk}zLl&*m#2sMW?RjO5;~>ZIJm>3fHP>T< zTH<)H=UIg%Nc4u(M=j=Y@t(F>{5S-DdfF$Zf zpl4kP{cwbCf-=xP(ukaA2Kr>-Tmm}3+LYan-PAeaj#b|>7%*_H+QxDwBz^e e?7VvU7l!`sge&0D-A_NGM<3%~g3tZ0>DxaLdIuN) diff --git a/docs/src/examples/zbl/zbl_jupyter.zip b/docs/src/examples/zbl/zbl_jupyter.zip deleted file mode 100644 index a9dbfb39d4d3302871790d89a169d8616113ede9..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6854 zcmeHMTaO#J72Y&yAJRVNsSgDqKU51yBgtF4$pZV}CJtQKK^-LMO%k-!3?;GVkOPvl z+Ogq>{+Ira{G|Mbeuw0Yq+QwVB1K>qkuT!)@bK`Q?_Bi!>4zVEazdY9e*M?!cfaX> z|LoT%C-m=E7%OBctjlE8=;f=A8SCXjDc3t^Q*;=kA^L>jg>Nc>sb0zLi^6PmZ_rMc z!t;WAj@ehQyHRIqo70X+ED)OMd+b}wrIvclI4eyd6x&L_VSoR_GnN}`Mee21Zl-7Y z+l_S0*WOsk6)QxEac{X7j&01AdBf0?`mJ=bNJzw`bgC1ct&C-!sEcb}R!ZPx?WFUL znHA$R?dl~hL&t9f6Mn;+chZLwpfFE4{QFzTXuKumv2bfXH8R2(lw)TnT(bwaIzZoNqgVQc0#H?O|rG&a+Z zpmUx}1=%|}ejplOg@@ZSYHJogc6*B<7)&YzaY^m4SXH_0l32&T-p7ah8@_Dck^eAmwS7 zKm=k9Q-rCKl|1_9@l4}2VI3Ui3JZaOkwSZ+9hw*{VO}y%?}N+U>AE>^Vh1qfwG5K^Sa?S56dIzT(P>G_}JkQ~Rp+0sZD3v11?>rNv(^4$VmmlagsFyu3uqFA1tH~G7|lR@u@h)|AodlBlvaJ- z$@)i9ntS)yH^K)MsjC1&(Ra{8{so!@w8S_^gVcp|BF;x}FXU<^EKELV0;@Pwka1mL z5p)nR=`sc?Td9CU?X`h?1io6>bs~wr6D{E#944#`<_v2K?u3X_1CL6s1ypyw<^9&a zPGsx?%9H`sUBz?JA7>8+>|r(@u>0fj=}bo7o--? 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If you have your own dataset, you can simply replace the dataset file name with yours. diff --git a/examples/0-beginner/04-parity_plot.py b/examples/0-beginner/04-parity_plot.py index 5e2d136a03..d5d23cf12e 100644 --- a/examples/0-beginner/04-parity_plot.py +++ b/examples/0-beginner/04-parity_plot.py @@ -98,13 +98,15 @@ for frame in targets + predictions: frame.arrays["forces"] = frame.get_forces() + # a workaround, because the chemiscope interface for getting forces is broken with ASE # 3.23 # %% # Plot the energy parity plot with Chemiscope. This can be rendered as a widget in a # Jupyter notebook. -cs = chemiscope.show( + +chemiscope.show( targets, # reading structures from the dataset properties={ "Target energy": {"values": e_targets, "target": "structure", "units": "kcal"}, @@ -133,7 +135,6 @@ }, ), ) -cs # %% # You can check the structures by clicking the red dots on the parity plot, or diff --git a/examples/1-advanced/05-multi-gpu.py b/examples/1-advanced/05-multi-gpu.py index 393c946d67..d0293a37f5 100644 --- a/examples/1-advanced/05-multi-gpu.py +++ b/examples/1-advanced/05-multi-gpu.py @@ -1,5 +1,4 @@ """ -================== Multi-GPU training ================== @@ -13,16 +12,17 @@ The different gradients obtained on each device are then summed. This approach allows the user to reduce the time it takes to train models. -To know if the model supports multi-GPU training, please check `Available Architectures -<../../docs/src/architectures/index.rst>`_ and see if the default hyperparameters have -the ``distributed`` option. +To know if the model supports multi-GPU training, please check +:ref:`available-architectures` and see if the default hyperparameters have the +``distributed`` option. Input file ---------- + To do this, you only need to switch on the ``distributed`` option in the ``.yaml`` file -for the training. Let's take `this tutorial -<../beginner_tutorials/train-from-scratch.rst>`_ as an example. Now, the -``options.yaml`` is +for the training. Let's take the +:ref:`sphx_glr_generated_examples_0-beginner_03-train_from_scratch.py` example and +adjust the ``options.yaml`` file. .. literalinclude:: options-distributed.yaml :language: yaml @@ -35,8 +35,8 @@ configurations vary from clusters to clusters, so you have to modify it. Different scheduler will require similar options. ``metatrain`` will automatically use all the GPUs that you have asked for. You should make a single GPU visible for each process -(setting `--gpus-per-node` equal to the number of GPUs, or setting `--gpus-per-task=1`, -depending on your cluster configuration). +(setting ``--gpus-per-node`` equal to the number of GPUs, or setting +``--gpus-per-task=1``, depending on your cluster configuration). .. code-block:: bash @@ -84,7 +84,8 @@ Multi-GPU fine-tuning --------------------- + You can use multi-GPU for fine-tuning too, by writing ``distributed: True`` in the -``.yaml`` input. For information about fine-tuning, please refer to `this tutorial on -fine-tuning <../../getting-started/finetuning-example.rst>`_. +``.yaml`` input. For information about fine-tuning, please refer to the +:ref:`sphx_glr_generated_examples_0-beginner_02-fine-tuning.py` example. """ diff --git a/examples/programmatic/data_preparation/data.xyz b/examples/programmatic/data_preparation/data.xyz deleted file mode 100644 index 4f7575ee81..0000000000 --- a/examples/programmatic/data_preparation/data.xyz +++ /dev/null @@ -1,1201 +0,0 @@ -5 -Properties=species:S:1:pos:R:3:forces:R:3:my_tensor:R:9 A=157.7118 B=157.70997 C=157.70699 mu=0 alpha=13.21 epsHOMO=-0.3877 epsLUMO=0.1171 epsgap=0.5048 =35.3641 zpve=0.044749 U0=-100.0 U=-40.476062 H=-40.475117 G=-40.498597 Cv=6.469 pbc="F F F" -C -0.01269814 1.08580416 0.00800100 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 0.00215042 -0.00603132 0.00197612 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 1.01173084 1.46375116 0.00027657 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 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A=39.37691 B=10.04033 C=8.90353 mu=1.1502 alpha=28.13 epsHOMO=-0.2525 epsLUMO=0.091 epsgap=0.3435 =187.1015 zpve=0.079534 U0=-100.0 U=-154.956045 H=-154.9551 G=-154.985747 Cv=12.934 pbc="F F F" -C -0.01482147 1.39241234 0.00567104 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -O -0.00471537 -0.01360660 0.01459658 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 0.63794893 -0.55329676 -1.11358232 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 1.00528358 1.81015807 0.00465631 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.54689560 1.79343530 -0.87251051 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.53002895 1.72291969 0.91101696 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 0.13993792 -0.25599333 -2.05098375 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 1.69265275 -0.23868382 -1.17477743 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 0.59959435 -1.64180161 -1.02407616 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -9 -Properties=species:S:1:pos:R:3:forces:R:3:my_tensor:R:9 A=20.15852 B=20.15302 C=12.5891 mu=0.0005 alpha=30.82 epsHOMO=-0.2888 epsLUMO=0.1042 epsgap=0.393 =155.8145 zpve=0.081231 U0=-100.0 U=-117.821426 H=-117.820482 G=-117.849087 Cv=11.041 pbc="F F F" -C -0.01193280 1.51433198 0.01031700 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 1.30299118 0.77886562 -0.00617842 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 0.00867174 0.00767042 0.00201032 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.30541464 2.01702117 0.92533239 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.32275498 2.02680156 -0.89347839 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 1.88493396 0.79145020 -0.92119393 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 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0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -O -0.00024018 -0.00771006 0.00204055 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.31763326 1.88586598 0.93475751 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.33527583 1.89576673 -0.90397578 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 1.83246682 0.56256848 -0.93193290 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 1.85008258 0.55267938 0.90680147 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -10 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alpha=31.42 epsHOMO=-0.2594 epsLUMO=0.0584 epsgap=0.3179 =297.8398 zpve=0.085172 U0=-100.0 U=-230.177723 H=-230.176779 G=-230.211195 Cv=16.837 pbc="F F F" -O -0.01418735 1.42642242 -0.05423651 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C -0.00391839 0.00717714 0.03496062 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C -1.42317019 -0.55560600 -0.01256639 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -O -2.11229413 -0.10563875 -1.17228313 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.18987882 1.64154705 -0.97555148 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 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Cv=14.821 pbc="F F F" -N -0.00829694 1.35362838 0.00995971 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 1.28033254 1.82457421 -0.00022459 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 2.12216639 0.73698898 -0.01445600 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 1.30849904 -0.43109580 -0.01284154 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C -0.00342782 -0.01836884 0.00235736 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.83243234 1.92772668 0.02122995 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 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0.01000855 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 1.29535422 1.79575578 -0.00047017 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 2.05550519 0.65637438 -0.01422927 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -N 1.25385745 -0.46275132 -0.01260894 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 0.02301170 -0.00706382 0.00200146 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.83195718 1.93618436 0.02132575 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 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0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 2.11664681 0.74375623 -0.01441494 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 1.30397796 -0.43852387 -0.01283316 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 0.01629568 -0.00777185 0.00191294 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 1.37951089 2.86161135 0.00427433 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 3.19376406 0.79865501 -0.02458604 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 1.63858893 -1.46383271 -0.02156949 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.93736295 -0.50899925 0.00843891 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -8 -Properties=species:S:1:pos:R:3:forces:R:3:my_tensor:R:9 A=10.10926 B=9.69372 C=4.94857 mu=1.5081 alpha=35.17 epsHOMO=-0.2509 epsLUMO=0.001 epsgap=0.2519 =269.2444 zpve=0.058593 U0=-100.0 U=-246.025614 H=-246.024669 G=-246.055309 Cv=12.04 pbc="F F F" -O -0.03808298 1.35643965 0.00975193 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 1.26496982 1.76855319 -0.00061744 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 2.04955750 0.66630033 -0.01406047 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 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z+ahNjeb!*b7;2ssJ?swZ*BaQ^$xz4A+%)6;zP9?T?RpqP&9fFM)g_z$+*G}<7HVm3 zIx#NdoIdN=WyVnRtXBD3@7906wqK=%TAG{s@4GWcpY{AHW2kx7s7}NE^;vz-YN3|q zCZDP+^6InZes2sl&$=f2ls@{bagkc6rMYSEvI;ZwS^MSALZRkai-j&WGfTJ^&_XTE zO@p(J_^8jiyrwbKJZm23hY|YvI`Df_MC}XH8tDW)5!OMor23uEyL5-)*(*Fm(>RsXh diff --git a/tox.ini b/tox.ini index 0081a723d0..a5395a98c4 100644 --- a/tox.ini +++ b/tox.ini @@ -223,7 +223,7 @@ extras = # these architectures are used in the documentation flashmd soap-bpnn commands = - {env:ENV_INSTALLER:python -m pip} install --no-build-isolation sphericart torch-spex>=0.1,<0.2 wigners + uv pip install --no-build-isolation sphericart torch-spex>=0.1,<0.2 wigners # Run all .sh files in the example folder (including subfolders) bash -c "set -e && for f in $(find examples -name '*.sh'); do cd $(dirname $f); bash $(basename $f); cd -; done" From 2a29ddb1dcce75ea4d9a19b86d38de7a2089007d Mon Sep 17 00:00:00 2001 From: Philip Loche Date: Mon, 27 Oct 2025 13:14:41 +0100 Subject: [PATCH 3/7] renamed concepts section --- docs/src/architectures/nanopet.rst | 3 +-- docs/src/architectures/pet.rst | 3 +-- docs/src/architectures/soap-bpnn.rst | 3 +-- docs/src/{advanced-concepts => concepts}/auxiliary-outputs.rst | 0 docs/src/{advanced-concepts => concepts}/index.rst | 0 docs/src/{advanced-concepts => concepts}/loss-functions.rst | 0 docs/src/{advanced-concepts => concepts}/output-naming.rst | 0 docs/src/index.rst | 2 +- 8 files changed, 4 insertions(+), 7 deletions(-) rename docs/src/{advanced-concepts => concepts}/auxiliary-outputs.rst (100%) rename docs/src/{advanced-concepts => concepts}/index.rst (100%) rename docs/src/{advanced-concepts => concepts}/loss-functions.rst (100%) rename docs/src/{advanced-concepts => concepts}/output-naming.rst (100%) diff --git a/docs/src/architectures/nanopet.rst b/docs/src/architectures/nanopet.rst index 821c93a004..a410ca9f7f 100644 --- a/docs/src/architectures/nanopet.rst +++ b/docs/src/architectures/nanopet.rst @@ -59,8 +59,7 @@ hyperparameters to tune are (in decreasing order of importance): neural network. Depending on the dataset, increasing this hyperparameter might lead to better accuracy, at the cost of increased training and evaluation time. - ``loss``: This section describes the loss function to be used. See the - :doc:`dedicated documentation page <../advanced-concepts/loss-functions>` for more - details. + :ref:`loss-functions` for more details. - ``long_range``: In some systems and datasets, enabling long-range Coulomb interactions might be beneficial for the accuracy of the model and/or its physical correctness. See below for a breakdown of the long-range section of the model hyperparameters. diff --git a/docs/src/architectures/pet.rst b/docs/src/architectures/pet.rst index e3d5a4442a..c64bb40eee 100644 --- a/docs/src/architectures/pet.rst +++ b/docs/src/architectures/pet.rst @@ -62,8 +62,7 @@ hyperparameters to tune are (in decreasing order of importance): neural network. Depending on the dataset, increasing this hyperparameter might lead to better accuracy, at the cost of increased training and evaluation time. - ``loss``: This section describes the loss function to be used. See the - :doc:`dedicated documentation page <../advanced-concepts/loss-functions>` for more - details. + :ref:`loss-functions` for more details. - ``long_range``: In some systems and datasets, enabling long-range Coulomb interactions might be beneficial for the accuracy of the model and/or its physical correctness. See below for a breakdown of the long-range section of the model hyperparameters. diff --git a/docs/src/architectures/soap-bpnn.rst b/docs/src/architectures/soap-bpnn.rst index 5cc62d0266..7ec55e0fe8 100644 --- a/docs/src/architectures/soap-bpnn.rst +++ b/docs/src/architectures/soap-bpnn.rst @@ -57,8 +57,7 @@ We explain below the model-specific hypers for SOAP-BPNN. this hyperparameter to ``false`` will lead to slower convergence of training, but might lead to better generalization outside of the training set distribution. - ``loss``: This section describes the loss function to be used. See the - :doc:`dedicated documentation page <../advanced-concepts/loss-functions>` for more - details. + :ref:`loss-functions` for more details. In addition to these model-specific hypers, we re-highlight that the following additive models (``zbl`` and ``long_range``) may be needed to achieve better description at the diff --git a/docs/src/advanced-concepts/auxiliary-outputs.rst b/docs/src/concepts/auxiliary-outputs.rst similarity index 100% rename from docs/src/advanced-concepts/auxiliary-outputs.rst rename to docs/src/concepts/auxiliary-outputs.rst diff --git a/docs/src/advanced-concepts/index.rst b/docs/src/concepts/index.rst similarity index 100% rename from docs/src/advanced-concepts/index.rst rename to docs/src/concepts/index.rst diff --git a/docs/src/advanced-concepts/loss-functions.rst b/docs/src/concepts/loss-functions.rst similarity index 100% rename from docs/src/advanced-concepts/loss-functions.rst rename to docs/src/concepts/loss-functions.rst diff --git a/docs/src/advanced-concepts/output-naming.rst b/docs/src/concepts/output-naming.rst similarity index 100% rename from docs/src/advanced-concepts/output-naming.rst rename to docs/src/concepts/output-naming.rst diff --git a/docs/src/index.rst b/docs/src/index.rst index 0ef1288481..411d386689 100644 --- a/docs/src/index.rst +++ b/docs/src/index.rst @@ -20,7 +20,7 @@ configuration/index architectures/index generated_examples/index - advanced-concepts/index + concepts/index faq cite dev-docs/index From 7fae43c33dc1522f57bc6621a8cb3109411762a7 Mon Sep 17 00:00:00 2001 From: Philip Loche Date: Tue, 21 Oct 2025 17:39:51 +0200 Subject: [PATCH 4/7] Reorganized example files --- docs/src/examples/ase/ase.zip | Bin 0 -> 6176 bytes docs/src/examples/ase/ase_jupyter.zip | Bin 0 -> 10186 bytes docs/src/examples/ase/ethanol_reduced_100.xyz | 1100 +++++++++++++ .../images/thumb/sphx_glr_run_ase_thumb.png | Bin 0 -> 41946 bytes docs/src/examples/ase/index.rst | 61 + docs/src/examples/ase/options.yaml | 15 + docs/src/examples/ase/run_ase.codeobj.json | 926 +++++++++++ docs/src/examples/ase/run_ase.ipynb | 266 ++++ docs/src/examples/ase/run_ase.py | 207 +++ docs/src/examples/ase/run_ase.py.md5 | 1 + docs/src/examples/ase/run_ase.rst | 403 +++++ docs/src/examples/ase/run_ase.zip | Bin 0 -> 16212 bytes docs/src/examples/ase/train.sh | 3 + docs/src/examples/basic_usage/basic_usage.zip | Bin 0 -> 4718 bytes docs/src/examples/basic_usage/eval.yaml | 6 + .../images/thumb/sphx_glr_usage_thumb.png | Bin 0 -> 41946 bytes docs/src/examples/basic_usage/index.rst | 57 + docs/src/examples/basic_usage/options.yaml | 18 + docs/src/examples/basic_usage/output.xyz | 1201 +++++++++++++++ .../examples/basic_usage/qm9_reduced_100.xyz | 1201 +++++++++++++++ docs/src/examples/basic_usage/usage.rst | 189 +++ docs/src/examples/basic_usage/usage.sh | 125 ++ docs/src/examples/basic_usage/usage.zip | Bin 0 -> 4718 bytes .../src/examples/llpr/ethanol_reduced_100.xyz | 1100 +++++++++++++ .../llpr/images/thumb/sphx_glr_llpr_thumb.png | Bin 0 -> 41946 bytes docs/src/examples/llpr/index.rst | 61 + docs/src/examples/llpr/llpr.codeobj.json | 728 +++++++++ docs/src/examples/llpr/llpr.ipynb | 68 + docs/src/examples/llpr/llpr.py | 121 ++ docs/src/examples/llpr/llpr.py.md5 | 1 + docs/src/examples/llpr/llpr.rst | 183 +++ docs/src/examples/llpr/llpr.zip | Bin 0 -> 3789 bytes docs/src/examples/llpr/llpr_jupyter.zip | Bin 0 -> 4720 bytes docs/src/examples/llpr/options-llpr.yaml | 20 + docs/src/examples/llpr/options.yaml | 21 + docs/src/examples/llpr/qm9_reduced_100.xyz | 1201 +++++++++++++++ docs/src/examples/llpr/train.sh | 4 + .../multi-gpu/ethanol_reduced_100.xyz | 1100 +++++++++++++ .../images/thumb/sphx_glr_multi-gpu_thumb.png | Bin 0 -> 41946 bytes docs/src/examples/multi-gpu/index.rst | 62 + docs/src/examples/multi-gpu/multi-gpu.ipynb | 32 + docs/src/examples/multi-gpu/multi-gpu.py | 91 ++ docs/src/examples/multi-gpu/multi-gpu.py.md5 | 1 + docs/src/examples/multi-gpu/multi-gpu.rst | 134 ++ docs/src/examples/multi-gpu/multi-gpu.zip | Bin 0 -> 7233 bytes .../examples/multi-gpu/multi-gpu_jupyter.zip | Bin 0 -> 3859 bytes .../examples/multi-gpu/multi-gpu_python.zip | Bin 0 -> 3396 bytes .../multi-gpu/options-distributed.yaml | 22 + .../data_preparation/carbon_reduced_100.xyz | 600 ++++++++ .../programmatic/data_preparation/data.xyz | 1201 +++++++++++++++ .../data_preparation.codeobj.json | 1366 +++++++++++++++++ .../data_preparation/data_preparation.ipynb | 169 ++ .../data_preparation/data_preparation.py | 242 +++ .../data_preparation/data_preparation.py.md5 | 1 + .../data_preparation/data_preparation.rst | 390 +++++ .../data_preparation/data_preparation.zip | Bin 0 -> 19743 bytes .../data_preparation_jupyter.zip | Bin 0 -> 11225 bytes .../data_preparation_python.zip | Bin 0 -> 8540 bytes .../thumb/sphx_glr_data_preparation_thumb.png | Bin 0 -> 41946 bytes .../programmatic/data_preparation/index.rst | 61 + .../data_preparation/options.yaml | 38 + .../data_preparation/qm9_reduced_100.xyz | 1201 +++++++++++++++ .../programmatic/flashmd/flashmd.codeobj.json | 870 +++++++++++ .../programmatic/flashmd/flashmd.ipynb | 97 ++ .../examples/programmatic/flashmd/flashmd.py | 120 ++ .../programmatic/flashmd/flashmd.py.md5 | 1 + .../examples/programmatic/flashmd/flashmd.rst | 225 +++ .../examples/programmatic/flashmd/flashmd.xyz | 680 ++++++++ .../examples/programmatic/flashmd/flashmd.zip | Bin 0 -> 9611 bytes .../programmatic/flashmd/flashmd_jupyter.zip | Bin 0 -> 5622 bytes .../programmatic/flashmd/flashmd_python.zip | Bin 0 -> 4011 bytes .../images/thumb/sphx_glr_flashmd_thumb.png | Bin 0 -> 41946 bytes .../examples/programmatic/flashmd/index.rst | 61 + .../programmatic/flashmd/options.yaml | 43 + .../thumb/sphx_glr_use_outside_thumb.png | Bin 0 -> 41946 bytes .../use_architectures_outside/index.rst | 61 + .../qm9_reduced_100.xyz | 1201 +++++++++++++++ .../use_architectures_outside_jupyter.zip | Bin 0 -> 4335 bytes .../use_architectures_outside_python.zip | Bin 0 -> 2709 bytes .../use_outside.codeobj.json | 673 ++++++++ .../use_outside.ipynb | 97 ++ .../use_architectures_outside/use_outside.py | 90 ++ .../use_outside.py.md5 | 1 + .../use_architectures_outside/use_outside.rst | 178 +++ .../use_architectures_outside/use_outside.zip | Bin 0 -> 7022 bytes .../ethanol_reduced_100.xyz | 1100 +++++++++++++ .../src/examples/train_from_scratch/eval.yaml | 8 + .../sphx_glr_train_from_scratch_thumb.png | Bin 0 -> 41946 bytes .../src/examples/train_from_scratch/index.rst | 61 + .../examples/train_from_scratch/options.yaml | 22 + .../train_from_scratch.ipynb | 32 + .../train_from_scratch/train_from_scratch.py | 151 ++ .../train_from_scratch.py.md5 | 1 + .../train_from_scratch/train_from_scratch.rst | 195 +++ .../train_from_scratch/train_from_scratch.zip | Bin 0 -> 12380 bytes .../train_from_scratch_jupyter.zip | Bin 0 -> 6122 bytes .../train_from_scratch_python.zip | Bin 0 -> 6280 bytes .../_datasets/fig_parity_plot_001.json.gz | Bin 0 -> 62349 bytes .../thumb/sphx_glr_parity_plot_thumb.png | Bin 0 -> 41946 bytes docs/src/examples/validation/index.rst | 61 + docs/src/examples/validation/output.xyz | 1100 +++++++++++++ .../validation/parity_plot.codeobj.json | 382 +++++ .../src/examples/validation/parity_plot.ipynb | 154 ++ docs/src/examples/validation/parity_plot.py | 147 ++ .../examples/validation/parity_plot.py.md5 | 1 + docs/src/examples/validation/parity_plot.rst | 281 ++++ docs/src/examples/validation/parity_plot.zip | Bin 0 -> 12844 bytes .../validation/validation_jupyter.zip | Bin 0 -> 7676 bytes .../examples/validation/validation_python.zip | Bin 0 -> 5190 bytes docs/src/examples/zbl/dimers.codeobj.json | 478 ++++++ docs/src/examples/zbl/dimers.ipynb | 140 ++ docs/src/examples/zbl/dimers.py | 147 ++ docs/src/examples/zbl/dimers.py.md5 | 1 + docs/src/examples/zbl/dimers.rst | 287 ++++ docs/src/examples/zbl/dimers.zip | Bin 0 -> 11424 bytes docs/src/examples/zbl/ethanol_reduced_100.xyz | 1100 +++++++++++++ .../images/thumb/sphx_glr_dimers_thumb.png | Bin 0 -> 41946 bytes 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+ +.. only:: html + + .. image:: /examples/ase/images/thumb/sphx_glr_run_ase_thumb.png + :alt: + + :ref:`sphx_glr_examples_ase_run_ase.py` + +.. raw:: html + +
Running molecular dynamics with ASE
+
+ + +.. thumbnail-parent-div-close + +.. raw:: html + + + + +.. toctree:: + :hidden: + + /examples/ase/run_ase + + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-gallery + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download all examples as source code: ase.zip ` + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download all examples in Jupyter notebooks: ase_jupyter.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/ase/options.yaml b/docs/src/examples/ase/options.yaml new file mode 100644 index 0000000000..87300b9263 --- /dev/null +++ b/docs/src/examples/ase/options.yaml @@ -0,0 +1,15 @@ +seed: 42 + +architecture: + name: gap + +# training set section +training_set: + systems: ethanol_reduced_100.xyz + targets: + energy: + key: energy + unit: kcal/mol # very important to run simulations + +validation_set: 0.1 +test_set: 0.0 diff --git a/docs/src/examples/ase/run_ase.codeobj.json b/docs/src/examples/ase/run_ase.codeobj.json new file mode 100644 index 0000000000..44011ab7cd --- /dev/null +++ b/docs/src/examples/ase/run_ase.codeobj.json @@ -0,0 +1,926 @@ +{ + "Analysis": [ + { + "is_class": true, + "is_explicit": false, + "module": "ase.geometry.analysis", + "module_short": "ase.geometry.analysis", + "name": "Analysis" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.geometry", + "module_short": "ase.geometry", + "name": "Analysis" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Analysis" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase.geometry.analysis", + "module_short": "ase.geometry.analysis", + "name": "Analysis" + } + ], + "MetatomicCalculator": [ + { + "is_class": true, + "is_explicit": false, + "module": "metatomic.torch.ase_calculator", + "module_short": "metatomic.torch.ase_calculator", + "name": "MetatomicCalculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "metatomic.torch", + "module_short": "metatomic.torch", + "name": "MetatomicCalculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "metatomic", + "module_short": "metatomic", + "name": "MetatomicCalculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.calculators.calculator", + "module_short": "ase.calculators.calculator", + "name": "Calculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.calculators", + "module_short": "ase.calculators", + "name": "Calculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Calculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.calculators.calculator", + "module_short": "ase.calculators.calculator", + "name": "BaseCalculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.calculators", + "module_short": "ase.calculators", + "name": "BaseCalculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "BaseCalculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.calculators.abc", + "module_short": "ase.calculators.abc", + "name": "GetPropertiesMixin" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.calculators", + "module_short": "ase.calculators", + "name": "GetPropertiesMixin" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "GetPropertiesMixin" + }, + { + "is_class": true, + "is_explicit": false, + "module": "abc", + "module_short": "abc", + "name": "ABC" + }, + { + "is_class": false, + "is_explicit": false, + "module": "metatomic.torch.ase_calculator", + "module_short": "metatomic.torch.ase_calculator", + "name": "MetatomicCalculator" + } + ], + "ana_train": [ + { + "is_class": false, + "is_explicit": false, + "module": "ase.geometry.analysis", + "module_short": "ase.geometry.analysis", + "name": "Analysis" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase.geometry", + "module_short": "ase.geometry", + "name": "Analysis" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Analysis" + } + ], + "ana_train.get_rdf": [ + { + "is_class": false, + "is_explicit": false, + "module": "ase.geometry.analysis", + "module_short": "ase.geometry.analysis", + "name": "Analysis.get_rdf" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase.geometry", + "module_short": "ase.geometry", + "name": "Analysis.get_rdf" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Analysis.get_rdf" + } + ], + "ana_traj": [ + { + "is_class": false, + "is_explicit": false, + "module": "ase.geometry.analysis", + "module_short": "ase.geometry.analysis", + "name": "Analysis" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase.geometry", + "module_short": "ase.geometry", + "name": "Analysis" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Analysis" + } + ], + "ana_traj.get_rdf": [ + { + "is_class": false, + "is_explicit": false, + "module": "ase.geometry.analysis", + "module_short": "ase.geometry.analysis", + "name": "Analysis.get_rdf" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase.geometry", + "module_short": "ase.geometry", + "name": "Analysis.get_rdf" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Analysis.get_rdf" + } + ], + "animation": [ + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib.animation", + "module_short": "matplotlib.animation", + "name": "FuncAnimation" + }, + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib", + "module_short": "matplotlib", + "name": "FuncAnimation" + } + ], + "ase.geometry.analysis.Analysis": [ + { + "is_class": false, + "is_explicit": true, + "module": "ase.geometry.analysis", + "module_short": "ase.geometry.analysis", + "name": "Analysis" + } + ], + "ase.geometry.analysis.Analysis.get_rdf": [ + { + "is_class": false, + "is_explicit": true, + "module": "ase.geometry.analysis.Analysis", + "module_short": "ase.geometry.analysis.Analysis", + "name": "get_rdf" + } + ], + "ase.io.read": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + } + ], + "ase.md.VelocityVerlet": [ + { + "is_class": true, + "is_explicit": false, + "module": "ase.md.verlet", + "module_short": "ase.md", + "name": "VelocityVerlet" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.md", + "module_short": "ase.md", + "name": "VelocityVerlet" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "VelocityVerlet" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.md.md", + "module_short": "ase.md.md", + "name": "MolecularDynamics" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.md", + "module_short": "ase.md", + "name": "MolecularDynamics" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "MolecularDynamics" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.optimize.optimize", + "module_short": "ase.optimize.optimize", + "name": "Dynamics" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.optimize", + "module_short": "ase.optimize", + "name": "Dynamics" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Dynamics" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.utils", + "module_short": "ase.utils", + "name": "IOContext" + }, + { + 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false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Atoms.get_kinetic_energy" + } + ], + "atoms.get_potential_energy": [ + { + "is_class": false, + "is_explicit": false, + "module": "ase.atoms", + "module_short": "ase", + "name": "Atoms.get_potential_energy" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Atoms.get_potential_energy" + } + ], + "atoms.get_total_energy": [ + { + "is_class": false, + "is_explicit": false, + "module": "ase.atoms", + "module_short": "ase", + "name": "Atoms.get_total_energy" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Atoms.get_total_energy" + } + ], + "atoms.pbc": [ + { + "is_class": false, + "is_explicit": false, + "module": "ase.atoms", + "module_short": "ase", + "name": "Atoms.pbc" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": 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}, + { + "is_class": false, + "is_explicit": false, + "module": "ase.md.md", + "module_short": "ase.md.md", + "name": "MolecularDynamics.run" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase.md", + "module_short": "ase.md", + "name": "MolecularDynamics.run" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "MolecularDynamics.run" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase.optimize.optimize", + "module_short": "ase.optimize.optimize", + "name": "Dynamics.run" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase.optimize", + "module_short": "ase.optimize", + "name": "Dynamics.run" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Dynamics.run" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase.utils", + "module_short": "ase.utils", + "name": "IOContext.run" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "IOContext.run" + } + ], + "kinetic_energy": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "ndarray" + } + ], + "kinetic_energy.mean": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "builtin_function_or_method" + } + ], + "n_steps": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "int" + } + ], + "np.mean": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "_ArrayFunctionDispatcher" + }, + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "mean" + } + ], + "np.ones": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "ones" + } + ], + "np.zeros": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "builtin_function_or_method" + }, + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "zeros" + } + ], + "plt.legend": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib.pyplot", + "module_short": "matplotlib.pyplot", + "name": "legend" + } + ], + "plt.plot": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib.pyplot", + "module_short": "matplotlib.pyplot", + "name": "plot" + } + ], + "plt.show": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib.pyplot", + "module_short": "matplotlib.pyplot", + "name": "show" + } + ], + "plt.xlabel": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib.pyplot", + "module_short": "matplotlib.pyplot", + "name": "xlabel" + } + ], + "plt.ylabel": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib.pyplot", + "module_short": "matplotlib.pyplot", + "name": "ylabel" + } + ], + "potential_energy": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "ndarray" + } + ], + "potential_energy.mean": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "builtin_function_or_method" + } + ], + "rdf_train": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "list" + } + ], + "rdf_train_mean": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "ndarray" + } + ], + "rdf_traj": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "list" + } + ], + "rdf_traj_mean": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "ndarray" + } + ], + "step": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "int" + } + ], + "total_energy": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "ndarray" + } + ], + "total_energy.mean": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "builtin_function_or_method" + } + ], + "train_frames": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "list" + } + ], + "trajectory": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "list" + } + ], + "trajectory.append": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "builtin_function_or_method" + } + ] +} diff --git a/docs/src/examples/ase/run_ase.ipynb b/docs/src/examples/ase/run_ase.ipynb new file mode 100644 index 0000000000..61d76e3cb0 --- /dev/null +++ b/docs/src/examples/ase/run_ase.ipynb @@ -0,0 +1,266 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Running molecular dynamics with ASE\n\nThis tutorial demonstrates how to use an already trained and exported model to run an\nASE simulation of a single ethanol molecule in vacuum. We use a model that was trained\nusing the `architecture-soap-bpnn` architecture on 100 ethanol systems\ncontaining energies and forces. You can obtain the :download:`dataset file\n` used in this example from our website. The dataset is a\nsubset of the [rMD17 dataset](https://iopscience.iop.org/article/10.1088/2632-2153/abba6f/meta).\n\nThe model was trained using the following training options.\n\n.. literalinclude:: options.yaml\n :language: yaml\n\nYou can train the same model yourself with\n\n.. literalinclude:: train.sh\n :language: bash\n\nA detailed step-by-step introduction on how to train a model is provided in\nthe `label_basic_usage` tutorial.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we start by importing the necessary libraries, including the integration of ASE\ncalculators for metatensor atomistic models.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import ase.md\nimport ase.md.velocitydistribution\nimport ase.units\nimport ase.visualize.plot\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom ase.geometry.analysis import Analysis\nfrom metatomic.torch.ase_calculator import MetatomicCalculator" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting up the simulation\n\nNext, we initialize the simulation by extracting the initial positions from the\ndataset file which we initially trained the model on.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "train_frames = ase.io.read(\"ethanol_reduced_100.xyz\", \":\")\natoms = train_frames[0].copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below we show the initial configuration of a single ethanol molecule in vacuum.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ase.visualize.plot.plot_atoms(atoms)\n\nplt.xlabel(\"\u00c5\")\nplt.ylabel(\"\u00c5\")\n\nplt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our initial coordinates do not include velocities. We initialize the velocities\naccording to a Maxwell-Boltzmann Distribution at 300 K.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ase.md.velocitydistribution.MaxwellBoltzmannDistribution(atoms, temperature_K=300)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now register our exported model as the energy calculator to obtain energies and\nforces.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "atoms.calc = MetatomicCalculator(\"model.pt\", extensions_directory=\"extensions/\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we define the integrator which we use to obtain new positions and velocities\nbased on our energy calculator. We use a common timestep of 0.5 fs.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "integrator = ase.md.VelocityVerlet(atoms, timestep=0.5 * ase.units.fs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run the simulation\n\nWe now have everything ready to run the MD simulation at constant energy (NVE). To\nkeep the execution time of this tutorial small we run the simulations only for 100\nsteps. If you want to run a longer simulation you can increase the ``n_steps``\nvariable.\n\nDuring the simulation loop we collect data about the simulation for later analysis.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n_steps = 100\n\npotential_energy = np.zeros(n_steps)\nkinetic_energy = np.zeros(n_steps)\ntotal_energy = np.zeros(n_steps)\ntrajectory = []\n\nfor step in range(n_steps):\n # run a single simulation step\n integrator.run(1)\n\n trajectory.append(atoms.copy())\n potential_energy[step] = atoms.get_potential_energy()\n kinetic_energy[step] = atoms.get_kinetic_energy()\n total_energy[step] = atoms.get_total_energy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyse the results\n\n### Energy conservation\n\nFor a first analysis, we plot the evolution of the mean of the kinetic, potential, and\ntotal energy which is an important measure for the stability of a simulation.\n\nAs shown below we see that both the kinetic, potential, and total energy\nfluctuate but the total energy is conserved over the length of the simulation.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "plt.plot(potential_energy - potential_energy.mean(), label=\"potential energy\")\nplt.plot(kinetic_energy - kinetic_energy.mean(), label=\"kinetic energy\")\nplt.plot(total_energy - total_energy.mean(), label=\"total energy\")\n\nplt.xlabel(\"step\")\nplt.ylabel(\"energy / kcal/mol\")\nplt.legend()\n\nplt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Inspect the systems\n\nEven though the total energy is conserved, we also have to verify that the ethanol\nmolecule is stable and the bonds did not break.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "animation = ase.visualize.plot.animate(trajectory, interval=100, save_count=None)\nplt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Carbon-hydrogen radial distribution function\n\nAs a final analysis we also calculate and plot the carbon-hydrogen radial distribution\nfunction (RDF) from the trajectory and compare this to the RDF from the training set.\n\nTo use the RDF code from ase we first have to define a unit cell for our systems.\nWe choose a cubic one with a side length of 10 \u00c5.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "for atoms in train_frames:\n atoms.cell = 10 * np.ones(3)\n atoms.pbc = True\n\nfor atoms in trajectory:\n atoms.cell = 10 * np.ones(3)\n atoms.pbc = True" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now can initilize the :py:class:`ase.geometry.analysis.Analysis` objects and\ncompute the the RDF using the :py:meth:`ase.geometry.analysis.Analysis.get_rdf`\nmethod.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ana_traj = Analysis(trajectory)\nana_train = Analysis(train_frames)\n\nrdf_traj = ana_traj.get_rdf(rmax=5, nbins=50, elements=[\"C\", \"H\"], return_dists=True)\nrdf_train = ana_train.get_rdf(rmax=5, nbins=50, elements=[\"C\", \"H\"], return_dists=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We extract the bin positions from the returned values and and averege the RDF over the\nwhole trajectory and dataset, respectively.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "bins = rdf_traj[0][1]\nrdf_traj_mean = np.mean([rdf_traj[i][0] for i in range(n_steps)], axis=0)\nrdf_train_mean = np.mean([rdf_train[i][0] for i in range(n_steps)], axis=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plotting the RDF verifies that the hydrogen bonds are stable, confirming that we\nperformed an energy-conserving and stable simulation.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "plt.plot(bins, rdf_traj_mean, label=\"trajectory\")\nplt.plot(bins, rdf_train_mean, label=\"training set\")\n\nplt.legend()\nplt.xlabel(\"r / \u00c5\")\nplt.ylabel(\"radial distribution function\")\n\nplt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/docs/src/examples/ase/run_ase.py b/docs/src/examples/ase/run_ase.py new file mode 100644 index 0000000000..9310d95b25 --- /dev/null +++ b/docs/src/examples/ase/run_ase.py @@ -0,0 +1,207 @@ +""" +Running molecular dynamics with ASE +=================================== + +This tutorial demonstrates how to use an already trained and exported model to run an +ASE simulation of a single ethanol molecule in vacuum. We use a model that was trained +using the :ref:`architecture-soap-bpnn` architecture on 100 ethanol systems +containing energies and forces. You can obtain the :download:`dataset file +` used in this example from our website. The dataset is a +subset of the `rMD17 dataset +`_. + +The model was trained using the following training options. + +.. literalinclude:: options-ase.yaml + :language: yaml + +We first train the model same model but and before import the necessary libraries and +run the training process and the integration of ASE. + +""" +# %% +# + +import subprocess + +import ase.md +import ase.md.velocitydistribution +import ase.units +import ase.visualize.plot +import matplotlib.pyplot as plt +import numpy as np +from ase.geometry.analysis import Analysis +from metatomic.torch.ase_calculator import MetatomicCalculator + + +# %% +# + +subprocess.run(["mtt", "train", "options-ase.yaml", "--output", "model-md.pt"]) + +# %% +# +# A detailed step-by-step introduction on how to train a model is provided in the +# :ref:`label_basic_usage` tutorial. +# +# Setting up the simulation +# ------------------------- +# +# Next, we initialize the simulation by extracting the initial positions from the +# dataset file which we initially trained the model on. + +train_frames = ase.io.read("ethanol_reduced_100.xyz", ":") +atoms = train_frames[0].copy() + +# %% +# +# Below we show the initial configuration of a single ethanol molecule in vacuum. + +ase.visualize.plot.plot_atoms(atoms) + +plt.xlabel("Ã…") +plt.ylabel("Ã…") + +plt.show() + + +# %% +# +# Our initial coordinates do not include velocities. We initialize the velocities +# according to a Maxwell-Boltzmann Distribution at 300 K. + +ase.md.velocitydistribution.MaxwellBoltzmannDistribution(atoms, temperature_K=300) + +# %% +# +# We now register our exported model as the energy calculator to obtain energies and +# forces. + +atoms.calc = MetatomicCalculator("model-md.pt", extensions_directory="extensions/") + +# %% +# +# Finally, we define the integrator which we use to obtain new positions and velocities +# based on our energy calculator. We use a common timestep of 0.5 fs. + +integrator = ase.md.VelocityVerlet(atoms, timestep=0.5 * ase.units.fs) + + +# %% +# +# Run the simulation +# ------------------ +# +# We now have everything ready to run the MD simulation at constant energy (NVE). To +# keep the execution time of this tutorial small we run the simulations only for 100 +# steps. If you want to run a longer simulation you can increase the ``n_steps`` +# variable. +# +# During the simulation loop we collect data about the simulation for later analysis. + + +n_steps = 100 + +potential_energy = np.zeros(n_steps) +kinetic_energy = np.zeros(n_steps) +total_energy = np.zeros(n_steps) +trajectory = [] + +for step in range(n_steps): + # run a single simulation step + integrator.run(1) + + trajectory.append(atoms.copy()) + potential_energy[step] = atoms.get_potential_energy() + kinetic_energy[step] = atoms.get_kinetic_energy() + total_energy[step] = atoms.get_total_energy() + +# %% +# +# Analyse the results +# ------------------- +# +# Energy conservation +# ################### +# +# For a first analysis, we plot the evolution of the mean of the kinetic, potential, and +# total energy which is an important measure for the stability of a simulation. +# +# As shown below we see that both the kinetic, potential, and total energy +# fluctuate but the total energy is conserved over the length of the simulation. + + +plt.plot(potential_energy - potential_energy.mean(), label="potential energy") +plt.plot(kinetic_energy - kinetic_energy.mean(), label="kinetic energy") +plt.plot(total_energy - total_energy.mean(), label="total energy") + +plt.xlabel("step") +plt.ylabel("energy / kcal/mol") +plt.legend() + +plt.show() + +# %% +# +# Inspect the systems +# ################### +# +# Even though the total energy is conserved, we also have to verify that the ethanol +# molecule is stable and the bonds did not break. + +animation = ase.visualize.plot.animate(trajectory, interval=100, save_count=None) +plt.show() + +# %% +# +# Carbon-hydrogen radial distribution function +# ############################################ +# +# As a final analysis we also calculate and plot the carbon-hydrogen radial distribution +# function (RDF) from the trajectory and compare this to the RDF from the training set. +# +# To use the RDF code from ase we first have to define a unit cell for our systems. +# We choose a cubic one with a side length of 10 Ã…. + +for atoms in train_frames: + atoms.cell = 10 * np.ones(3) + atoms.pbc = True + +for atoms in trajectory: + atoms.cell = 10 * np.ones(3) + atoms.pbc = True + +# %% +# +# We now can initilize the :py:class:`ase.geometry.analysis.Analysis` objects and +# compute the the RDF using the :py:meth:`ase.geometry.analysis.Analysis.get_rdf` +# method. + +ana_traj = Analysis(trajectory) +ana_train = Analysis(train_frames) + +rdf_traj = ana_traj.get_rdf(rmax=5, nbins=50, elements=["C", "H"], return_dists=True) +rdf_train = ana_train.get_rdf(rmax=5, nbins=50, elements=["C", "H"], return_dists=True) + +# %% +# +# We extract the bin positions from the returned values and and averege the RDF over the +# whole trajectory and dataset, respectively. + +bins = rdf_traj[0][1] +rdf_traj_mean = np.mean([rdf_traj[i][0] for i in range(n_steps)], axis=0) +rdf_train_mean = np.mean([rdf_train[i][0] for i in range(n_steps)], axis=0) + +# %% +# +# Plotting the RDF verifies that the hydrogen bonds are stable, confirming that we +# performed an energy-conserving and stable simulation. + +plt.plot(bins, rdf_traj_mean, label="trajectory") +plt.plot(bins, rdf_train_mean, label="training set") + +plt.legend() +plt.xlabel("r / Ã…") +plt.ylabel("radial distribution function") + +plt.show() diff --git a/docs/src/examples/ase/run_ase.py.md5 b/docs/src/examples/ase/run_ase.py.md5 new file mode 100644 index 0000000000..31f5391e73 --- /dev/null +++ b/docs/src/examples/ase/run_ase.py.md5 @@ -0,0 +1 @@ +ac05d2993cd2bb401fd7750e31d970c6 \ No newline at end of file diff --git a/docs/src/examples/ase/run_ase.rst b/docs/src/examples/ase/run_ase.rst new file mode 100644 index 0000000000..b1401d1108 --- /dev/null +++ b/docs/src/examples/ase/run_ase.rst @@ -0,0 +1,403 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "examples/ase/run_ase.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code. + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_examples_ase_run_ase.py: + + +Running molecular dynamics with ASE +=================================== + +This tutorial demonstrates how to use an already trained and exported model to run an +ASE simulation of a single ethanol molecule in vacuum. We use a model that was trained +using the :ref:`architecture-soap-bpnn` architecture on 100 ethanol systems +containing energies and forces. You can obtain the :download:`dataset file +` used in this example from our website. The dataset is a +subset of the `rMD17 dataset +`_. + +The model was trained using the following training options. + +.. literalinclude:: options.yaml + :language: yaml + +You can train the same model yourself with + +.. literalinclude:: train.sh + :language: bash + +A detailed step-by-step introduction on how to train a model is provided in +the :ref:`label_basic_usage` tutorial. + +.. GENERATED FROM PYTHON SOURCE LINES 28-30 + +First, we start by importing the necessary libraries, including the integration of ASE +calculators for metatensor atomistic models. + +.. GENERATED FROM PYTHON SOURCE LINES 31-42 + +.. code-block:: Python + + + import ase.md + import ase.md.velocitydistribution + import ase.units + import ase.visualize.plot + import matplotlib.pyplot as plt + import numpy as np + from ase.geometry.analysis import Analysis + from metatomic.torch.ase_calculator import MetatomicCalculator + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 43-48 + +Setting up the simulation +------------------------- + +Next, we initialize the simulation by extracting the initial positions from the +dataset file which we initially trained the model on. + +.. GENERATED FROM PYTHON SOURCE LINES 49-53 + +.. code-block:: Python + + + train_frames = ase.io.read("ethanol_reduced_100.xyz", ":") + atoms = train_frames[0].copy() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 54-55 + +Below we show the initial configuration of a single ethanol molecule in vacuum. + +.. GENERATED FROM PYTHON SOURCE LINES 56-65 + +.. code-block:: Python + + + ase.visualize.plot.plot_atoms(atoms) + + plt.xlabel("Ã…") + plt.ylabel("Ã…") + + plt.show() + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 66-68 + +Our initial coordinates do not include velocities. We initialize the velocities +according to a Maxwell-Boltzmann Distribution at 300 K. + +.. GENERATED FROM PYTHON SOURCE LINES 69-72 + +.. code-block:: Python + + + ase.md.velocitydistribution.MaxwellBoltzmannDistribution(atoms, temperature_K=300) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 73-75 + +We now register our exported model as the energy calculator to obtain energies and +forces. + +.. GENERATED FROM PYTHON SOURCE LINES 76-79 + +.. code-block:: Python + + + atoms.calc = MetatomicCalculator("model.pt", extensions_directory="extensions/") + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 80-82 + +Finally, we define the integrator which we use to obtain new positions and velocities +based on our energy calculator. We use a common timestep of 0.5 fs. + +.. GENERATED FROM PYTHON SOURCE LINES 83-87 + +.. code-block:: Python + + + integrator = ase.md.VelocityVerlet(atoms, timestep=0.5 * ase.units.fs) + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 88-97 + +Run the simulation +------------------ + +We now have everything ready to run the MD simulation at constant energy (NVE). To +keep the execution time of this tutorial small we run the simulations only for 100 +steps. If you want to run a longer simulation you can increase the ``n_steps`` +variable. + +During the simulation loop we collect data about the simulation for later analysis. + +.. GENERATED FROM PYTHON SOURCE LINES 98-116 + +.. code-block:: Python + + + + n_steps = 100 + + potential_energy = np.zeros(n_steps) + kinetic_energy = np.zeros(n_steps) + total_energy = np.zeros(n_steps) + trajectory = [] + + for step in range(n_steps): + # run a single simulation step + integrator.run(1) + + trajectory.append(atoms.copy()) + potential_energy[step] = atoms.get_potential_energy() + kinetic_energy[step] = atoms.get_kinetic_energy() + total_energy[step] = atoms.get_total_energy() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 117-128 + +Analyse the results +------------------- + +Energy conservation +################### + +For a first analysis, we plot the evolution of the mean of the kinetic, potential, and +total energy which is an important measure for the stability of a simulation. + +As shown below we see that both the kinetic, potential, and total energy +fluctuate but the total energy is conserved over the length of the simulation. + +.. GENERATED FROM PYTHON SOURCE LINES 129-141 + +.. code-block:: Python + + + + plt.plot(potential_energy - potential_energy.mean(), label="potential energy") + plt.plot(kinetic_energy - kinetic_energy.mean(), label="kinetic energy") + plt.plot(total_energy - total_energy.mean(), label="total energy") + + plt.xlabel("step") + plt.ylabel("energy / kcal/mol") + plt.legend() + + plt.show() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 142-147 + +Inspect the systems +################### + +Even though the total energy is conserved, we also have to verify that the ethanol +molecule is stable and the bonds did not break. + +.. GENERATED FROM PYTHON SOURCE LINES 148-152 + +.. code-block:: Python + + + animation = ase.visualize.plot.animate(trajectory, interval=100, save_count=None) + plt.show() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 153-161 + +Carbon-hydrogen radial distribution function +############################################ + +As a final analysis we also calculate and plot the carbon-hydrogen radial distribution +function (RDF) from the trajectory and compare this to the RDF from the training set. + +To use the RDF code from ase we first have to define a unit cell for our systems. +We choose a cubic one with a side length of 10 Ã…. + +.. GENERATED FROM PYTHON SOURCE LINES 162-171 + +.. code-block:: Python + + + for atoms in train_frames: + atoms.cell = 10 * np.ones(3) + atoms.pbc = True + + for atoms in trajectory: + atoms.cell = 10 * np.ones(3) + atoms.pbc = True + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 172-175 + +We now can initilize the :py:class:`ase.geometry.analysis.Analysis` objects and +compute the the RDF using the :py:meth:`ase.geometry.analysis.Analysis.get_rdf` +method. + +.. GENERATED FROM PYTHON SOURCE LINES 176-183 + +.. code-block:: Python + + + ana_traj = Analysis(trajectory) + ana_train = Analysis(train_frames) + + rdf_traj = ana_traj.get_rdf(rmax=5, nbins=50, elements=["C", "H"], return_dists=True) + rdf_train = ana_train.get_rdf(rmax=5, nbins=50, elements=["C", "H"], return_dists=True) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 184-186 + +We extract the bin positions from the returned values and and averege the RDF over the +whole trajectory and dataset, respectively. + +.. GENERATED FROM PYTHON SOURCE LINES 187-192 + +.. code-block:: Python + + + bins = rdf_traj[0][1] + rdf_traj_mean = np.mean([rdf_traj[i][0] for i in range(n_steps)], axis=0) + rdf_train_mean = np.mean([rdf_train[i][0] for i in range(n_steps)], axis=0) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 193-195 + +Plotting the RDF verifies that the hydrogen bonds are stable, confirming that we +performed an energy-conserving and stable simulation. + +.. GENERATED FROM PYTHON SOURCE LINES 196-205 + +.. code-block:: Python + + + plt.plot(bins, rdf_traj_mean, label="trajectory") + plt.plot(bins, rdf_train_mean, label="training set") + + plt.legend() + plt.xlabel("r / Ã…") + plt.ylabel("radial distribution function") + + plt.show() + + + + + + + + +.. _sphx_glr_download_examples_ase_run_ase.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: run_ase.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: run_ase.py ` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: run_ase.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/ase/run_ase.zip b/docs/src/examples/ase/run_ase.zip new file mode 100644 index 0000000000000000000000000000000000000000..074a0cf90b7fc095beb556e85cee490919d5a9a0 GIT binary patch literal 16212 zcmdU0+iv5=8FrE$00O-W(AxoxpmJc-cHC`KAr7$F0}?EDyG<5p3)7HCF=%0s&MJ-3X6hS`sec$`KmNJo3mZ?-kb;=7C&n3^2sAOkJHDeFIdEy;j z{o{GBW=YAax>7~LQx=O%$+9YVB}z6^XRK1JE(PO~@w5;;o-@2mq=@k-X5u_oMTHNU zibYC`fhFE}IG&YB2E?jFNv0-@+3qFGXjsHzLGQ!1YCjB_bZSob-@`TKi&?%Z--Rw66CNXZH} zA%H@PVw&K_bf<|bB2fnHuc~Gd?ybhO%p4l4GnpzL4^LuVff*H>B&qN|vv(R5BCaD5 zk8oaaK7aH130)_q8&*VSan7?GB$yN`W2!FLnHZPAKVYwLr3NGxa<8n%1RjK=+nyB9 zAMgLZS>b&)tE#*l?(QZkFQY`rNCfyZP{nkY7gZ9aVt0Qp*x&o~)7?*gcks#1C;JZ$ zcKLYBe>d69M8!Wp83n|8VF}%#F)(guJW*+?&h#guKmDO{;%AA?LBLX=Uhp)LQCi1h zICO94JWD;s*f8aCTJxzGGX2PFXsY?CZ&vcGL1GSylp>vI0bYH8-WQa!1>j?jN8Uq7 zFIbg=;NUPsVZKAZLH(*w;ERz&*@(5lfm;fI=y{<|lUTFMYeg*OW08(gVd!7>sXw@S}8?VEPZ$zc3Ugt*2 zoGAh|cP{$TOnf2E4G$sk6-*+oz5s&wibVy7L|JQO*Roteok>BAibINUUZdA!D%ja9 ziDo1sc1zkCRFXMkN0kH{wS0~y1r(%Yhnk&<3djZz{0r=jzs>xizvU5SXipc!(cWX7LRh{Kr{SZ(WpKywpV$4N4+TSA}4FKtCyg6rl?mS`GUnY-Wq0DxfynQ^kr&2%)t^%t|@bOS=@iqv2%x7Jzva zX)y3A3O?#NKR**`y7Prft2Y^!l0EKd3bc(Kz?D1$dyIOmRH(p0aX`8-3=y^&d{7SS zNuF!;>=019e1RX7U`!#VI9(LlS1kBi@`~V>c3tz1g`j&_m(_J^xT<@Fw7K5KmwNT+zA{al_7#*RRXOc;|~YA7d&7SZ5XX>1r|o1 zvN-tG8pLl!k&4PO$8JBQy&r4c)0Q!qls$Pw6t-X-K#FDaq794X>5QL(Yp0@^BP4+N zutAlHoQR{(A9o`sNE}oV{zO*Iy$3J8eX@m+LE)^|0(aDMaE>rgdkwm}i5p;HU0_#c z;0tLw1!%OPjm}EY6=sMW3*r_4CBi{-zM3$&WW--Mx{2&qs^k;`+3wbBC{BX%;V#5f zKtDN=BMs){1OQLr>i9SnfhNS`x@hcX2TZC|PWVL#Iv{1*YBD}nH4g8gs}Y4W1Qv}W zfjWBjJaCpS;pGZqOZIzYrR5O5D0m|ZRSxXZE$=nt6Rz{JC6%f!hXEV=ozV&$eRS-3 zL`N-pi24e|Kf;|iq_Bl;ni#nWhdU&oU3y8YE&aLR7Xcxy}A6OS3d;@I3jaIV@^c0MbeRq)|OM18f~!wb|xs&H^H{O=Qiw- zm7#4)3~->nO{0d!kWsJ>+1FtnDGC6UFzH%IwcJ*GoFGu16Lj*SO-^KV;bFKlI%eOD*f;HMqVUv7P3E7l5+MR{-h?7r=BsF5=TA z1v#v41VS3J5QA8ovCCe=OYS1aw(nChCAG5vn)t+pCtt}jC)=eNZ_~_+#q`OkAXBXB zso`@MN7iMRmR+8fifQKoTMYIk6GV`jIpjabfc?DEp?K%TD6yz-9FJe4@DxCNA%%Tp1g^sT9Z8(QaB?J zU5yf{l|_mTzJC04%fW+xyD?x5%WX8p83iINLXfI$48=pXq5-H>K#68bQN@Nu^%!X(SYj$dWPET!tMU7L z?57_g0pzf>cK~C1DTDD>Hd>?u$+sdLf+Gdga&TZAhgp!1DVls$)M6Q;b&xlP=M)Ur zqqx|F8_2X=b~eoC!zkrtiJH)=R5NID%o7C1gpRKDfMCS88a9)-&~T!yfDl;ZqqD1^ zBDbwZMLeNkht{iDYZf1o+JG$0=^ZsfmL~Ok5WT-3kv8jtnE}iOhF$($bJn29`1#?3 zZ6?QwEDs+*6-6pCL_OuLgaAtF-Xqy6LN_TP=P)7NpK_FhMBU2=@2TD=m|iGCCZlJfJUJVe4~H-KRmfJ)@i z+rnyT_YywWRTjvGXlqa9NoVh!88tSY439DP+vMb7I5HDq!#$i)2?CiWv^)c)5v&;J zwqv6KT1Ygv&Y~9rkeAdhn+OT&T4B_;b*{ek3|%MR+hGaR-QsLvzp{yr+||JeWgzh3-iV}t(vs#8%<^0^#;k0975vs(H?cBCU>dPG0^ z@u-^T0~A-(q4AtV5O2T#!96Wunj_j_fN4OX++@fZud3XKGL*Mcmk;H` zTPe(kvbCA-Qj`xRq;gGF`B1LEEFa2#UH+(7mp>l7Yh8XTFsyG1f!D6f)Ap+>^Pz++ zE|lhJ-`f}GFFFOLAdx7~FP=d4dD^qa3VkTaft*}coe!mNHS}2jwyL>)t-hVQKXk#B zwy!Sx6M3(3T?#f^116f{k%{ABvt*s7&XRTQo24ijMHFgyjM5osNEp8 z()RS&0b6AjdmW1q2?;e#bkz+NI$H`#WOk zt@K($xrSEjy_Sf!`G(RF+5a1LiSI}T5YsL9po#oe-`Ch}4(0lr&B&+M+G`G_Yc&%Q zZlTkBuSL7pBd43VHeJ)%ThS#4->+PmUG+MmZf%od*(tIA<}mK7WO3z}_RO0C$`eLc7ar7}>uRz?VAtAXC)}>#C;q=`Hm*0Ys2=FVFFso6 z#)nJe0WMd%!GJ*M_a_AUu)6AW`HVp5>$O(!G?azBj!czkL?! zQc!TO#hs!wQ=!Ehb6FT==Mpq6OIl*}M=^_Ve9pgoJBvb1iT|!`VlgXufEmtnF z>Wxqtd1@x8kgK?Aic2?^C$$#luJkT9c-6^p+C+s8x8KYPeVV?ysF!uw5LO5B%9IC# z0(u_6Um7g%UyDMDl!o|dn95ER_?TVHQ$8Qz3(k53>m|NBgMsCPc7mb#qA7OyxqjQe zYj&CE2Syd_l!D)E93_ZIx@S_hoh3z~iV=Mqs`Co74-H66ju9sAEWE<79(`L1>kpO& zBIp}{BiAb+TdU&$^A`r@j5KGpM~ zqZAV#dWF3}hmtM+{z0&R5d6W?3zyL}^W_+!8%8N`z;6k4_h5vfLq$;cydPdZd++@Z zKe+hI?B-kU8yo-p8~ueZx!A9^clWIt*=zTGjjzQw`}6?c%V$6T1-*VB|CRXt@6YMe Fe*q$4Rrvq_ literal 0 HcmV?d00001 diff --git a/docs/src/examples/ase/train.sh b/docs/src/examples/ase/train.sh new file mode 100644 index 0000000000..f73b048e61 --- /dev/null +++ b/docs/src/examples/ase/train.sh @@ -0,0 +1,3 @@ +#!/bin/bash + +mtt train options.yaml diff --git a/docs/src/examples/basic_usage/basic_usage.zip b/docs/src/examples/basic_usage/basic_usage.zip new file mode 100644 index 0000000000000000000000000000000000000000..29173744cf9d766480cb629534615cf1c3358a9b GIT binary patch literal 4718 zcmb7IO^+ML5tZX42=?Z{m)r^x3`oLqsN@j9jAKB89oP_7Sx0LmMh-j8nI@; 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+ +.. only:: html + + .. image:: /examples/basic_usage/images/thumb/sphx_glr_usage_thumb.png + :alt: + + :ref:`sphx_glr_examples_basic_usage_usage.sh` + +.. raw:: html + +
Basic Usage
+
+ + +.. thumbnail-parent-div-close + +.. raw:: html + + + + +.. toctree:: + :hidden: + + /examples/basic_usage/usage + + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-gallery + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download all examples as source code: basic_usage.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/basic_usage/options.yaml b/docs/src/examples/basic_usage/options.yaml new file mode 100644 index 0000000000..c0c0ddf7bf --- /dev/null +++ b/docs/src/examples/basic_usage/options.yaml @@ -0,0 +1,18 @@ +# architecture used to train the model +architecture: + name: soap_bpnn + training: + num_epochs: 5 # a very short training run + batch_size: 10 + +# Mandatory section defining the parameters for system and target data of the +# training set +training_set: + systems: qm9_reduced_100.xyz # file where the positions are stored + targets: + energy: + key: U0 # name of the target value + unit: eV # unit of the target value + +test_set: 0.1 # 10 % of the training_set are randomly split and taken for test set +validation_set: 0.1 # 10 % of the training_set are randomly split and for validation diff --git a/docs/src/examples/basic_usage/output.xyz b/docs/src/examples/basic_usage/output.xyz new file mode 100644 index 0000000000..37cb4a2653 --- /dev/null +++ b/docs/src/examples/basic_usage/output.xyz @@ -0,0 +1,1201 @@ +5 +Properties=species:S:1:pos:R:3 energy=-40.47401479185843 pbc="F F F" +C -0.01269814 1.08580416 0.00800100 +H 0.00215042 -0.00603132 0.00197612 +H 1.01173084 1.46375116 0.00027657 +H -0.54081507 1.44752661 -0.87664372 +H -0.52381363 1.43793264 0.90639729 +4 +Properties=species:S:1:pos:R:3 energy=-56.53954696998065 pbc="F F F" +N -0.04042605 1.02410775 0.06256380 +H 0.01725746 0.01254521 -0.02737716 +H 0.91578937 1.35874519 -0.02875776 +H -0.52027774 1.34353213 -0.77554261 +3 +Properties=species:S:1:pos:R:3 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epsgap=0.3351 =59.5248 zpve=0.026841 U0=-77.308427 U=-77.305527 H=-77.304583 G=-77.327429 Cv=8.574 pbc="F F F" +C 0.59953949 0.00000000 1.00000000 +C -0.59953949 0.00000000 1.00000000 +H -1.66163859 0.00000000 1.00000000 +H 1.66163859 0.00000000 1.00000000 +3 +Properties=species:S:1:pos:R:3 A=0 B=44.593883 C=44.593883 mu=2.8937 alpha=12.99 epsHOMO=-0.3604 epsLUMO=0.0191 epsgap=0.3796 =48.7476 zpve=0.016601 U0=-93.411888 U=-93.40937 H=-93.408425 G=-93.431246 Cv=6.278 pbc="F F F" +C -0.01332393 1.13246572 0.00827589 +N 0.00231072 -0.01915859 0.00192873 +H -0.02780270 2.19894930 0.01415379 +4 +Properties=species:S:1:pos:R:3 A=285.48839 B=38.9823 C=34.29892 mu=2.1089 alpha=14.18 epsHOMO=-0.267 epsLUMO=-0.0406 epsgap=0.2263 =59.9891 zpve=0.026603 U0=-114.483613 U=-114.480746 H=-114.479802 G=-114.505268 Cv=6.413 pbc="F F F" +C -0.01397770 1.18021143 0.00775250 +O 0.00231399 -0.01966366 0.00216107 +H 0.91496029 1.78951117 0.00395656 +H -0.95910972 1.76401798 0.01718284 +8 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B=10.11122 C=9.07368 mu=2.5682 alpha=25.11 epsHOMO=-0.254 epsLUMO=-0.0198 epsgap=0.2342 =166.9728 zpve=0.055355 U0=-153.787612 U=-153.783728 H=-153.782784 G=-153.812518 Cv=11.219 pbc="F F F" +C -0.00294482 1.50991366 0.00867278 +C 0.02608284 0.00327563 -0.03745912 +O 0.94228801 -0.65507035 -0.45682576 +H 0.92278802 1.92634242 -0.39146557 +H -0.86201540 1.87852481 -0.56479538 +H -0.15050638 1.84393383 1.04289100 +H -0.89443009 -0.48643408 0.35774865 +6 +Properties=species:S:1:pos:R:3 A=73.8472 B=11.34793 C=9.83639 mu=3.7286 alpha=21.57 epsHOMO=-0.2543 epsLUMO=0.0302 epsgap=0.2845 =145.3078 zpve=0.045279 U0=-169.860788 U=-169.856903 H=-169.855958 G=-169.885594 Cv=10.89 pbc="F F F" +N -0.02589986 1.34614561 0.00889391 +C 0.04646728 -0.01174341 0.00120425 +O 1.07183520 -0.65258782 -0.01113313 +H 0.82535497 1.88504948 0.00373769 +H -0.90837680 1.82679638 0.01891992 +H -0.96144100 -0.47500376 0.00807391 +11 +Properties=species:S:1:pos:R:3 A=29.45018 B=8.37701 C=7.42076 mu=0.0597 alpha=34.75 epsHOMO=-0.323 epsLUMO=0.0949 epsgap=0.4179 =227.1361 zpve=0.103182 U0=-119.052475 U=-119.047927 H=-119.046983 G=-119.078157 Cv=14.84 pbc="F F F" +C -0.03113825 1.54081582 0.03192126 +C 0.01215347 0.01092235 -0.01603259 +C 0.72169129 -0.52583353 -1.26230570 +H 0.97955987 1.96459116 0.03098367 +H -0.55840223 1.94831192 -0.83816075 +H -0.54252252 1.90153531 0.93005671 +H 0.51522791 -0.36840234 0.88231134 +H -1.01070641 -0.38456999 0.02051783 +H 1.75851210 -0.17376585 -1.30871516 +H 0.74087192 -1.62024959 -1.27516511 +H 0.22023351 -0.19051179 -2.17729020 +9 +Properties=species:S:1:pos:R:3 A=35.09545 B=9.3686 C=8.1497 mu=1.4131 alpha=27.87 epsHOMO=-0.2619 epsLUMO=0.0798 epsgap=0.3417 =193.1659 zpve=0.079754 U0=-154.972731 U=-154.968412 H=-154.967467 G=-154.998148 Cv=13.546 pbc="F F F" +C -0.00860504 1.50203829 -0.00681217 +C 0.01099310 -0.01764877 -0.01377035 +O 0.68088841 -0.44041803 -1.19313210 +H 1.01153334 1.89662030 -0.01920155 +H -0.53159862 1.88076109 -0.88974659 +H -0.51674572 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0.77886562 -0.00617842 +C 0.00867174 0.00767042 0.00201032 +H -0.30541464 2.01702117 0.92533239 +H -0.32275498 2.02680156 -0.89347839 +H 1.88493396 0.79145020 -0.92119393 +H 1.90226631 0.78165391 0.89766373 +H -0.27088043 -0.51289125 0.91137474 +H -0.28821983 -0.50310409 -0.90740824 +7 +Properties=species:S:1:pos:R:3 A=25.77385 B=22.12109 C=14.18066 mu=1.7675 alpha=24.04 epsHOMO=-0.2682 epsLUMO=0.1042 epsgap=0.3724 =129.891 zpve=0.057289 U0=-153.742562 U=-153.73941 H=-153.738466 G=-153.766642 Cv=9.176 pbc="F F F" +C 0.01537720 1.41764380 0.00956287 +C 1.26478161 0.64923984 -0.00655044 +O -0.00024018 -0.00771006 0.00204055 +H -0.31763326 1.88586598 0.93475751 +H -0.33527583 1.89576673 -0.90397578 +H 1.83246682 0.56256848 -0.93193290 +H 1.85008258 0.55267938 0.90680147 +10 +Properties=species:S:1:pos:R:3 A=10.12193 B=8.49011 C=4.89615 mu=2.7362 alpha=35.53 epsHOMO=-0.2431 epsLUMO=-0.0087 epsgap=0.2344 =292.4367 zpve=0.083382 U0=-193.08834 U=-193.082969 H=-193.082024 G=-193.116476 Cv=16.893 pbc="F F F" +C -0.00310189 1.48050328 -0.17259867 +C -0.04527202 -0.02887059 0.00139493 +C 1.29705800 -0.72194415 0.16889042 +O -1.08643172 -0.64273343 0.00630393 +H 0.59403529 1.74668579 -1.05225043 +H -1.01540407 1.87034023 -0.28382265 +H 0.48099600 1.95070605 0.69099376 +H 1.82512144 -0.32470731 1.04328658 +H 1.14892507 -1.79597689 0.28554393 +H 1.93682635 -0.52949692 -0.69995403 +9 +Properties=species:S:1:pos:R:3 A=10.78886 B=9.27509 C=5.14977 mu=3.6367 alpha=31.83 epsHOMO=-0.2436 epsLUMO=0.0347 epsgap=0.2783 =267.6148 zpve=0.07319 U0=-209.159302 U=-209.15402 H=-209.153076 G=-209.187468 Cv=16.561 pbc="F F F" +C 0.00679889 1.49969736 -0.02567030 +C -0.02740385 -0.01752051 -0.14894811 +N 1.17826667 -0.63324298 0.05691721 +O -1.03721965 -0.63980649 -0.40727572 +H 0.91390280 1.92923755 -0.46100067 +H -0.87080896 1.91124186 -0.52345176 +H -0.02826124 1.78517271 1.03149125 +H 1.98405139 -0.13175861 0.38674906 +H 1.19049421 -1.63919563 0.10006209 +8 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-0.54817239 1.91268414 0.90208039 +H -0.99752525 -0.37310578 0.00977297 +H 1.75070021 -0.15586784 -1.33269631 +H 0.73446691 -1.60328288 -1.29939696 +H 0.19367011 -0.16085589 -2.17195508 +H 0.23611990 -0.18382017 2.17005260 +H 0.75964083 -1.61713153 1.27226101 +H 1.77635866 -0.16985318 1.30053019 +12 +Properties=species:S:1:pos:R:3 A=8.67553 B=8.00568 C=4.75542 mu=1.4259 alpha=38.58 epsHOMO=-0.2612 epsLUMO=0.074 epsgap=0.3351 =318.3721 zpve=0.107673 U0=-194.267232 U=-194.261748 H=-194.260804 G=-194.294663 Cv=19.052 pbc="F F F" +C -0.03315871 1.54782566 -0.00438815 +C -0.01108534 0.01859095 0.01676117 +C 0.70938384 -0.53596711 1.23978444 +O -1.33219841 -0.51712554 0.05236057 +H 0.98215692 1.95715900 -0.03182225 +H -0.56388100 1.92087623 -0.88845865 +H -0.54082548 1.93340945 0.88575489 +H 0.51042122 -0.33767241 -0.88819176 +H 0.22240492 -0.18785860 2.15650245 +H 0.68242024 -1.62887638 1.23305141 +H 1.75434301 -0.21246950 1.25524188 +H -1.81818681 -0.15577435 -0.69553962 +6 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zpve=0.015951 U0=-185.648533 U=-185.644825 H=-185.64388 G=-185.667652 Cv=10.398 pbc="F F F" +N 0.01745734 -1.16134217 -0.00415342 +C 0.00253212 -0.00344278 0.00179939 +C -0.01611419 1.37220987 0.00934873 +N -0.03264302 2.53008276 0.01609090 +6 +Properties=species:S:1:pos:R:3 A=68.5171 B=4.8345 C=4.51586 mu=2.7824 alpha=31.14 epsHOMO=-0.2777 epsLUMO=-0.0735 epsgap=0.2042 =268.3921 zpve=0.037208 U0=-190.624631 U=-190.620363 H=-190.619419 G=-190.650543 Cv=13.049 pbc="F F F" +O -0.06776120 -0.04233838 0.00282489 +C -0.00562945 1.16382879 0.00743862 +C 1.22985272 1.91339997 0.00028781 +C 2.23625322 2.57430970 -0.00529277 +H -0.91670701 1.79467546 0.01781352 +H 3.13429662 3.14370267 -0.01037656 +5 +Properties=species:S:1:pos:R:3 A=67.88408 B=5.00823 C=4.66413 mu=2.3112 alpha=26.25 epsHOMO=-0.3166 epsLUMO=-0.11 epsgap=0.2066 =251.0007 zpve=0.02654 U0=-206.721858 U=-206.717875 H=-206.716931 G=-206.747625 Cv=11.329 pbc="F F F" +O -0.04055978 -0.03799144 0.00266513 +C -0.01768870 1.16193251 0.00746008 +C 1.23315534 1.93381336 0.00043371 +N 2.19857378 2.57055097 -0.00483320 +H -0.92627534 1.78959196 0.01764859 +6 +Properties=species:S:1:pos:R:3 A=56.25048 B=4.77441 C=4.40088 mu=0.002 alpha=26.12 epsHOMO=-0.2668 epsLUMO=-0.1113 epsgap=0.1555 =266.8164 zpve=0.036943 U0=-227.798785 U=-227.79457 H=-227.793626 G=-227.825074 Cv=12.147 pbc="F F F" +O 0.00337122 -0.03146586 0.00157206 +C -0.01713174 1.17081409 0.00676444 +C 1.26555783 1.99719410 -0.00007703 +O 1.24507069 3.19946589 0.00681275 +H -0.94690483 1.77678142 0.01852668 +H 2.19534410 1.39121374 -0.01006739 +10 +Properties=species:S:1:pos:R:3 A=80.28766 B=3.3688 C=3.3688 mu=0 alpha=42.32 epsHOMO=-0.2412 epsLUMO=0.0684 epsgap=0.3096 =400.2236 zpve=0.083896 U0=-155.908941 U=-155.90318 H=-155.902236 G=-155.937641 Cv=17.447 pbc="F F F" +C -0.01787959 1.46676893 0.01011180 +C 0.00186644 0.01072913 0.00211109 +C 0.01819704 -1.19272861 -0.00450723 +C 0.03795170 -2.64876793 -0.01251193 +H 0.86460312 1.87589979 0.51479719 +H -0.03217624 1.87210581 -1.00791002 +H -0.90233115 1.85185524 0.53003953 +H -0.83477011 -3.06339007 0.50451436 +H 0.03276422 -3.04315564 -1.03491223 +H 0.93213486 -3.03931642 0.48626606 +10 +Properties=species:S:1:pos:R:3 A=27.39459 B=4.53005 C=4.08342 mu=0.7067 alpha=40.09 epsHOMO=-0.2592 epsLUMO=0.0566 epsgap=0.3157 =333.9589 zpve=0.084338 U0=-155.897345 U=-155.892291 H=-155.891347 G=-155.924226 Cv=17.13 pbc="F F F" +C -0.03095840 1.54775025 0.03167945 +C 0.01485375 0.00962471 -0.02082154 +C 0.69099073 -0.49954575 -1.20857624 +C 1.25222275 -0.89514467 -2.19527879 +H 0.97899300 1.96621609 0.03373834 +H -0.56082354 1.95002902 -0.83580745 +H -0.54576708 1.88225519 0.93709062 +H 0.51920135 -0.37425398 0.87496224 +H -1.00644802 -0.39030838 0.01340632 +H 1.74594541 -1.25604187 -3.06299415 +9 +Properties=species:S:1:pos:R:3 A=28.0016 B=4.67752 C=4.21765 mu=3.9233 alpha=35.38 epsHOMO=-0.3213 epsLUMO=0.034 epsgap=0.3553 =314.5335 zpve=0.07419 U0=-172.006141 U=-172.001467 H=-172.000523 G=-172.032826 Cv=14.988 pbc="F F F" +C -0.02522005 1.54773054 0.02133370 +C 0.01183939 0.00999278 -0.01556702 +C 0.68915114 -0.50836846 -1.20331488 +N 1.22565733 -0.90360846 -2.14737482 +H 0.98571620 1.96212990 0.02370153 +H -0.55522192 1.94599662 -0.84711771 +H -0.53962556 1.88294354 0.92536604 +H 0.52481910 -0.38226048 0.86933355 +H -1.00453542 -0.39832109 0.00530071 +8 +Properties=species:S:1:pos:R:3 A=32.19893 B=4.76299 C=4.30696 mu=4.4361 alpha=31.81 epsHOMO=-0.2683 epsLUMO=0.0173 epsgap=0.2855 =295.6635 zpve=0.063305 U0=-188.042067 U=-188.037478 H=-188.036534 G=-188.06863 Cv=14.488 pbc="F F F" +N -0.03608099 1.48762533 0.03678072 +C -0.01039471 0.02517605 -0.01506608 +C -1.37180956 -0.52108920 0.02739272 +N -2.44026695 -0.95956444 0.03370519 +H 0.91729752 1.83547547 0.05202682 +H -0.47162178 1.85677205 -0.80275569 +H 0.48946161 -0.40880401 -0.89715168 +H 0.52827150 -0.34533338 0.86469547 +8 +Properties=species:S:1:pos:R:3 A=35.11615 B=4.66117 C=4.22354 mu=1.7211 alpha=33.1 epsHOMO=-0.2595 epsLUMO=0.0277 epsgap=0.2872 =300.0993 zpve=0.060632 U0=-191.810916 U=-191.806025 H=-191.805081 G=-191.837634 Cv=15.855 pbc="F F F" +O -0.02013197 1.45738137 0.01010195 +C 0.00168478 0.03579980 0.00224736 +C -1.36366691 -0.47481101 0.01255056 +C -2.47902835 -0.91970202 0.02082451 +H 0.89446911 1.75736553 0.00327363 +H 0.51979028 -0.35175413 -0.88980302 +H 0.53670458 -0.36127661 0.88000894 +H -3.46773502 -1.30737009 0.02818120 +7 +Properties=species:S:1:pos:R:3 A=35.93172 B=4.83443 C=4.37827 mu=4.6788 alpha=28.56 epsHOMO=-0.3018 epsLUMO=0.0022 epsgap=0.3039 =280.6659 zpve=0.050262 U0=-207.916786 U=-207.912215 H=-207.911271 G=-207.943384 Cv=13.845 pbc="F F F" +O -0.03738221 1.44547841 0.01010769 +C 0.01292906 0.03441661 0.00209165 +C -1.35747452 -0.48874523 0.01239738 +N -2.42135277 -0.93685184 0.02016053 +H 0.86898057 1.76972949 0.00417577 +H 0.51692713 -0.36329790 -0.89215814 +H 0.53387616 -0.37278749 0.88223921 +10 +Properties=species:S:1:pos:R:3 A=26.14564 B=4.2893 C=4.12773 mu=2.6741 alpha=35.83 epsHOMO=-0.25 epsLUMO=-0.0205 epsgap=0.2295 =333.3276 zpve=0.084175 U0=-193.075202 U=-193.070116 H=-193.069171 G=-193.102798 Cv=15.954 pbc="F F F" +C 0.16879340 1.52513042 -0.15747056 +C -0.18737625 0.06194763 0.14679374 +C 0.50917645 -0.43996215 1.39125850 +O 1.18191079 -1.43565585 1.45816384 +H 1.23126511 1.63137254 -0.39632693 +H -0.40744668 1.89431681 -1.00969246 +H -0.04931034 2.17266260 0.69857432 +H -1.26905160 -0.01668067 0.32854433 +H 0.06279062 -0.60250479 -0.68474036 +H 0.35384841 0.20663370 2.28871052 +9 +Properties=species:S:1:pos:R:3 A=19.98749 B=6.11589 C=4.82584 mu=3.7071 alpha=32.78 epsHOMO=-0.2516 epsLUMO=0.0335 epsgap=0.2851 =279.7863 zpve=0.074166 U0=-209.144909 U=-209.139976 H=-209.139032 G=-209.172305 Cv=15.058 pbc="F F F" +C 0.01474916 1.47888256 -0.03509326 +N 0.18708077 0.03831317 -0.02906851 +C 0.45703661 -0.66858895 1.10069001 +O 0.58457996 -0.19876202 2.21119705 +H 0.76025289 1.96736104 -0.67198126 +H -0.98462423 1.75984483 -0.38485103 +H 0.14269849 1.82387718 0.99149397 +H 0.10386349 -0.46937726 -0.89461379 +H 0.55047568 -1.75253452 0.88868242 +8 +Properties=species:S:1:pos:R:3 A=48.62351 B=4.67298 C=4.38187 mu=3.92 alpha=29.47 epsHOMO=-0.2814 epsLUMO=0.0074 epsgap=0.2888 =293.917 zpve=0.061327 U0=-229.013797 U=-229.009003 H=-229.008059 G=-229.041086 Cv=13.885 pbc="F F F" +C 0.10653781 1.43512049 0.03336100 +O 0.42686564 0.04716929 0.09309945 +C 0.50714862 -0.63652665 -1.06318297 +O 0.76927014 -1.79955017 -1.11010475 +H 0.92349251 2.00385139 0.48465728 +H -0.03757815 1.76927183 -1.00057425 +H -0.81269569 1.60801916 0.59873287 +H 0.30458460 -0.00231902 -1.94965553 +8 +Properties=species:S:1:pos:R:3 A=36.49282 B=4.31175 C=4.04493 mu=1.7341 alpha=28.53 epsHOMO=-0.2537 epsLUMO=-0.0341 epsgap=0.2196 =303.8129 zpve=0.060508 U0=-228.992613 U=-228.987769 H=-228.986825 G=-229.019918 Cv=14.78 pbc="F F F" +O 0.19375856 1.37170995 0.31398297 +C 0.04992637 0.01405481 -0.04526239 +C -1.40888025 -0.39635680 -0.20601574 +O -1.77729462 -1.37233860 -0.80361689 +H -0.01723186 1.91269132 -0.45337758 +H 0.60440478 -0.26519766 -0.95205725 +H 0.46740406 -0.57521679 0.78176270 +H -2.11835055 0.30075962 0.29870515 +14 +Properties=species:S:1:pos:R:3 A=23.44662 B=3.60588 C=3.38683 mu=0 alpha=45.71 epsHOMO=-0.317 epsLUMO=0.0937 epsgap=0.4107 =426.2996 zpve=0.131708 U0=-158.340943 U=-158.33517 H=-158.334226 G=-158.36894 Cv=19.668 pbc="F F F" +C -0.03007624 1.55804912 0.02980774 +C 0.00707277 0.02834877 -0.00712529 +C 0.71320792 -0.52989340 -1.24765581 +C 0.75036895 -2.05959366 -1.28458957 +H 0.98195330 1.97845113 0.02800797 +H -0.55582433 1.96212343 -0.84270157 +H -0.54083762 1.92692755 0.92493697 +H 0.50928672 -0.34909049 0.89361319 +H -1.01733481 -0.36542299 0.03207393 +H 0.21098948 -0.15245770 -2.14839302 +H 1.73761331 -0.13611625 -1.28685672 +H 1.27616854 -2.46366410 -0.41210980 +H -0.26165699 -2.48000447 -1.28273552 +H 1.26108491 -2.42846444 -2.17974803 +12 +Properties=species:S:1:pos:R:3 A=26.59789 B=3.79186 C=3.53779 mu=1.3402 alpha=38.61 epsHOMO=-0.2619 epsLUMO=0.081 epsgap=0.3429 =382.8628 zpve=0.108241 U0=-194.261089 U=-194.255495 H=-194.254551 G=-194.28893 Cv=18.431 pbc="F F F" +C -0.00379012 1.52196790 0.01027996 +C -0.03589372 -0.00778254 0.00225671 +C -1.45219016 -0.56653711 0.01284386 +O -1.36799785 -1.98394604 0.00453606 +H 1.02477576 1.89411231 0.00239793 +H -0.51286674 1.93764614 -0.86653888 +H -0.49584080 1.92801344 0.90121721 +H 0.48140784 -0.39530552 -0.88316064 +H 0.49828589 -0.40486256 0.87330348 +H -1.98734220 -0.20399494 0.90666062 +H -2.00431485 -0.19459473 -0.86666460 +H -2.26171819 -2.33824298 0.01034748 +12 +Properties=species:S:1:pos:R:3 A=28.29567 B=4.14901 C=3.88349 mu=1.0363 alpha=39.34 epsHOMO=-0.2503 epsLUMO=0.0925 epsgap=0.3428 =368.9331 zpve=0.107895 U0=-194.254127 U=-194.248585 H=-194.247641 G=-194.281899 Cv=17.888 pbc="F F F" +C -0.00519551 1.50868224 0.03308760 +C 0.01316580 -0.01037819 0.01988643 +O 0.69758944 -0.46344140 1.16868979 +C 0.76688491 -1.86508183 1.24602669 +H 1.01445476 1.90423799 0.02996018 +H -0.52990866 1.89184059 -0.84726911 +H -0.51263765 1.87805843 0.92875424 +H -1.01599507 -0.40998729 0.00714282 +H 0.51093143 -0.38369435 -0.89219395 +H -0.23415841 -2.32451489 1.28977780 +H 1.30129276 -2.29853663 0.38486095 +H 1.30990287 -2.11811998 2.16013206 +10 +Properties=species:S:1:pos:R:3 A=14.79671 B=5.6956 C=4.58846 mu=0.0075 alpha=31.42 epsHOMO=-0.2594 epsLUMO=0.0584 epsgap=0.3179 =297.8398 zpve=0.085172 U0=-230.183076 U=-230.177723 H=-230.176779 G=-230.211195 Cv=16.837 pbc="F F F" +O -0.01418735 1.42642242 -0.05423651 +C -0.00391839 0.00717714 0.03496062 +C -1.42317019 -0.55560600 -0.01256639 +O -2.11229413 -0.10563875 -1.17228313 +H -0.18987882 1.64154705 -0.97555148 +H 0.59659504 -0.43315430 -0.77409095 +H 0.47311116 -0.24903733 0.98747216 +H -1.39494859 -1.64975279 -0.06283188 +H -1.96497417 -0.27216915 0.90131972 +H -2.35925991 0.80944396 -1.00614597 +12 +Properties=species:S:1:pos:R:3 A=15.61472 B=6.31363 C=5.54514 mu=0.1136 alpha=41.96 epsHOMO=-0.2727 epsLUMO=0.1012 epsgap=0.3738 =298.6061 zpve=0.109284 U0=-157.116735 U=-157.11209 H=-157.111146 G=-157.143262 Cv=16.49 pbc="F F F" +C -0.03602245 1.52092949 0.01569161 +C 0.01938487 0.01004403 0.04814953 +C 1.30891671 -0.71251995 -0.24920416 +C 0.75347423 -0.70267714 1.15564676 +H 0.90105691 1.95389291 0.38283338 +H -0.19640365 1.89437369 -1.00208569 +H -0.84685273 1.90588930 0.64451322 +H -0.88416809 -0.48001889 -0.30566335 +H 1.26900901 -1.63903068 -0.81150371 +H 2.19010738 -0.11131209 -0.44927513 +H 1.26248862 -0.09487455 1.89690028 +H 0.33540836 -1.62248918 1.54980695 +10 +Properties=species:S:1:pos:R:3 A=18.21931 B=6.63877 C=5.92459 mu=1.812 alpha=35.01 epsHOMO=-0.2633 epsLUMO=0.1052 epsgap=0.3685 =267.2979 zpve=0.085275 U0=-193.039603 U=-193.035186 H=-193.034242 G=-193.065979 Cv=14.764 pbc="F F F" +C -0.01646343 1.51830705 0.00771445 +C -0.02743227 0.01657694 -0.10889741 +C 0.77296553 -0.70131886 -1.10863115 +O 1.17212293 -0.66702969 0.26278829 +H 0.91865980 1.92914217 -0.38285880 +H -0.85158014 1.95456506 -0.55109460 +H -0.11487702 1.82596470 1.05397777 +H -0.93582598 -0.47350600 0.24682793 +H 0.44443922 -1.67201495 -1.47810622 +H 1.40538284 -0.13600339 -1.79271491 +11 +Properties=species:S:1:pos:R:3 A=16.58914 B=7.18798 C=6.11415 mu=1.1353 alpha=39.02 epsHOMO=-0.2304 epsLUMO=0.0968 epsgap=0.3271 =270.5508 zpve=0.097671 U0=-173.147782 U=-173.143343 H=-173.142399 G=-173.174073 Cv=15.298 pbc="F F F" +C -0.05275541 1.47419574 0.00999108 +N 0.01940516 0.02189947 0.06334866 +C 1.32511048 -0.56446850 -0.21420725 +C 0.78051759 -0.54057993 1.17242760 +H 0.86784202 1.97176076 0.36297285 +H -0.23845155 1.79095005 -1.02142866 +H -0.88683448 1.81938959 0.62950398 +H 1.31992909 -1.48186782 -0.79602038 +H 2.15202820 0.11020204 -0.43285248 +H 1.24563570 0.14996098 1.87499245 +H 0.37013358 -1.44020447 1.62233365 +10 +Properties=species:S:1:pos:R:3 A=16.79477 B=6.71015 C=5.86781 mu=1.3894 alpha=34.64 epsHOMO=-0.239 epsLUMO=0.0775 epsgap=0.3166 =263.966 zpve=0.085106 U0=-193.034988 U=-193.030356 H=-193.029411 G=-193.061689 Cv=15.679 pbc="F F F" +O 0.21577583 1.35386859 -0.10695107 +C 0.01628099 -0.02832718 -0.02915694 +C -0.39081465 -0.71497009 1.24315669 +C -1.35118138 -0.64280380 0.06273589 +H -0.43642338 1.79062849 0.45006761 +H 0.74981167 -0.52404050 -0.65624767 +H 0.05959375 -1.66840483 1.49729237 +H -0.61369353 -0.08848006 2.10170640 +H -2.19821932 0.03058430 0.15411451 +H -1.56395912 -1.54640426 -0.49826515 +12 +Properties=species:S:1:pos:R:3 A=10.66107 B=10.66107 C=6.30452 mu=0 alpha=41.83 epsHOMO=-0.2982 epsLUMO=0.0956 epsgap=0.3938 =268.4432 zpve=0.110511 U0=-157.115484 U=-157.111322 H=-157.110378 G=-157.141657 Cv=14.696 pbc="F F F" +C -0.02542957 1.54032578 -0.04215944 +C 1.52564399 1.50419989 0.04121276 +C 1.42146727 0.06146790 0.60844885 +C -0.02638833 -0.01070956 0.04935088 +H -0.46591372 1.99639894 0.84962894 +H -0.47900239 2.00028207 -0.92365389 +H 1.98248278 1.51375334 -0.95303183 +H 2.02191365 2.25986579 0.65522129 +H 2.14291679 -0.67956358 0.25531480 +H 1.43105853 0.05751479 1.70261879 +H -0.79053440 -0.48530296 0.66996573 +H -0.05233245 -0.47238446 -0.94236394 +10 +Properties=species:S:1:pos:R:3 A=12.06545 B=11.77731 C=6.71997 mu=1.7978 alpha=34.56 epsHOMO=-0.2424 epsLUMO=0.0859 epsgap=0.3283 =236.9759 zpve=0.086675 U0=-193.034094 U=-193.029968 H=-193.029024 G=-193.060777 Cv=12.915 pbc="F F F" +C -0.03489789 1.55259455 -0.08507691 +C 1.49349183 1.45459700 0.10114677 +O 1.42725884 0.02348990 0.27254886 +C -0.00634758 0.02236494 0.10944630 +H -0.56579210 2.11301721 0.68591527 +H -0.36628352 1.89393380 -1.06692327 +H 2.10031724 1.73525720 -0.76898349 +H 1.90023090 1.95506611 0.98896748 +H -0.51987849 -0.35595179 1.00235948 +H -0.31970062 -0.57567421 -0.75559275 +12 +Properties=species:S:1:pos:R:3 A=8.63483 B=4.11977 C=2.88825 mu=0.6875 alpha=45.37 epsHOMO=-0.2392 epsLUMO=0.0192 epsgap=0.2584 =452.5112 zpve=0.100501 U0=-248.375248 U=-248.368823 H=-248.367879 G=-248.405354 Cv=21.616 pbc="F F F" +C -0.10255370 1.49907127 -0.00133642 +C -0.00519774 -0.00343114 -0.02723470 +C -0.00688536 -0.76890740 1.26434744 +N 0.08186799 -0.69202791 -1.10087306 +O 0.07439067 0.13215394 -2.24195258 +H 0.78342093 1.94690862 -0.46354050 +H -0.96150430 1.83614082 -0.59021965 +H -0.19854173 1.87462989 1.01931607 +H -0.92620012 -0.57297946 1.82982479 +H 0.07256154 -1.84064782 1.07516863 +H 0.82937178 -0.45553340 1.90133089 +H 0.13975924 -0.51254299 -2.95389260 +10 +Properties=species:S:1:pos:R:3 A=9.17136 B=9.04195 C=4.5531 mu=1.8689 alpha=43.14 epsHOMO=-0.2029 epsLUMO=0.0499 epsgap=0.2528 =303.9808 zpve=0.082433 U0=-210.101789 U=-210.097816 H=-210.096872 G=-210.12818 Cv=14.821 pbc="F F F" +N -0.00829694 1.35362838 0.00995971 +C 1.28033254 1.82457421 -0.00022459 +C 2.12216639 0.73698898 -0.01445600 +C 1.30849904 -0.43109580 -0.01284154 +C -0.00342782 -0.01836884 0.00235736 +H -0.83243234 1.92772668 0.02122995 +H 1.48471820 2.88312630 0.00354313 +H 3.20066918 0.77343077 -0.02488416 +H 1.64810599 -1.45539683 -0.02180394 +H -0.92551647 -0.57697054 0.00839402 +9 +Properties=species:S:1:pos:R:3 A=9.77329 B=9.41192 C=4.7946 mu=3.6193 alpha=39.13 epsHOMO=-0.2253 epsLUMO=0.0332 epsgap=0.2585 =283.6817 zpve=0.071145 U0=-226.160842 U=-226.157088 H=-226.156144 G=-226.187104 Cv=13.371 pbc="F F F" +N -0.00996090 1.35636172 0.01000855 +C 1.29535422 1.79575578 -0.00047017 +C 2.05550519 0.65637438 -0.01422927 +N 1.25385745 -0.46275132 -0.01260894 +C 0.02301170 -0.00706382 0.00200146 +H -0.83195718 1.93618436 0.02132575 +H 1.54553961 2.84344998 0.00289054 +H 3.13177434 0.57569766 -0.02524155 +H -0.87667864 -0.60414531 0.00750794 +9 +Properties=species:S:1:pos:R:3 A=9.50228 B=9.29497 C=4.69874 mu=0.5571 alpha=39.2 epsHOMO=-0.2246 epsLUMO=0.0199 epsgap=0.2445 =289.005 zpve=0.069883 U0=-229.969129 U=-229.965414 H=-229.96447 G=-229.995393 Cv=13.358 pbc="F F F" +O -0.03255819 1.35065130 0.00967376 +C 1.25319237 1.79167710 -0.00030351 +C 2.11664681 0.74375623 -0.01441494 +C 1.30397796 -0.43852387 -0.01283316 +C 0.01629568 -0.00777185 0.00191294 +H 1.37951089 2.86161135 0.00427433 +H 3.19376406 0.79865501 -0.02458604 +H 1.63858893 -1.46383271 -0.02156949 +H -0.93736295 -0.50899925 0.00843891 +8 +Properties=species:S:1:pos:R:3 A=10.10926 B=9.69372 C=4.94857 mu=1.5081 alpha=35.17 epsHOMO=-0.2509 epsLUMO=0.001 epsgap=0.2519 =269.2444 zpve=0.058593 U0=-246.02915 U=-246.025614 H=-246.024669 G=-246.055309 Cv=12.04 pbc="F F F" +O -0.03808298 1.35643965 0.00975193 +C 1.26496982 1.76855319 -0.00061744 +C 2.04955750 0.66630033 -0.01406047 +N 1.24227075 -0.46732066 -0.01237541 +C 0.04019820 0.00561577 0.00183569 +H 1.43513907 2.83095265 0.00340823 +H 3.12532898 0.59783015 -0.02480757 +H -0.89387707 -0.53342154 0.00796935 +17 +Properties=species:S:1:pos:R:3 A=4.4178 B=4.41753 C=4.41717 mu=0.0003 alpha=56.01 epsHOMO=-0.3145 epsLUMO=0.0737 epsgap=0.3882 =486.2719 zpve=0.158836 U0=-197.632222 U=-197.625241 H=-197.624297 G=-197.661411 Cv=26.084 pbc="F F F" +C -0.01859029 1.53486266 0.01039904 +C 0.00210320 -0.00413672 0.00196980 +C 0.74643954 -0.51390760 1.24887638 +C -1.44190166 -0.53673615 0.01311457 +C 0.72234971 -0.50049606 -1.26445632 +H 0.99776928 1.94420529 0.00435474 +H -0.54181883 1.92841415 -0.86819911 +H -0.52758402 1.91888376 0.90148530 +H 0.25549576 -0.17602728 2.16833067 +H 0.77815389 -1.60892372 1.27088904 +H 1.77964786 -0.14975834 1.26944035 +H -1.99871895 -0.18888794 -0.86408066 +H -1.45956909 -1.63227235 0.00649916 +H -1.98088827 -0.19979639 0.90561580 +H 1.75482473 -0.13554018 -1.30118083 +H 0.75396989 -1.59524036 -1.29822280 +H 0.21358221 -0.15343900 -2.17067181 +15 +Properties=species:S:1:pos:R:3 A=4.69474 B=4.66938 C=4.47909 mu=1.384 alpha=49.04 epsHOMO=-0.2601 epsLUMO=0.0664 epsgap=0.3265 =449.0573 zpve=0.134977 U0=-233.560626 U=-233.553779 H=-233.552834 G=-233.589759 Cv=25.128 pbc="F F F" +C -0.00855999 1.54270147 0.00152717 +C 0.00506780 0.00702635 0.01856160 +C 0.76137731 -0.51839196 1.24766443 +C -1.42012774 -0.54933183 -0.02116008 +O 0.62590477 -0.48363745 -1.17657057 +H 1.01361406 1.94020776 0.00281128 +H -0.51459669 1.90702466 -0.89705781 +H -0.52214815 1.94882514 0.87918344 +H 0.28130613 -0.20165548 2.17943598 +H 0.79965506 -1.61131746 1.23022549 +H 1.79096916 -0.14072935 1.26083003 +H -1.93854829 -0.19910724 -0.91828281 +H -1.39937632 -1.64259062 -0.04563833 +H -1.98850725 -0.23059656 0.85726520 +H 1.53111188 -0.15488046 -1.19210222 +9 +Properties=species:S:1:pos:R:3 A=10.21876 B=4.04324 C=2.94978 mu=2.8579 alpha=42.02 epsHOMO=-0.2654 epsLUMO=-0.0575 epsgap=0.2079 =416.7799 zpve=0.065175 U0=-229.927277 U=-229.921622 H=-229.920677 G=-229.955843 Cv=18.723 pbc="F F F" +C -0.00693851 1.49559567 -0.01063358 +C -0.02986170 -0.01594623 0.06248673 +O -0.08815239 -0.63215062 1.10334812 +C 0.02336981 -0.70256747 -1.22062379 +C 0.06738132 -1.26058341 -2.28623129 +H 0.90429806 1.83281887 -0.51655384 +H -0.85338524 1.85556926 -0.60552819 +H -0.05237764 1.91099807 0.99695949 +H 0.10466868 -1.77036047 -3.21832617 +8 +Properties=species:S:1:pos:R:3 A=10.16063 B=4.15847 C=3.0056 mu=3.3351 alpha=37.08 epsHOMO=-0.3007 epsLUMO=-0.0889 epsgap=0.2118 =397.7757 zpve=0.05451 U0=-246.027383 U=-246.022024 H=-246.02108 G=-246.055769 Cv=16.963 pbc="F F F" +C -0.01171097 1.49973776 -0.01213268 +C -0.02919740 -0.00338745 0.07151218 +O -0.09164028 -0.64592927 1.08732241 +C 0.03831104 -0.69690258 -1.23748557 +N 0.09136286 -1.22982650 -2.26228068 +H 0.90093132 1.83370670 -0.51814928 +H -0.85663933 1.84864400 -0.61618135 +H -0.06416540 1.92131542 0.99203818 +8 +Properties=species:S:1:pos:R:3 A=10.68423 B=4.3198 C=3.07993 mu=5.1815 alpha=38.31 epsHOMO=-0.2739 epsLUMO=-0.0438 epsgap=0.2301 =384.5574 zpve=0.056619 U0=-242.19573 U=-242.190591 H=-242.189646 G=-242.223513 Cv=17.465 pbc="F F F" +N -0.05232108 1.34991769 0.15535538 +C 0.08549378 -0.01476951 0.02668890 +N 1.14555215 -0.71993906 -0.03966037 +C -1.19426556 -0.71152088 0.01802151 +N -2.22747610 -1.22790543 0.00132148 +H 0.75474469 1.91998737 -0.04411334 +H -0.94458527 1.76600473 -0.05932293 +H 1.98312429 -0.13415500 -0.02878551 +9 +Properties=species:S:1:pos:R:3 A=9.08854 B=4.42607 C=3.03169 mu=0.9461 alpha=36.51 epsHOMO=-0.2538 epsLUMO=-0.0964 epsgap=0.1574 =399.222 zpve=0.064992 U0=-267.10335 U=-267.097658 H=-267.096714 G=-267.132534 Cv=17.806 pbc="F F F" +C -0.43126066 1.30586664 -0.12758585 +C 0.25285715 -0.01026988 0.13138867 +O 0.69872099 -0.36890333 1.19604690 +C 0.38978943 -0.96773680 -1.06226208 +O -0.01611268 -0.71564293 -2.16583718 +H 0.10006565 1.85324490 -0.91332597 +H -1.44270433 1.13069361 -0.50923371 +H -0.46757554 1.89548158 0.78836479 +H 0.90329886 -1.91558845 -0.79531972 +9 +Properties=species:S:1:pos:R:3 A=10.08927 B=4.54115 C=3.13487 mu=2.7707 alpha=38.09 epsHOMO=-0.2561 epsLUMO=-0.064 epsgap=0.192 =384.4827 zpve=0.067668 U0=-263.278851 U=-263.273589 H=-263.272645 G=-263.306835 Cv=17.972 pbc="F F F" +N -0.16032929 1.08528962 -0.45398652 +C 0.18649127 -0.08762327 0.14758021 +N 1.28979627 -0.46427423 0.67927030 +C -0.94204384 -1.08440264 0.20423621 +O -2.01802205 -0.89007687 -0.30583149 +H 0.54574437 1.73837930 -0.74689246 +H -1.04684920 1.09570857 -0.93607796 +H 2.01738195 0.24991828 0.59881811 +H -0.67740148 -2.01003995 0.74746441 +13 +Properties=species:S:1:pos:R:3 A=7.92874 B=3.8272 C=2.82581 mu=0.6578 alpha=51.2 epsHOMO=-0.2589 epsLUMO=0.0571 epsgap=0.316 =481.9854 zpve=0.112471 U0=-195.186772 U=-195.180446 H=-195.179502 G=-195.215658 Cv=22.569 pbc="F F F" +C -0.01763434 1.54859468 -0.00253584 +C -0.01719180 0.00735789 0.03269604 +C 0.75818463 -0.52820155 1.25278862 +C -1.38213303 -0.51768438 0.00775516 +C -2.50796392 -0.94005629 0.00478552 +H 1.00944396 1.92639613 -0.02632830 +H -0.54514614 1.92217052 -0.88393459 +H -0.51243000 1.95339892 0.88560642 +H 0.49366179 -0.34904155 -0.87265890 +H 0.28968458 -0.19345372 2.18334679 +H 0.77833392 -1.62102506 1.25783727 +H 1.78952492 -0.16181727 1.23574954 +H -3.49998244 -1.31810165 -0.00723572 +12 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0.76268677 -0.47101822 1.19250856 +C -1.38541954 -0.52099256 -0.00194097 +N -2.45680847 -0.95636072 0.00694969 +H 1.02043523 1.90106589 0.00392365 +H -0.52873161 1.91193452 -0.88031480 +H -0.51849536 1.94150431 0.89013197 +H 0.50418828 -0.35805500 -0.86276072 +H 0.30070681 -0.18364934 2.05155305 +H 0.78148992 -1.48670299 1.20428183 +11 +Properties=species:S:1:pos:R:3 A=8.55919 B=3.89952 C=2.91869 mu=1.3582 alpha=44.03 epsHOMO=-0.2665 epsLUMO=0.0336 epsgap=0.3001 =444.6452 zpve=0.088908 U0=-231.108368 U=-231.102292 H=-231.101348 G=-231.137061 Cv=21.306 pbc="F F F" +C -0.00754278 1.55566144 0.08650633 +C 0.01207541 0.02096553 0.04935916 +O -1.29150251 -0.52785128 0.22126386 +C 0.65093362 -0.47718651 -1.17430507 +C 1.13902673 -0.86577343 -2.20205518 +H 1.00824243 1.95969458 0.04929733 +H -0.56272245 1.95192010 -0.76978085 +H -0.49328161 1.88929438 1.00704123 +H 0.57811967 -0.34464907 0.91458864 +H -1.78516532 -0.35903262 -0.58909400 +H 1.58078877 -1.22365060 -3.09919447 +10 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-1.87620455 -1.23582351 -0.87399559 +H 0.98823696 1.94625080 0.03705522 +H -0.50725090 1.92153422 -0.91285565 +H -0.57756514 1.92360883 0.85796386 +H 0.50584680 -0.36421091 -0.90619124 +H 0.22958380 -0.23909403 2.16431052 +H 0.81692736 -1.62559499 1.22973438 +H 1.76267153 -0.12846566 1.29050755 +H -2.00861510 -0.22792742 0.87121666 +11 +Properties=species:S:1:pos:R:3 A=8.31096 B=4.07963 C=2.92918 mu=2.8354 alpha=39.16 epsHOMO=-0.255 epsLUMO=-0.0279 epsgap=0.2271 =432.1489 zpve=0.088443 U0=-268.287661 U=-268.281505 H=-268.280561 G=-268.316982 Cv=20.344 pbc="F F F" +C -0.05483118 1.46032843 -0.17068647 +C 0.07105714 -0.03524485 0.11225841 +O 0.58204486 -0.33316656 1.40176256 +C -1.27639279 -0.74383027 0.03051185 +O -2.11855775 -0.49643175 -0.79163412 +H 0.93158072 1.93091398 -0.23353204 +H -0.57713995 1.61290939 -1.11835562 +H -0.62444315 1.94776119 0.62573670 +H 0.69687521 -0.50071620 -0.67159471 +H 1.45603316 0.06066659 1.47858584 +H -1.40723217 -1.53388627 0.80211181 +12 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-0.17379913 -0.76196233 0.96223129 +C 0.06068107 -0.73615179 -1.38825252 +O 0.00877771 -2.12574390 -1.27749486 +H 0.92062950 1.81463913 -0.39522215 +H -0.83050523 1.86542281 -0.56780350 +H -0.10911975 1.78049045 1.06493675 +H -0.75015067 -0.35235060 -2.03056074 +H 1.00312898 -0.40389029 -1.85592942 +H -0.09063255 -2.29450960 -0.32608019 +13 +Properties=species:S:1:pos:R:3 A=9.55216 B=3.57844 C=2.7341 mu=2.6168 alpha=46.19 epsHOMO=-0.2423 epsLUMO=-0.0072 epsgap=0.2351 =489.8518 zpve=0.112006 U0=-232.377706 U=-232.371073 H=-232.370129 G=-232.408256 Cv=21.716 pbc="F F F" +C -0.04384102 1.54099793 -0.03913814 +C 0.02765132 0.01885817 0.04778773 +C 0.76814644 -0.48842218 1.28000847 +C 0.87850208 -1.99746312 1.42393323 +O 1.24179142 0.26651340 2.09788702 +H 0.95796454 1.97690198 -0.06816012 +H -0.58447717 1.85356031 -0.93690369 +H -0.55173626 1.95737759 0.83440529 +H -0.97692824 -0.42709877 0.05353178 +H 0.51853486 -0.40806507 -0.83809409 +H 1.39924980 -2.42678188 0.56053177 +H 1.42001187 -2.24449367 2.33776426 +H -0.11750602 -2.45360651 1.45141965 +12 +Properties=species:S:1:pos:R:3 A=9.78802 B=3.78441 C=2.84416 mu=3.499 alpha=42.54 epsHOMO=-0.2438 epsLUMO=0.0355 epsgap=0.2793 =457.447 zpve=0.101847 U0=-248.448467 U=-248.441988 H=-248.441044 G=-248.478935 Cv=21.374 pbc="F F F" +C -0.00220144 1.53344329 -0.05960369 +C -0.03235837 0.01206167 0.06840265 +C 0.69666086 -0.48196462 1.31828322 +N 0.98104007 -1.82034162 1.31984698 +O 1.00392124 0.24606232 2.24136749 +H 1.02111190 1.89525169 -0.19182672 +H -0.59707204 1.86206427 -0.91645604 +H -0.39728839 1.99920712 0.84545201 +H -1.06936083 -0.34552935 0.13040806 +H 0.39947165 -0.46906818 -0.81777020 +H 0.63281951 -2.43917647 0.60874785 +H 1.35708014 -2.22217793 2.16303522 +12 +Properties=species:S:1:pos:R:3 A=10.01185 B=3.84195 C=2.87635 mu=3.5402 alpha=43.61 epsHOMO=-0.2418 epsLUMO=0.0387 epsgap=0.2805 =458.9803 zpve=0.101735 U0=-248.443503 U=-248.436899 H=-248.435955 G=-248.474272 Cv=20.967 pbc="F F F" +C 0.00619941 1.45933202 -0.03893859 +N -0.06411616 0.01422963 -0.12046901 +C -1.20595394 -0.68196796 0.16117120 +C -1.08982704 -2.19284899 0.02095775 +O -2.24482911 -0.14443077 0.49994418 +H 0.75053989 1.78372520 0.69715573 +H 0.25692001 1.90395263 -1.00876215 +H -0.97856026 1.81121438 0.27082194 +H 0.75820982 -0.49543660 -0.39464024 +H -0.09909703 -2.53543104 -0.28970003 +H -1.34097671 -2.65395851 0.97972885 +H -1.82903100 -2.53483935 -0.70813076 +11 +Properties=species:S:1:pos:R:3 A=10.44045 B=4.04692 C=2.98547 mu=3.5648 alpha=39.51 epsHOMO=-0.2436 epsLUMO=0.0599 epsgap=0.3034 =428.404 zpve=0.091713 U0=-264.504487 U=-264.498452 H=-264.497508 G=-264.533633 Cv=20.274 pbc="F F F" +C 0.00922630 1.45021814 -0.03194543 +N -0.01429488 -0.00054646 -0.01782667 +C -1.21576579 -0.67252403 0.04421896 +N -1.09733352 -2.04644898 -0.15997741 +O -2.28599299 -0.12893711 0.24196027 +H 0.89659038 1.81885022 0.49120949 +H 0.00233950 1.86388504 -1.04914155 +H -0.88562684 1.79943346 0.48353298 +H 0.75257305 -0.47506631 -0.46719265 +H -0.24882333 -2.48772194 0.16375654 +H -1.93419610 -2.53898393 0.11482880 +12 +Properties=species:S:1:pos:R:3 A=9.69683 B=4.1533 C=3.01703 mu=1.1876 alpha=43.57 epsHOMO=-0.2595 epsLUMO=0.0352 epsgap=0.2948 =443.1687 zpve=0.102062 U0=-248.416462 U=-248.410358 H=-248.409414 G=-248.445651 Cv=20.256 pbc="F F F" +C -0.09160695 1.37540046 -0.00729426 +O -0.06964534 -0.04831581 -0.11148812 +C 0.68114625 -0.56142673 -1.10732458 +C 0.60405298 -2.06701550 -1.08766892 +N 1.32645533 0.19266343 -1.89967731 +H 0.91463916 1.76843655 0.16257460 +H -0.48562826 1.82624615 -0.92235216 +H -0.73971430 1.60007423 0.84110605 +H -0.43430065 -2.39242162 -1.20325205 +H 1.20627110 -2.50049977 -1.88745640 +H 0.95651337 -2.44976670 -0.12501838 +H 1.83504115 -0.36104361 -2.58486055 +11 +Properties=species:S:1:pos:R:3 A=10.20392 B=4.16834 C=3.07281 mu=1.7569 alpha=39.33 epsHOMO=-0.2685 epsLUMO=0.0174 epsgap=0.2859 =427.6606 zpve=0.089436 U0=-268.32127 U=-268.315051 H=-268.314106 G=-268.351214 Cv=19.501 pbc="F F F" +C 0.07295254 1.38840919 0.06442046 +O 0.21049114 -0.03740028 0.04612736 +C 0.50102805 -0.57224288 -1.15975346 +C 0.62218715 -2.07378323 -1.06030684 +O 0.63922154 0.08224418 -2.16157325 +H 1.00029720 1.87062570 -0.25532064 +H -0.73338957 1.70782089 -0.60080454 +H -0.15751280 1.65154060 1.09692954 +H -0.31330001 -2.50202851 -0.68941906 +H 0.85646588 -2.48430275 -2.04157352 +H 1.40651685 -2.34055496 -0.34634301 +10 +Properties=species:S:1:pos:R:3 A=10.68182 B=4.39532 C=3.17969 mu=2.329 alpha=35.5 epsHOMO=-0.2669 epsLUMO=0.0575 epsgap=0.3244 =398.5908 zpve=0.079271 U0=-284.385189 U=-284.379361 H=-284.378417 G=-284.414085 Cv=19.084 pbc="F F F" +C -0.02645771 1.38305753 0.02085660 +O 0.04139566 -0.04439503 -0.00277452 +C 0.66775338 -0.56001625 -1.09000344 +N 0.72605063 -1.92219983 -0.98822954 +O 1.12952381 0.09648143 -1.99322180 +H 0.97529779 1.82044307 0.02387306 +H -0.57085698 1.76214621 -0.84790425 +H -0.55410722 1.63835731 0.94043594 +H 0.12662696 -2.38401590 -0.32566929 +H 0.97230070 -2.41798421 -1.82732573 +10 +Properties=species:S:1:pos:R:3 A=10.20696 B=3.93135 C=2.9219 mu=4.5676 alpha=35.87 epsHOMO=-0.246 epsLUMO=0.0273 epsgap=0.2733 =418.5967 zpve=0.078222 U0=-284.360325 U=-284.354148 H=-284.353204 G=-284.390143 Cv=20.089 pbc="F F F" +N -0.08115607 1.31807026 0.01276742 +C 0.05442259 -0.04477733 0.08155388 +O 1.12163074 -0.59783950 0.20940553 +C -1.28687786 -0.79011642 0.06178435 +O -1.04605114 -2.13954195 -0.23957158 +H 0.76623366 1.85991128 -0.04483425 +H -0.93021346 1.74567447 -0.31565615 +H -1.74559696 -0.65521334 1.05526700 +H -1.96026061 -0.31149222 -0.67026256 +H -1.85637296 -2.63017646 -0.07625478 +10 +Properties=species:S:1:pos:R:3 A=10.14389 B=4.0714 C=3.02223 mu=5.3004 alpha=35.19 epsHOMO=-0.2527 epsLUMO=0.0208 epsgap=0.2735 =408.0279 zpve=0.080317 U0=-284.372483 U=-284.367172 H=-284.366228 G=-284.40095 Cv=17.931 pbc="F F F" +N 0.01838100 0.93057357 0.04943058 +H -0.70674506 0.56116798 -0.55550134 +H 0.68248307 0.18430002 0.21487099 +H 0.21928084 2.79858558 -0.32670585 +C -0.54586914 1.44688585 1.30274586 +C -0.80466975 2.96030569 1.21052835 +O -0.21468822 3.53648884 0.15531687 +O -1.45295676 3.56338618 2.02143528 +H -1.47662579 0.96512183 1.62072169 +H 0.17781209 1.30324286 2.11157176 +13 +Properties=species:S:1:pos:R:3 A=6.6508 B=4.19317 C=3.39441 mu=2.2854 alpha=42.01 epsHOMO=-0.2597 epsLUMO=0.0631 epsgap=0.3228 =438.3028 zpve=0.11345 U0=-269.479234 U=-269.472993 H=-269.472049 G=-269.508213 Cv=22.072 pbc="F F F" +C -0.02775167 1.53421433 0.00651151 +C 0.08343246 0.01218277 0.05258986 +O 0.95856392 -0.48200710 -0.96694113 +C 0.69767574 -0.49516451 1.35948812 +O 2.02247062 -0.02842530 1.52222186 +H 0.94333763 1.98841434 0.21841589 +H -0.37320516 1.87354041 -0.97716908 +H -0.74773817 1.89256394 0.74926853 +H -0.91671848 -0.43697036 -0.06040528 +H 0.76726710 -0.01412177 -1.78502043 +H 0.11650002 -0.13351914 2.21392431 +H 0.65814685 -1.59563445 1.36274474 +H 2.46378566 -0.22762476 0.68698431 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epsgap=0.335 =521.8605 zpve=0.136091 U0=-233.555951 U=-233.549143 H=-233.548199 G=-233.585602 Cv=23.99 pbc="F F F" +C -0.03841227 1.55621396 0.00935837 +C 0.01907135 0.02716079 -0.02074203 +C 0.73141506 -0.52709328 -1.25454422 +C 0.83975868 -2.05206973 -1.22872655 +O -0.01226470 -0.08514394 -2.38973758 +H 0.96845664 1.98863424 0.03526234 +H -0.54377006 1.93824134 -0.88074407 +H -0.57652955 1.91383386 0.89256698 +H 0.53166867 -0.34991486 0.87240999 +H -0.99727186 -0.38638654 -0.00185289 +H 1.74909346 -0.09943025 -1.28826184 +H 1.34143968 -2.42487137 -2.12957624 +H 1.41822012 -2.39229108 -0.36340871 +H -0.15616765 -2.50443859 -1.18308875 +H 0.43090825 -0.40901616 -3.17979642 +15 +Properties=species:S:1:pos:R:3 A=7.55335 B=3.78323 C=2.80566 mu=1.0758 alpha=49.8 epsHOMO=-0.2476 epsLUMO=0.086 epsgap=0.3336 =507.9614 zpve=0.135681 U0=-233.545899 U=-233.539034 H=-233.53809 G=-233.57582 Cv=23.573 pbc="F F F" +C -0.00857605 1.38097833 0.09454670 +O -0.02255106 -0.00739461 -0.13062576 +C -1.30243341 -0.61997805 -0.02088309 +C -1.18687286 -1.97885751 -0.70303918 +C -1.75587824 -0.74662760 1.43714508 +H 1.00321550 1.73258087 -0.12443496 +H -0.71511468 1.90838275 -0.56674280 +H -0.24909065 1.64967237 1.13384681 +H -2.03737978 -0.00432241 -0.56884198 +H -0.44792779 -2.59945605 -0.18596870 +H -2.14751041 -2.50225525 -0.69102581 +H -0.86524212 -1.85860679 -1.74077836 +H -2.73321482 -1.23674908 1.49205117 +H -1.03621350 -1.34483828 2.00544671 +H -1.84898410 0.22935483 1.92198087 +15 +Properties=species:S:1:pos:R:3 A=6.14183 B=5.17679 C=3.79297 mu=0.1068 alpha=52.92 epsHOMO=-0.2633 epsLUMO=0.0893 epsgap=0.3526 =439.1643 zpve=0.137025 U0=-196.409349 U=-196.403398 H=-196.402453 G=-196.437676 Cv=22.258 pbc="F F F" +C -0.03913204 1.50456305 0.04978913 +C 0.00985856 -0.01113880 0.01248619 +C -1.24167981 -0.66663044 -0.53989979 +C 1.33803387 -0.66895748 -0.27400430 +C 0.75126442 -0.72221586 1.11862571 +H 0.88633040 1.92490040 0.45579358 +H -0.18183878 1.92045743 -0.95489291 +H -0.86832076 1.85815718 0.67439648 +H -1.43570890 -0.34344387 -1.56976411 +H -1.15326757 -1.75757800 -0.54433832 +H -2.12221416 -0.40570820 0.05951361 +H 1.34398374 -1.58235208 -0.86005084 +H 2.20196897 -0.03329084 -0.43929326 +H 1.21949576 -0.12246018 1.89246580 +H 0.36153319 -1.67153717 1.47171932 +13 +Properties=species:S:1:pos:R:3 A=6.49534 B=5.52329 C=3.9108 mu=1.8235 alpha=45.7 epsHOMO=-0.2596 epsLUMO=0.091 epsgap=0.3505 =405.521 zpve=0.112851 U0=-232.335768 U=-232.329981 H=-232.329037 G=-232.364091 Cv=20.719 pbc="F F F" +C -0.03970330 1.50954998 -0.04112233 +C -0.03023556 -0.00335329 -0.06498361 +C -1.24613189 -0.68356648 0.52531190 +C 0.78937201 -0.71129193 -1.06027559 +O 1.22546798 -0.60059036 0.29712283 +H 0.88992772 1.90948478 -0.45296558 +H -0.87952723 1.90350108 -0.62376642 +H -0.14448576 1.87590604 0.98604429 +H -2.15218727 -0.41012616 -0.02629495 +H -1.13412584 -1.77005431 0.49747032 +H -1.38586631 -0.38055453 1.56882757 +H 0.50712690 -1.71386874 -1.38085018 +H 1.37088104 -0.14369322 -1.78640853 +13 +Properties=species:S:1:pos:R:3 A=6.57648 B=5.31603 C=3.98453 mu=1.3822 alpha=45.85 epsHOMO=-0.2494 epsLUMO=0.0776 epsgap=0.327 =401.4888 zpve=0.113237 U0=-232.33341 U=-232.327734 H=-232.32679 G=-232.36147 Cv=21.17 pbc="F F F" +C -0.03714052 1.53215920 0.01332310 +C 0.01344335 0.02117019 0.01922005 +O -1.17476104 -0.59735284 -0.40931260 +C 1.30744798 -0.69902106 -0.27389650 +C 0.70844749 -0.71373504 1.12226318 +H 0.89026861 1.96240279 0.40276135 +H -0.17846151 1.91561262 -1.00518522 +H -0.87011232 1.88955201 0.62730953 +H -1.28352265 -0.40225391 -1.34690641 +H 1.23973813 -1.61642115 -0.84783774 +H 2.20543993 -0.11523820 -0.44744474 +H 1.19539159 -0.13889834 1.90240284 +H 0.24735439 -1.63670241 1.45378480 +10 +Properties=species:S:1:pos:R:3 A=12.18399 B=5.1384 C=3.78868 mu=2.5732 alpha=40.19 epsHOMO=-0.263 epsLUMO=0.0277 epsgap=0.2907 =352.0378 zpve=0.080558 U0=-247.201165 U=-247.196699 H=-247.195755 G=-247.22841 Cv=15.51 pbc="F F F" +N -0.04445152 1.25623533 0.31681915 +C -0.03914559 0.04913710 -0.01670558 +C -1.08749619 -1.04164446 -0.11488033 +C 0.09869688 -1.90754844 -0.58225894 +O 0.99647508 -0.76306629 -0.44028010 +H 0.88935980 1.66481350 0.24916893 +H -1.54324114 -1.32235812 0.83572172 +H -1.86910036 -0.85713839 -0.85325910 +H 0.39739753 -2.72227350 0.08158721 +H 0.06945080 -2.25418345 -1.61796084 +11 +Properties=species:S:1:pos:R:3 A=10.79571 B=4.78732 C=3.54292 mu=2.7119 alpha=42.77 epsHOMO=-0.2415 epsLUMO=-0.0194 epsgap=0.2222 =379.6371 zpve=0.090544 U0=-231.15578 U=-231.150804 H=-231.149859 G=-231.184401 Cv=17.076 pbc="F F F" +O -0.00101746 0.01846628 -0.20114742 +C -0.01405811 1.19658059 0.00205306 +C -1.13542911 2.22979148 0.21957980 +C -0.03500884 3.29097637 0.53052223 +C 1.08469664 2.25947795 0.18994331 +H -1.71642814 2.41772662 -0.68901078 +H -1.83392757 1.98415059 1.02444244 +H -0.05534741 4.17585264 -0.10670963 +H -0.02541477 3.61399048 1.57278917 +H 1.81074389 2.03288534 0.97578973 +H 1.63600801 2.46255380 -0.73376244 +10 +Properties=species:S:1:pos:R:3 A=12.23436 B=4.97895 C=3.70561 mu=3.6671 alpha=39.73 epsHOMO=-0.25 epsLUMO=0.038 epsgap=0.288 =355.0934 zpve=0.080442 U0=-247.225618 U=-247.220897 H=-247.219953 G=-247.253218 Cv=16.1 pbc="F F F" +O 0.03184134 -0.03461682 0.00849836 +C -0.02440334 1.16440533 0.02160811 +C -1.14541322 2.23712809 0.03012901 +C 0.00928805 3.27945878 0.04557758 +N 0.93843838 2.14652109 0.03554396 +H -1.77030529 2.23779730 -0.86428437 +H -1.77632809 2.21765386 0.92007934 +H 0.07170564 3.91863516 -0.84072193 +H 0.06622603 3.89826625 0.94657952 +H 1.94616840 2.09535713 0.03855945 +9 +Properties=species:S:1:pos:R:3 A=12.44643 B=5.23328 C=3.86599 mu=3.9339 alpha=35.4 epsHOMO=-0.2788 epsLUMO=0.0089 epsgap=0.2878 =336.0792 zpve=0.068574 U0=-267.106213 U=-267.101929 H=-267.100985 G=-267.13332 Cv=14.483 pbc="F F F" +O 0.04424004 0.03933243 -0.33071104 +C -0.04854479 1.17416230 0.00410076 +C -1.14211656 2.23611850 0.15338361 +C 0.02332538 3.12357010 0.61681625 +O 0.95663808 2.01190122 0.42603746 +H -1.62573755 2.51482212 -0.78401102 +H -1.90009213 1.99553386 0.90028663 +H 0.28368077 3.96172751 -0.03229287 +H 0.00782250 3.43942655 1.66162725 +10 +Properties=species:S:1:pos:R:3 A=11.54185 B=4.90368 C=3.63309 mu=2.5257 alpha=39.34 epsHOMO=-0.2437 epsLUMO=-0.0258 epsgap=0.2179 =359.152 zpve=0.07965 U0=-247.190194 U=-247.185543 H=-247.184599 G=-247.217688 Cv=16.083 pbc="F F F" +O 0.00053891 0.00665475 -0.09743049 +C -0.01371990 1.19213881 0.03139936 +C -1.10668681 2.26612284 0.17247913 +N -0.03543445 3.25123475 0.48533693 +C 1.05328615 2.29521506 0.14383904 +H -1.65047970 2.42796175 -0.76886054 +H -1.83606786 2.11370530 0.97567410 +H -0.05434600 4.07193609 -0.10727051 +H 1.80754206 2.16277968 0.92736250 +H 1.56737937 2.47130277 -0.81152688 +9 +Properties=species:S:1:pos:R:3 A=12.18264 B=4.94228 C=3.68162 mu=0.8477 alpha=35.94 epsHOMO=-0.2647 epsLUMO=-0.0352 epsgap=0.2295 =344.5473 zpve=0.066989 U0=-267.068488 U=-267.063976 H=-267.063032 G=-267.095887 Cv=15.115 pbc="F F F" +O -0.00031692 0.00676321 -0.17139774 +C -0.01417098 1.18672865 -0.00980279 +C -1.08283120 2.27653381 0.15574094 +O -0.03856115 3.26460423 0.27513986 +C 1.02857270 2.30515693 0.12774311 +H -1.72923855 2.43647507 -0.71671274 +H -1.70219898 2.19346725 1.05800754 +H 1.67366781 2.23923293 1.01324179 +H 1.64705068 2.48224548 -0.76148355 +15 +Properties=species:S:1:pos:R:3 A=9.63416 B=4.10254 C=3.28665 mu=0.095 alpha=52.94 epsHOMO=-0.2896 epsLUMO=0.0927 epsgap=0.3823 =455.0215 zpve=0.138424 U0=-196.407957 U=-196.40245 H=-196.401505 G=-196.436159 Cv=20.299 pbc="F F F" +C -0.47005934 1.44206547 -0.06684548 +C 0.14287306 0.05485144 0.03747984 +C 1.37254693 -0.30295273 -0.84679168 +C 1.95947994 -1.09364561 0.35326039 +C 0.99887433 -0.30768787 1.28563099 +H 0.29210839 2.21844829 0.06844100 +H -0.93614604 1.60094605 -1.04612799 +H -1.24148161 1.59707353 0.69627593 +H -0.63517341 -0.70560132 -0.10055095 +H 1.19338376 -0.84118177 -1.78129390 +H 1.96867777 0.58857708 -1.07004184 +H 1.69873597 -2.15500718 0.30521100 +H 3.03253510 -1.01077235 0.54148058 +H 1.48290386 0.58242428 1.70211026 +H 0.51304296 -0.84979686 2.10121062 +13 +Properties=species:S:1:pos:R:3 A=9.93129 B=4.56362 C=3.63704 mu=1.6826 alpha=45.72 epsHOMO=-0.241 epsLUMO=0.0915 epsgap=0.3325 =407.6909 zpve=0.11459 U0=-232.33123 U=-232.325877 H=-232.324933 G=-232.359589 Cv=18.582 pbc="F F F" +C -0.32308228 1.53751451 0.01947624 +C 0.11847214 0.08900172 -0.00523140 +C 0.87489284 -0.43763303 -1.24721738 +C 1.97841048 -0.80657483 -0.23740356 +O 1.27344926 -0.15419108 0.83794040 +H 0.49211985 2.19658795 -0.29527664 +H -1.17615835 1.68923490 -0.65134808 +H -0.62776963 1.82747912 1.03013205 +H -0.71410200 -0.56389187 0.29597305 +H 0.41033314 -1.26269706 -1.78910031 +H 1.15560811 0.34596269 -1.95459519 +H 2.09501012 -1.88349764 -0.05606083 +H 2.96970344 -0.36924147 -0.40408009 +13 +Properties=species:S:1:pos:R:3 A=10.13697 B=4.32512 C=3.50912 mu=1.8995 alpha=45.45 epsHOMO=-0.2419 epsLUMO=0.0814 epsgap=0.3233 =414.1575 zpve=0.114694 U0=-232.325947 U=-232.320548 H=-232.319604 G=-232.354576 Cv=18.443 pbc="F F F" +C -0.18371124 1.54668909 -0.01671129 +C 0.04513382 0.04386724 0.02270494 +C 1.20301104 -0.54034580 -0.81983464 +O 1.80061686 -1.15530615 0.33924991 +C 0.83391983 -0.54313754 1.21648003 +H 0.75102609 2.09256015 0.15344911 +H -0.58319248 1.86155707 -0.98694169 +H -0.89843724 1.85917126 0.75244153 +H -0.89506736 -0.48768372 -0.14843995 +H 0.93560472 -1.27857588 -1.58543917 +H 1.86392938 0.21379924 -1.27081649 +H 1.29414565 0.20948993 1.87273952 +H 0.31517042 -1.28326856 1.83756393 +13 +Properties=species:S:1:pos:R:3 A=10.09703 B=4.26865 C=3.40487 mu=1.4604 alpha=45.55 epsHOMO=-0.256 epsLUMO=0.0801 epsgap=0.3361 =413.9118 zpve=0.115063 U0=-232.333258 U=-232.328097 H=-232.327153 G=-232.361103 Cv=19.023 pbc="F F F" +O 0.54622847 1.03270424 -0.58398083 +C -0.00112269 -0.11036621 0.01898238 +C 0.13945950 -0.34310429 1.54777117 +C -1.26863784 -0.99876517 1.51632654 +C -1.53844651 -0.26418236 0.17423510 +H 0.20332379 1.80433874 -0.12071219 +H 0.41296647 -0.95857955 -0.53560402 +H 0.98890738 -0.92719626 1.90921867 +H 0.11786549 0.61323034 2.08356053 +H -1.20828813 -2.08191064 1.38036699 +H -1.94143614 -0.79119746 2.35014116 +H -2.00495682 0.71307933 0.34581493 +H -2.09160311 -0.78230202 -0.61249171 +11 +Properties=species:S:1:pos:R:3 A=10.85394 B=4.52867 C=3.62328 mu=2.4158 alpha=38.58 epsHOMO=-0.2465 epsLUMO=0.0604 epsgap=0.3069 =374.4087 zpve=0.091005 U0=-268.248371 U=-268.243234 H=-268.24229 G=-268.276572 Cv=17.344 pbc="F F F" +O 0.07721714 1.41738724 -0.14439016 +C 0.01675156 0.02484791 0.03233817 +C 0.11193823 -0.54392541 1.47000744 +O -1.16387804 -1.20093191 1.32441605 +C -1.41930178 -0.52040930 0.07750673 +H 1.00022214 1.67889479 -0.21697466 +H 0.66036495 -0.50380879 -0.68180327 +H 0.91806589 -1.25385265 1.68878628 +H 0.09146208 0.24427487 2.23499232 +H -2.16139683 0.28183960 0.18222436 +H -1.72867456 -1.20941157 -0.71550887 +15 +Properties=species:S:1:pos:R:3 A=7.14221 B=4.37083 C=3.30265 mu=0.1023 alpha=52.92 epsHOMO=-0.2632 epsLUMO=0.094 epsgap=0.3573 =465.3301 zpve=0.137341 U0=-196.406419 U=-196.40034 H=-196.399396 G=-196.435152 Cv=22.005 pbc="F F F" +C -0.05189850 1.55217435 -0.10318438 +C 0.01226648 0.04690675 0.03747520 +C 1.30115692 -0.70661553 -0.19725724 +C 0.70431939 -0.64893239 1.19003562 +C 1.39750324 0.09481280 2.31078011 +H 0.87404209 2.03543171 0.22162228 +H -0.21641357 1.83695278 -1.14852958 +H -0.87157646 1.97673684 0.48797046 +H -0.88508206 -0.45596941 -0.31580786 +H 1.27663658 -1.64149022 -0.74697967 +H 2.19424067 -0.11404121 -0.37569359 +H 0.21672461 -1.56377934 1.51902400 +H 1.91317356 0.99051175 1.95271747 +H 0.68738009 0.40901468 3.08446972 +H 2.14934633 -0.54151807 2.79111543 +13 +Properties=species:S:1:pos:R:3 A=7.36997 B=4.78662 C=3.53123 mu=1.3092 alpha=45.84 epsHOMO=-0.2518 epsLUMO=0.0822 epsgap=0.334 =418.298 zpve=0.113827 U0=-232.32907 U=-232.323327 H=-232.322383 G=-232.357426 Cv=20.702 pbc="F F F" +C -0.07992786 1.55914739 -0.03875994 +C 0.00042206 0.05111569 0.04346551 +C 1.31748624 -0.66230034 -0.20269965 +C 0.71624326 -0.61854924 1.18128126 +O 1.28134126 0.21474798 2.15341739 +H 0.80207677 2.01979988 0.41147972 +H -0.15235410 1.89225261 -1.07968732 +H -0.95836082 1.93564781 0.49643179 +H -0.88615174 -0.46939753 -0.30918496 +H 1.32384464 -1.60086657 -0.74666557 +H 2.18619315 -0.03316813 -0.37068758 +H 0.28509951 -1.54025035 1.57291630 +H 2.12931661 -0.16234387 2.40895930 +14 +Properties=species:S:1:pos:R:3 A=7.53724 B=4.74514 C=3.54049 mu=1.2483 alpha=49.79 epsHOMO=-0.2199 epsLUMO=0.0931 epsgap=0.313 =432.5081 zpve=0.125556 U0=-212.438188 U=-212.432286 H=-212.431342 G=-212.466725 Cv=20.993 pbc="F F F" +C -0.17034561 1.55765735 -0.10065470 +C 0.01075256 0.05574638 -0.05225833 +C 0.71794239 -0.67802425 -1.14204150 +N 1.29811574 -0.59777508 0.19386866 +C 2.50047290 0.20256923 0.36428818 +H 0.60876703 2.05717913 -0.68119301 +H -1.13238698 1.79957181 -0.56489127 +H -0.17350949 1.98946323 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a/docs/src/examples/basic_usage/usage.rst b/docs/src/examples/basic_usage/usage.rst new file mode 100644 index 0000000000..a65bd19678 --- /dev/null +++ b/docs/src/examples/basic_usage/usage.rst @@ -0,0 +1,189 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "examples/basic_usage/usage.sh" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code. + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_examples_basic_usage_usage.sh: + +.. _label_basic_usage: + +Basic Usage +=========== + +``metatrain`` is designed for a direct usage from the command line (cli). The program +is registered via the abbreviation ``mtt`` to your command line. The general help of +``metatrain`` can be accessed using + +.. GENERATED FROM PYTHON SOURCE LINES 10-11 + +.. code-block:: Bash + + mtt --help + +.. GENERATED FROM PYTHON SOURCE LINES 12-52 + +We now demonstrate how to ``train`` and ``evaluate`` a model from the command line. +For this example we use the :ref:`architecture-soap-bpnn` architecture and a subset of +the `QM9 dataset `_. You can obtain the +dataset for this example here: :download:`qm9_reduced_100.xyz +<../../../../examples/basic_usage/qm9_reduced_100.xyz>`. + + +Training +-------- + +To train models, ``metatrain`` uses a dynamic override strategy for your training +options. We allow a dynamical composition and override of the default architecture +with either your custom ``options.yaml`` and even command line override grammar. For +reference and reproducibility purposes ``metatrain`` always writes the fully expanded, +including the overwritten option to ``options_restart.yaml``. The restart options file +is written into a subfolder named with the current *date* and *time* inside the +``output`` directory of your current training run. + +The sub-command to start a model training is + +.. code-block:: bash + + mtt train + +To train a model you have to define your options. This includes the specific +architecture you want to use and the data including the training systems and target +values + +The default model and training hyperparameter for each model are listed in their +corresponding documentation page. We will use these minimal options to run an example +training using the default hyperparameters of an SOAP BPNN model + +.. literalinclude:: ../../../../examples/basic_usage/options.yaml + :language: yaml + +For each training run a new output directory in the format +``outputs/YYYY-MM-DD/HH-MM-SS`` based on the current *date* and *time* is created. We +use this output directory to store checkpoints, the restart ``options_restart.yaml`` +file as well as the log files. To start the training, create an ``options.yaml`` file +in the current directory and type + +.. GENERATED FROM PYTHON SOURCE LINES 55-57 + +.. code-block:: Bash + + + mtt train options.yaml + +.. GENERATED FROM PYTHON SOURCE LINES 58-62 + +The functions saves the final model `model.pt` to the current output folder for later +evaluation. An `extensions/` folder, which contains the compiled extensions for the +model, might also be saved depending on the architecture. All command line flags of +the train sub-command can be listed via + +.. GENERATED FROM PYTHON SOURCE LINES 66-67 + +.. code-block:: Bash + + mtt train --help + +.. GENERATED FROM PYTHON SOURCE LINES 68-96 + +After the training has finished, the ``mtt train`` command generates the +``model.ckpt`` (final checkpoint) and ``model.pt`` (exported model) files in the +current directory, as well as in the ``output/YYYY-MM-DD/HH-MM-SS`` directory. + +Evaluation +---------- + +The sub-command to evaluate an already trained model is + +.. code-block:: bash + + mtt eval + +Besides the trained ``model``, you will also have to provide a file containing the +system and possible target values for evaluation. The system section of this +``eval.yaml`` is exactly the same as for a dataset in the ``options.yaml`` file. + +.. literalinclude:: ../../../../examples/basic_usage/eval.yaml + :language: yaml + +Note that the ``targets`` section is optional. If the ``targets`` section is present, +the function will calculate and report RMSE values of the predictions with respect to +the real values as loaded from the ``targets`` section. You can run an evaluation by +typing + +We now evaluate the model on the training dataset, where the first arguments specifies +trained model and the second an option file containing the path of the dataset for +evaulation. The extensions of the model, if any, can be specified via the ``-e`` flag. + +.. GENERATED FROM PYTHON SOURCE LINES 99-100 + +.. code-block:: Bash + + mtt eval model.pt eval.yaml -e extensions/ + +.. GENERATED FROM PYTHON SOURCE LINES 101-105 + +The evaluation command predicts those properties the model was trained against; here +``"U0"``. The predictions together with the systems have been written in a file named +``output.xyz`` in the current directory. The written file starts with the following +lines + +.. GENERATED FROM PYTHON SOURCE LINES 108-109 + +.. code-block:: Bash + + head -n 20 output.xyz + +.. GENERATED FROM PYTHON SOURCE LINES 110-111 + +All command line flags of the eval sub-command can be listed via + +.. GENERATED FROM PYTHON SOURCE LINES 114-115 + +.. code-block:: Bash + + mtt eval --help + +.. GENERATED FROM PYTHON SOURCE LINES 115-123 + +An important parameter of ``mtt eval`` is the ``-b`` (or ``--batch-size``) option, +which allows you to specify the batch size for the evaluation. + +Molecular simulations +--------------------- + +The trained model can also be used to run molecular simulations. +You can find how in the :ref:`tutorials` section. + + +.. _sphx_glr_download_examples_basic_usage_usage.sh: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Bash source code: usage.sh ` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: usage.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/basic_usage/usage.sh b/docs/src/examples/basic_usage/usage.sh new file mode 100644 index 0000000000..cc35dfc1e2 --- /dev/null +++ b/docs/src/examples/basic_usage/usage.sh @@ -0,0 +1,125 @@ +# .. _label_basic_usage: +# +# Basic Usage +# =========== +# +# ``metatrain`` is designed for a direct usage from the command line (cli). The program +# is registered via the abbreviation ``mtt`` to your command line. The general help of +# ``metatrain`` can be accessed using +# + +mtt --help + +# %% +# +# We now demonstrate how to ``train`` and ``evaluate`` a model from the command line. +# For this example we use the :ref:`architecture-soap-bpnn` architecture and a subset of +# the `QM9 dataset `_. You can obtain the +# dataset for this example here: :download:`qm9_reduced_100.xyz `. +# +# +# Training +# -------- +# +# To train models, ``metatrain`` uses a dynamic override strategy for your training +# options. We allow a dynamical composition and override of the default architecture +# with either your custom ``options.yaml`` and even command line override grammar. For +# reference and reproducibility purposes ``metatrain`` always writes the fully expanded, +# including the overwritten option to ``options_restart.yaml``. The restart options file +# is written into a subfolder named with the current *date* and *time* inside the +# ``output`` directory of your current training run. +# +# The sub-command to start a model training is +# +# .. code-block:: bash +# +# mtt train +# +# To train a model you have to define your options. This includes the specific +# architecture you want to use and the data including the training systems and target +# values +# +# The default model and training hyperparameter for each model are listed in their +# corresponding documentation page. We will use these minimal options to run an example +# training using the default hyperparameters of an SOAP BPNN model +# +# .. literalinclude:: options-basic.yaml +# :language: yaml +# +# For each training run a new output directory in the format +# ``outputs/YYYY-MM-DD/HH-MM-SS`` based on the current *date* and *time* is created. We +# use this output directory to store checkpoints, the restart ``options_restart.yaml`` +# file as well as the log files. To start the training, create an ``options.yaml`` file +# in the current directory and type + + +mtt train options-basic.yaml + +# %% +# +# The functions saves the final model `model.pt` to the current output folder for later +# evaluation. An `extensions/` folder, which contains the compiled extensions for the +# model, might also be saved depending on the architecture. All command line flags of +# the train sub-command can be listed via +# + +mtt train --help + +# %% +# +# After the training has finished, the ``mtt train`` command generates the +# ``model.ckpt`` (final checkpoint) and ``model.pt`` (exported model) files in the +# current directory, as well as in the ``output/YYYY-MM-DD/HH-MM-SS`` directory. +# +# Evaluation +# ---------- +# +# The sub-command to evaluate an already trained model is +# +# .. code-block:: bash +# +# mtt eval +# +# Besides the trained ``model``, you will also have to provide a file containing the +# system and possible target values for evaluation. The system section of this +# ``eval.yaml`` is exactly the same as for a dataset in the ``options.yaml`` file. +# +# .. literalinclude:: eval-basic.yaml +# :language: yaml +# +# Note that the ``targets`` section is optional. If the ``targets`` section is present, +# the function will calculate and report RMSE values of the predictions with respect to +# the real values as loaded from the ``targets`` section. You can run an evaluation by +# typing +# +# We now evaluate the model on the training dataset, where the first arguments specifies +# trained model and the second an option file containing the path of the dataset for +# evaulation. The extensions of the model, if any, can be specified via the ``-e`` flag. + +mtt eval model.pt eval-basic.yaml -e extensions/ + +# %% +# +# The evaluation command predicts those properties the model was trained against; here +# ``"U0"``. The predictions together with the systems have been written in a file named +# ``output.xyz`` in the current directory. The written file starts with the following +# lines + +head -n 20 output.xyz + +# %% +# +# All command line flags of the eval sub-command can be listed via + +mtt eval --help + +# %% +# +# An important parameter of ``mtt eval`` is the ``-b`` (or ``--batch-size``) option, +# which allows you to specify the batch size for the evaluation. +# +# Molecular simulations +# --------------------- +# +# The trained model can also be used to run molecular simulations. +# You can find how in the :ref:`tutorials` section. diff --git a/docs/src/examples/basic_usage/usage.zip b/docs/src/examples/basic_usage/usage.zip new file mode 100644 index 0000000000000000000000000000000000000000..29173744cf9d766480cb629534615cf1c3358a9b GIT binary patch literal 4718 zcmb7IO^+ML5tZX42=?Z{m)r^x3`oLqsN@j9jAKB89oP_7Sx0LmMh-j8nI@; z{o@CZ9`Vm_aBm7~tEY7ZTRtK{kbW0oONl|OOZX$_2+IZJf>@+p5@72(fHG`(7jWy3Iy2Aa@yRBC} z_JJj@w585kBowg&n4a}ap+o*xo znpD&15U|Y@%&@uf9MsA{{=9`4&mx|svoVUorWJ;!d-d2S=Gunoryqk@_G@R8W-zv7-0J7 zKJmti!n6)LrcfE@`m2kNsZ~+&-A}q0$FN#1M>T36b|!WWe5;U*WqxM)um0mt>+6cX zaT75*ZXJ(FG$kWaMX?ZX=0n^+ruLo}>uzGvuW4OZ!aO zDMXY+U6<|ls<+G<*wi%2x40eZ7G*2F!HlZUT> z;FSBy?$kce&VyP(oZd{)7fWV90SR+A&b9ysSwblaR7~M!@`x?cbFfc8mk6In)8o<%fzz0{ z2?(b+POJw>a-83z4hqc4M#(SvX%JG0=%8p8MUdc&1Obmy-Ofu)5UxQexo8$^>zZ#@ zD*|~t`GD|Z*^mXNv@Ev3G3wL}Ty{{A4Qrp!t|TAc0GI@N&Pf<`V>YG%W+&y&rgjRj zz!a+mC!W#b61|nSIfAfEs}>&denH28q26~ zgh&^>E(!^|b4;SxDe~<3shfd0fTeH$`tp)qUA}pf+%2g@fpCM!kRzS5I^p78fhxIT zlEV?aR#pw$3Bjx=-NbDcbpdB~R6?LZ?d6{)vxexukDiy`|3moU!HBQZJIt$0F5vKtjs#;hHGM=SAEj9~V z#Kuw2zQU%PX=p^uDWLeW)|mkmODwPDPc=pvCD_dZY6dC`6SqAJJek!i-yTiwXtf;S5}tRapO+i1`q>sY zg$~(w&tO*QVC+c^cx7Q`j%_#$u7A9V9sh69rUeKBly? zD7S}N?o6G??~DyOeu{8&Hskmq7sM-`*IIQ zB&iDs!WavJS)+PWH^{vpDf=9J>f?D;z!VBVSX6lclw7jArF7F6+eFL}%#?s$!Fk}y z!T6Nt&_g!zjFpET7Lo)#n(Y6t%(EZx>H%4P<1n+wPN8kWJ524;1p_!(0%LU)uMiV` znTELso*SW!Xl6Vov9+QyDJ6}v&BS6MKw~5WdFgK#Z$B>}=RpxhT9egDMjmdLd|-+$ z+Xa(sSp+C~z6K5Elhi%V9j5ovH|J#1de3I}qYSyY%x9XJyc~&CSm`fCu}k(?)i7+P zkc|&K<8Gy^2g<_=j}y13U&a%_j9Gw7B%ypCM`=`;k~2PqGhh&m!c7KMRQC#=9*Jmv zJ}aolpX^H}F)ks7=jc!I)PNPkeU|9+bnOjmW&&hL=n53 zQLb7{-F%|O*c44JFqKQqrdavMVRjRJR}T~1AI6; 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+ +.. only:: html + + .. image:: /examples/llpr/images/thumb/sphx_glr_llpr_thumb.png + :alt: + + :ref:`sphx_glr_examples_llpr_llpr.py` + +.. raw:: html + +
Computing LLPR uncertainties
+
+ + +.. thumbnail-parent-div-close + +.. raw:: html + + + + +.. toctree:: + :hidden: + + /examples/llpr/llpr + + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-gallery + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download all examples as source code: llpr.zip ` + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download all examples in Jupyter notebooks: llpr_jupyter.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/llpr/llpr.codeobj.json b/docs/src/examples/llpr/llpr.codeobj.json new file mode 100644 index 0000000000..bd7f237401 --- /dev/null +++ b/docs/src/examples/llpr/llpr.codeobj.json @@ -0,0 +1,728 @@ +{ + "LogNorm": [ + { + "is_class": true, + "is_explicit": false, + "module": "matplotlib.colors", + "module_short": "matplotlib.colors", + "name": "LogNorm" + }, + { + "is_class": true, + "is_explicit": false, + "module": "matplotlib", + "module_short": "matplotlib", + "name": "LogNorm" + }, + { + "is_class": true, + "is_explicit": false, + "module": "matplotlib.colors", + "module_short": "matplotlib.colors", + "name": "Normalize" + }, + { + "is_class": true, + "is_explicit": false, + "module": "matplotlib", + "module_short": "matplotlib", + "name": "Normalize" + }, + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib.colors", + "module_short": "matplotlib.colors", + "name": "LogNorm" + } + ], + "MetatomicCalculator": [ + { + "is_class": true, + "is_explicit": false, + "module": "metatomic.torch.ase_calculator", + "module_short": "metatomic.torch.ase_calculator", + "name": "MetatomicCalculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "metatomic.torch", + "module_short": "metatomic.torch", + "name": "MetatomicCalculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "metatomic", + "module_short": "metatomic", + "name": "MetatomicCalculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.calculators.calculator", + "module_short": "ase.calculators.calculator", + "name": "Calculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.calculators", + "module_short": "ase.calculators", + "name": "Calculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Calculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.calculators.calculator", + "module_short": "ase.calculators.calculator", + "name": "BaseCalculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.calculators", + "module_short": "ase.calculators", + "name": "BaseCalculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "BaseCalculator" + }, + { + "is_class": true, + "is_explicit": false, + "module": "ase.calculators.abc", + "module_short": 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false, + "module": "numpy", + "module_short": "numpy", + "name": "ndarray" + } + ], + "custom_ticks": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "list" + } + ], + "energies": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "ndarray" + } + ], + "local_uncertainty": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "ndarray" + } + ], + "norm": [ + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib.colors", + "module_short": "matplotlib.colors", + "name": "LogNorm" + }, + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib", + "module_short": "matplotlib", + "name": "LogNorm" + } + ], + "plot_atoms": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase.visualize.plot", + "module_short": "ase.visualize.plot", + "name": "plot_atoms" + } + ], + "plt.cm.ScalarMappable": [ + { + "is_class": true, + "is_explicit": false, + "module": "matplotlib.colorizer", + "module_short": "matplotlib.colorizer", + "name": "_ScalarMappable" + }, + { + "is_class": true, + "is_explicit": false, + "module": "matplotlib", + "module_short": "matplotlib", + "name": "_ScalarMappable" + }, + { + "is_class": true, + "is_explicit": false, + "module": "matplotlib.colorizer", + "module_short": "matplotlib.colorizer", + "name": "_ColorizerInterface" + }, + { + "is_class": true, + "is_explicit": false, + "module": "matplotlib", + "module_short": "matplotlib", + "name": "_ColorizerInterface" + }, + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib.pyplot.cm", + "module_short": "matplotlib.pyplot.cm", + "name": "ScalarMappable" + } + ], + "plt.colorbar": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib.pyplot", + "module_short": "matplotlib.pyplot", + "name": "colorbar" + } + ], + "plt.get_cmap": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib.pyplot", + "module_short": "matplotlib.pyplot", + "name": "get_cmap" + } + ], + "plt.show": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib.pyplot", + "module_short": "matplotlib.pyplot", + "name": "show" + } + ], + "predictions": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "dict" + } + ], + "structure": [ + { + "is_class": false, + "is_explicit": false, + "module": "ase.atoms", + "module_short": "ase", + "name": "Atoms" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Atoms" + } + ], + "structures": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "list" + } + ], + "uncertainties": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "ndarray" + } + ] +} diff --git a/docs/src/examples/llpr/llpr.ipynb b/docs/src/examples/llpr/llpr.ipynb new file mode 100644 index 0000000000..5c74493b0f --- /dev/null +++ b/docs/src/examples/llpr/llpr.ipynb @@ -0,0 +1,68 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Computing LLPR uncertainties\n\nThis tutorial demonstrates how to train a model with uncertainties using metatrain.\nIt involves the computation of the uncertainties on ethanol molecules, using the\nlast-layer prediction rigidity ([LLPR](LLPR_)) approximation. Both total and local\n([LPR](LPR_)) uncertainties are computed\n\n\nThe baseline model was trained using the following training options, where the training\nset consists of 100 structures from the QM9 dataset.\n\n.. literalinclude:: options.yaml\n :language: yaml\n\nOnce a model is trained, you can add LLPR uncertainties to it by launching a training\nrun with the \"llpr\" architecture, on the same data. In this case, the training options\nto add LLPR uncertainties are as follows:\n\n.. literalinclude:: options-llpr.yaml\n :language: yaml\n\nAdding LLPR uncertainties is very cheap compared to training a model, as it only\ninvolves one pass through the training data (equivalent to one epoch of training).\n\nYou can repeat the same training yourself with\n\n.. literalinclude:: train.sh\n :language: bash\n\nA detailed step-by-step introduction on how to train a model is provided in\nthe `label_basic_usage` tutorial.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As an example, we will compute the energies and uncertainties of the LLPR model on a\nfew ethanol structures.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import ase.io\nimport matplotlib.pyplot as plt\nfrom ase.visualize.plot import plot_atoms\nfrom matplotlib.colors import LogNorm\nfrom metatomic.torch import ModelOutput\nfrom metatomic.torch.ase_calculator import MetatomicCalculator\n\n\n# load 5 ethanol structures\nstructures = ase.io.read(\"ethanol_reduced_100.xyz\", \":5\")\n\n# load the model as an ASE calculator\ncalc = MetatomicCalculator(\n \"model-llpr.pt\", extensions_directory=\"extensions/\", device=\"cpu\"\n)\n\n# the uncertainties are available throguh the ``run_model`` method of the calculator\npredictions = calc.run_model(\n structures,\n {\n \"energy\": ModelOutput(per_atom=False),\n \"energy_uncertainty\": ModelOutput(per_atom=False),\n },\n)\n\n# print the energies and uncertainties\n\nenergies = predictions[\"energy\"].block().values.squeeze().numpy()\nuncertainties = predictions[\"energy_uncertainty\"].block().values.squeeze().numpy()\n\nprint(energies)\nprint(uncertainties)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also obtain per-atom uncertainties (local prediction rigidity, LPR). As an\nexample, we will compute the uncertainties on an ethanol structure.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "structure = structures[0]\npredictions = calc.run_model(\n structure,\n {\n # here, we use per_atom=True to request per-atom uncertainties\n \"energy_uncertainty\": ModelOutput(per_atom=True),\n },\n)\nlocal_uncertainty = predictions[\"energy_uncertainty\"].block().values.squeeze().numpy()\nlocal_uncertainty = local_uncertainty * 1000.0 # convert from eV to meV\n\nnorm = LogNorm(vmin=min(local_uncertainty), vmax=max(local_uncertainty))\ncolormap = plt.get_cmap(\"viridis\")\ncolors = colormap(norm(local_uncertainty))\nax = plot_atoms(structure, colors=colors, rotation=\"180x,0y,0z\")\ncustom_ticks = [0.7, 0.8, 0.9, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5]\ncbar = plt.colorbar(\n plt.cm.ScalarMappable(norm=norm, cmap=colormap),\n ax=ax,\n label=\"Local energy uncertainty (meV)\",\n ticks=custom_ticks,\n)\ncbar.ax.set_yticklabels([f\"{tick}\" for tick in custom_ticks])\ncbar.minorticks_off()\nax.set_xticks([])\nax.set_yticks([])\nplt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/docs/src/examples/llpr/llpr.py b/docs/src/examples/llpr/llpr.py new file mode 100644 index 0000000000..8979340c17 --- /dev/null +++ b/docs/src/examples/llpr/llpr.py @@ -0,0 +1,121 @@ +""".. _llprexample: + +Computing LLPR uncertainties +============================ + +This tutorial demonstrates how to train a model with uncertainties using metatrain. +It involves the computation of the uncertainties on ethanol molecules, using the +last-layer prediction rigidity (`LLPR `_) approximation. Both total and local +(`LPR `_) uncertainties are computed + +.. _LLPR: https://arxiv.org/html/2403.02251v1 +.. _LPR: https://pubs.acs.org/doi/10.1021/acs.jctc.3c00704 + +The baseline model was trained using the following training options, where the training +set consists of 100 structures from the QM9 dataset. + +.. literalinclude:: options-no-llpr.yaml + :language: yaml + +Once a model is trained, you can add LLPR uncertainties to it by launching a training +run with the "llpr" architecture, on the same data. In this case, the training options +to add LLPR uncertainties are as follows: + +.. literalinclude:: options-llpr.yaml + :language: yaml + +Adding LLPR uncertainties is very cheap compared to training a model, as it only +involves one pass through the training data (equivalent to one epoch of training). + +You can repeat the same training yourself with + +""" +# %% +# + +import subprocess + +import ase.io +import matplotlib.pyplot as plt +from ase.visualize.plot import plot_atoms +from matplotlib.colors import LogNorm +from metatomic.torch import ModelOutput +from metatomic.torch.ase_calculator import MetatomicCalculator + + +# %% +# +# We first train the baseline model without uncertainties and then the LLPR model. + + +subprocess.run(["mtt", "train", "options-no-llpr.yaml", "-o", "model.pt"]) +subprocess.run(["mtt", "train", "options-llpr.yaml", "-o", "model-llpr.pt"]) + +# %% +# +# A detailed step-by-step introduction on how to train a model is provided in +# the :ref:`label_basic_usage` tutorial. +# +# As an example, we will compute the energies and uncertainties of the LLPR model on a +# few ethanol structures. +# +# Next we load 5 ethanol structures +structures = ase.io.read("ethanol_reduced_100.xyz", ":5") + +# %% +# +# and load the model as an ASE calculator +calc = MetatomicCalculator( + "model-llpr.pt", extensions_directory="extensions/", device="cpu" +) + +# the uncertainties are available throguh the ``run_model`` method of the calculator +predictions = calc.run_model( + structures, + { + "energy": ModelOutput(per_atom=False), + "energy_uncertainty": ModelOutput(per_atom=False), + }, +) + +# print the energies and uncertainties + +energies = predictions["energy"].block().values.squeeze().numpy() +uncertainties = predictions["energy_uncertainty"].block().values.squeeze().numpy() + +print(energies) +print(uncertainties) + + +# %% +# +# We can also obtain per-atom uncertainties (local prediction rigidity, LPR). As an +# example, we will compute the uncertainties on an ethanol structure. + +structure = structures[0] +predictions = calc.run_model( + structure, + { + # here, we use per_atom=True to request per-atom uncertainties + "energy_uncertainty": ModelOutput(per_atom=True), + }, +) +local_uncertainty = predictions["energy_uncertainty"].block().values.squeeze().numpy() +local_uncertainty = local_uncertainty * 1000.0 # convert from eV to meV + +norm = LogNorm(vmin=min(local_uncertainty), vmax=max(local_uncertainty)) +colormap = plt.get_cmap("viridis") +colors = colormap(norm(local_uncertainty)) +ax = plot_atoms(structure, colors=colors, rotation="180x,0y,0z") +custom_ticks = [0.7, 0.8, 0.9, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5] +cbar = plt.colorbar( + plt.cm.ScalarMappable(norm=norm, cmap=colormap), + ax=ax, + label="Local energy uncertainty (meV)", + ticks=custom_ticks, +) +cbar.ax.set_yticklabels([f"{tick}" for tick in custom_ticks]) +cbar.minorticks_off() +ax.set_xticks([]) +ax.set_yticks([]) +plt.show() diff --git a/docs/src/examples/llpr/llpr.py.md5 b/docs/src/examples/llpr/llpr.py.md5 new file mode 100644 index 0000000000..d9979d99e5 --- /dev/null +++ b/docs/src/examples/llpr/llpr.py.md5 @@ -0,0 +1 @@ +76970f9c741099e3b7ea8946bb48d8e0 \ No newline at end of file diff --git a/docs/src/examples/llpr/llpr.rst b/docs/src/examples/llpr/llpr.rst new file mode 100644 index 0000000000..aed660fd85 --- /dev/null +++ b/docs/src/examples/llpr/llpr.rst @@ -0,0 +1,183 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "examples/llpr/llpr.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code. + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_examples_llpr_llpr.py: + +.. _llprexample: + +Computing LLPR uncertainties +============================ + +This tutorial demonstrates how to train a model with uncertainties using metatrain. +It involves the computation of the uncertainties on ethanol molecules, using the +last-layer prediction rigidity (`LLPR `_) approximation. Both total and local +(`LPR `_) uncertainties are computed + +.. _LLPR: https://arxiv.org/html/2403.02251v1 +.. _LPR: https://pubs.acs.org/doi/10.1021/acs.jctc.3c00704 + +The baseline model was trained using the following training options, where the training +set consists of 100 structures from the QM9 dataset. + +.. literalinclude:: options.yaml + :language: yaml + +Once a model is trained, you can add LLPR uncertainties to it by launching a training +run with the "llpr" architecture, on the same data. In this case, the training options +to add LLPR uncertainties are as follows: + +.. literalinclude:: options-llpr.yaml + :language: yaml + +Adding LLPR uncertainties is very cheap compared to training a model, as it only +involves one pass through the training data (equivalent to one epoch of training). + +You can repeat the same training yourself with + +.. literalinclude:: train.sh + :language: bash + +A detailed step-by-step introduction on how to train a model is provided in +the :ref:`label_basic_usage` tutorial. + +.. GENERATED FROM PYTHON SOURCE LINES 40-42 + +As an example, we will compute the energies and uncertainties of the LLPR model on a +few ethanol structures. + +.. GENERATED FROM PYTHON SOURCE LINES 43-78 + +.. code-block:: Python + + + import ase.io + import matplotlib.pyplot as plt + from ase.visualize.plot import plot_atoms + from matplotlib.colors import LogNorm + from metatomic.torch import ModelOutput + from metatomic.torch.ase_calculator import MetatomicCalculator + + + # load 5 ethanol structures + structures = ase.io.read("ethanol_reduced_100.xyz", ":5") + + # load the model as an ASE calculator + calc = MetatomicCalculator( + "model-llpr.pt", extensions_directory="extensions/", device="cpu" + ) + + # the uncertainties are available throguh the ``run_model`` method of the calculator + predictions = calc.run_model( + structures, + { + "energy": ModelOutput(per_atom=False), + "energy_uncertainty": ModelOutput(per_atom=False), + }, + ) + + # print the energies and uncertainties + + energies = predictions["energy"].block().values.squeeze().numpy() + uncertainties = predictions["energy_uncertainty"].block().values.squeeze().numpy() + + print(energies) + print(uncertainties) + + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + [-154.97468067 -154.97642503 -154.97632212 -154.97600337 -154.97451059] + [0.0097369 0.01553008 0.02451612 0.01447105 0.01356071] + + + + +.. GENERATED FROM PYTHON SOURCE LINES 79-81 + +We can also obtain per-atom uncertainties (local prediction rigidity, LPR). As an +example, we will compute the uncertainties on an ethanol structure. + +.. GENERATED FROM PYTHON SOURCE LINES 82-110 + +.. code-block:: Python + + + structure = structures[0] + predictions = calc.run_model( + structure, + { + # here, we use per_atom=True to request per-atom uncertainties + "energy_uncertainty": ModelOutput(per_atom=True), + }, + ) + local_uncertainty = predictions["energy_uncertainty"].block().values.squeeze().numpy() + local_uncertainty = local_uncertainty * 1000.0 # convert from eV to meV + + norm = LogNorm(vmin=min(local_uncertainty), vmax=max(local_uncertainty)) + colormap = plt.get_cmap("viridis") + colors = colormap(norm(local_uncertainty)) + ax = plot_atoms(structure, colors=colors, rotation="180x,0y,0z") + custom_ticks = [0.7, 0.8, 0.9, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5] + cbar = plt.colorbar( + plt.cm.ScalarMappable(norm=norm, cmap=colormap), + ax=ax, + label="Local energy uncertainty (meV)", + ticks=custom_ticks, + ) + cbar.ax.set_yticklabels([f"{tick}" for tick in custom_ticks]) + cbar.minorticks_off() + ax.set_xticks([]) + ax.set_yticks([]) + plt.show() + + + + + + + + +.. _sphx_glr_download_examples_llpr_llpr.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: llpr.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: llpr.py ` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: llpr.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/llpr/llpr.zip b/docs/src/examples/llpr/llpr.zip new file mode 100644 index 0000000000000000000000000000000000000000..46bdfa75339844db082667a71e1500958b666a9a GIT binary patch literal 3789 zcmb_fU5_NS60ZPG;#cG$Z~Vaggq-WD>W|$X!b4_M)vogO_5C>a_}0~vN3Xv5fc_qR^^fb;L(6;hoBTK_iEiY}MjHtuQBB#dG?oh6M z1>vi$2@heyU;gu_fB*8vg9miI1{31KdXprH5PV8cYAws!s4-(7+p=zgt`_|A^6GEA 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U0=-115.679136 U=-115.675816 H=-115.674872 G=-115.701876 Cv=8.751 pbc="F F F" +C -0.00828817 1.39046978 -0.00560069 +O -0.00797038 -0.02504537 0.02030606 +H 1.00658338 1.81556366 0.00348335 +H -0.54657475 1.79916975 -0.87390126 +H -0.52288871 1.72555240 0.89907326 +H 0.44142019 -0.33354425 -0.77152059 +7 +Properties=species:S:1:pos:R:3 A=160.28041 B=8.59323 C=8.59321 mu=0.7156 alpha=28.78 epsHOMO=-0.2609 epsLUMO=0.0613 epsgap=0.3222 =177.1963 zpve=0.05541 U0=-116.609549 U=-116.60555 H=-116.604606 G=-116.633775 Cv=12.482 pbc="F F F" +C -0.01782102 1.46435788 0.01009397 +C 0.00208816 0.00950777 0.00201200 +C 0.01834085 -1.19180518 -0.00450508 +H 0.99782210 1.87425349 0.00260606 +H -0.54220434 1.85801178 -0.86721192 +H -0.52533306 1.84834356 0.90148138 +H 0.03231658 -2.25314823 -0.01025999 +6 +Properties=species:S:1:pos:R:3 A=159.03567 B=9.22327 C=9.22324 mu=3.8266 alpha=24.45 epsHOMO=-0.3264 epsLUMO=0.0376 epsgap=0.364 =160.7223 zpve=0.045286 U0=-132.71815 U=-132.714563 H=-132.713619 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-0.02589986 1.34614561 0.00889391 +C 0.04646728 -0.01174341 0.00120425 +O 1.07183520 -0.65258782 -0.01113313 +H 0.82535497 1.88504948 0.00373769 +H -0.90837680 1.82679638 0.01891992 +H -0.96144100 -0.47500376 0.00807391 +11 +Properties=species:S:1:pos:R:3 A=29.45018 B=8.37701 C=7.42076 mu=0.0597 alpha=34.75 epsHOMO=-0.323 epsLUMO=0.0949 epsgap=0.4179 =227.1361 zpve=0.103182 U0=-119.052475 U=-119.047927 H=-119.046983 G=-119.078157 Cv=14.84 pbc="F F F" +C -0.03113825 1.54081582 0.03192126 +C 0.01215347 0.01092235 -0.01603259 +C 0.72169129 -0.52583353 -1.26230570 +H 0.97955987 1.96459116 0.03098367 +H -0.55840223 1.94831192 -0.83816075 +H -0.54252252 1.90153531 0.93005671 +H 0.51522791 -0.36840234 0.88231134 +H -1.01070641 -0.38456999 0.02051783 +H 1.75851210 -0.17376585 -1.30871516 +H 0.74087192 -1.62024959 -1.27516511 +H 0.22023351 -0.19051179 -2.17729020 +9 +Properties=species:S:1:pos:R:3 A=35.09545 B=9.3686 C=8.1497 mu=1.4131 alpha=27.87 epsHOMO=-0.2619 epsLUMO=0.0798 epsgap=0.3417 =193.1659 zpve=0.079754 U0=-154.972731 U=-154.968412 H=-154.967467 G=-154.998148 Cv=13.546 pbc="F F F" +C -0.00860504 1.50203829 -0.00681217 +C 0.01099310 -0.01764877 -0.01377035 +O 0.68088841 -0.44041803 -1.19313210 +H 1.01153334 1.89662030 -0.01920155 +H -0.53159862 1.88076109 -0.88974659 +H -0.51674572 1.87611771 0.88710739 +H 0.52377121 -0.38912295 0.88824082 +H -1.02027544 -0.40507261 0.01690670 +H 0.69529573 -1.40179568 -1.20148495 +9 +Properties=species:S:1:pos:R:3 A=39.37691 B=10.04033 C=8.90353 mu=1.1502 alpha=28.13 epsHOMO=-0.2525 epsLUMO=0.091 epsgap=0.3435 =187.1015 zpve=0.079534 U0=-154.960361 U=-154.956045 H=-154.9551 G=-154.985747 Cv=12.934 pbc="F F F" +C -0.01482147 1.39241234 0.00567104 +O -0.00471537 -0.01360660 0.01459658 +C 0.63794893 -0.55329676 -1.11358232 +H 1.00528358 1.81015807 0.00465631 +H -0.54689560 1.79343530 -0.87251051 +H -0.53002895 1.72291969 0.91101696 +H 0.13993792 -0.25599333 -2.05098375 +H 1.69265275 -0.23868382 -1.17477743 +H 0.59959435 -1.64180161 -1.02407616 +9 +Properties=species:S:1:pos:R:3 A=20.15852 B=20.15302 C=12.5891 mu=0.0005 alpha=30.82 epsHOMO=-0.2888 epsLUMO=0.1042 epsgap=0.393 =155.8145 zpve=0.081231 U0=-117.824798 U=-117.821426 H=-117.820482 G=-117.849087 Cv=11.041 pbc="F F F" +C -0.01193280 1.51433198 0.01031700 +C 1.30299118 0.77886562 -0.00617842 +C 0.00867174 0.00767042 0.00201032 +H -0.30541464 2.01702117 0.92533239 +H -0.32275498 2.02680156 -0.89347839 +H 1.88493396 0.79145020 -0.92119393 +H 1.90226631 0.78165391 0.89766373 +H -0.27088043 -0.51289125 0.91137474 +H -0.28821983 -0.50310409 -0.90740824 +7 +Properties=species:S:1:pos:R:3 A=25.77385 B=22.12109 C=14.18066 mu=1.7675 alpha=24.04 epsHOMO=-0.2682 epsLUMO=0.1042 epsgap=0.3724 =129.891 zpve=0.057289 U0=-153.742562 U=-153.73941 H=-153.738466 G=-153.766642 Cv=9.176 pbc="F F F" +C 0.01537720 1.41764380 0.00956287 +C 1.26478161 0.64923984 -0.00655044 +O -0.00024018 -0.00771006 0.00204055 +H -0.31763326 1.88586598 0.93475751 +H -0.33527583 1.89576673 -0.90397578 +H 1.83246682 0.56256848 -0.93193290 +H 1.85008258 0.55267938 0.90680147 +10 +Properties=species:S:1:pos:R:3 A=10.12193 B=8.49011 C=4.89615 mu=2.7362 alpha=35.53 epsHOMO=-0.2431 epsLUMO=-0.0087 epsgap=0.2344 =292.4367 zpve=0.083382 U0=-193.08834 U=-193.082969 H=-193.082024 G=-193.116476 Cv=16.893 pbc="F F F" +C -0.00310189 1.48050328 -0.17259867 +C -0.04527202 -0.02887059 0.00139493 +C 1.29705800 -0.72194415 0.16889042 +O -1.08643172 -0.64273343 0.00630393 +H 0.59403529 1.74668579 -1.05225043 +H -1.01540407 1.87034023 -0.28382265 +H 0.48099600 1.95070605 0.69099376 +H 1.82512144 -0.32470731 1.04328658 +H 1.14892507 -1.79597689 0.28554393 +H 1.93682635 -0.52949692 -0.69995403 +9 +Properties=species:S:1:pos:R:3 A=10.78886 B=9.27509 C=5.14977 mu=3.6367 alpha=31.83 epsHOMO=-0.2436 epsLUMO=0.0347 epsgap=0.2783 =267.6148 zpve=0.07319 U0=-209.159302 U=-209.15402 H=-209.153076 G=-209.187468 Cv=16.561 pbc="F F F" +C 0.00679889 1.49969736 -0.02567030 +C -0.02740385 -0.01752051 -0.14894811 +N 1.17826667 -0.63324298 0.05691721 +O -1.03721965 -0.63980649 -0.40727572 +H 0.91390280 1.92923755 -0.46100067 +H -0.87080896 1.91124186 -0.52345176 +H -0.02826124 1.78517271 1.03149125 +H 1.98405139 -0.13175861 0.38674906 +H 1.19049421 -1.63919563 0.10006209 +8 +Properties=species:S:1:pos:R:3 A=11.1475 B=10.36388 C=5.42091 mu=3.4869 alpha=28.07 epsHOMO=-0.2495 epsLUMO=0.0556 epsgap=0.3051 =244.2308 zpve=0.063824 U0=-225.221461 U=-225.217075 H=-225.216131 G=-225.247724 Cv=15.292 pbc="F F F" +N 0.03605270 1.36077873 -0.12416403 +C -0.02591138 -0.02076560 0.00200645 +N 1.21968479 -0.62334220 0.11963222 +O -1.06822866 -0.64174597 0.00865573 +H 0.80749439 1.83455148 0.32144915 +H -0.85825775 1.80373500 0.02243770 +H 1.99927148 -0.17303054 -0.33602496 +H 1.18204327 -1.62147901 -0.02109234 +14 +Properties=species:S:1:pos:R:3 A=7.75166 B=7.74847 C=4.48668 mu=0.0897 alpha=45.46 epsHOMO=-0.3167 epsLUMO=0.0843 epsgap=0.401 =355.0621 zpve=0.131146 U0=-158.342346 U=-158.336603 H=-158.335658 G=-158.370016 Cv=20.273 pbc="F F F" +C -0.03215888 1.54021598 0.01074456 +C 0.03381741 0.00745852 0.00180716 +C 0.71375572 -0.50856404 -1.27296949 +C 0.73848999 -0.52220886 1.25750907 +H 0.97522736 1.97414341 0.00528362 +H -0.56196769 1.92232326 -0.86835418 +H -0.54817239 1.91268414 0.90208039 +H -0.99752525 -0.37310578 0.00977297 +H 1.75070021 -0.15586784 -1.33269631 +H 0.73446691 -1.60328288 -1.29939696 +H 0.19367011 -0.16085589 -2.17195508 +H 0.23611990 -0.18382017 2.17005260 +H 0.75964083 -1.61713153 1.27226101 +H 1.77635866 -0.16985318 1.30053019 +12 +Properties=species:S:1:pos:R:3 A=8.67553 B=8.00568 C=4.75542 mu=1.4259 alpha=38.58 epsHOMO=-0.2612 epsLUMO=0.074 epsgap=0.3351 =318.3721 zpve=0.107673 U0=-194.267232 U=-194.261748 H=-194.260804 G=-194.294663 Cv=19.052 pbc="F F F" +C -0.03315871 1.54782566 -0.00438815 +C -0.01108534 0.01859095 0.01676117 +C 0.70938384 -0.53596711 1.23978444 +O -1.33219841 -0.51712554 0.05236057 +H 0.98215692 1.95715900 -0.03182225 +H -0.56388100 1.92087623 -0.88845865 +H -0.54082548 1.93340945 0.88575489 +H 0.51042122 -0.33767241 -0.88819176 +H 0.22240492 -0.18785860 2.15650245 +H 0.68242024 -1.62887638 1.23305141 +H 1.75434301 -0.21246950 1.25524188 +H -1.81818681 -0.15577435 -0.69553962 +6 +Properties=species:S:1:pos:R:3 A=0 B=4.4259726 C=4.4259726 mu=0 alpha=38.52 epsHOMO=-0.2599 epsLUMO=-0.0214 epsgap=0.2386 =278.6264 zpve=0.037354 U0=-153.459846 U=-153.455442 H=-153.454498 G=-153.482621 Cv=15.312 pbc="F F F" +C 0.68098021 0.00000000 0.00000000 +C -0.68098021 0.00000000 0.00000000 +C -1.88766603 0.00000000 0.00000000 +C 1.88766603 0.00000000 0.00000000 +H -2.94960000 0.00000000 0.00000000 +H 2.94960000 0.00000000 0.00000000 +5 +Properties=species:S:1:pos:R:3 A=0 B=4.5793222 C=4.5793222 mu=3.792 alpha=32.66 epsHOMO=-0.3102 epsLUMO=-0.0543 epsgap=0.2559 =260.1896 zpve=0.027259 U0=-169.557758 U=-169.553764 H=-169.55282 G=-169.581024 Cv=12.93 pbc="F F F" +C 0.01510390 0.00000000 1.00000000 +C 1.38233149 0.00000000 1.00000000 +C -1.18862525 0.00000000 1.00000000 +H -2.25177201 0.00000000 1.00000000 +N 2.54296187 0.00000000 1.00000000 +4 +Properties=species:S:1:pos:R:3 A=0 B=4.73269 C=4.73269 mu=0.0023 alpha=27.7 epsHOMO=-0.3696 epsLUMO=-0.0926 epsgap=0.277 =242.9308 zpve=0.015951 U0=-185.648533 U=-185.644825 H=-185.64388 G=-185.667652 Cv=10.398 pbc="F F F" +N 0.01745734 -1.16134217 -0.00415342 +C 0.00253212 -0.00344278 0.00179939 +C -0.01611419 1.37220987 0.00934873 +N -0.03264302 2.53008276 0.01609090 +6 +Properties=species:S:1:pos:R:3 A=68.5171 B=4.8345 C=4.51586 mu=2.7824 alpha=31.14 epsHOMO=-0.2777 epsLUMO=-0.0735 epsgap=0.2042 =268.3921 zpve=0.037208 U0=-190.624631 U=-190.620363 H=-190.619419 G=-190.650543 Cv=13.049 pbc="F F F" +O -0.06776120 -0.04233838 0.00282489 +C -0.00562945 1.16382879 0.00743862 +C 1.22985272 1.91339997 0.00028781 +C 2.23625322 2.57430970 -0.00529277 +H -0.91670701 1.79467546 0.01781352 +H 3.13429662 3.14370267 -0.01037656 +5 +Properties=species:S:1:pos:R:3 A=67.88408 B=5.00823 C=4.66413 mu=2.3112 alpha=26.25 epsHOMO=-0.3166 epsLUMO=-0.11 epsgap=0.2066 =251.0007 zpve=0.02654 U0=-206.721858 U=-206.717875 H=-206.716931 G=-206.747625 Cv=11.329 pbc="F F F" +O -0.04055978 -0.03799144 0.00266513 +C -0.01768870 1.16193251 0.00746008 +C 1.23315534 1.93381336 0.00043371 +N 2.19857378 2.57055097 -0.00483320 +H -0.92627534 1.78959196 0.01764859 +6 +Properties=species:S:1:pos:R:3 A=56.25048 B=4.77441 C=4.40088 mu=0.002 alpha=26.12 epsHOMO=-0.2668 epsLUMO=-0.1113 epsgap=0.1555 =266.8164 zpve=0.036943 U0=-227.798785 U=-227.79457 H=-227.793626 G=-227.825074 Cv=12.147 pbc="F F F" +O 0.00337122 -0.03146586 0.00157206 +C -0.01713174 1.17081409 0.00676444 +C 1.26555783 1.99719410 -0.00007703 +O 1.24507069 3.19946589 0.00681275 +H -0.94690483 1.77678142 0.01852668 +H 2.19534410 1.39121374 -0.01006739 +10 +Properties=species:S:1:pos:R:3 A=80.28766 B=3.3688 C=3.3688 mu=0 alpha=42.32 epsHOMO=-0.2412 epsLUMO=0.0684 epsgap=0.3096 =400.2236 zpve=0.083896 U0=-155.908941 U=-155.90318 H=-155.902236 G=-155.937641 Cv=17.447 pbc="F F F" +C -0.01787959 1.46676893 0.01011180 +C 0.00186644 0.01072913 0.00211109 +C 0.01819704 -1.19272861 -0.00450723 +C 0.03795170 -2.64876793 -0.01251193 +H 0.86460312 1.87589979 0.51479719 +H -0.03217624 1.87210581 -1.00791002 +H -0.90233115 1.85185524 0.53003953 +H -0.83477011 -3.06339007 0.50451436 +H 0.03276422 -3.04315564 -1.03491223 +H 0.93213486 -3.03931642 0.48626606 +10 +Properties=species:S:1:pos:R:3 A=27.39459 B=4.53005 C=4.08342 mu=0.7067 alpha=40.09 epsHOMO=-0.2592 epsLUMO=0.0566 epsgap=0.3157 =333.9589 zpve=0.084338 U0=-155.897345 U=-155.892291 H=-155.891347 G=-155.924226 Cv=17.13 pbc="F F F" +C -0.03095840 1.54775025 0.03167945 +C 0.01485375 0.00962471 -0.02082154 +C 0.69099073 -0.49954575 -1.20857624 +C 1.25222275 -0.89514467 -2.19527879 +H 0.97899300 1.96621609 0.03373834 +H -0.56082354 1.95002902 -0.83580745 +H -0.54576708 1.88225519 0.93709062 +H 0.51920135 -0.37425398 0.87496224 +H -1.00644802 -0.39030838 0.01340632 +H 1.74594541 -1.25604187 -3.06299415 +9 +Properties=species:S:1:pos:R:3 A=28.0016 B=4.67752 C=4.21765 mu=3.9233 alpha=35.38 epsHOMO=-0.3213 epsLUMO=0.034 epsgap=0.3553 =314.5335 zpve=0.07419 U0=-172.006141 U=-172.001467 H=-172.000523 G=-172.032826 Cv=14.988 pbc="F F F" +C -0.02522005 1.54773054 0.02133370 +C 0.01183939 0.00999278 -0.01556702 +C 0.68915114 -0.50836846 -1.20331488 +N 1.22565733 -0.90360846 -2.14737482 +H 0.98571620 1.96212990 0.02370153 +H -0.55522192 1.94599662 -0.84711771 +H -0.53962556 1.88294354 0.92536604 +H 0.52481910 -0.38226048 0.86933355 +H -1.00453542 -0.39832109 0.00530071 +8 +Properties=species:S:1:pos:R:3 A=32.19893 B=4.76299 C=4.30696 mu=4.4361 alpha=31.81 epsHOMO=-0.2683 epsLUMO=0.0173 epsgap=0.2855 =295.6635 zpve=0.063305 U0=-188.042067 U=-188.037478 H=-188.036534 G=-188.06863 Cv=14.488 pbc="F F F" +N -0.03608099 1.48762533 0.03678072 +C -0.01039471 0.02517605 -0.01506608 +C -1.37180956 -0.52108920 0.02739272 +N -2.44026695 -0.95956444 0.03370519 +H 0.91729752 1.83547547 0.05202682 +H -0.47162178 1.85677205 -0.80275569 +H 0.48946161 -0.40880401 -0.89715168 +H 0.52827150 -0.34533338 0.86469547 +8 +Properties=species:S:1:pos:R:3 A=35.11615 B=4.66117 C=4.22354 mu=1.7211 alpha=33.1 epsHOMO=-0.2595 epsLUMO=0.0277 epsgap=0.2872 =300.0993 zpve=0.060632 U0=-191.810916 U=-191.806025 H=-191.805081 G=-191.837634 Cv=15.855 pbc="F F F" +O -0.02013197 1.45738137 0.01010195 +C 0.00168478 0.03579980 0.00224736 +C -1.36366691 -0.47481101 0.01255056 +C -2.47902835 -0.91970202 0.02082451 +H 0.89446911 1.75736553 0.00327363 +H 0.51979028 -0.35175413 -0.88980302 +H 0.53670458 -0.36127661 0.88000894 +H -3.46773502 -1.30737009 0.02818120 +7 +Properties=species:S:1:pos:R:3 A=35.93172 B=4.83443 C=4.37827 mu=4.6788 alpha=28.56 epsHOMO=-0.3018 epsLUMO=0.0022 epsgap=0.3039 =280.6659 zpve=0.050262 U0=-207.916786 U=-207.912215 H=-207.911271 G=-207.943384 Cv=13.845 pbc="F F F" +O -0.03738221 1.44547841 0.01010769 +C 0.01292906 0.03441661 0.00209165 +C -1.35747452 -0.48874523 0.01239738 +N -2.42135277 -0.93685184 0.02016053 +H 0.86898057 1.76972949 0.00417577 +H 0.51692713 -0.36329790 -0.89215814 +H 0.53387616 -0.37278749 0.88223921 +10 +Properties=species:S:1:pos:R:3 A=26.14564 B=4.2893 C=4.12773 mu=2.6741 alpha=35.83 epsHOMO=-0.25 epsLUMO=-0.0205 epsgap=0.2295 =333.3276 zpve=0.084175 U0=-193.075202 U=-193.070116 H=-193.069171 G=-193.102798 Cv=15.954 pbc="F F F" +C 0.16879340 1.52513042 -0.15747056 +C -0.18737625 0.06194763 0.14679374 +C 0.50917645 -0.43996215 1.39125850 +O 1.18191079 -1.43565585 1.45816384 +H 1.23126511 1.63137254 -0.39632693 +H -0.40744668 1.89431681 -1.00969246 +H -0.04931034 2.17266260 0.69857432 +H -1.26905160 -0.01668067 0.32854433 +H 0.06279062 -0.60250479 -0.68474036 +H 0.35384841 0.20663370 2.28871052 +9 +Properties=species:S:1:pos:R:3 A=19.98749 B=6.11589 C=4.82584 mu=3.7071 alpha=32.78 epsHOMO=-0.2516 epsLUMO=0.0335 epsgap=0.2851 =279.7863 zpve=0.074166 U0=-209.144909 U=-209.139976 H=-209.139032 G=-209.172305 Cv=15.058 pbc="F F F" +C 0.01474916 1.47888256 -0.03509326 +N 0.18708077 0.03831317 -0.02906851 +C 0.45703661 -0.66858895 1.10069001 +O 0.58457996 -0.19876202 2.21119705 +H 0.76025289 1.96736104 -0.67198126 +H -0.98462423 1.75984483 -0.38485103 +H 0.14269849 1.82387718 0.99149397 +H 0.10386349 -0.46937726 -0.89461379 +H 0.55047568 -1.75253452 0.88868242 +8 +Properties=species:S:1:pos:R:3 A=48.62351 B=4.67298 C=4.38187 mu=3.92 alpha=29.47 epsHOMO=-0.2814 epsLUMO=0.0074 epsgap=0.2888 =293.917 zpve=0.061327 U0=-229.013797 U=-229.009003 H=-229.008059 G=-229.041086 Cv=13.885 pbc="F F F" +C 0.10653781 1.43512049 0.03336100 +O 0.42686564 0.04716929 0.09309945 +C 0.50714862 -0.63652665 -1.06318297 +O 0.76927014 -1.79955017 -1.11010475 +H 0.92349251 2.00385139 0.48465728 +H -0.03757815 1.76927183 -1.00057425 +H -0.81269569 1.60801916 0.59873287 +H 0.30458460 -0.00231902 -1.94965553 +8 +Properties=species:S:1:pos:R:3 A=36.49282 B=4.31175 C=4.04493 mu=1.7341 alpha=28.53 epsHOMO=-0.2537 epsLUMO=-0.0341 epsgap=0.2196 =303.8129 zpve=0.060508 U0=-228.992613 U=-228.987769 H=-228.986825 G=-229.019918 Cv=14.78 pbc="F F F" +O 0.19375856 1.37170995 0.31398297 +C 0.04992637 0.01405481 -0.04526239 +C -1.40888025 -0.39635680 -0.20601574 +O -1.77729462 -1.37233860 -0.80361689 +H -0.01723186 1.91269132 -0.45337758 +H 0.60440478 -0.26519766 -0.95205725 +H 0.46740406 -0.57521679 0.78176270 +H -2.11835055 0.30075962 0.29870515 +14 +Properties=species:S:1:pos:R:3 A=23.44662 B=3.60588 C=3.38683 mu=0 alpha=45.71 epsHOMO=-0.317 epsLUMO=0.0937 epsgap=0.4107 =426.2996 zpve=0.131708 U0=-158.340943 U=-158.33517 H=-158.334226 G=-158.36894 Cv=19.668 pbc="F F F" +C -0.03007624 1.55804912 0.02980774 +C 0.00707277 0.02834877 -0.00712529 +C 0.71320792 -0.52989340 -1.24765581 +C 0.75036895 -2.05959366 -1.28458957 +H 0.98195330 1.97845113 0.02800797 +H -0.55582433 1.96212343 -0.84270157 +H -0.54083762 1.92692755 0.92493697 +H 0.50928672 -0.34909049 0.89361319 +H -1.01733481 -0.36542299 0.03207393 +H 0.21098948 -0.15245770 -2.14839302 +H 1.73761331 -0.13611625 -1.28685672 +H 1.27616854 -2.46366410 -0.41210980 +H -0.26165699 -2.48000447 -1.28273552 +H 1.26108491 -2.42846444 -2.17974803 +12 +Properties=species:S:1:pos:R:3 A=26.59789 B=3.79186 C=3.53779 mu=1.3402 alpha=38.61 epsHOMO=-0.2619 epsLUMO=0.081 epsgap=0.3429 =382.8628 zpve=0.108241 U0=-194.261089 U=-194.255495 H=-194.254551 G=-194.28893 Cv=18.431 pbc="F F F" +C -0.00379012 1.52196790 0.01027996 +C -0.03589372 -0.00778254 0.00225671 +C -1.45219016 -0.56653711 0.01284386 +O -1.36799785 -1.98394604 0.00453606 +H 1.02477576 1.89411231 0.00239793 +H -0.51286674 1.93764614 -0.86653888 +H -0.49584080 1.92801344 0.90121721 +H 0.48140784 -0.39530552 -0.88316064 +H 0.49828589 -0.40486256 0.87330348 +H -1.98734220 -0.20399494 0.90666062 +H -2.00431485 -0.19459473 -0.86666460 +H -2.26171819 -2.33824298 0.01034748 +12 +Properties=species:S:1:pos:R:3 A=28.29567 B=4.14901 C=3.88349 mu=1.0363 alpha=39.34 epsHOMO=-0.2503 epsLUMO=0.0925 epsgap=0.3428 =368.9331 zpve=0.107895 U0=-194.254127 U=-194.248585 H=-194.247641 G=-194.281899 Cv=17.888 pbc="F F F" +C -0.00519551 1.50868224 0.03308760 +C 0.01316580 -0.01037819 0.01988643 +O 0.69758944 -0.46344140 1.16868979 +C 0.76688491 -1.86508183 1.24602669 +H 1.01445476 1.90423799 0.02996018 +H -0.52990866 1.89184059 -0.84726911 +H -0.51263765 1.87805843 0.92875424 +H -1.01599507 -0.40998729 0.00714282 +H 0.51093143 -0.38369435 -0.89219395 +H -0.23415841 -2.32451489 1.28977780 +H 1.30129276 -2.29853663 0.38486095 +H 1.30990287 -2.11811998 2.16013206 +10 +Properties=species:S:1:pos:R:3 A=14.79671 B=5.6956 C=4.58846 mu=0.0075 alpha=31.42 epsHOMO=-0.2594 epsLUMO=0.0584 epsgap=0.3179 =297.8398 zpve=0.085172 U0=-230.183076 U=-230.177723 H=-230.176779 G=-230.211195 Cv=16.837 pbc="F F F" +O -0.01418735 1.42642242 -0.05423651 +C -0.00391839 0.00717714 0.03496062 +C -1.42317019 -0.55560600 -0.01256639 +O -2.11229413 -0.10563875 -1.17228313 +H -0.18987882 1.64154705 -0.97555148 +H 0.59659504 -0.43315430 -0.77409095 +H 0.47311116 -0.24903733 0.98747216 +H -1.39494859 -1.64975279 -0.06283188 +H -1.96497417 -0.27216915 0.90131972 +H -2.35925991 0.80944396 -1.00614597 +12 +Properties=species:S:1:pos:R:3 A=15.61472 B=6.31363 C=5.54514 mu=0.1136 alpha=41.96 epsHOMO=-0.2727 epsLUMO=0.1012 epsgap=0.3738 =298.6061 zpve=0.109284 U0=-157.116735 U=-157.11209 H=-157.111146 G=-157.143262 Cv=16.49 pbc="F F F" +C -0.03602245 1.52092949 0.01569161 +C 0.01938487 0.01004403 0.04814953 +C 1.30891671 -0.71251995 -0.24920416 +C 0.75347423 -0.70267714 1.15564676 +H 0.90105691 1.95389291 0.38283338 +H -0.19640365 1.89437369 -1.00208569 +H -0.84685273 1.90588930 0.64451322 +H -0.88416809 -0.48001889 -0.30566335 +H 1.26900901 -1.63903068 -0.81150371 +H 2.19010738 -0.11131209 -0.44927513 +H 1.26248862 -0.09487455 1.89690028 +H 0.33540836 -1.62248918 1.54980695 +10 +Properties=species:S:1:pos:R:3 A=18.21931 B=6.63877 C=5.92459 mu=1.812 alpha=35.01 epsHOMO=-0.2633 epsLUMO=0.1052 epsgap=0.3685 =267.2979 zpve=0.085275 U0=-193.039603 U=-193.035186 H=-193.034242 G=-193.065979 Cv=14.764 pbc="F F F" +C -0.01646343 1.51830705 0.00771445 +C -0.02743227 0.01657694 -0.10889741 +C 0.77296553 -0.70131886 -1.10863115 +O 1.17212293 -0.66702969 0.26278829 +H 0.91865980 1.92914217 -0.38285880 +H -0.85158014 1.95456506 -0.55109460 +H -0.11487702 1.82596470 1.05397777 +H -0.93582598 -0.47350600 0.24682793 +H 0.44443922 -1.67201495 -1.47810622 +H 1.40538284 -0.13600339 -1.79271491 +11 +Properties=species:S:1:pos:R:3 A=16.58914 B=7.18798 C=6.11415 mu=1.1353 alpha=39.02 epsHOMO=-0.2304 epsLUMO=0.0968 epsgap=0.3271 =270.5508 zpve=0.097671 U0=-173.147782 U=-173.143343 H=-173.142399 G=-173.174073 Cv=15.298 pbc="F F F" +C -0.05275541 1.47419574 0.00999108 +N 0.01940516 0.02189947 0.06334866 +C 1.32511048 -0.56446850 -0.21420725 +C 0.78051759 -0.54057993 1.17242760 +H 0.86784202 1.97176076 0.36297285 +H -0.23845155 1.79095005 -1.02142866 +H -0.88683448 1.81938959 0.62950398 +H 1.31992909 -1.48186782 -0.79602038 +H 2.15202820 0.11020204 -0.43285248 +H 1.24563570 0.14996098 1.87499245 +H 0.37013358 -1.44020447 1.62233365 +10 +Properties=species:S:1:pos:R:3 A=16.79477 B=6.71015 C=5.86781 mu=1.3894 alpha=34.64 epsHOMO=-0.239 epsLUMO=0.0775 epsgap=0.3166 =263.966 zpve=0.085106 U0=-193.034988 U=-193.030356 H=-193.029411 G=-193.061689 Cv=15.679 pbc="F F F" +O 0.21577583 1.35386859 -0.10695107 +C 0.01628099 -0.02832718 -0.02915694 +C -0.39081465 -0.71497009 1.24315669 +C -1.35118138 -0.64280380 0.06273589 +H -0.43642338 1.79062849 0.45006761 +H 0.74981167 -0.52404050 -0.65624767 +H 0.05959375 -1.66840483 1.49729237 +H -0.61369353 -0.08848006 2.10170640 +H -2.19821932 0.03058430 0.15411451 +H -1.56395912 -1.54640426 -0.49826515 +12 +Properties=species:S:1:pos:R:3 A=10.66107 B=10.66107 C=6.30452 mu=0 alpha=41.83 epsHOMO=-0.2982 epsLUMO=0.0956 epsgap=0.3938 =268.4432 zpve=0.110511 U0=-157.115484 U=-157.111322 H=-157.110378 G=-157.141657 Cv=14.696 pbc="F F F" +C -0.02542957 1.54032578 -0.04215944 +C 1.52564399 1.50419989 0.04121276 +C 1.42146727 0.06146790 0.60844885 +C -0.02638833 -0.01070956 0.04935088 +H -0.46591372 1.99639894 0.84962894 +H -0.47900239 2.00028207 -0.92365389 +H 1.98248278 1.51375334 -0.95303183 +H 2.02191365 2.25986579 0.65522129 +H 2.14291679 -0.67956358 0.25531480 +H 1.43105853 0.05751479 1.70261879 +H -0.79053440 -0.48530296 0.66996573 +H -0.05233245 -0.47238446 -0.94236394 +10 +Properties=species:S:1:pos:R:3 A=12.06545 B=11.77731 C=6.71997 mu=1.7978 alpha=34.56 epsHOMO=-0.2424 epsLUMO=0.0859 epsgap=0.3283 =236.9759 zpve=0.086675 U0=-193.034094 U=-193.029968 H=-193.029024 G=-193.060777 Cv=12.915 pbc="F F F" +C -0.03489789 1.55259455 -0.08507691 +C 1.49349183 1.45459700 0.10114677 +O 1.42725884 0.02348990 0.27254886 +C -0.00634758 0.02236494 0.10944630 +H -0.56579210 2.11301721 0.68591527 +H -0.36628352 1.89393380 -1.06692327 +H 2.10031724 1.73525720 -0.76898349 +H 1.90023090 1.95506611 0.98896748 +H -0.51987849 -0.35595179 1.00235948 +H -0.31970062 -0.57567421 -0.75559275 +12 +Properties=species:S:1:pos:R:3 A=8.63483 B=4.11977 C=2.88825 mu=0.6875 alpha=45.37 epsHOMO=-0.2392 epsLUMO=0.0192 epsgap=0.2584 =452.5112 zpve=0.100501 U0=-248.375248 U=-248.368823 H=-248.367879 G=-248.405354 Cv=21.616 pbc="F F F" +C -0.10255370 1.49907127 -0.00133642 +C -0.00519774 -0.00343114 -0.02723470 +C -0.00688536 -0.76890740 1.26434744 +N 0.08186799 -0.69202791 -1.10087306 +O 0.07439067 0.13215394 -2.24195258 +H 0.78342093 1.94690862 -0.46354050 +H -0.96150430 1.83614082 -0.59021965 +H -0.19854173 1.87462989 1.01931607 +H -0.92620012 -0.57297946 1.82982479 +H 0.07256154 -1.84064782 1.07516863 +H 0.82937178 -0.45553340 1.90133089 +H 0.13975924 -0.51254299 -2.95389260 +10 +Properties=species:S:1:pos:R:3 A=9.17136 B=9.04195 C=4.5531 mu=1.8689 alpha=43.14 epsHOMO=-0.2029 epsLUMO=0.0499 epsgap=0.2528 =303.9808 zpve=0.082433 U0=-210.101789 U=-210.097816 H=-210.096872 G=-210.12818 Cv=14.821 pbc="F F F" +N -0.00829694 1.35362838 0.00995971 +C 1.28033254 1.82457421 -0.00022459 +C 2.12216639 0.73698898 -0.01445600 +C 1.30849904 -0.43109580 -0.01284154 +C -0.00342782 -0.01836884 0.00235736 +H -0.83243234 1.92772668 0.02122995 +H 1.48471820 2.88312630 0.00354313 +H 3.20066918 0.77343077 -0.02488416 +H 1.64810599 -1.45539683 -0.02180394 +H -0.92551647 -0.57697054 0.00839402 +9 +Properties=species:S:1:pos:R:3 A=9.77329 B=9.41192 C=4.7946 mu=3.6193 alpha=39.13 epsHOMO=-0.2253 epsLUMO=0.0332 epsgap=0.2585 =283.6817 zpve=0.071145 U0=-226.160842 U=-226.157088 H=-226.156144 G=-226.187104 Cv=13.371 pbc="F F F" +N -0.00996090 1.35636172 0.01000855 +C 1.29535422 1.79575578 -0.00047017 +C 2.05550519 0.65637438 -0.01422927 +N 1.25385745 -0.46275132 -0.01260894 +C 0.02301170 -0.00706382 0.00200146 +H -0.83195718 1.93618436 0.02132575 +H 1.54553961 2.84344998 0.00289054 +H 3.13177434 0.57569766 -0.02524155 +H -0.87667864 -0.60414531 0.00750794 +9 +Properties=species:S:1:pos:R:3 A=9.50228 B=9.29497 C=4.69874 mu=0.5571 alpha=39.2 epsHOMO=-0.2246 epsLUMO=0.0199 epsgap=0.2445 =289.005 zpve=0.069883 U0=-229.969129 U=-229.965414 H=-229.96447 G=-229.995393 Cv=13.358 pbc="F F F" +O -0.03255819 1.35065130 0.00967376 +C 1.25319237 1.79167710 -0.00030351 +C 2.11664681 0.74375623 -0.01441494 +C 1.30397796 -0.43852387 -0.01283316 +C 0.01629568 -0.00777185 0.00191294 +H 1.37951089 2.86161135 0.00427433 +H 3.19376406 0.79865501 -0.02458604 +H 1.63858893 -1.46383271 -0.02156949 +H -0.93736295 -0.50899925 0.00843891 +8 +Properties=species:S:1:pos:R:3 A=10.10926 B=9.69372 C=4.94857 mu=1.5081 alpha=35.17 epsHOMO=-0.2509 epsLUMO=0.001 epsgap=0.2519 =269.2444 zpve=0.058593 U0=-246.02915 U=-246.025614 H=-246.024669 G=-246.055309 Cv=12.04 pbc="F F F" +O -0.03808298 1.35643965 0.00975193 +C 1.26496982 1.76855319 -0.00061744 +C 2.04955750 0.66630033 -0.01406047 +N 1.24227075 -0.46732066 -0.01237541 +C 0.04019820 0.00561577 0.00183569 +H 1.43513907 2.83095265 0.00340823 +H 3.12532898 0.59783015 -0.02480757 +H -0.89387707 -0.53342154 0.00796935 +17 +Properties=species:S:1:pos:R:3 A=4.4178 B=4.41753 C=4.41717 mu=0.0003 alpha=56.01 epsHOMO=-0.3145 epsLUMO=0.0737 epsgap=0.3882 =486.2719 zpve=0.158836 U0=-197.632222 U=-197.625241 H=-197.624297 G=-197.661411 Cv=26.084 pbc="F F F" +C -0.01859029 1.53486266 0.01039904 +C 0.00210320 -0.00413672 0.00196980 +C 0.74643954 -0.51390760 1.24887638 +C -1.44190166 -0.53673615 0.01311457 +C 0.72234971 -0.50049606 -1.26445632 +H 0.99776928 1.94420529 0.00435474 +H -0.54181883 1.92841415 -0.86819911 +H -0.52758402 1.91888376 0.90148530 +H 0.25549576 -0.17602728 2.16833067 +H 0.77815389 -1.60892372 1.27088904 +H 1.77964786 -0.14975834 1.26944035 +H -1.99871895 -0.18888794 -0.86408066 +H -1.45956909 -1.63227235 0.00649916 +H -1.98088827 -0.19979639 0.90561580 +H 1.75482473 -0.13554018 -1.30118083 +H 0.75396989 -1.59524036 -1.29822280 +H 0.21358221 -0.15343900 -2.17067181 +15 +Properties=species:S:1:pos:R:3 A=4.69474 B=4.66938 C=4.47909 mu=1.384 alpha=49.04 epsHOMO=-0.2601 epsLUMO=0.0664 epsgap=0.3265 =449.0573 zpve=0.134977 U0=-233.560626 U=-233.553779 H=-233.552834 G=-233.589759 Cv=25.128 pbc="F F F" +C -0.00855999 1.54270147 0.00152717 +C 0.00506780 0.00702635 0.01856160 +C 0.76137731 -0.51839196 1.24766443 +C -1.42012774 -0.54933183 -0.02116008 +O 0.62590477 -0.48363745 -1.17657057 +H 1.01361406 1.94020776 0.00281128 +H -0.51459669 1.90702466 -0.89705781 +H -0.52214815 1.94882514 0.87918344 +H 0.28130613 -0.20165548 2.17943598 +H 0.79965506 -1.61131746 1.23022549 +H 1.79096916 -0.14072935 1.26083003 +H -1.93854829 -0.19910724 -0.91828281 +H -1.39937632 -1.64259062 -0.04563833 +H -1.98850725 -0.23059656 0.85726520 +H 1.53111188 -0.15488046 -1.19210222 +9 +Properties=species:S:1:pos:R:3 A=10.21876 B=4.04324 C=2.94978 mu=2.8579 alpha=42.02 epsHOMO=-0.2654 epsLUMO=-0.0575 epsgap=0.2079 =416.7799 zpve=0.065175 U0=-229.927277 U=-229.921622 H=-229.920677 G=-229.955843 Cv=18.723 pbc="F F F" +C -0.00693851 1.49559567 -0.01063358 +C -0.02986170 -0.01594623 0.06248673 +O -0.08815239 -0.63215062 1.10334812 +C 0.02336981 -0.70256747 -1.22062379 +C 0.06738132 -1.26058341 -2.28623129 +H 0.90429806 1.83281887 -0.51655384 +H -0.85338524 1.85556926 -0.60552819 +H -0.05237764 1.91099807 0.99695949 +H 0.10466868 -1.77036047 -3.21832617 +8 +Properties=species:S:1:pos:R:3 A=10.16063 B=4.15847 C=3.0056 mu=3.3351 alpha=37.08 epsHOMO=-0.3007 epsLUMO=-0.0889 epsgap=0.2118 =397.7757 zpve=0.05451 U0=-246.027383 U=-246.022024 H=-246.02108 G=-246.055769 Cv=16.963 pbc="F F F" +C -0.01171097 1.49973776 -0.01213268 +C -0.02919740 -0.00338745 0.07151218 +O -0.09164028 -0.64592927 1.08732241 +C 0.03831104 -0.69690258 -1.23748557 +N 0.09136286 -1.22982650 -2.26228068 +H 0.90093132 1.83370670 -0.51814928 +H -0.85663933 1.84864400 -0.61618135 +H -0.06416540 1.92131542 0.99203818 +8 +Properties=species:S:1:pos:R:3 A=10.68423 B=4.3198 C=3.07993 mu=5.1815 alpha=38.31 epsHOMO=-0.2739 epsLUMO=-0.0438 epsgap=0.2301 =384.5574 zpve=0.056619 U0=-242.19573 U=-242.190591 H=-242.189646 G=-242.223513 Cv=17.465 pbc="F F F" +N -0.05232108 1.34991769 0.15535538 +C 0.08549378 -0.01476951 0.02668890 +N 1.14555215 -0.71993906 -0.03966037 +C -1.19426556 -0.71152088 0.01802151 +N -2.22747610 -1.22790543 0.00132148 +H 0.75474469 1.91998737 -0.04411334 +H -0.94458527 1.76600473 -0.05932293 +H 1.98312429 -0.13415500 -0.02878551 +9 +Properties=species:S:1:pos:R:3 A=9.08854 B=4.42607 C=3.03169 mu=0.9461 alpha=36.51 epsHOMO=-0.2538 epsLUMO=-0.0964 epsgap=0.1574 =399.222 zpve=0.064992 U0=-267.10335 U=-267.097658 H=-267.096714 G=-267.132534 Cv=17.806 pbc="F F F" +C -0.43126066 1.30586664 -0.12758585 +C 0.25285715 -0.01026988 0.13138867 +O 0.69872099 -0.36890333 1.19604690 +C 0.38978943 -0.96773680 -1.06226208 +O -0.01611268 -0.71564293 -2.16583718 +H 0.10006565 1.85324490 -0.91332597 +H -1.44270433 1.13069361 -0.50923371 +H -0.46757554 1.89548158 0.78836479 +H 0.90329886 -1.91558845 -0.79531972 +9 +Properties=species:S:1:pos:R:3 A=10.08927 B=4.54115 C=3.13487 mu=2.7707 alpha=38.09 epsHOMO=-0.2561 epsLUMO=-0.064 epsgap=0.192 =384.4827 zpve=0.067668 U0=-263.278851 U=-263.273589 H=-263.272645 G=-263.306835 Cv=17.972 pbc="F F F" +N -0.16032929 1.08528962 -0.45398652 +C 0.18649127 -0.08762327 0.14758021 +N 1.28979627 -0.46427423 0.67927030 +C -0.94204384 -1.08440264 0.20423621 +O -2.01802205 -0.89007687 -0.30583149 +H 0.54574437 1.73837930 -0.74689246 +H -1.04684920 1.09570857 -0.93607796 +H 2.01738195 0.24991828 0.59881811 +H -0.67740148 -2.01003995 0.74746441 +13 +Properties=species:S:1:pos:R:3 A=7.92874 B=3.8272 C=2.82581 mu=0.6578 alpha=51.2 epsHOMO=-0.2589 epsLUMO=0.0571 epsgap=0.316 =481.9854 zpve=0.112471 U0=-195.186772 U=-195.180446 H=-195.179502 G=-195.215658 Cv=22.569 pbc="F F F" +C -0.01763434 1.54859468 -0.00253584 +C -0.01719180 0.00735789 0.03269604 +C 0.75818463 -0.52820155 1.25278862 +C -1.38213303 -0.51768438 0.00775516 +C -2.50796392 -0.94005629 0.00478552 +H 1.00944396 1.92639613 -0.02632830 +H -0.54514614 1.92217052 -0.88393459 +H -0.51243000 1.95339892 0.88560642 +H 0.49366179 -0.34904155 -0.87265890 +H 0.28968458 -0.19345372 2.18334679 +H 0.77833392 -1.62102506 1.25783727 +H 1.78952492 -0.16181727 1.23574954 +H -3.49998244 -1.31810165 -0.00723572 +12 +Properties=species:S:1:pos:R:3 A=7.90009 B=3.95466 C=2.88643 mu=3.9512 alpha=46.23 epsHOMO=-0.318 epsLUMO=0.0365 epsgap=0.3545 =460.694 zpve=0.102281 U0=-211.295796 U=-211.289821 H=-211.288877 G=-211.324525 Cv=20.467 pbc="F F F" +C -0.00242350 1.55035837 0.01056187 +C -0.01856600 0.01004352 -0.02261770 +C -1.45045318 -0.55766178 0.01293347 +C 0.70125749 -0.48583168 -1.20172764 +N 1.26190946 -0.87126766 -2.13619740 +H 1.01959496 1.93673215 0.00349668 +H -0.52972844 1.96141639 -0.85498386 +H -0.50134251 1.90346839 0.91755862 +H 0.52481877 -0.36222456 0.85525462 +H -2.02427903 -0.21428716 -0.85251869 +H -1.44438296 -1.65026927 0.00754107 +H -1.95805494 -0.21725882 0.91996760 +11 +Properties=species:S:1:pos:R:3 A=8.30095 B=3.97615 C=2.93764 mu=2.7429 alpha=42.87 epsHOMO=-0.2704 epsLUMO=0.0278 epsgap=0.2983 =440.0738 zpve=0.091554 U0=-227.338075 U=-227.332253 H=-227.331309 G=-227.366638 Cv=19.946 pbc="F F F" +C -0.01064295 1.54128691 0.00722087 +C -0.00203811 0.00756015 0.03863463 +N 0.76268677 -0.47101822 1.19250856 +C -1.38541954 -0.52099256 -0.00194097 +N -2.45680847 -0.95636072 0.00694969 +H 1.02043523 1.90106589 0.00392365 +H -0.52873161 1.91193452 -0.88031480 +H -0.51849536 1.94150431 0.89013197 +H 0.50418828 -0.35805500 -0.86276072 +H 0.30070681 -0.18364934 2.05155305 +H 0.78148992 -1.48670299 1.20428183 +11 +Properties=species:S:1:pos:R:3 A=8.55919 B=3.89952 C=2.91869 mu=1.3582 alpha=44.03 epsHOMO=-0.2665 epsLUMO=0.0336 epsgap=0.3001 =444.6452 zpve=0.088908 U0=-231.108368 U=-231.102292 H=-231.101348 G=-231.137061 Cv=21.306 pbc="F F F" +C -0.00754278 1.55566144 0.08650633 +C 0.01207541 0.02096553 0.04935916 +O -1.29150251 -0.52785128 0.22126386 +C 0.65093362 -0.47718651 -1.17430507 +C 1.13902673 -0.86577343 -2.20205518 +H 1.00824243 1.95969458 0.04929733 +H -0.56272245 1.95192010 -0.76978085 +H -0.49328161 1.88929438 1.00704123 +H 0.57811967 -0.34464907 0.91458864 +H -1.78516532 -0.35903262 -0.58909400 +H 1.58078877 -1.22365060 -3.09919447 +10 +Properties=species:S:1:pos:R:3 A=8.55908 B=4.01671 C=2.97882 mu=3.269 alpha=39.28 epsHOMO=-0.3051 epsLUMO=0.0115 epsgap=0.3166 =424.3395 zpve=0.078602 U0=-247.214861 U=-247.209162 H=-247.208218 G=-247.243338 Cv=19.252 pbc="F F F" +C -0.00992462 1.54951908 0.00732200 +C 0.00740485 0.01722020 0.05031270 +O 0.72085722 -0.49630479 1.15864583 +C -1.36995095 -0.52355929 0.00894432 +N -2.45081572 -0.93233138 0.00716912 +H 1.01892314 1.91630447 0.02724149 +H -0.50261490 1.90969362 -0.89997951 +H -0.54800426 1.95377611 0.87053664 +H 0.53602846 -0.36494195 -0.82970945 +H 0.27702643 -0.20644534 1.96421773 +13 +Properties=species:S:1:pos:R:3 A=7.66289 B=3.70925 C=2.79937 mu=2.6921 alpha=46.58 epsHOMO=-0.2469 epsLUMO=-0.0188 epsgap=0.2281 =482.0475 zpve=0.112328 U0=-232.364952 U=-232.358577 H=-232.357633 G=-232.394589 Cv=21.434 pbc="F F F" +C -0.02629647 1.53877116 -0.00783747 +C 0.01468904 0.00095310 0.00166037 +C 0.74737862 -0.53388859 1.24420471 +C -1.40211264 -0.53859257 -0.01557820 +O -1.87620455 -1.23582351 -0.87399559 +H 0.98823696 1.94625080 0.03705522 +H -0.50725090 1.92153422 -0.91285565 +H -0.57756514 1.92360883 0.85796386 +H 0.50584680 -0.36421091 -0.90619124 +H 0.22958380 -0.23909403 2.16431052 +H 0.81692736 -1.62559499 1.22973438 +H 1.76267153 -0.12846566 1.29050755 +H -2.00861510 -0.22792742 0.87121666 +11 +Properties=species:S:1:pos:R:3 A=8.31096 B=4.07963 C=2.92918 mu=2.8354 alpha=39.16 epsHOMO=-0.255 epsLUMO=-0.0279 epsgap=0.2271 =432.1489 zpve=0.088443 U0=-268.287661 U=-268.281505 H=-268.280561 G=-268.316982 Cv=20.344 pbc="F F F" +C -0.05483118 1.46032843 -0.17068647 +C 0.07105714 -0.03524485 0.11225841 +O 0.58204486 -0.33316656 1.40176256 +C -1.27639279 -0.74383027 0.03051185 +O -2.11855775 -0.49643175 -0.79163412 +H 0.93158072 1.93091398 -0.23353204 +H -0.57713995 1.61290939 -1.11835562 +H -0.62444315 1.94776119 0.62573670 +H 0.69687521 -0.50071620 -0.67159471 +H 1.45603316 0.06066659 1.47858584 +H -1.40723217 -1.53388627 0.80211181 +12 +Properties=species:S:1:pos:R:3 A=8.9331 B=4.19405 C=2.96046 mu=3.7163 alpha=44.42 epsHOMO=-0.2424 epsLUMO=0.0327 epsgap=0.2751 =441.85 zpve=0.10227 U0=-248.430371 U=-248.424309 H=-248.423365 G=-248.459383 Cv=19.918 pbc="F F F" +C -0.09776499 1.44917268 0.04561984 +N 0.01043053 0.00373818 -0.00070600 +C 0.05323028 -0.71432112 1.25264891 +C 0.06728716 -0.64067225 -1.20060778 +O 0.03636432 -0.11006515 -2.29109228 +H 0.75650660 1.88747109 0.57647421 +H -0.11648577 1.81632103 -0.98049172 +H -1.01641106 1.75521548 0.56151415 +H -0.85453556 -0.53657675 1.84390168 +H 0.13297722 -1.78696336 1.05851351 +H 0.91637348 -0.40737846 1.85761345 +H 0.14762240 -1.73801339 -1.06805139 +11 +Properties=species:S:1:pos:R:3 A=10.13982 B=3.81346 C=2.86965 mu=2.9514 alpha=39.33 epsHOMO=-0.2699 epsLUMO=-0.0262 epsgap=0.2437 =440.1727 zpve=0.088924 U0=-268.301176 U=-268.295084 H=-268.29414 G=-268.331307 Cv=19.824 pbc="F F F" +C -0.01708808 1.44281136 0.03225501 +C -0.05544542 -0.06560572 -0.02375999 +O -0.17379913 -0.76196233 0.96223129 +C 0.06068107 -0.73615179 -1.38825252 +O 0.00877771 -2.12574390 -1.27749486 +H 0.92062950 1.81463913 -0.39522215 +H -0.83050523 1.86542281 -0.56780350 +H -0.10911975 1.78049045 1.06493675 +H -0.75015067 -0.35235060 -2.03056074 +H 1.00312898 -0.40389029 -1.85592942 +H -0.09063255 -2.29450960 -0.32608019 +13 +Properties=species:S:1:pos:R:3 A=9.55216 B=3.57844 C=2.7341 mu=2.6168 alpha=46.19 epsHOMO=-0.2423 epsLUMO=-0.0072 epsgap=0.2351 =489.8518 zpve=0.112006 U0=-232.377706 U=-232.371073 H=-232.370129 G=-232.408256 Cv=21.716 pbc="F F F" +C -0.04384102 1.54099793 -0.03913814 +C 0.02765132 0.01885817 0.04778773 +C 0.76814644 -0.48842218 1.28000847 +C 0.87850208 -1.99746312 1.42393323 +O 1.24179142 0.26651340 2.09788702 +H 0.95796454 1.97690198 -0.06816012 +H -0.58447717 1.85356031 -0.93690369 +H -0.55173626 1.95737759 0.83440529 +H -0.97692824 -0.42709877 0.05353178 +H 0.51853486 -0.40806507 -0.83809409 +H 1.39924980 -2.42678188 0.56053177 +H 1.42001187 -2.24449367 2.33776426 +H -0.11750602 -2.45360651 1.45141965 +12 +Properties=species:S:1:pos:R:3 A=9.78802 B=3.78441 C=2.84416 mu=3.499 alpha=42.54 epsHOMO=-0.2438 epsLUMO=0.0355 epsgap=0.2793 =457.447 zpve=0.101847 U0=-248.448467 U=-248.441988 H=-248.441044 G=-248.478935 Cv=21.374 pbc="F F F" +C -0.00220144 1.53344329 -0.05960369 +C -0.03235837 0.01206167 0.06840265 +C 0.69666086 -0.48196462 1.31828322 +N 0.98104007 -1.82034162 1.31984698 +O 1.00392124 0.24606232 2.24136749 +H 1.02111190 1.89525169 -0.19182672 +H -0.59707204 1.86206427 -0.91645604 +H -0.39728839 1.99920712 0.84545201 +H -1.06936083 -0.34552935 0.13040806 +H 0.39947165 -0.46906818 -0.81777020 +H 0.63281951 -2.43917647 0.60874785 +H 1.35708014 -2.22217793 2.16303522 +12 +Properties=species:S:1:pos:R:3 A=10.01185 B=3.84195 C=2.87635 mu=3.5402 alpha=43.61 epsHOMO=-0.2418 epsLUMO=0.0387 epsgap=0.2805 =458.9803 zpve=0.101735 U0=-248.443503 U=-248.436899 H=-248.435955 G=-248.474272 Cv=20.967 pbc="F F F" +C 0.00619941 1.45933202 -0.03893859 +N -0.06411616 0.01422963 -0.12046901 +C -1.20595394 -0.68196796 0.16117120 +C -1.08982704 -2.19284899 0.02095775 +O -2.24482911 -0.14443077 0.49994418 +H 0.75053989 1.78372520 0.69715573 +H 0.25692001 1.90395263 -1.00876215 +H -0.97856026 1.81121438 0.27082194 +H 0.75820982 -0.49543660 -0.39464024 +H -0.09909703 -2.53543104 -0.28970003 +H -1.34097671 -2.65395851 0.97972885 +H -1.82903100 -2.53483935 -0.70813076 +11 +Properties=species:S:1:pos:R:3 A=10.44045 B=4.04692 C=2.98547 mu=3.5648 alpha=39.51 epsHOMO=-0.2436 epsLUMO=0.0599 epsgap=0.3034 =428.404 zpve=0.091713 U0=-264.504487 U=-264.498452 H=-264.497508 G=-264.533633 Cv=20.274 pbc="F F F" +C 0.00922630 1.45021814 -0.03194543 +N -0.01429488 -0.00054646 -0.01782667 +C -1.21576579 -0.67252403 0.04421896 +N -1.09733352 -2.04644898 -0.15997741 +O -2.28599299 -0.12893711 0.24196027 +H 0.89659038 1.81885022 0.49120949 +H 0.00233950 1.86388504 -1.04914155 +H -0.88562684 1.79943346 0.48353298 +H 0.75257305 -0.47506631 -0.46719265 +H -0.24882333 -2.48772194 0.16375654 +H -1.93419610 -2.53898393 0.11482880 +12 +Properties=species:S:1:pos:R:3 A=9.69683 B=4.1533 C=3.01703 mu=1.1876 alpha=43.57 epsHOMO=-0.2595 epsLUMO=0.0352 epsgap=0.2948 =443.1687 zpve=0.102062 U0=-248.416462 U=-248.410358 H=-248.409414 G=-248.445651 Cv=20.256 pbc="F F F" +C -0.09160695 1.37540046 -0.00729426 +O -0.06964534 -0.04831581 -0.11148812 +C 0.68114625 -0.56142673 -1.10732458 +C 0.60405298 -2.06701550 -1.08766892 +N 1.32645533 0.19266343 -1.89967731 +H 0.91463916 1.76843655 0.16257460 +H -0.48562826 1.82624615 -0.92235216 +H -0.73971430 1.60007423 0.84110605 +H -0.43430065 -2.39242162 -1.20325205 +H 1.20627110 -2.50049977 -1.88745640 +H 0.95651337 -2.44976670 -0.12501838 +H 1.83504115 -0.36104361 -2.58486055 +11 +Properties=species:S:1:pos:R:3 A=10.20392 B=4.16834 C=3.07281 mu=1.7569 alpha=39.33 epsHOMO=-0.2685 epsLUMO=0.0174 epsgap=0.2859 =427.6606 zpve=0.089436 U0=-268.32127 U=-268.315051 H=-268.314106 G=-268.351214 Cv=19.501 pbc="F F F" +C 0.07295254 1.38840919 0.06442046 +O 0.21049114 -0.03740028 0.04612736 +C 0.50102805 -0.57224288 -1.15975346 +C 0.62218715 -2.07378323 -1.06030684 +O 0.63922154 0.08224418 -2.16157325 +H 1.00029720 1.87062570 -0.25532064 +H -0.73338957 1.70782089 -0.60080454 +H -0.15751280 1.65154060 1.09692954 +H -0.31330001 -2.50202851 -0.68941906 +H 0.85646588 -2.48430275 -2.04157352 +H 1.40651685 -2.34055496 -0.34634301 +10 +Properties=species:S:1:pos:R:3 A=10.68182 B=4.39532 C=3.17969 mu=2.329 alpha=35.5 epsHOMO=-0.2669 epsLUMO=0.0575 epsgap=0.3244 =398.5908 zpve=0.079271 U0=-284.385189 U=-284.379361 H=-284.378417 G=-284.414085 Cv=19.084 pbc="F F F" +C -0.02645771 1.38305753 0.02085660 +O 0.04139566 -0.04439503 -0.00277452 +C 0.66775338 -0.56001625 -1.09000344 +N 0.72605063 -1.92219983 -0.98822954 +O 1.12952381 0.09648143 -1.99322180 +H 0.97529779 1.82044307 0.02387306 +H -0.57085698 1.76214621 -0.84790425 +H -0.55410722 1.63835731 0.94043594 +H 0.12662696 -2.38401590 -0.32566929 +H 0.97230070 -2.41798421 -1.82732573 +10 +Properties=species:S:1:pos:R:3 A=10.20696 B=3.93135 C=2.9219 mu=4.5676 alpha=35.87 epsHOMO=-0.246 epsLUMO=0.0273 epsgap=0.2733 =418.5967 zpve=0.078222 U0=-284.360325 U=-284.354148 H=-284.353204 G=-284.390143 Cv=20.089 pbc="F F F" +N -0.08115607 1.31807026 0.01276742 +C 0.05442259 -0.04477733 0.08155388 +O 1.12163074 -0.59783950 0.20940553 +C -1.28687786 -0.79011642 0.06178435 +O -1.04605114 -2.13954195 -0.23957158 +H 0.76623366 1.85991128 -0.04483425 +H -0.93021346 1.74567447 -0.31565615 +H -1.74559696 -0.65521334 1.05526700 +H -1.96026061 -0.31149222 -0.67026256 +H -1.85637296 -2.63017646 -0.07625478 +10 +Properties=species:S:1:pos:R:3 A=10.14389 B=4.0714 C=3.02223 mu=5.3004 alpha=35.19 epsHOMO=-0.2527 epsLUMO=0.0208 epsgap=0.2735 =408.0279 zpve=0.080317 U0=-284.372483 U=-284.367172 H=-284.366228 G=-284.40095 Cv=17.931 pbc="F F F" +N 0.01838100 0.93057357 0.04943058 +H -0.70674506 0.56116798 -0.55550134 +H 0.68248307 0.18430002 0.21487099 +H 0.21928084 2.79858558 -0.32670585 +C -0.54586914 1.44688585 1.30274586 +C -0.80466975 2.96030569 1.21052835 +O -0.21468822 3.53648884 0.15531687 +O -1.45295676 3.56338618 2.02143528 +H -1.47662579 0.96512183 1.62072169 +H 0.17781209 1.30324286 2.11157176 +13 +Properties=species:S:1:pos:R:3 A=6.6508 B=4.19317 C=3.39441 mu=2.2854 alpha=42.01 epsHOMO=-0.2597 epsLUMO=0.0631 epsgap=0.3228 =438.3028 zpve=0.11345 U0=-269.479234 U=-269.472993 H=-269.472049 G=-269.508213 Cv=22.072 pbc="F F F" +C -0.02775167 1.53421433 0.00651151 +C 0.08343246 0.01218277 0.05258986 +O 0.95856392 -0.48200710 -0.96694113 +C 0.69767574 -0.49516451 1.35948812 +O 2.02247062 -0.02842530 1.52222186 +H 0.94333763 1.98841434 0.21841589 +H -0.37320516 1.87354041 -0.97716908 +H -0.74773817 1.89256394 0.74926853 +H -0.91671848 -0.43697036 -0.06040528 +H 0.76726710 -0.01412177 -1.78502043 +H 0.11650002 -0.13351914 2.21392431 +H 0.65814685 -1.59563445 1.36274474 +H 2.46378566 -0.22762476 0.68698431 +17 +Properties=species:S:1:pos:R:3 A=7.26511 B=3.32874 C=2.54491 mu=0.0618 alpha=56.26 epsHOMO=-0.3085 epsLUMO=0.085 epsgap=0.3934 =565.8412 zpve=0.159632 U0=-197.629387 U=-197.622325 H=-197.621381 G=-197.659365 Cv=25.169 pbc="F F F" +C 0.05879035 1.56174169 -0.01945240 +C -0.01544024 0.03182306 0.00308332 +C -1.44103237 -0.54862028 0.05028629 +C -1.40777007 -2.07231030 -0.13012010 +C -2.18400529 -0.17659759 1.34114303 +H 1.09435638 1.90233607 -0.11900072 +H -0.50689566 1.97217319 -0.86388098 +H -0.34407951 2.00628082 0.89579522 +H 0.48926758 -0.36011214 -0.88953902 +H 0.55254938 -0.35139446 0.86269887 +H -2.00031575 -0.12153682 -0.79559684 +H -0.85501131 -2.55065810 0.68775438 +H -2.41787513 -2.49543628 -0.13720127 +H -0.91928762 -2.35363802 -1.06914014 +H -3.19100197 -0.60699559 1.35137051 +H -1.65197294 -0.56010584 2.22063481 +H -2.28793759 0.90575132 1.46079572 +15 +Properties=species:S:1:pos:R:3 A=8.11598 B=3.43678 C=2.66869 mu=1.3894 alpha=49.45 epsHOMO=-0.2617 epsLUMO=0.0733 epsgap=0.335 =521.8605 zpve=0.136091 U0=-233.555951 U=-233.549143 H=-233.548199 G=-233.585602 Cv=23.99 pbc="F F F" +C -0.03841227 1.55621396 0.00935837 +C 0.01907135 0.02716079 -0.02074203 +C 0.73141506 -0.52709328 -1.25454422 +C 0.83975868 -2.05206973 -1.22872655 +O -0.01226470 -0.08514394 -2.38973758 +H 0.96845664 1.98863424 0.03526234 +H -0.54377006 1.93824134 -0.88074407 +H -0.57652955 1.91383386 0.89256698 +H 0.53166867 -0.34991486 0.87240999 +H -0.99727186 -0.38638654 -0.00185289 +H 1.74909346 -0.09943025 -1.28826184 +H 1.34143968 -2.42487137 -2.12957624 +H 1.41822012 -2.39229108 -0.36340871 +H -0.15616765 -2.50443859 -1.18308875 +H 0.43090825 -0.40901616 -3.17979642 +15 +Properties=species:S:1:pos:R:3 A=7.55335 B=3.78323 C=2.80566 mu=1.0758 alpha=49.8 epsHOMO=-0.2476 epsLUMO=0.086 epsgap=0.3336 =507.9614 zpve=0.135681 U0=-233.545899 U=-233.539034 H=-233.53809 G=-233.57582 Cv=23.573 pbc="F F F" +C -0.00857605 1.38097833 0.09454670 +O -0.02255106 -0.00739461 -0.13062576 +C -1.30243341 -0.61997805 -0.02088309 +C -1.18687286 -1.97885751 -0.70303918 +C -1.75587824 -0.74662760 1.43714508 +H 1.00321550 1.73258087 -0.12443496 +H -0.71511468 1.90838275 -0.56674280 +H -0.24909065 1.64967237 1.13384681 +H -2.03737978 -0.00432241 -0.56884198 +H -0.44792779 -2.59945605 -0.18596870 +H -2.14751041 -2.50225525 -0.69102581 +H -0.86524212 -1.85860679 -1.74077836 +H -2.73321482 -1.23674908 1.49205117 +H -1.03621350 -1.34483828 2.00544671 +H -1.84898410 0.22935483 1.92198087 +15 +Properties=species:S:1:pos:R:3 A=6.14183 B=5.17679 C=3.79297 mu=0.1068 alpha=52.92 epsHOMO=-0.2633 epsLUMO=0.0893 epsgap=0.3526 =439.1643 zpve=0.137025 U0=-196.409349 U=-196.403398 H=-196.402453 G=-196.437676 Cv=22.258 pbc="F F F" +C -0.03913204 1.50456305 0.04978913 +C 0.00985856 -0.01113880 0.01248619 +C -1.24167981 -0.66663044 -0.53989979 +C 1.33803387 -0.66895748 -0.27400430 +C 0.75126442 -0.72221586 1.11862571 +H 0.88633040 1.92490040 0.45579358 +H -0.18183878 1.92045743 -0.95489291 +H -0.86832076 1.85815718 0.67439648 +H -1.43570890 -0.34344387 -1.56976411 +H -1.15326757 -1.75757800 -0.54433832 +H -2.12221416 -0.40570820 0.05951361 +H 1.34398374 -1.58235208 -0.86005084 +H 2.20196897 -0.03329084 -0.43929326 +H 1.21949576 -0.12246018 1.89246580 +H 0.36153319 -1.67153717 1.47171932 +13 +Properties=species:S:1:pos:R:3 A=6.49534 B=5.52329 C=3.9108 mu=1.8235 alpha=45.7 epsHOMO=-0.2596 epsLUMO=0.091 epsgap=0.3505 =405.521 zpve=0.112851 U0=-232.335768 U=-232.329981 H=-232.329037 G=-232.364091 Cv=20.719 pbc="F F F" +C -0.03970330 1.50954998 -0.04112233 +C -0.03023556 -0.00335329 -0.06498361 +C -1.24613189 -0.68356648 0.52531190 +C 0.78937201 -0.71129193 -1.06027559 +O 1.22546798 -0.60059036 0.29712283 +H 0.88992772 1.90948478 -0.45296558 +H -0.87952723 1.90350108 -0.62376642 +H -0.14448576 1.87590604 0.98604429 +H -2.15218727 -0.41012616 -0.02629495 +H -1.13412584 -1.77005431 0.49747032 +H -1.38586631 -0.38055453 1.56882757 +H 0.50712690 -1.71386874 -1.38085018 +H 1.37088104 -0.14369322 -1.78640853 +13 +Properties=species:S:1:pos:R:3 A=6.57648 B=5.31603 C=3.98453 mu=1.3822 alpha=45.85 epsHOMO=-0.2494 epsLUMO=0.0776 epsgap=0.327 =401.4888 zpve=0.113237 U0=-232.33341 U=-232.327734 H=-232.32679 G=-232.36147 Cv=21.17 pbc="F F F" +C -0.03714052 1.53215920 0.01332310 +C 0.01344335 0.02117019 0.01922005 +O -1.17476104 -0.59735284 -0.40931260 +C 1.30744798 -0.69902106 -0.27389650 +C 0.70844749 -0.71373504 1.12226318 +H 0.89026861 1.96240279 0.40276135 +H -0.17846151 1.91561262 -1.00518522 +H -0.87011232 1.88955201 0.62730953 +H -1.28352265 -0.40225391 -1.34690641 +H 1.23973813 -1.61642115 -0.84783774 +H 2.20543993 -0.11523820 -0.44744474 +H 1.19539159 -0.13889834 1.90240284 +H 0.24735439 -1.63670241 1.45378480 +10 +Properties=species:S:1:pos:R:3 A=12.18399 B=5.1384 C=3.78868 mu=2.5732 alpha=40.19 epsHOMO=-0.263 epsLUMO=0.0277 epsgap=0.2907 =352.0378 zpve=0.080558 U0=-247.201165 U=-247.196699 H=-247.195755 G=-247.22841 Cv=15.51 pbc="F F F" +N -0.04445152 1.25623533 0.31681915 +C -0.03914559 0.04913710 -0.01670558 +C -1.08749619 -1.04164446 -0.11488033 +C 0.09869688 -1.90754844 -0.58225894 +O 0.99647508 -0.76306629 -0.44028010 +H 0.88935980 1.66481350 0.24916893 +H -1.54324114 -1.32235812 0.83572172 +H -1.86910036 -0.85713839 -0.85325910 +H 0.39739753 -2.72227350 0.08158721 +H 0.06945080 -2.25418345 -1.61796084 +11 +Properties=species:S:1:pos:R:3 A=10.79571 B=4.78732 C=3.54292 mu=2.7119 alpha=42.77 epsHOMO=-0.2415 epsLUMO=-0.0194 epsgap=0.2222 =379.6371 zpve=0.090544 U0=-231.15578 U=-231.150804 H=-231.149859 G=-231.184401 Cv=17.076 pbc="F F F" +O -0.00101746 0.01846628 -0.20114742 +C -0.01405811 1.19658059 0.00205306 +C -1.13542911 2.22979148 0.21957980 +C -0.03500884 3.29097637 0.53052223 +C 1.08469664 2.25947795 0.18994331 +H -1.71642814 2.41772662 -0.68901078 +H -1.83392757 1.98415059 1.02444244 +H -0.05534741 4.17585264 -0.10670963 +H -0.02541477 3.61399048 1.57278917 +H 1.81074389 2.03288534 0.97578973 +H 1.63600801 2.46255380 -0.73376244 +10 +Properties=species:S:1:pos:R:3 A=12.23436 B=4.97895 C=3.70561 mu=3.6671 alpha=39.73 epsHOMO=-0.25 epsLUMO=0.038 epsgap=0.288 =355.0934 zpve=0.080442 U0=-247.225618 U=-247.220897 H=-247.219953 G=-247.253218 Cv=16.1 pbc="F F F" +O 0.03184134 -0.03461682 0.00849836 +C -0.02440334 1.16440533 0.02160811 +C -1.14541322 2.23712809 0.03012901 +C 0.00928805 3.27945878 0.04557758 +N 0.93843838 2.14652109 0.03554396 +H -1.77030529 2.23779730 -0.86428437 +H -1.77632809 2.21765386 0.92007934 +H 0.07170564 3.91863516 -0.84072193 +H 0.06622603 3.89826625 0.94657952 +H 1.94616840 2.09535713 0.03855945 +9 +Properties=species:S:1:pos:R:3 A=12.44643 B=5.23328 C=3.86599 mu=3.9339 alpha=35.4 epsHOMO=-0.2788 epsLUMO=0.0089 epsgap=0.2878 =336.0792 zpve=0.068574 U0=-267.106213 U=-267.101929 H=-267.100985 G=-267.13332 Cv=14.483 pbc="F F F" +O 0.04424004 0.03933243 -0.33071104 +C -0.04854479 1.17416230 0.00410076 +C -1.14211656 2.23611850 0.15338361 +C 0.02332538 3.12357010 0.61681625 +O 0.95663808 2.01190122 0.42603746 +H -1.62573755 2.51482212 -0.78401102 +H -1.90009213 1.99553386 0.90028663 +H 0.28368077 3.96172751 -0.03229287 +H 0.00782250 3.43942655 1.66162725 +10 +Properties=species:S:1:pos:R:3 A=11.54185 B=4.90368 C=3.63309 mu=2.5257 alpha=39.34 epsHOMO=-0.2437 epsLUMO=-0.0258 epsgap=0.2179 =359.152 zpve=0.07965 U0=-247.190194 U=-247.185543 H=-247.184599 G=-247.217688 Cv=16.083 pbc="F F F" +O 0.00053891 0.00665475 -0.09743049 +C -0.01371990 1.19213881 0.03139936 +C -1.10668681 2.26612284 0.17247913 +N -0.03543445 3.25123475 0.48533693 +C 1.05328615 2.29521506 0.14383904 +H -1.65047970 2.42796175 -0.76886054 +H -1.83606786 2.11370530 0.97567410 +H -0.05434600 4.07193609 -0.10727051 +H 1.80754206 2.16277968 0.92736250 +H 1.56737937 2.47130277 -0.81152688 +9 +Properties=species:S:1:pos:R:3 A=12.18264 B=4.94228 C=3.68162 mu=0.8477 alpha=35.94 epsHOMO=-0.2647 epsLUMO=-0.0352 epsgap=0.2295 =344.5473 zpve=0.066989 U0=-267.068488 U=-267.063976 H=-267.063032 G=-267.095887 Cv=15.115 pbc="F F F" +O -0.00031692 0.00676321 -0.17139774 +C -0.01417098 1.18672865 -0.00980279 +C -1.08283120 2.27653381 0.15574094 +O -0.03856115 3.26460423 0.27513986 +C 1.02857270 2.30515693 0.12774311 +H -1.72923855 2.43647507 -0.71671274 +H -1.70219898 2.19346725 1.05800754 +H 1.67366781 2.23923293 1.01324179 +H 1.64705068 2.48224548 -0.76148355 +15 +Properties=species:S:1:pos:R:3 A=9.63416 B=4.10254 C=3.28665 mu=0.095 alpha=52.94 epsHOMO=-0.2896 epsLUMO=0.0927 epsgap=0.3823 =455.0215 zpve=0.138424 U0=-196.407957 U=-196.40245 H=-196.401505 G=-196.436159 Cv=20.299 pbc="F F F" +C -0.47005934 1.44206547 -0.06684548 +C 0.14287306 0.05485144 0.03747984 +C 1.37254693 -0.30295273 -0.84679168 +C 1.95947994 -1.09364561 0.35326039 +C 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+ +.. only:: html + + .. image:: /examples/multi-gpu/images/thumb/sphx_glr_multi-gpu_thumb.png + :alt: + + :ref:`sphx_glr_examples_multi-gpu_multi-gpu.py` + +.. raw:: html + +
Multi-GPU training
+
+ + +.. thumbnail-parent-div-close + +.. raw:: html + + + + +.. toctree:: + :hidden: + + /examples/multi-gpu/multi-gpu + + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-gallery + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download all examples in Python source code: multi-gpu_python.zip ` + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download all examples in Jupyter notebooks: multi-gpu_jupyter.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/multi-gpu/multi-gpu.ipynb b/docs/src/examples/multi-gpu/multi-gpu.ipynb new file mode 100644 index 0000000000..6cc034d0f1 --- /dev/null +++ b/docs/src/examples/multi-gpu/multi-gpu.ipynb @@ -0,0 +1,32 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Multi-GPU training\n\n``metatrain`` supports training a model with several GPUs, which can accelerate the\ntraining, especially when the training dataset is large / there are many training\nepochs. This feature is enabled by the :py:mod:`torch.distributed` module, and thus can\ndo multiprocess parallelism across several nodes.\n\nIn multi-GPU training, every batch of samples is split into smaller mini-batches and the\ncomputation is run for each of the smaller mini-batches in parallel on different GPUs.\nThe different gradients obtained on each device are then summed. This approach allows\nthe user to reduce the time it takes to train models.\n\nTo know if the model supports multi-GPU training, please check [Available Architectures](../../docs/src/architectures/index.rst) and see if the default hyperparameters have\nthe ``distributed`` option.\n\n## Input file\nTo do this, you only need to switch on the ``distributed`` option in the ``.yaml`` file\nfor the training. Let's take [this tutorial](../beginner_tutorials/train-from-scratch.rst) as an example. Now, the\n``options.yaml`` is\n\n.. literalinclude:: ./options-distributed.yaml\n :language: yaml\n :linenos:\n\n## Slurm script\n\nBelow is an example Slurm script for submitting the job. Please be aware that the actual\nconfigurations vary from clusters to clusters, so you have to modify it. Different\nscheduler will require similar options. ``metatrain`` will automatically use all the\nGPUs that you have asked for. You should make a single GPU visible for each process\n(setting `--gpus-per-node` equal to the number of GPUs, or setting `--gpus-per-task=1`,\ndepending on your cluster configuration).\n\n```bash\n#!/bin/bash\n#SBATCH --nodes 1\n#SBATCH --ntasks 2 # must equal to the number of GPUs\n#SBATCH --ntasks-per-node 2\n#SBATCH --gpus-per-node 2 # use 2 GPUs\n#SBATCH --cpus-per-task 8\n#SBATCH --exclusive\n#SBATCH --partition=h100 # adapt this to your cluster\n#SBATCH --time=1:00:00\n\n# load modules and/or virtual environments and/or containers here\n\nsrun mtt train options-distributed.yaml\n```\n## Performance\n\nIf the multi-GPU training runs successfully, you should see this in the training log:\n\n```bash\n[2025-10-08 11:34:22][INFO] - Distributed environment set up with MASTER_ADDR=kh080,\nMASTER_PORT=39591, WORLD_SIZE=2, RANK=0, LOCAL_RANK=0\n[2025-10-08 11:34:23][INFO] - Training on 2 devices with dtype torch.float32\n```\nThis 100-epoch training takes 23 seconds.\n\n```bash\n[2025-10-08 11:34:22][INFO] - Starting training from scratch\n...\n[2025-10-08 11:34:45][INFO] - Training finished!\n```\nNow let's switch off the multi-GPU training by writing ``distributed: false``, and\nsubmit this job again. The training takes 69 seconds.\n\n```bash\n[2025-10-08 11:37:38][INFO] - Setting up model\n...\n[2025-10-08 11:38:47][INFO] - Training finished!\n```\n## Multi-GPU fine-tuning\nYou can use multi-GPU for fine-tuning too, by writing ``distributed: True`` in the\n``.yaml`` input. For information about fine-tuning, please refer to [this tutorial on\nfine-tuning](../../getting-started/finetuning-example.rst).\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/docs/src/examples/multi-gpu/multi-gpu.py b/docs/src/examples/multi-gpu/multi-gpu.py new file mode 100644 index 0000000000..d0293a37f5 --- /dev/null +++ b/docs/src/examples/multi-gpu/multi-gpu.py @@ -0,0 +1,91 @@ +""" +Multi-GPU training +================== + +``metatrain`` supports training a model with several GPUs, which can accelerate the +training, especially when the training dataset is large / there are many training +epochs. This feature is enabled by the :py:mod:`torch.distributed` module, and thus can +do multiprocess parallelism across several nodes. + +In multi-GPU training, every batch of samples is split into smaller mini-batches and the +computation is run for each of the smaller mini-batches in parallel on different GPUs. +The different gradients obtained on each device are then summed. This approach allows +the user to reduce the time it takes to train models. + +To know if the model supports multi-GPU training, please check +:ref:`available-architectures` and see if the default hyperparameters have the +``distributed`` option. + +Input file +---------- + +To do this, you only need to switch on the ``distributed`` option in the ``.yaml`` file +for the training. Let's take the +:ref:`sphx_glr_generated_examples_0-beginner_03-train_from_scratch.py` example and +adjust the ``options.yaml`` file. + +.. literalinclude:: options-distributed.yaml + :language: yaml + :linenos: + +Slurm script +------------ + +Below is an example Slurm script for submitting the job. Please be aware that the actual +configurations vary from clusters to clusters, so you have to modify it. Different +scheduler will require similar options. ``metatrain`` will automatically use all the +GPUs that you have asked for. You should make a single GPU visible for each process +(setting ``--gpus-per-node`` equal to the number of GPUs, or setting +``--gpus-per-task=1``, depending on your cluster configuration). + +.. code-block:: bash + + #!/bin/bash + #SBATCH --nodes 1 + #SBATCH --ntasks 2 # must equal to the number of GPUs + #SBATCH --ntasks-per-node 2 + #SBATCH --gpus-per-node 2 # use 2 GPUs + #SBATCH --cpus-per-task 8 + #SBATCH --exclusive + #SBATCH --partition=h100 # adapt this to your cluster + #SBATCH --time=1:00:00 + + # load modules and/or virtual environments and/or containers here + + srun mtt train options-distributed.yaml + +Performance +----------- + +If the multi-GPU training runs successfully, you should see this in the training log: + +.. code-block:: bash + + [2025-10-08 11:34:22][INFO] - Distributed environment set up with MASTER_ADDR=kh080, + MASTER_PORT=39591, WORLD_SIZE=2, RANK=0, LOCAL_RANK=0 + [2025-10-08 11:34:23][INFO] - Training on 2 devices with dtype torch.float32 + +This 100-epoch training takes 23 seconds. + +.. code-block:: bash + + [2025-10-08 11:34:22][INFO] - Starting training from scratch + ... + [2025-10-08 11:34:45][INFO] - Training finished! + +Now let's switch off the multi-GPU training by writing ``distributed: false``, and +submit this job again. The training takes 69 seconds. + +.. code-block:: bash + + [2025-10-08 11:37:38][INFO] - Setting up model + ... + [2025-10-08 11:38:47][INFO] - Training finished! + +Multi-GPU fine-tuning +--------------------- + +You can use multi-GPU for fine-tuning too, by writing ``distributed: True`` in the +``.yaml`` input. For information about fine-tuning, please refer to the +:ref:`sphx_glr_generated_examples_0-beginner_02-fine-tuning.py` example. +""" diff --git a/docs/src/examples/multi-gpu/multi-gpu.py.md5 b/docs/src/examples/multi-gpu/multi-gpu.py.md5 new file mode 100644 index 0000000000..86e00a927d --- /dev/null +++ b/docs/src/examples/multi-gpu/multi-gpu.py.md5 @@ -0,0 +1 @@ +4666621a9eb6317d63ffb285c171dd9a \ No newline at end of file diff --git a/docs/src/examples/multi-gpu/multi-gpu.rst b/docs/src/examples/multi-gpu/multi-gpu.rst new file mode 100644 index 0000000000..e1d6b46bf7 --- /dev/null +++ b/docs/src/examples/multi-gpu/multi-gpu.rst @@ -0,0 +1,134 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "examples/multi-gpu/multi-gpu.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code. + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_examples_multi-gpu_multi-gpu.py: + + +================== +Multi-GPU training +================== + +``metatrain`` supports training a model with several GPUs, which can accelerate the +training, especially when the training dataset is large / there are many training +epochs. This feature is enabled by the :py:mod:`torch.distributed` module, and thus can +do multiprocess parallelism across several nodes. + +In multi-GPU training, every batch of samples is split into smaller mini-batches and the +computation is run for each of the smaller mini-batches in parallel on different GPUs. +The different gradients obtained on each device are then summed. This approach allows +the user to reduce the time it takes to train models. + +To know if the model supports multi-GPU training, please check `Available Architectures +<../../docs/src/architectures/index.rst>`_ and see if the default hyperparameters have +the ``distributed`` option. + +Input file +---------- +To do this, you only need to switch on the ``distributed`` option in the ``.yaml`` file +for the training. Let's take `this tutorial +<../beginner_tutorials/train-from-scratch.rst>`_ as an example. Now, the +``options.yaml`` is + +.. literalinclude:: ./options-distributed.yaml + :language: yaml + :linenos: + +Slurm script +------------ + +Below is an example Slurm script for submitting the job. Please be aware that the actual +configurations vary from clusters to clusters, so you have to modify it. Different +scheduler will require similar options. ``metatrain`` will automatically use all the +GPUs that you have asked for. You should make a single GPU visible for each process +(setting `--gpus-per-node` equal to the number of GPUs, or setting `--gpus-per-task=1`, +depending on your cluster configuration). + +.. code-block:: bash + + #!/bin/bash + #SBATCH --nodes 1 + #SBATCH --ntasks 2 # must equal to the number of GPUs + #SBATCH --ntasks-per-node 2 + #SBATCH --gpus-per-node 2 # use 2 GPUs + #SBATCH --cpus-per-task 8 + #SBATCH --exclusive + #SBATCH --partition=h100 # adapt this to your cluster + #SBATCH --time=1:00:00 + + # load modules and/or virtual environments and/or containers here + + srun mtt train options-distributed.yaml + +Performance +----------- + +If the multi-GPU training runs successfully, you should see this in the training log: + +.. code-block:: bash + + [2025-10-08 11:34:22][INFO] - Distributed environment set up with MASTER_ADDR=kh080, + MASTER_PORT=39591, WORLD_SIZE=2, RANK=0, LOCAL_RANK=0 + [2025-10-08 11:34:23][INFO] - Training on 2 devices with dtype torch.float32 + +This 100-epoch training takes 23 seconds. + +.. code-block:: bash + + [2025-10-08 11:34:22][INFO] - Starting training from scratch + ... + [2025-10-08 11:34:45][INFO] - Training finished! + +Now let's switch off the multi-GPU training by writing ``distributed: false``, and +submit this job again. The training takes 69 seconds. + +.. code-block:: bash + + [2025-10-08 11:37:38][INFO] - Setting up model + ... + [2025-10-08 11:38:47][INFO] - Training finished! + +Multi-GPU fine-tuning +--------------------- +You can use multi-GPU for fine-tuning too, by writing ``distributed: True`` in the +``.yaml`` input. For information about fine-tuning, please refer to `this tutorial on +fine-tuning <../../getting-started/finetuning-example.rst>`_. + + +.. _sphx_glr_download_examples_multi-gpu_multi-gpu.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: multi-gpu.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: multi-gpu.py ` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: multi-gpu.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/multi-gpu/multi-gpu.zip b/docs/src/examples/multi-gpu/multi-gpu.zip new file mode 100644 index 0000000000000000000000000000000000000000..b4c6adaa55d42c51bb1e2789ea2fc089a57de639 GIT binary patch literal 7233 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"module_short": "pathlib", + "name": "PurePathBase.mkdir" + } + ], + "s": [ + { + "is_class": false, + "is_explicit": false, + "module": "ase.atoms", + "module_short": "ase", + "name": "Atoms" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Atoms" + } + ], + "s.arrays": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "dict" + } + ], + "s.get_cell": [ + { + "is_class": false, + "is_explicit": false, + "module": "ase.atoms", + "module_short": "ase", + "name": "Atoms.get_cell" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Atoms.get_cell" + } + ], + "s.get_positions": [ + { + "is_class": false, + "is_explicit": false, + "module": "ase.atoms", + "module_short": "ase", + "name": "Atoms.get_positions" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Atoms.get_positions" + } + ], + "s.get_potential_energy": [ + { + "is_class": false, + "is_explicit": false, + "module": "ase.atoms", + "module_short": "ase", + "name": "Atoms.get_potential_energy" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Atoms.get_potential_energy" + } + ], + "s.get_volume": [ + { + "is_class": false, + "is_explicit": false, + "module": "ase.atoms", + "module_short": "ase", + "name": "Atoms.get_volume" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Atoms.get_volume" + } + ], + "s.info": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "dict" + } + ], + "s.numbers": [ + { + "is_class": false, + "is_explicit": false, + "module": "ase.atoms", + "module_short": "ase", + "name": "Atoms.numbers" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Atoms.numbers" + } + ], + "s_mm": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "memmap" + } + ], + "s_mm.flush": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "memmap.flush" + }, + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "ndarray.flush" + } + ], + "s_path": [ + { + "is_class": false, + "is_explicit": false, + "module": "pathlib._local", + "module_short": "pathlib", + "name": "PosixPath" + }, + { + "is_class": false, + "is_explicit": false, + "module": "pathlib", + "module_short": "pathlib", + "name": "PosixPath" + } + ], + "structures": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "list" + } + ], + "subprocess.run": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "subprocess", + "module_short": "subprocess", + "name": "run" + } + ], + "system": [ + { + "is_class": false, + "is_explicit": false, + "module": "torch", + "module_short": "torch", + "name": "ScriptObject" + } + ], + "systems": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "list" + } + ], + "systems_to_torch": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "metatomic.torch", + "module_short": "metatomic.torch", + "name": "systems_to_torch" + } + ], + "torch.float64": [ + { + "is_class": false, + "is_explicit": false, + "module": "torch", + "module_short": "torch", + "name": "dtype" + }, + { + "is_class": false, + "is_explicit": false, + "module": "torch", + "module_short": "torch", + "name": "float64" + } + ], + "torch.tensor": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "builtin_function_or_method" + }, + { + "is_class": false, + "is_explicit": false, + "module": "torch", + "module_short": "torch", + "name": "tensor" + } + ], + "x_mm": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "memmap" + } + ], + "x_mm.flush": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "memmap.flush" + }, + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "ndarray.flush" + } + ], + "x_path": [ + { + "is_class": false, + "is_explicit": false, + "module": "pathlib._local", + "module_short": "pathlib", + "name": "PosixPath" + }, + { + "is_class": false, + "is_explicit": false, + "module": "pathlib", + "module_short": "pathlib", + "name": "PosixPath" + } + ] +} diff --git a/docs/src/examples/programmatic/data_preparation/data_preparation.ipynb b/docs/src/examples/programmatic/data_preparation/data_preparation.ipynb new file mode 100644 index 0000000000..bf87a8c055 --- /dev/null +++ b/docs/src/examples/programmatic/data_preparation/data_preparation.ipynb @@ -0,0 +1,169 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# How to prepare data for training\n\n.. attention::\n\n This tutorial is only relevant for users who need to prepare their data from scratch\n from several files or for big datasets. If you already have your data in a common\n file format (like XYZ or `ASE database`_), you can skip this tutorial and directly\n start training.\n\n\nXYZ, ASE databases, and also from metrain's\n:class:`metatrain.utils.data.dataset.DiskDataset ` file.\n\nFor the small datasets (<10k structures), you can simply provide an XYZ file or an ASE\ndatabase to ``metatrain``, and it will handle the data loading for you. Large datasets\n(>10k structures) may not fit into the GPU memory. In such cases, it is useful to\npre-process the dataset, save it to disk and load it on the fly during training.\n\nIn this tutorial, we will show how to prepare data for training using three different\nformats. You can choose the one that best fits your needs.\n\nWe start by importing the necessary packages.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import subprocess\nfrom pathlib import Path\n\nimport ase.io\nimport numpy as np\nimport torch\nfrom metatensor.torch import Labels, TensorBlock, TensorMap\nfrom metatomic.torch import NeighborListOptions, systems_to_torch\n\nfrom metatrain.utils.data.writers import DiskDatasetWriter\nfrom metatrain.utils.neighbor_lists import get_system_with_neighbor_lists" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create a XYZ training file (small datasets)\n\nFirst, we will show how to create a XYZ file with fields corresponding to the target\nproperties. On modern HPC systems, this format is suitable for datasets up to around\n1M structures. As an example, we will use 100 structures from a file read by ASE_.\nSince files from reference calculations may be located in different directories, we\nfirst create a list of all path that we want to read from. Here, for simplicity, we\nassume that all files are located in the same directory.\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "filelist = 100 * [\"qm9_reduced_100.xyz\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now read the structures using the ASE package. Check the ase documentation for\nmore details on how to read different file formats. Instead of creating the ``atoms``\nobject by reading from disk, you can also create an\n:class:`ase.Atoms` object containing the chemical ``symbols``, ``positions``, the\n``cell`` and the periodic boundary conditions (``pbc``) by hand using its constructor.\n\n.. hint::\n\n If a property is not read by the :func:`ase.io.read` function, you can add custom\n scalar properties to the ``info`` dictionary. Vector properties (e.g. forces) can be\n added to the ``arrays`` dictionary. Tensor properties (e.g. stress) must\n be flattened before adding them to the ``arrays`` dictionary.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "frames = []\nfor i, fname in enumerate(filelist):\n atoms = ase.io.read(fname, index=i)\n\n n_atoms = len(atoms)\n # scalar\n atoms.info[\"U0\"] = -100.0\n # vector\n atoms.arrays[\"forces\"] = np.zeros((n_atoms, 3))\n # tensor\n atoms.arrays[\"my_tensor\"] = np.zeros((n_atoms, 3, 3)).reshape(n_atoms, 9)\n\n frames.append(atoms)\n\nase.io.write(\"data.xyz\", frames)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Note

The names of the added properties (like, ``U0``, etc.) must be referenced correctly\n in the ``options.yaml`` file.

\n\n## Create a ``DiskDataset`` (large datasets)\n\nIn addition to the systems and targets (as above), we also save the neighbor\nlists that the model will use during training. We first create the writer object that\nwill write the data to a zip file.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "disk_dataset_writer = DiskDatasetWriter(\"qm9_reduced_100.zip\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we loop over all structures, convert them to the internal torch format using\n:func:`metatomic.torch.systems_to_torch`, compute the neighbor lists using\n:func:`metatrain.utils.neighbor_lists.get_system_with_neighbor_lists` and write\neverything to disk using the writer's ``write()`` method.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "for i, fname in enumerate(filelist):\n atoms = ase.io.read(fname, index=i)\n\n system = systems_to_torch(atoms, dtype=torch.float64)\n system = get_system_with_neighbor_lists(\n system,\n [NeighborListOptions(cutoff=5.0, full_list=True, strict=True)],\n )\n energy = TensorMap(\n keys=Labels.single(),\n blocks=[\n TensorBlock(\n values=torch.tensor([[atoms.info[\"U0\"]]], dtype=torch.float64),\n samples=Labels(\n names=[\"system\"],\n values=torch.tensor([[i]]),\n ),\n components=[],\n properties=Labels(\"energy\", torch.tensor([[0]])),\n )\n ],\n )\n disk_dataset_writer.write([system], {\"energy\": energy})\n\ndisk_dataset_writer.finish()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, you can also write the whole dataset at once, which might be more\nefficient (but also potentially run into memory issues). We use the same ``frames``\nthat we created above.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "disk_dataset_writer = DiskDatasetWriter(\"qm9_reduced_100_all_at_once.zip\")\n\nsystems = systems_to_torch(frames, dtype=torch.float64)\nsystems = [\n get_system_with_neighbor_lists(\n system,\n [NeighborListOptions(cutoff=5.0, full_list=True, strict=True)],\n )\n for system in systems\n]\nenergy = TensorMap(\n keys=Labels.single(),\n blocks=[\n TensorBlock(\n values=torch.tensor(\n [frame.info[\"U0\"] for frame in frames], dtype=torch.float64\n ).reshape(-1, 1),\n samples=Labels.range(\"system\", len(frames)),\n components=[],\n properties=Labels(\"energy\", torch.tensor([[0]])),\n )\n ],\n)\n\ndisk_dataset_writer.write(systems, {\"energy\": energy})\ndisk_dataset_writer.finish()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The dataset is saved to disk. You can now provide it to ``metatrain`` as a\ndataset to train from, simply by replacing your ``.xyz`` file with the newly created\nzip file (e.g. ``read_from: qm9_reduced_100.zip``).\n\n## Create a ``MemmapDataset`` (large datasets, parallel filesystems)\n\nIf your dataset is large and you are using a parallel filesystem (e.g. on an HPC\ncluster), it is recommended to use a ``MemmapDataset`` instead of a ``DiskDataset``.\nThe ``MemmapDataset`` stores the data inside memory-mapped numpy arrays instead of a\nzip file. Reading from this format avoids I/O bottlenecks, but it does not support\nspherical targets or storing neighbor lists.\n\nAs an example, we will use 100 structures from a dataset of carbon structures. The\nnumpy arrays must be saved inside a directory, using the following format.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "structures = ase.io.read(\"carbon_reduced_100.xyz\", index=\":\")\n\nroot = Path(\"carbon_reduced_100_memmap/\")\nroot.mkdir()\n\nns_path = root / \"ns.npy\"\nna_path = root / \"na.npy\"\na_path = root / \"a.bin\"\nx_path = root / \"x.bin\"\nc_path = root / \"c.bin\"\ne_path = root / \"e.bin\"\nf_path = root / \"f.bin\"\ns_path = root / \"s.bin\"\n\nns = len(structures)\nna = np.cumsum(np.array([0] + [len(s) for s in structures], dtype=np.int64))\nnp.save(ns_path, ns)\nnp.save(na_path, na)\n\na_mm = np.memmap(a_path, dtype=\"int32\", mode=\"w+\", shape=(na[-1],))\nx_mm = np.memmap(x_path, dtype=\"float32\", mode=\"w+\", shape=(na[-1], 3))\nc_mm = np.memmap(c_path, dtype=\"float32\", mode=\"w+\", shape=(ns, 3, 3))\ne_mm = np.memmap(e_path, dtype=\"float32\", mode=\"w+\", shape=(ns, 1))\nf_mm = np.memmap(f_path, dtype=\"float32\", mode=\"w+\", shape=(na[-1], 3))\ns_mm = np.memmap(s_path, dtype=\"float32\", mode=\"w+\", shape=(ns, 3, 3))\n\nfor i, s in enumerate(structures):\n a_mm[na[i] : na[i + 1]] = s.numbers\n x_mm[na[i] : na[i + 1]] = s.get_positions()\n c_mm[i] = s.get_cell()[:]\n e_mm[i] = s.get_potential_energy()\n f_mm[na[i] : na[i + 1]] = s.arrays[\"force\"]\n s_mm[i] = -s.info[\"virial\"] / s.get_volume()\n\na_mm.flush()\nx_mm.flush()\nc_mm.flush()\ne_mm.flush()\nf_mm.flush()\ns_mm.flush()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The dataset is saved to disk. You can now provide it to ``metatrain`` as a\ndataset to train from, simply by specifying the newly created\ndirectory as the path from which to read the systems\n(e.g. ``read_from: carbon_reduced_100_memmap/``).\n\nFor example, you can use the following options file:\n\n.. literalinclude:: options.yaml\n :language: yaml\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "subprocess.run([\"mtt\", \"train\", \"options.yaml\"])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/docs/src/examples/programmatic/data_preparation/data_preparation.py b/docs/src/examples/programmatic/data_preparation/data_preparation.py new file mode 100644 index 0000000000..a8dfa80f25 --- /dev/null +++ b/docs/src/examples/programmatic/data_preparation/data_preparation.py @@ -0,0 +1,242 @@ +""" +How to prepare data for training +================================ + +.. attention:: + + This tutorial is only relevant for users who need to prepare their data from scratch + from several files or for big datasets. If you already have your data in a common + file format (like XYZ or `ASE database`_), you can skip this tutorial and directly + start training. + +.. _ASE database: https://ase-lib.org/ase/db/db.html + +XYZ, ASE databases, and also from metrain's +:class:`metatrain.utils.data.dataset.DiskDataset ` file. + +For the small datasets (<10k structures), you can simply provide an XYZ file or an ASE +database to ``metatrain``, and it will handle the data loading for you. Large datasets +(>10k structures) may not fit into the GPU memory. In such cases, it is useful to +pre-process the dataset, save it to disk and load it on the fly during training. + +In this tutorial, we will show how to prepare data for training using three different +formats. You can choose the one that best fits your needs. + +We start by importing the necessary packages. +""" + +# %% +# +import subprocess +from pathlib import Path + +import ase.io +import numpy as np +import torch +from metatensor.torch import Labels, TensorBlock, TensorMap +from metatomic.torch import NeighborListOptions, systems_to_torch + +from metatrain.utils.data.writers import DiskDatasetWriter +from metatrain.utils.neighbor_lists import get_system_with_neighbor_lists + + +# %% +# Create a XYZ training file (small datasets) +# ------------------------------------------- +# +# First, we will show how to create a XYZ file with fields corresponding to the target +# properties. On modern HPC systems, this format is suitable for datasets up to around +# 1M structures. As an example, we will use 100 structures from a file read by ASE_. +# Since files from reference calculations may be located in different directories, we +# first create a list of all path that we want to read from. Here, for simplicity, we +# assume that all files are located in the same directory. +# +# .. _ASE: https://ase-lib.org/ + +filelist = 100 * ["qm9_reduced_100.xyz"] + +# %% +# +# We will now read the structures using the ASE package. Check the ase documentation for +# more details on how to read different file formats. Instead of creating the ``atoms`` +# object by reading from disk, you can also create an +# :class:`ase.Atoms` object containing the chemical ``symbols``, ``positions``, the +# ``cell`` and the periodic boundary conditions (``pbc``) by hand using its constructor. +# +# .. hint:: +# +# If a property is not read by the :func:`ase.io.read` function, you can add custom +# scalar properties to the ``info`` dictionary. Vector properties (e.g. forces) can be +# added to the ``arrays`` dictionary. Tensor properties (e.g. stress) must +# be flattened before adding them to the ``arrays`` dictionary. + +frames = [] +for i, fname in enumerate(filelist): + atoms = ase.io.read(fname, index=i) + + n_atoms = len(atoms) + # scalar + atoms.info["U0"] = -100.0 + # vector + atoms.arrays["forces"] = np.zeros((n_atoms, 3)) + # tensor + atoms.arrays["my_tensor"] = np.zeros((n_atoms, 3, 3)).reshape(n_atoms, 9) + + frames.append(atoms) + +ase.io.write("data.xyz", frames) + +# %% +# +# .. note:: +# +# The names of the added properties (like, ``U0``, etc.) must be referenced correctly +# in the ``options.yaml`` file. +# +# Create a ``DiskDataset`` (large datasets) +# ----------------------------------------- +# +# In addition to the systems and targets (as above), we also save the neighbor +# lists that the model will use during training. We first create the writer object that +# will write the data to a zip file. + +disk_dataset_writer = DiskDatasetWriter("qm9_reduced_100.zip") + +# %% +# +# Then we loop over all structures, convert them to the internal torch format using +# :func:`metatomic.torch.systems_to_torch`, compute the neighbor lists using +# :func:`metatrain.utils.neighbor_lists.get_system_with_neighbor_lists` and write +# everything to disk using the writer's ``write()`` method. + +for i, fname in enumerate(filelist): + atoms = ase.io.read(fname, index=i) + + system = systems_to_torch(atoms, dtype=torch.float64) + system = get_system_with_neighbor_lists( + system, + [NeighborListOptions(cutoff=5.0, full_list=True, strict=True)], + ) + energy = TensorMap( + keys=Labels.single(), + blocks=[ + TensorBlock( + values=torch.tensor([[atoms.info["U0"]]], dtype=torch.float64), + samples=Labels( + names=["system"], + values=torch.tensor([[i]]), + ), + components=[], + properties=Labels("energy", torch.tensor([[0]])), + ) + ], + ) + disk_dataset_writer.write([system], {"energy": energy}) + +disk_dataset_writer.finish() + +# %% +# +# Alternatively, you can also write the whole dataset at once, which might be more +# efficient (but also potentially run into memory issues). We use the same ``frames`` +# that we created above. + +disk_dataset_writer = DiskDatasetWriter("qm9_reduced_100_all_at_once.zip") + +systems = systems_to_torch(frames, dtype=torch.float64) +systems = [ + get_system_with_neighbor_lists( + system, + [NeighborListOptions(cutoff=5.0, full_list=True, strict=True)], + ) + for system in systems +] +energy = TensorMap( + keys=Labels.single(), + blocks=[ + TensorBlock( + values=torch.tensor( + [frame.info["U0"] for frame in frames], dtype=torch.float64 + ).reshape(-1, 1), + samples=Labels.range("system", len(frames)), + components=[], + properties=Labels("energy", torch.tensor([[0]])), + ) + ], +) + +disk_dataset_writer.write(systems, {"energy": energy}) +disk_dataset_writer.finish() + +# %% +# +# The dataset is saved to disk. You can now provide it to ``metatrain`` as a +# dataset to train from, simply by replacing your ``.xyz`` file with the newly created +# zip file (e.g. ``read_from: qm9_reduced_100.zip``). +# +# Create a ``MemmapDataset`` (large datasets, parallel filesystems) +# ----------------------------------------------------------------- +# +# If your dataset is large and you are using a parallel filesystem (e.g. on an HPC +# cluster), it is recommended to use a ``MemmapDataset`` instead of a ``DiskDataset``. +# The ``MemmapDataset`` stores the data inside memory-mapped numpy arrays instead of a +# zip file. Reading from this format avoids I/O bottlenecks, but it does not support +# spherical targets or storing neighbor lists. +# +# As an example, we will use 100 structures from a dataset of carbon structures. The +# numpy arrays must be saved inside a directory, using the following format. + +structures = ase.io.read("carbon_reduced_100.xyz", index=":") + +root = Path("carbon_reduced_100_memmap/") +root.mkdir(exist_ok=True) + +ns_path = root / "ns.npy" +na_path = root / "na.npy" +a_path = root / "a.bin" +x_path = root / "x.bin" +c_path = root / "c.bin" +e_path = root / "e.bin" +f_path = root / "f.bin" +s_path = root / "s.bin" + +ns = len(structures) +na = np.cumsum(np.array([0] + [len(s) for s in structures], dtype=np.int64)) +np.save(ns_path, ns) +np.save(na_path, na) + +a_mm = np.memmap(a_path, dtype="int32", mode="w+", shape=(na[-1],)) +x_mm = np.memmap(x_path, dtype="float32", mode="w+", shape=(na[-1], 3)) +c_mm = np.memmap(c_path, dtype="float32", mode="w+", shape=(ns, 3, 3)) +e_mm = np.memmap(e_path, dtype="float32", mode="w+", shape=(ns, 1)) +f_mm = np.memmap(f_path, dtype="float32", mode="w+", shape=(na[-1], 3)) +s_mm = np.memmap(s_path, dtype="float32", mode="w+", shape=(ns, 3, 3)) + +for i, s in enumerate(structures): + a_mm[na[i] : na[i + 1]] = s.numbers + x_mm[na[i] : na[i + 1]] = s.get_positions() + c_mm[i] = s.get_cell()[:] + e_mm[i] = s.get_potential_energy() + f_mm[na[i] : na[i + 1]] = s.arrays["force"] + s_mm[i] = -s.info["virial"] / s.get_volume() + +a_mm.flush() +x_mm.flush() +c_mm.flush() +e_mm.flush() +f_mm.flush() +s_mm.flush() + +# %% +# +# The dataset is saved to disk. You can now provide it to ``metatrain`` as a +# dataset to train from, simply by specifying the newly created +# directory as the path from which to read the systems +# (e.g. ``read_from: carbon_reduced_100_memmap/``). +# +# For example, you can use the following options file: +# +# .. literalinclude:: options-memmap.yaml +# :language: yaml + +subprocess.run(["mtt", "train", "options-memmap.yaml"]) diff --git a/docs/src/examples/programmatic/data_preparation/data_preparation.py.md5 b/docs/src/examples/programmatic/data_preparation/data_preparation.py.md5 new file mode 100644 index 0000000000..ec11fcf764 --- /dev/null +++ b/docs/src/examples/programmatic/data_preparation/data_preparation.py.md5 @@ -0,0 +1 @@ +54ca454338c67496640c3b734e586992 \ No newline at end of file diff --git a/docs/src/examples/programmatic/data_preparation/data_preparation.rst b/docs/src/examples/programmatic/data_preparation/data_preparation.rst new file mode 100644 index 0000000000..6aa58fb836 --- /dev/null +++ b/docs/src/examples/programmatic/data_preparation/data_preparation.rst @@ -0,0 +1,390 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "examples/programmatic/data_preparation/data_preparation.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code. + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_examples_programmatic_data_preparation_data_preparation.py: + + +How to prepare data for training +================================ + +.. attention:: + + This tutorial is only relevant for users who need to prepare their data from scratch + from several files or for big datasets. If you already have your data in a common + file format (like XYZ or `ASE database`_), you can skip this tutorial and directly + start training. + +.. _ASE database: https://ase-lib.org/ase/db/db.html + +XYZ, ASE databases, and also from metrain's +:class:`metatrain.utils.data.dataset.DiskDataset ` file. + +For the small datasets (<10k structures), you can simply provide an XYZ file or an ASE +database to ``metatrain``, and it will handle the data loading for you. Large datasets +(>10k structures) may not fit into the GPU memory. In such cases, it is useful to +pre-process the dataset, save it to disk and load it on the fly during training. + +In this tutorial, we will show how to prepare data for training using three different +formats. You can choose the one that best fits your needs. + +We start by importing the necessary packages. + +.. GENERATED FROM PYTHON SOURCE LINES 30-43 + +.. code-block:: Python + + import subprocess + from pathlib import Path + + import ase.io + import numpy as np + import torch + from metatensor.torch import Labels, TensorBlock, TensorMap + from metatomic.torch import NeighborListOptions, systems_to_torch + + from metatrain.utils.data.writers import DiskDatasetWriter + from metatrain.utils.neighbor_lists import get_system_with_neighbor_lists + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 44-55 + +Create a XYZ training file (small datasets) +------------------------------------------- + +First, we will show how to create a XYZ file with fields corresponding to the target +properties. On modern HPC systems, this format is suitable for datasets up to around +1M structures. As an example, we will use 100 structures from a file read by ASE_. +Since files from reference calculations may be located in different directories, we +first create a list of all path that we want to read from. Here, for simplicity, we +assume that all files are located in the same directory. + +.. _ASE: https://ase-lib.org/ + +.. GENERATED FROM PYTHON SOURCE LINES 55-58 + +.. code-block:: Python + + + filelist = 100 * ["qm9_reduced_100.xyz"] + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 59-71 + +We will now read the structures using the ASE package. Check the ase documentation for +more details on how to read different file formats. Instead of creating the ``atoms`` +object by reading from disk, you can also create an +:class:`ase.Atoms` object containing the chemical ``symbols``, ``positions``, the +``cell`` and the periodic boundary conditions (``pbc``) by hand using its constructor. + +.. hint:: + + If a property is not read by the :func:`ase.io.read` function, you can add custom + scalar properties to the ``info`` dictionary. Vector properties (e.g. forces) can be + added to the ``arrays`` dictionary. Tensor properties (e.g. stress) must + be flattened before adding them to the ``arrays`` dictionary. + +.. GENERATED FROM PYTHON SOURCE LINES 72-89 + +.. code-block:: Python + + + frames = [] + for i, fname in enumerate(filelist): + atoms = ase.io.read(fname, index=i) + + n_atoms = len(atoms) + # scalar + atoms.info["U0"] = -100.0 + # vector + atoms.arrays["forces"] = np.zeros((n_atoms, 3)) + # tensor + atoms.arrays["my_tensor"] = np.zeros((n_atoms, 3, 3)).reshape(n_atoms, 9) + + frames.append(atoms) + + ase.io.write("data.xyz", frames) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 90-101 + +.. note:: + + The names of the added properties (like, ``U0``, etc.) must be referenced correctly + in the ``options.yaml`` file. + +Create a ``DiskDataset`` (large datasets) +----------------------------------------- + +In addition to the systems and targets (as above), we also save the neighbor +lists that the model will use during training. We first create the writer object that +will write the data to a zip file. + +.. GENERATED FROM PYTHON SOURCE LINES 102-105 + +.. code-block:: Python + + + disk_dataset_writer = DiskDatasetWriter("qm9_reduced_100.zip") + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 106-110 + +Then we loop over all structures, convert them to the internal torch format using +:func:`metatomic.torch.systems_to_torch`, compute the neighbor lists using +:func:`metatrain.utils.neighbor_lists.get_system_with_neighbor_lists` and write +everything to disk using the writer's ``write()`` method. + +.. GENERATED FROM PYTHON SOURCE LINES 111-138 + +.. code-block:: Python + + + for i, fname in enumerate(filelist): + atoms = ase.io.read(fname, index=i) + + system = systems_to_torch(atoms, dtype=torch.float64) + system = get_system_with_neighbor_lists( + system, + [NeighborListOptions(cutoff=5.0, full_list=True, strict=True)], + ) + energy = TensorMap( + keys=Labels.single(), + blocks=[ + TensorBlock( + values=torch.tensor([[atoms.info["U0"]]], dtype=torch.float64), + samples=Labels( + names=["system"], + values=torch.tensor([[i]]), + ), + components=[], + properties=Labels("energy", torch.tensor([[0]])), + ) + ], + ) + disk_dataset_writer.write([system], {"energy": energy}) + + disk_dataset_writer.finish() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 139-142 + +Alternatively, you can also write the whole dataset at once, which might be more +efficient (but also potentially run into memory issues). We use the same ``frames`` +that we created above. + +.. GENERATED FROM PYTHON SOURCE LINES 143-171 + +.. code-block:: Python + + + disk_dataset_writer = DiskDatasetWriter("qm9_reduced_100_all_at_once.zip") + + systems = systems_to_torch(frames, dtype=torch.float64) + systems = [ + get_system_with_neighbor_lists( + system, + [NeighborListOptions(cutoff=5.0, full_list=True, strict=True)], + ) + for system in systems + ] + energy = TensorMap( + keys=Labels.single(), + blocks=[ + TensorBlock( + values=torch.tensor( + [frame.info["U0"] for frame in frames], dtype=torch.float64 + ).reshape(-1, 1), + samples=Labels.range("system", len(frames)), + components=[], + properties=Labels("energy", torch.tensor([[0]])), + ) + ], + ) + + disk_dataset_writer.write(systems, {"energy": energy}) + disk_dataset_writer.finish() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 172-187 + +The dataset is saved to disk. You can now provide it to ``metatrain`` as a +dataset to train from, simply by replacing your ``.xyz`` file with the newly created +zip file (e.g. ``read_from: qm9_reduced_100.zip``). + +Create a ``MemmapDataset`` (large datasets, parallel filesystems) +----------------------------------------------------------------- + +If your dataset is large and you are using a parallel filesystem (e.g. on an HPC +cluster), it is recommended to use a ``MemmapDataset`` instead of a ``DiskDataset``. +The ``MemmapDataset`` stores the data inside memory-mapped numpy arrays instead of a +zip file. Reading from this format avoids I/O bottlenecks, but it does not support +spherical targets or storing neighbor lists. + +As an example, we will use 100 structures from a dataset of carbon structures. The +numpy arrays must be saved inside a directory, using the following format. + +.. GENERATED FROM PYTHON SOURCE LINES 188-230 + +.. code-block:: Python + + + structures = ase.io.read("carbon_reduced_100.xyz", index=":") + + root = Path("carbon_reduced_100_memmap/") + root.mkdir() + + ns_path = root / "ns.npy" + na_path = root / "na.npy" + a_path = root / "a.bin" + x_path = root / "x.bin" + c_path = root / "c.bin" + e_path = root / "e.bin" + f_path = root / "f.bin" + s_path = root / "s.bin" + + ns = len(structures) + na = np.cumsum(np.array([0] + [len(s) for s in structures], dtype=np.int64)) + np.save(ns_path, ns) + np.save(na_path, na) + + a_mm = np.memmap(a_path, dtype="int32", mode="w+", shape=(na[-1],)) + x_mm = np.memmap(x_path, dtype="float32", mode="w+", shape=(na[-1], 3)) + c_mm = np.memmap(c_path, dtype="float32", mode="w+", shape=(ns, 3, 3)) + e_mm = np.memmap(e_path, dtype="float32", mode="w+", shape=(ns, 1)) + f_mm = np.memmap(f_path, dtype="float32", mode="w+", shape=(na[-1], 3)) + s_mm = np.memmap(s_path, dtype="float32", mode="w+", shape=(ns, 3, 3)) + + for i, s in enumerate(structures): + a_mm[na[i] : na[i + 1]] = s.numbers + x_mm[na[i] : na[i + 1]] = s.get_positions() + c_mm[i] = s.get_cell()[:] + e_mm[i] = s.get_potential_energy() + f_mm[na[i] : na[i + 1]] = s.arrays["force"] + s_mm[i] = -s.info["virial"] / s.get_volume() + + a_mm.flush() + x_mm.flush() + c_mm.flush() + e_mm.flush() + f_mm.flush() + s_mm.flush() + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 231-240 + +The dataset is saved to disk. You can now provide it to ``metatrain`` as a +dataset to train from, simply by specifying the newly created +directory as the path from which to read the systems +(e.g. ``read_from: carbon_reduced_100_memmap/``). + +For example, you can use the following options file: + +.. literalinclude:: options.yaml + :language: yaml + +.. GENERATED FROM PYTHON SOURCE LINES 241-243 + +.. code-block:: Python + + + subprocess.run(["mtt", "train", "options.yaml"]) + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + + CompletedProcess(args=['mtt', 'train', 'options.yaml'], returncode=0) + + + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** (0 minutes 10.476 seconds) + + +.. _sphx_glr_download_examples_programmatic_data_preparation_data_preparation.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: data_preparation.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: data_preparation.py ` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: data_preparation.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by 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+ +.. only:: html + + .. image:: /examples/programmatic/data_preparation/images/thumb/sphx_glr_data_preparation_thumb.png + :alt: + + :ref:`sphx_glr_examples_programmatic_data_preparation_data_preparation.py` + +.. raw:: html + +
How to prepare data for training
+
+ + +.. thumbnail-parent-div-close + +.. raw:: html + + + + +.. toctree:: + :hidden: + + /examples/programmatic/data_preparation/data_preparation + + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-gallery + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download all examples in Python source code: data_preparation_python.zip ` + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download all examples in Jupyter notebooks: data_preparation_jupyter.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/programmatic/data_preparation/options.yaml b/docs/src/examples/programmatic/data_preparation/options.yaml new file mode 100644 index 0000000000..f8de24c036 --- /dev/null +++ b/docs/src/examples/programmatic/data_preparation/options.yaml @@ -0,0 +1,38 @@ +seed: 42 + +architecture: + name: pet + training: + num_epochs: 1 + batch_size: 8 + +training_set: + systems: + read_from: carbon_reduced_100_memmap/ + length_unit: angstrom + targets: + energy: + key: e + unit: eV + forces: + key: f + stress: + key: s + non_conservative_forces: + key: f + quantity: force + unit: eV/A + per_atom: true + type: + cartesian: + rank: 1 + non_conservative_stress: + key: s + quantity: pressure + unit: eV/A^3 + type: + cartesian: + rank: 2 + +test_set: 0.1 +validation_set: 0.1 diff --git a/docs/src/examples/programmatic/data_preparation/qm9_reduced_100.xyz b/docs/src/examples/programmatic/data_preparation/qm9_reduced_100.xyz new file mode 100644 index 0000000000..14b2a0416e --- /dev/null +++ b/docs/src/examples/programmatic/data_preparation/qm9_reduced_100.xyz @@ -0,0 +1,1201 @@ +5 +Properties=species:S:1:pos:R:3 A=157.7118 B=157.70997 C=157.70699 mu=0 alpha=13.21 epsHOMO=-0.3877 epsLUMO=0.1171 epsgap=0.5048 =35.3641 zpve=0.044749 U0=-40.47893 U=-40.476062 H=-40.475117 G=-40.498597 Cv=6.469 pbc="F F F" +C -0.01269814 1.08580416 0.00800100 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=59.5248 zpve=0.026841 U0=-77.308427 U=-77.305527 H=-77.304583 G=-77.327429 Cv=8.574 pbc="F F F" +C 0.59953949 0.00000000 1.00000000 +C -0.59953949 0.00000000 1.00000000 +H -1.66163859 0.00000000 1.00000000 +H 1.66163859 0.00000000 1.00000000 +3 +Properties=species:S:1:pos:R:3 A=0 B=44.593883 C=44.593883 mu=2.8937 alpha=12.99 epsHOMO=-0.3604 epsLUMO=0.0191 epsgap=0.3796 =48.7476 zpve=0.016601 U0=-93.411888 U=-93.40937 H=-93.408425 G=-93.431246 Cv=6.278 pbc="F F F" +C -0.01332393 1.13246572 0.00827589 +N 0.00231072 -0.01915859 0.00192873 +H -0.02780270 2.19894930 0.01415379 +4 +Properties=species:S:1:pos:R:3 A=285.48839 B=38.9823 C=34.29892 mu=2.1089 alpha=14.18 epsHOMO=-0.267 epsLUMO=-0.0406 epsgap=0.2263 =59.9891 zpve=0.026603 U0=-114.483613 U=-114.480746 H=-114.479802 G=-114.505268 Cv=6.413 pbc="F F F" +C -0.01397770 1.18021143 0.00775250 +O 0.00231399 -0.01966366 0.00216107 +H 0.91496029 1.78951117 0.00395656 +H -0.95910972 1.76401798 0.01718284 +8 +Properties=species:S:1:pos:R:3 A=80.46225 B=19.90649 C=19.90633 mu=0 alpha=23.95 epsHOMO=-0.3385 epsLUMO=0.1041 epsgap=0.4426 =109.5031 zpve=0.074542 U0=-79.764152 U=-79.760666 H=-79.759722 G=-79.787269 Cv=10.098 pbc="F F F" +C -0.01870400 1.52558201 0.01043281 +C 0.00210374 -0.00388191 0.00199882 +H 0.99487275 1.93974324 0.00294120 +H -0.54207611 1.92361063 -0.86511735 +H -0.52524112 1.91417308 0.90002399 +H 0.52548654 -0.40190784 0.87754395 +H -1.01147651 -0.41803380 0.00950849 +H 0.50862619 -0.39247040 -0.88760117 +6 +Properties=species:S:1:pos:R:3 A=127.83497 B=24.85872 C=23.97872 mu=1.5258 alpha=16.97 epsHOMO=-0.2653 epsLUMO=0.0784 epsgap=0.3437 =83.794 zpve=0.051208 U0=-115.679136 U=-115.675816 H=-115.674872 G=-115.701876 Cv=8.751 pbc="F F F" +C -0.00828817 1.39046978 -0.00560069 +O -0.00797038 -0.02504537 0.02030606 +H 1.00658338 1.81556366 0.00348335 +H -0.54657475 1.79916975 -0.87390126 +H -0.52288871 1.72555240 0.89907326 +H 0.44142019 -0.33354425 -0.77152059 +7 +Properties=species:S:1:pos:R:3 A=160.28041 B=8.59323 C=8.59321 mu=0.7156 alpha=28.78 epsHOMO=-0.2609 epsLUMO=0.0613 epsgap=0.3222 =177.1963 zpve=0.05541 U0=-116.609549 U=-116.60555 H=-116.604606 G=-116.633775 Cv=12.482 pbc="F F F" +C -0.01782102 1.46435788 0.01009397 +C 0.00208816 0.00950777 0.00201200 +C 0.01834085 -1.19180518 -0.00450508 +H 0.99782210 1.87425349 0.00260606 +H -0.54220434 1.85801178 -0.86721192 +H -0.52533306 1.84834356 0.90148138 +H 0.03231658 -2.25314823 -0.01025999 +6 +Properties=species:S:1:pos:R:3 A=159.03567 B=9.22327 C=9.22324 mu=3.8266 alpha=24.45 epsHOMO=-0.3264 epsLUMO=0.0376 epsgap=0.364 =160.7223 zpve=0.045286 U0=-132.71815 U=-132.714563 H=-132.713619 G=-132.742149 Cv=10.287 pbc="F F F" +C -0.01788629 1.46712787 0.01011311 +C 0.00173819 0.01035260 0.00207588 +N 0.01765235 -1.14452926 -0.00420445 +H 1.00202941 1.86089927 0.00245401 +H -0.54398848 1.84479901 -0.87075460 +H -0.52707783 1.83518166 0.90486329 +7 +Properties=species:S:1:pos:R:3 A=57.22434 B=10.11122 C=9.07368 mu=2.5682 alpha=25.11 epsHOMO=-0.254 epsLUMO=-0.0198 epsgap=0.2342 =166.9728 zpve=0.055355 U0=-153.787612 U=-153.783728 H=-153.782784 G=-153.812518 Cv=11.219 pbc="F F F" +C -0.00294482 1.50991366 0.00867278 +C 0.02608284 0.00327563 -0.03745912 +O 0.94228801 -0.65507035 -0.45682576 +H 0.92278802 1.92634242 -0.39146557 +H -0.86201540 1.87852481 -0.56479538 +H -0.15050638 1.84393383 1.04289100 +H -0.89443009 -0.48643408 0.35774865 +6 +Properties=species:S:1:pos:R:3 A=73.8472 B=11.34793 C=9.83639 mu=3.7286 alpha=21.57 epsHOMO=-0.2543 epsLUMO=0.0302 epsgap=0.2845 =145.3078 zpve=0.045279 U0=-169.860788 U=-169.856903 H=-169.855958 G=-169.885594 Cv=10.89 pbc="F F F" +N -0.02589986 1.34614561 0.00889391 +C 0.04646728 -0.01174341 0.00120425 +O 1.07183520 -0.65258782 -0.01113313 +H 0.82535497 1.88504948 0.00373769 +H -0.90837680 1.82679638 0.01891992 +H -0.96144100 -0.47500376 0.00807391 +11 +Properties=species:S:1:pos:R:3 A=29.45018 B=8.37701 C=7.42076 mu=0.0597 alpha=34.75 epsHOMO=-0.323 epsLUMO=0.0949 epsgap=0.4179 =227.1361 zpve=0.103182 U0=-119.052475 U=-119.047927 H=-119.046983 G=-119.078157 Cv=14.84 pbc="F F F" +C -0.03113825 1.54081582 0.03192126 +C 0.01215347 0.01092235 -0.01603259 +C 0.72169129 -0.52583353 -1.26230570 +H 0.97955987 1.96459116 0.03098367 +H -0.55840223 1.94831192 -0.83816075 +H -0.54252252 1.90153531 0.93005671 +H 0.51522791 -0.36840234 0.88231134 +H -1.01070641 -0.38456999 0.02051783 +H 1.75851210 -0.17376585 -1.30871516 +H 0.74087192 -1.62024959 -1.27516511 +H 0.22023351 -0.19051179 -2.17729020 +9 +Properties=species:S:1:pos:R:3 A=35.09545 B=9.3686 C=8.1497 mu=1.4131 alpha=27.87 epsHOMO=-0.2619 epsLUMO=0.0798 epsgap=0.3417 =193.1659 zpve=0.079754 U0=-154.972731 U=-154.968412 H=-154.967467 G=-154.998148 Cv=13.546 pbc="F F F" +C -0.00860504 1.50203829 -0.00681217 +C 0.01099310 -0.01764877 -0.01377035 +O 0.68088841 -0.44041803 -1.19313210 +H 1.01153334 1.89662030 -0.01920155 +H -0.53159862 1.88076109 -0.88974659 +H -0.51674572 1.87611771 0.88710739 +H 0.52377121 -0.38912295 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alpha=28.07 epsHOMO=-0.2495 epsLUMO=0.0556 epsgap=0.3051 =244.2308 zpve=0.063824 U0=-225.221461 U=-225.217075 H=-225.216131 G=-225.247724 Cv=15.292 pbc="F F F" +N 0.03605270 1.36077873 -0.12416403 +C -0.02591138 -0.02076560 0.00200645 +N 1.21968479 -0.62334220 0.11963222 +O -1.06822866 -0.64174597 0.00865573 +H 0.80749439 1.83455148 0.32144915 +H -0.85825775 1.80373500 0.02243770 +H 1.99927148 -0.17303054 -0.33602496 +H 1.18204327 -1.62147901 -0.02109234 +14 +Properties=species:S:1:pos:R:3 A=7.75166 B=7.74847 C=4.48668 mu=0.0897 alpha=45.46 epsHOMO=-0.3167 epsLUMO=0.0843 epsgap=0.401 =355.0621 zpve=0.131146 U0=-158.342346 U=-158.336603 H=-158.335658 G=-158.370016 Cv=20.273 pbc="F F F" +C -0.03215888 1.54021598 0.01074456 +C 0.03381741 0.00745852 0.00180716 +C 0.71375572 -0.50856404 -1.27296949 +C 0.73848999 -0.52220886 1.25750907 +H 0.97522736 1.97414341 0.00528362 +H -0.56196769 1.92232326 -0.86835418 +H -0.54817239 1.91268414 0.90208039 +H -0.99752525 -0.37310578 0.00977297 +H 1.75070021 -0.15586784 -1.33269631 +H 0.73446691 -1.60328288 -1.29939696 +H 0.19367011 -0.16085589 -2.17195508 +H 0.23611990 -0.18382017 2.17005260 +H 0.75964083 -1.61713153 1.27226101 +H 1.77635866 -0.16985318 1.30053019 +12 +Properties=species:S:1:pos:R:3 A=8.67553 B=8.00568 C=4.75542 mu=1.4259 alpha=38.58 epsHOMO=-0.2612 epsLUMO=0.074 epsgap=0.3351 =318.3721 zpve=0.107673 U0=-194.267232 U=-194.261748 H=-194.260804 G=-194.294663 Cv=19.052 pbc="F F F" +C -0.03315871 1.54782566 -0.00438815 +C -0.01108534 0.01859095 0.01676117 +C 0.70938384 -0.53596711 1.23978444 +O -1.33219841 -0.51712554 0.05236057 +H 0.98215692 1.95715900 -0.03182225 +H -0.56388100 1.92087623 -0.88845865 +H -0.54082548 1.93340945 0.88575489 +H 0.51042122 -0.33767241 -0.88819176 +H 0.22240492 -0.18785860 2.15650245 +H 0.68242024 -1.62887638 1.23305141 +H 1.75434301 -0.21246950 1.25524188 +H -1.81818681 -0.15577435 -0.69553962 +6 +Properties=species:S:1:pos:R:3 A=0 B=4.4259726 C=4.4259726 mu=0 alpha=38.52 epsHOMO=-0.2599 epsLUMO=-0.0214 epsgap=0.2386 =278.6264 zpve=0.037354 U0=-153.459846 U=-153.455442 H=-153.454498 G=-153.482621 Cv=15.312 pbc="F F F" +C 0.68098021 0.00000000 0.00000000 +C -0.68098021 0.00000000 0.00000000 +C -1.88766603 0.00000000 0.00000000 +C 1.88766603 0.00000000 0.00000000 +H -2.94960000 0.00000000 0.00000000 +H 2.94960000 0.00000000 0.00000000 +5 +Properties=species:S:1:pos:R:3 A=0 B=4.5793222 C=4.5793222 mu=3.792 alpha=32.66 epsHOMO=-0.3102 epsLUMO=-0.0543 epsgap=0.2559 =260.1896 zpve=0.027259 U0=-169.557758 U=-169.553764 H=-169.55282 G=-169.581024 Cv=12.93 pbc="F F F" +C 0.01510390 0.00000000 1.00000000 +C 1.38233149 0.00000000 1.00000000 +C -1.18862525 0.00000000 1.00000000 +H -2.25177201 0.00000000 1.00000000 +N 2.54296187 0.00000000 1.00000000 +4 +Properties=species:S:1:pos:R:3 A=0 B=4.73269 C=4.73269 mu=0.0023 alpha=27.7 epsHOMO=-0.3696 epsLUMO=-0.0926 epsgap=0.277 =242.9308 zpve=0.015951 U0=-185.648533 U=-185.644825 H=-185.64388 G=-185.667652 Cv=10.398 pbc="F F F" +N 0.01745734 -1.16134217 -0.00415342 +C 0.00253212 -0.00344278 0.00179939 +C -0.01611419 1.37220987 0.00934873 +N -0.03264302 2.53008276 0.01609090 +6 +Properties=species:S:1:pos:R:3 A=68.5171 B=4.8345 C=4.51586 mu=2.7824 alpha=31.14 epsHOMO=-0.2777 epsLUMO=-0.0735 epsgap=0.2042 =268.3921 zpve=0.037208 U0=-190.624631 U=-190.620363 H=-190.619419 G=-190.650543 Cv=13.049 pbc="F F F" +O -0.06776120 -0.04233838 0.00282489 +C -0.00562945 1.16382879 0.00743862 +C 1.22985272 1.91339997 0.00028781 +C 2.23625322 2.57430970 -0.00529277 +H -0.91670701 1.79467546 0.01781352 +H 3.13429662 3.14370267 -0.01037656 +5 +Properties=species:S:1:pos:R:3 A=67.88408 B=5.00823 C=4.66413 mu=2.3112 alpha=26.25 epsHOMO=-0.3166 epsLUMO=-0.11 epsgap=0.2066 =251.0007 zpve=0.02654 U0=-206.721858 U=-206.717875 H=-206.716931 G=-206.747625 Cv=11.329 pbc="F F F" +O -0.04055978 -0.03799144 0.00266513 +C -0.01768870 1.16193251 0.00746008 +C 1.23315534 1.93381336 0.00043371 +N 2.19857378 2.57055097 -0.00483320 +H -0.92627534 1.78959196 0.01764859 +6 +Properties=species:S:1:pos:R:3 A=56.25048 B=4.77441 C=4.40088 mu=0.002 alpha=26.12 epsHOMO=-0.2668 epsLUMO=-0.1113 epsgap=0.1555 =266.8164 zpve=0.036943 U0=-227.798785 U=-227.79457 H=-227.793626 G=-227.825074 Cv=12.147 pbc="F F F" +O 0.00337122 -0.03146586 0.00157206 +C -0.01713174 1.17081409 0.00676444 +C 1.26555783 1.99719410 -0.00007703 +O 1.24507069 3.19946589 0.00681275 +H -0.94690483 1.77678142 0.01852668 +H 2.19534410 1.39121374 -0.01006739 +10 +Properties=species:S:1:pos:R:3 A=80.28766 B=3.3688 C=3.3688 mu=0 alpha=42.32 epsHOMO=-0.2412 epsLUMO=0.0684 epsgap=0.3096 =400.2236 zpve=0.083896 U0=-155.908941 U=-155.90318 H=-155.902236 G=-155.937641 Cv=17.447 pbc="F F F" +C -0.01787959 1.46676893 0.01011180 +C 0.00186644 0.01072913 0.00211109 +C 0.01819704 -1.19272861 -0.00450723 +C 0.03795170 -2.64876793 -0.01251193 +H 0.86460312 1.87589979 0.51479719 +H -0.03217624 1.87210581 -1.00791002 +H -0.90233115 1.85185524 0.53003953 +H -0.83477011 -3.06339007 0.50451436 +H 0.03276422 -3.04315564 -1.03491223 +H 0.93213486 -3.03931642 0.48626606 +10 +Properties=species:S:1:pos:R:3 A=27.39459 B=4.53005 C=4.08342 mu=0.7067 alpha=40.09 epsHOMO=-0.2592 epsLUMO=0.0566 epsgap=0.3157 =333.9589 zpve=0.084338 U0=-155.897345 U=-155.892291 H=-155.891347 G=-155.924226 Cv=17.13 pbc="F F F" +C -0.03095840 1.54775025 0.03167945 +C 0.01485375 0.00962471 -0.02082154 +C 0.69099073 -0.49954575 -1.20857624 +C 1.25222275 -0.89514467 -2.19527879 +H 0.97899300 1.96621609 0.03373834 +H -0.56082354 1.95002902 -0.83580745 +H -0.54576708 1.88225519 0.93709062 +H 0.51920135 -0.37425398 0.87496224 +H -1.00644802 -0.39030838 0.01340632 +H 1.74594541 -1.25604187 -3.06299415 +9 +Properties=species:S:1:pos:R:3 A=28.0016 B=4.67752 C=4.21765 mu=3.9233 alpha=35.38 epsHOMO=-0.3213 epsLUMO=0.034 epsgap=0.3553 =314.5335 zpve=0.07419 U0=-172.006141 U=-172.001467 H=-172.000523 G=-172.032826 Cv=14.988 pbc="F F F" +C -0.02522005 1.54773054 0.02133370 +C 0.01183939 0.00999278 -0.01556702 +C 0.68915114 -0.50836846 -1.20331488 +N 1.22565733 -0.90360846 -2.14737482 +H 0.98571620 1.96212990 0.02370153 +H -0.55522192 1.94599662 -0.84711771 +H -0.53962556 1.88294354 0.92536604 +H 0.52481910 -0.38226048 0.86933355 +H -1.00453542 -0.39832109 0.00530071 +8 +Properties=species:S:1:pos:R:3 A=32.19893 B=4.76299 C=4.30696 mu=4.4361 alpha=31.81 epsHOMO=-0.2683 epsLUMO=0.0173 epsgap=0.2855 =295.6635 zpve=0.063305 U0=-188.042067 U=-188.037478 H=-188.036534 G=-188.06863 Cv=14.488 pbc="F F F" +N -0.03608099 1.48762533 0.03678072 +C -0.01039471 0.02517605 -0.01506608 +C -1.37180956 -0.52108920 0.02739272 +N -2.44026695 -0.95956444 0.03370519 +H 0.91729752 1.83547547 0.05202682 +H -0.47162178 1.85677205 -0.80275569 +H 0.48946161 -0.40880401 -0.89715168 +H 0.52827150 -0.34533338 0.86469547 +8 +Properties=species:S:1:pos:R:3 A=35.11615 B=4.66117 C=4.22354 mu=1.7211 alpha=33.1 epsHOMO=-0.2595 epsLUMO=0.0277 epsgap=0.2872 =300.0993 zpve=0.060632 U0=-191.810916 U=-191.806025 H=-191.805081 G=-191.837634 Cv=15.855 pbc="F F F" +O -0.02013197 1.45738137 0.01010195 +C 0.00168478 0.03579980 0.00224736 +C -1.36366691 -0.47481101 0.01255056 +C -2.47902835 -0.91970202 0.02082451 +H 0.89446911 1.75736553 0.00327363 +H 0.51979028 -0.35175413 -0.88980302 +H 0.53670458 -0.36127661 0.88000894 +H -3.46773502 -1.30737009 0.02818120 +7 +Properties=species:S:1:pos:R:3 A=35.93172 B=4.83443 C=4.37827 mu=4.6788 alpha=28.56 epsHOMO=-0.3018 epsLUMO=0.0022 epsgap=0.3039 =280.6659 zpve=0.050262 U0=-207.916786 U=-207.912215 H=-207.911271 G=-207.943384 Cv=13.845 pbc="F F F" +O -0.03738221 1.44547841 0.01010769 +C 0.01292906 0.03441661 0.00209165 +C -1.35747452 -0.48874523 0.01239738 +N -2.42135277 -0.93685184 0.02016053 +H 0.86898057 1.76972949 0.00417577 +H 0.51692713 -0.36329790 -0.89215814 +H 0.53387616 -0.37278749 0.88223921 +10 +Properties=species:S:1:pos:R:3 A=26.14564 B=4.2893 C=4.12773 mu=2.6741 alpha=35.83 epsHOMO=-0.25 epsLUMO=-0.0205 epsgap=0.2295 =333.3276 zpve=0.084175 U0=-193.075202 U=-193.070116 H=-193.069171 G=-193.102798 Cv=15.954 pbc="F F F" +C 0.16879340 1.52513042 -0.15747056 +C -0.18737625 0.06194763 0.14679374 +C 0.50917645 -0.43996215 1.39125850 +O 1.18191079 -1.43565585 1.45816384 +H 1.23126511 1.63137254 -0.39632693 +H -0.40744668 1.89431681 -1.00969246 +H -0.04931034 2.17266260 0.69857432 +H -1.26905160 -0.01668067 0.32854433 +H 0.06279062 -0.60250479 -0.68474036 +H 0.35384841 0.20663370 2.28871052 +9 +Properties=species:S:1:pos:R:3 A=19.98749 B=6.11589 C=4.82584 mu=3.7071 alpha=32.78 epsHOMO=-0.2516 epsLUMO=0.0335 epsgap=0.2851 =279.7863 zpve=0.074166 U0=-209.144909 U=-209.139976 H=-209.139032 G=-209.172305 Cv=15.058 pbc="F F F" +C 0.01474916 1.47888256 -0.03509326 +N 0.18708077 0.03831317 -0.02906851 +C 0.45703661 -0.66858895 1.10069001 +O 0.58457996 -0.19876202 2.21119705 +H 0.76025289 1.96736104 -0.67198126 +H -0.98462423 1.75984483 -0.38485103 +H 0.14269849 1.82387718 0.99149397 +H 0.10386349 -0.46937726 -0.89461379 +H 0.55047568 -1.75253452 0.88868242 +8 +Properties=species:S:1:pos:R:3 A=48.62351 B=4.67298 C=4.38187 mu=3.92 alpha=29.47 epsHOMO=-0.2814 epsLUMO=0.0074 epsgap=0.2888 =293.917 zpve=0.061327 U0=-229.013797 U=-229.009003 H=-229.008059 G=-229.041086 Cv=13.885 pbc="F F F" +C 0.10653781 1.43512049 0.03336100 +O 0.42686564 0.04716929 0.09309945 +C 0.50714862 -0.63652665 -1.06318297 +O 0.76927014 -1.79955017 -1.11010475 +H 0.92349251 2.00385139 0.48465728 +H -0.03757815 1.76927183 -1.00057425 +H -0.81269569 1.60801916 0.59873287 +H 0.30458460 -0.00231902 -1.94965553 +8 +Properties=species:S:1:pos:R:3 A=36.49282 B=4.31175 C=4.04493 mu=1.7341 alpha=28.53 epsHOMO=-0.2537 epsLUMO=-0.0341 epsgap=0.2196 =303.8129 zpve=0.060508 U0=-228.992613 U=-228.987769 H=-228.986825 G=-229.019918 Cv=14.78 pbc="F F F" +O 0.19375856 1.37170995 0.31398297 +C 0.04992637 0.01405481 -0.04526239 +C -1.40888025 -0.39635680 -0.20601574 +O -1.77729462 -1.37233860 -0.80361689 +H -0.01723186 1.91269132 -0.45337758 +H 0.60440478 -0.26519766 -0.95205725 +H 0.46740406 -0.57521679 0.78176270 +H -2.11835055 0.30075962 0.29870515 +14 +Properties=species:S:1:pos:R:3 A=23.44662 B=3.60588 C=3.38683 mu=0 alpha=45.71 epsHOMO=-0.317 epsLUMO=0.0937 epsgap=0.4107 =426.2996 zpve=0.131708 U0=-158.340943 U=-158.33517 H=-158.334226 G=-158.36894 Cv=19.668 pbc="F F F" +C -0.03007624 1.55804912 0.02980774 +C 0.00707277 0.02834877 -0.00712529 +C 0.71320792 -0.52989340 -1.24765581 +C 0.75036895 -2.05959366 -1.28458957 +H 0.98195330 1.97845113 0.02800797 +H -0.55582433 1.96212343 -0.84270157 +H -0.54083762 1.92692755 0.92493697 +H 0.50928672 -0.34909049 0.89361319 +H -1.01733481 -0.36542299 0.03207393 +H 0.21098948 -0.15245770 -2.14839302 +H 1.73761331 -0.13611625 -1.28685672 +H 1.27616854 -2.46366410 -0.41210980 +H -0.26165699 -2.48000447 -1.28273552 +H 1.26108491 -2.42846444 -2.17974803 +12 +Properties=species:S:1:pos:R:3 A=26.59789 B=3.79186 C=3.53779 mu=1.3402 alpha=38.61 epsHOMO=-0.2619 epsLUMO=0.081 epsgap=0.3429 =382.8628 zpve=0.108241 U0=-194.261089 U=-194.255495 H=-194.254551 G=-194.28893 Cv=18.431 pbc="F F F" +C -0.00379012 1.52196790 0.01027996 +C -0.03589372 -0.00778254 0.00225671 +C -1.45219016 -0.56653711 0.01284386 +O -1.36799785 -1.98394604 0.00453606 +H 1.02477576 1.89411231 0.00239793 +H -0.51286674 1.93764614 -0.86653888 +H -0.49584080 1.92801344 0.90121721 +H 0.48140784 -0.39530552 -0.88316064 +H 0.49828589 -0.40486256 0.87330348 +H -1.98734220 -0.20399494 0.90666062 +H -2.00431485 -0.19459473 -0.86666460 +H -2.26171819 -2.33824298 0.01034748 +12 +Properties=species:S:1:pos:R:3 A=28.29567 B=4.14901 C=3.88349 mu=1.0363 alpha=39.34 epsHOMO=-0.2503 epsLUMO=0.0925 epsgap=0.3428 =368.9331 zpve=0.107895 U0=-194.254127 U=-194.248585 H=-194.247641 G=-194.281899 Cv=17.888 pbc="F F F" +C -0.00519551 1.50868224 0.03308760 +C 0.01316580 -0.01037819 0.01988643 +O 0.69758944 -0.46344140 1.16868979 +C 0.76688491 -1.86508183 1.24602669 +H 1.01445476 1.90423799 0.02996018 +H -0.52990866 1.89184059 -0.84726911 +H -0.51263765 1.87805843 0.92875424 +H -1.01599507 -0.40998729 0.00714282 +H 0.51093143 -0.38369435 -0.89219395 +H -0.23415841 -2.32451489 1.28977780 +H 1.30129276 -2.29853663 0.38486095 +H 1.30990287 -2.11811998 2.16013206 +10 +Properties=species:S:1:pos:R:3 A=14.79671 B=5.6956 C=4.58846 mu=0.0075 alpha=31.42 epsHOMO=-0.2594 epsLUMO=0.0584 epsgap=0.3179 =297.8398 zpve=0.085172 U0=-230.183076 U=-230.177723 H=-230.176779 G=-230.211195 Cv=16.837 pbc="F F F" +O -0.01418735 1.42642242 -0.05423651 +C -0.00391839 0.00717714 0.03496062 +C -1.42317019 -0.55560600 -0.01256639 +O -2.11229413 -0.10563875 -1.17228313 +H -0.18987882 1.64154705 -0.97555148 +H 0.59659504 -0.43315430 -0.77409095 +H 0.47311116 -0.24903733 0.98747216 +H -1.39494859 -1.64975279 -0.06283188 +H -1.96497417 -0.27216915 0.90131972 +H -2.35925991 0.80944396 -1.00614597 +12 +Properties=species:S:1:pos:R:3 A=15.61472 B=6.31363 C=5.54514 mu=0.1136 alpha=41.96 epsHOMO=-0.2727 epsLUMO=0.1012 epsgap=0.3738 =298.6061 zpve=0.109284 U0=-157.116735 U=-157.11209 H=-157.111146 G=-157.143262 Cv=16.49 pbc="F F F" +C -0.03602245 1.52092949 0.01569161 +C 0.01938487 0.01004403 0.04814953 +C 1.30891671 -0.71251995 -0.24920416 +C 0.75347423 -0.70267714 1.15564676 +H 0.90105691 1.95389291 0.38283338 +H -0.19640365 1.89437369 -1.00208569 +H -0.84685273 1.90588930 0.64451322 +H -0.88416809 -0.48001889 -0.30566335 +H 1.26900901 -1.63903068 -0.81150371 +H 2.19010738 -0.11131209 -0.44927513 +H 1.26248862 -0.09487455 1.89690028 +H 0.33540836 -1.62248918 1.54980695 +10 +Properties=species:S:1:pos:R:3 A=18.21931 B=6.63877 C=5.92459 mu=1.812 alpha=35.01 epsHOMO=-0.2633 epsLUMO=0.1052 epsgap=0.3685 =267.2979 zpve=0.085275 U0=-193.039603 U=-193.035186 H=-193.034242 G=-193.065979 Cv=14.764 pbc="F F F" +C -0.01646343 1.51830705 0.00771445 +C -0.02743227 0.01657694 -0.10889741 +C 0.77296553 -0.70131886 -1.10863115 +O 1.17212293 -0.66702969 0.26278829 +H 0.91865980 1.92914217 -0.38285880 +H -0.85158014 1.95456506 -0.55109460 +H -0.11487702 1.82596470 1.05397777 +H -0.93582598 -0.47350600 0.24682793 +H 0.44443922 -1.67201495 -1.47810622 +H 1.40538284 -0.13600339 -1.79271491 +11 +Properties=species:S:1:pos:R:3 A=16.58914 B=7.18798 C=6.11415 mu=1.1353 alpha=39.02 epsHOMO=-0.2304 epsLUMO=0.0968 epsgap=0.3271 =270.5508 zpve=0.097671 U0=-173.147782 U=-173.143343 H=-173.142399 G=-173.174073 Cv=15.298 pbc="F F F" +C -0.05275541 1.47419574 0.00999108 +N 0.01940516 0.02189947 0.06334866 +C 1.32511048 -0.56446850 -0.21420725 +C 0.78051759 -0.54057993 1.17242760 +H 0.86784202 1.97176076 0.36297285 +H -0.23845155 1.79095005 -1.02142866 +H -0.88683448 1.81938959 0.62950398 +H 1.31992909 -1.48186782 -0.79602038 +H 2.15202820 0.11020204 -0.43285248 +H 1.24563570 0.14996098 1.87499245 +H 0.37013358 -1.44020447 1.62233365 +10 +Properties=species:S:1:pos:R:3 A=16.79477 B=6.71015 C=5.86781 mu=1.3894 alpha=34.64 epsHOMO=-0.239 epsLUMO=0.0775 epsgap=0.3166 =263.966 zpve=0.085106 U0=-193.034988 U=-193.030356 H=-193.029411 G=-193.061689 Cv=15.679 pbc="F F F" +O 0.21577583 1.35386859 -0.10695107 +C 0.01628099 -0.02832718 -0.02915694 +C -0.39081465 -0.71497009 1.24315669 +C -1.35118138 -0.64280380 0.06273589 +H -0.43642338 1.79062849 0.45006761 +H 0.74981167 -0.52404050 -0.65624767 +H 0.05959375 -1.66840483 1.49729237 +H -0.61369353 -0.08848006 2.10170640 +H -2.19821932 0.03058430 0.15411451 +H -1.56395912 -1.54640426 -0.49826515 +12 +Properties=species:S:1:pos:R:3 A=10.66107 B=10.66107 C=6.30452 mu=0 alpha=41.83 epsHOMO=-0.2982 epsLUMO=0.0956 epsgap=0.3938 =268.4432 zpve=0.110511 U0=-157.115484 U=-157.111322 H=-157.110378 G=-157.141657 Cv=14.696 pbc="F F F" +C -0.02542957 1.54032578 -0.04215944 +C 1.52564399 1.50419989 0.04121276 +C 1.42146727 0.06146790 0.60844885 +C -0.02638833 -0.01070956 0.04935088 +H -0.46591372 1.99639894 0.84962894 +H -0.47900239 2.00028207 -0.92365389 +H 1.98248278 1.51375334 -0.95303183 +H 2.02191365 2.25986579 0.65522129 +H 2.14291679 -0.67956358 0.25531480 +H 1.43105853 0.05751479 1.70261879 +H -0.79053440 -0.48530296 0.66996573 +H -0.05233245 -0.47238446 -0.94236394 +10 +Properties=species:S:1:pos:R:3 A=12.06545 B=11.77731 C=6.71997 mu=1.7978 alpha=34.56 epsHOMO=-0.2424 epsLUMO=0.0859 epsgap=0.3283 =236.9759 zpve=0.086675 U0=-193.034094 U=-193.029968 H=-193.029024 G=-193.060777 Cv=12.915 pbc="F F F" +C -0.03489789 1.55259455 -0.08507691 +C 1.49349183 1.45459700 0.10114677 +O 1.42725884 0.02348990 0.27254886 +C -0.00634758 0.02236494 0.10944630 +H -0.56579210 2.11301721 0.68591527 +H -0.36628352 1.89393380 -1.06692327 +H 2.10031724 1.73525720 -0.76898349 +H 1.90023090 1.95506611 0.98896748 +H -0.51987849 -0.35595179 1.00235948 +H -0.31970062 -0.57567421 -0.75559275 +12 +Properties=species:S:1:pos:R:3 A=8.63483 B=4.11977 C=2.88825 mu=0.6875 alpha=45.37 epsHOMO=-0.2392 epsLUMO=0.0192 epsgap=0.2584 =452.5112 zpve=0.100501 U0=-248.375248 U=-248.368823 H=-248.367879 G=-248.405354 Cv=21.616 pbc="F F F" +C -0.10255370 1.49907127 -0.00133642 +C -0.00519774 -0.00343114 -0.02723470 +C -0.00688536 -0.76890740 1.26434744 +N 0.08186799 -0.69202791 -1.10087306 +O 0.07439067 0.13215394 -2.24195258 +H 0.78342093 1.94690862 -0.46354050 +H -0.96150430 1.83614082 -0.59021965 +H -0.19854173 1.87462989 1.01931607 +H -0.92620012 -0.57297946 1.82982479 +H 0.07256154 -1.84064782 1.07516863 +H 0.82937178 -0.45553340 1.90133089 +H 0.13975924 -0.51254299 -2.95389260 +10 +Properties=species:S:1:pos:R:3 A=9.17136 B=9.04195 C=4.5531 mu=1.8689 alpha=43.14 epsHOMO=-0.2029 epsLUMO=0.0499 epsgap=0.2528 =303.9808 zpve=0.082433 U0=-210.101789 U=-210.097816 H=-210.096872 G=-210.12818 Cv=14.821 pbc="F F F" +N -0.00829694 1.35362838 0.00995971 +C 1.28033254 1.82457421 -0.00022459 +C 2.12216639 0.73698898 -0.01445600 +C 1.30849904 -0.43109580 -0.01284154 +C -0.00342782 -0.01836884 0.00235736 +H -0.83243234 1.92772668 0.02122995 +H 1.48471820 2.88312630 0.00354313 +H 3.20066918 0.77343077 -0.02488416 +H 1.64810599 -1.45539683 -0.02180394 +H -0.92551647 -0.57697054 0.00839402 +9 +Properties=species:S:1:pos:R:3 A=9.77329 B=9.41192 C=4.7946 mu=3.6193 alpha=39.13 epsHOMO=-0.2253 epsLUMO=0.0332 epsgap=0.2585 =283.6817 zpve=0.071145 U0=-226.160842 U=-226.157088 H=-226.156144 G=-226.187104 Cv=13.371 pbc="F F F" +N -0.00996090 1.35636172 0.01000855 +C 1.29535422 1.79575578 -0.00047017 +C 2.05550519 0.65637438 -0.01422927 +N 1.25385745 -0.46275132 -0.01260894 +C 0.02301170 -0.00706382 0.00200146 +H -0.83195718 1.93618436 0.02132575 +H 1.54553961 2.84344998 0.00289054 +H 3.13177434 0.57569766 -0.02524155 +H -0.87667864 -0.60414531 0.00750794 +9 +Properties=species:S:1:pos:R:3 A=9.50228 B=9.29497 C=4.69874 mu=0.5571 alpha=39.2 epsHOMO=-0.2246 epsLUMO=0.0199 epsgap=0.2445 =289.005 zpve=0.069883 U0=-229.969129 U=-229.965414 H=-229.96447 G=-229.995393 Cv=13.358 pbc="F F F" +O -0.03255819 1.35065130 0.00967376 +C 1.25319237 1.79167710 -0.00030351 +C 2.11664681 0.74375623 -0.01441494 +C 1.30397796 -0.43852387 -0.01283316 +C 0.01629568 -0.00777185 0.00191294 +H 1.37951089 2.86161135 0.00427433 +H 3.19376406 0.79865501 -0.02458604 +H 1.63858893 -1.46383271 -0.02156949 +H -0.93736295 -0.50899925 0.00843891 +8 +Properties=species:S:1:pos:R:3 A=10.10926 B=9.69372 C=4.94857 mu=1.5081 alpha=35.17 epsHOMO=-0.2509 epsLUMO=0.001 epsgap=0.2519 =269.2444 zpve=0.058593 U0=-246.02915 U=-246.025614 H=-246.024669 G=-246.055309 Cv=12.04 pbc="F F F" +O -0.03808298 1.35643965 0.00975193 +C 1.26496982 1.76855319 -0.00061744 +C 2.04955750 0.66630033 -0.01406047 +N 1.24227075 -0.46732066 -0.01237541 +C 0.04019820 0.00561577 0.00183569 +H 1.43513907 2.83095265 0.00340823 +H 3.12532898 0.59783015 -0.02480757 +H -0.89387707 -0.53342154 0.00796935 +17 +Properties=species:S:1:pos:R:3 A=4.4178 B=4.41753 C=4.41717 mu=0.0003 alpha=56.01 epsHOMO=-0.3145 epsLUMO=0.0737 epsgap=0.3882 =486.2719 zpve=0.158836 U0=-197.632222 U=-197.625241 H=-197.624297 G=-197.661411 Cv=26.084 pbc="F F F" +C -0.01859029 1.53486266 0.01039904 +C 0.00210320 -0.00413672 0.00196980 +C 0.74643954 -0.51390760 1.24887638 +C -1.44190166 -0.53673615 0.01311457 +C 0.72234971 -0.50049606 -1.26445632 +H 0.99776928 1.94420529 0.00435474 +H -0.54181883 1.92841415 -0.86819911 +H -0.52758402 1.91888376 0.90148530 +H 0.25549576 -0.17602728 2.16833067 +H 0.77815389 -1.60892372 1.27088904 +H 1.77964786 -0.14975834 1.26944035 +H -1.99871895 -0.18888794 -0.86408066 +H -1.45956909 -1.63227235 0.00649916 +H -1.98088827 -0.19979639 0.90561580 +H 1.75482473 -0.13554018 -1.30118083 +H 0.75396989 -1.59524036 -1.29822280 +H 0.21358221 -0.15343900 -2.17067181 +15 +Properties=species:S:1:pos:R:3 A=4.69474 B=4.66938 C=4.47909 mu=1.384 alpha=49.04 epsHOMO=-0.2601 epsLUMO=0.0664 epsgap=0.3265 =449.0573 zpve=0.134977 U0=-233.560626 U=-233.553779 H=-233.552834 G=-233.589759 Cv=25.128 pbc="F F F" +C -0.00855999 1.54270147 0.00152717 +C 0.00506780 0.00702635 0.01856160 +C 0.76137731 -0.51839196 1.24766443 +C -1.42012774 -0.54933183 -0.02116008 +O 0.62590477 -0.48363745 -1.17657057 +H 1.01361406 1.94020776 0.00281128 +H -0.51459669 1.90702466 -0.89705781 +H -0.52214815 1.94882514 0.87918344 +H 0.28130613 -0.20165548 2.17943598 +H 0.79965506 -1.61131746 1.23022549 +H 1.79096916 -0.14072935 1.26083003 +H -1.93854829 -0.19910724 -0.91828281 +H -1.39937632 -1.64259062 -0.04563833 +H -1.98850725 -0.23059656 0.85726520 +H 1.53111188 -0.15488046 -1.19210222 +9 +Properties=species:S:1:pos:R:3 A=10.21876 B=4.04324 C=2.94978 mu=2.8579 alpha=42.02 epsHOMO=-0.2654 epsLUMO=-0.0575 epsgap=0.2079 =416.7799 zpve=0.065175 U0=-229.927277 U=-229.921622 H=-229.920677 G=-229.955843 Cv=18.723 pbc="F F F" +C -0.00693851 1.49559567 -0.01063358 +C -0.02986170 -0.01594623 0.06248673 +O -0.08815239 -0.63215062 1.10334812 +C 0.02336981 -0.70256747 -1.22062379 +C 0.06738132 -1.26058341 -2.28623129 +H 0.90429806 1.83281887 -0.51655384 +H -0.85338524 1.85556926 -0.60552819 +H -0.05237764 1.91099807 0.99695949 +H 0.10466868 -1.77036047 -3.21832617 +8 +Properties=species:S:1:pos:R:3 A=10.16063 B=4.15847 C=3.0056 mu=3.3351 alpha=37.08 epsHOMO=-0.3007 epsLUMO=-0.0889 epsgap=0.2118 =397.7757 zpve=0.05451 U0=-246.027383 U=-246.022024 H=-246.02108 G=-246.055769 Cv=16.963 pbc="F F F" +C -0.01171097 1.49973776 -0.01213268 +C -0.02919740 -0.00338745 0.07151218 +O -0.09164028 -0.64592927 1.08732241 +C 0.03831104 -0.69690258 -1.23748557 +N 0.09136286 -1.22982650 -2.26228068 +H 0.90093132 1.83370670 -0.51814928 +H -0.85663933 1.84864400 -0.61618135 +H -0.06416540 1.92131542 0.99203818 +8 +Properties=species:S:1:pos:R:3 A=10.68423 B=4.3198 C=3.07993 mu=5.1815 alpha=38.31 epsHOMO=-0.2739 epsLUMO=-0.0438 epsgap=0.2301 =384.5574 zpve=0.056619 U0=-242.19573 U=-242.190591 H=-242.189646 G=-242.223513 Cv=17.465 pbc="F F F" +N -0.05232108 1.34991769 0.15535538 +C 0.08549378 -0.01476951 0.02668890 +N 1.14555215 -0.71993906 -0.03966037 +C -1.19426556 -0.71152088 0.01802151 +N -2.22747610 -1.22790543 0.00132148 +H 0.75474469 1.91998737 -0.04411334 +H -0.94458527 1.76600473 -0.05932293 +H 1.98312429 -0.13415500 -0.02878551 +9 +Properties=species:S:1:pos:R:3 A=9.08854 B=4.42607 C=3.03169 mu=0.9461 alpha=36.51 epsHOMO=-0.2538 epsLUMO=-0.0964 epsgap=0.1574 =399.222 zpve=0.064992 U0=-267.10335 U=-267.097658 H=-267.096714 G=-267.132534 Cv=17.806 pbc="F F F" +C -0.43126066 1.30586664 -0.12758585 +C 0.25285715 -0.01026988 0.13138867 +O 0.69872099 -0.36890333 1.19604690 +C 0.38978943 -0.96773680 -1.06226208 +O -0.01611268 -0.71564293 -2.16583718 +H 0.10006565 1.85324490 -0.91332597 +H -1.44270433 1.13069361 -0.50923371 +H -0.46757554 1.89548158 0.78836479 +H 0.90329886 -1.91558845 -0.79531972 +9 +Properties=species:S:1:pos:R:3 A=10.08927 B=4.54115 C=3.13487 mu=2.7707 alpha=38.09 epsHOMO=-0.2561 epsLUMO=-0.064 epsgap=0.192 =384.4827 zpve=0.067668 U0=-263.278851 U=-263.273589 H=-263.272645 G=-263.306835 Cv=17.972 pbc="F F F" +N -0.16032929 1.08528962 -0.45398652 +C 0.18649127 -0.08762327 0.14758021 +N 1.28979627 -0.46427423 0.67927030 +C -0.94204384 -1.08440264 0.20423621 +O -2.01802205 -0.89007687 -0.30583149 +H 0.54574437 1.73837930 -0.74689246 +H -1.04684920 1.09570857 -0.93607796 +H 2.01738195 0.24991828 0.59881811 +H -0.67740148 -2.01003995 0.74746441 +13 +Properties=species:S:1:pos:R:3 A=7.92874 B=3.8272 C=2.82581 mu=0.6578 alpha=51.2 epsHOMO=-0.2589 epsLUMO=0.0571 epsgap=0.316 =481.9854 zpve=0.112471 U0=-195.186772 U=-195.180446 H=-195.179502 G=-195.215658 Cv=22.569 pbc="F F F" +C -0.01763434 1.54859468 -0.00253584 +C -0.01719180 0.00735789 0.03269604 +C 0.75818463 -0.52820155 1.25278862 +C -1.38213303 -0.51768438 0.00775516 +C -2.50796392 -0.94005629 0.00478552 +H 1.00944396 1.92639613 -0.02632830 +H -0.54514614 1.92217052 -0.88393459 +H -0.51243000 1.95339892 0.88560642 +H 0.49366179 -0.34904155 -0.87265890 +H 0.28968458 -0.19345372 2.18334679 +H 0.77833392 -1.62102506 1.25783727 +H 1.78952492 -0.16181727 1.23574954 +H -3.49998244 -1.31810165 -0.00723572 +12 +Properties=species:S:1:pos:R:3 A=7.90009 B=3.95466 C=2.88643 mu=3.9512 alpha=46.23 epsHOMO=-0.318 epsLUMO=0.0365 epsgap=0.3545 =460.694 zpve=0.102281 U0=-211.295796 U=-211.289821 H=-211.288877 G=-211.324525 Cv=20.467 pbc="F F F" +C -0.00242350 1.55035837 0.01056187 +C -0.01856600 0.01004352 -0.02261770 +C -1.45045318 -0.55766178 0.01293347 +C 0.70125749 -0.48583168 -1.20172764 +N 1.26190946 -0.87126766 -2.13619740 +H 1.01959496 1.93673215 0.00349668 +H -0.52972844 1.96141639 -0.85498386 +H -0.50134251 1.90346839 0.91755862 +H 0.52481877 -0.36222456 0.85525462 +H -2.02427903 -0.21428716 -0.85251869 +H -1.44438296 -1.65026927 0.00754107 +H -1.95805494 -0.21725882 0.91996760 +11 +Properties=species:S:1:pos:R:3 A=8.30095 B=3.97615 C=2.93764 mu=2.7429 alpha=42.87 epsHOMO=-0.2704 epsLUMO=0.0278 epsgap=0.2983 =440.0738 zpve=0.091554 U0=-227.338075 U=-227.332253 H=-227.331309 G=-227.366638 Cv=19.946 pbc="F F F" +C -0.01064295 1.54128691 0.00722087 +C -0.00203811 0.00756015 0.03863463 +N 0.76268677 -0.47101822 1.19250856 +C -1.38541954 -0.52099256 -0.00194097 +N -2.45680847 -0.95636072 0.00694969 +H 1.02043523 1.90106589 0.00392365 +H -0.52873161 1.91193452 -0.88031480 +H -0.51849536 1.94150431 0.89013197 +H 0.50418828 -0.35805500 -0.86276072 +H 0.30070681 -0.18364934 2.05155305 +H 0.78148992 -1.48670299 1.20428183 +11 +Properties=species:S:1:pos:R:3 A=8.55919 B=3.89952 C=2.91869 mu=1.3582 alpha=44.03 epsHOMO=-0.2665 epsLUMO=0.0336 epsgap=0.3001 =444.6452 zpve=0.088908 U0=-231.108368 U=-231.102292 H=-231.101348 G=-231.137061 Cv=21.306 pbc="F F F" +C -0.00754278 1.55566144 0.08650633 +C 0.01207541 0.02096553 0.04935916 +O -1.29150251 -0.52785128 0.22126386 +C 0.65093362 -0.47718651 -1.17430507 +C 1.13902673 -0.86577343 -2.20205518 +H 1.00824243 1.95969458 0.04929733 +H -0.56272245 1.95192010 -0.76978085 +H -0.49328161 1.88929438 1.00704123 +H 0.57811967 -0.34464907 0.91458864 +H -1.78516532 -0.35903262 -0.58909400 +H 1.58078877 -1.22365060 -3.09919447 +10 +Properties=species:S:1:pos:R:3 A=8.55908 B=4.01671 C=2.97882 mu=3.269 alpha=39.28 epsHOMO=-0.3051 epsLUMO=0.0115 epsgap=0.3166 =424.3395 zpve=0.078602 U0=-247.214861 U=-247.209162 H=-247.208218 G=-247.243338 Cv=19.252 pbc="F F F" +C -0.00992462 1.54951908 0.00732200 +C 0.00740485 0.01722020 0.05031270 +O 0.72085722 -0.49630479 1.15864583 +C -1.36995095 -0.52355929 0.00894432 +N -2.45081572 -0.93233138 0.00716912 +H 1.01892314 1.91630447 0.02724149 +H -0.50261490 1.90969362 -0.89997951 +H -0.54800426 1.95377611 0.87053664 +H 0.53602846 -0.36494195 -0.82970945 +H 0.27702643 -0.20644534 1.96421773 +13 +Properties=species:S:1:pos:R:3 A=7.66289 B=3.70925 C=2.79937 mu=2.6921 alpha=46.58 epsHOMO=-0.2469 epsLUMO=-0.0188 epsgap=0.2281 =482.0475 zpve=0.112328 U0=-232.364952 U=-232.358577 H=-232.357633 G=-232.394589 Cv=21.434 pbc="F F F" +C -0.02629647 1.53877116 -0.00783747 +C 0.01468904 0.00095310 0.00166037 +C 0.74737862 -0.53388859 1.24420471 +C -1.40211264 -0.53859257 -0.01557820 +O -1.87620455 -1.23582351 -0.87399559 +H 0.98823696 1.94625080 0.03705522 +H -0.50725090 1.92153422 -0.91285565 +H -0.57756514 1.92360883 0.85796386 +H 0.50584680 -0.36421091 -0.90619124 +H 0.22958380 -0.23909403 2.16431052 +H 0.81692736 -1.62559499 1.22973438 +H 1.76267153 -0.12846566 1.29050755 +H -2.00861510 -0.22792742 0.87121666 +11 +Properties=species:S:1:pos:R:3 A=8.31096 B=4.07963 C=2.92918 mu=2.8354 alpha=39.16 epsHOMO=-0.255 epsLUMO=-0.0279 epsgap=0.2271 =432.1489 zpve=0.088443 U0=-268.287661 U=-268.281505 H=-268.280561 G=-268.316982 Cv=20.344 pbc="F F F" +C -0.05483118 1.46032843 -0.17068647 +C 0.07105714 -0.03524485 0.11225841 +O 0.58204486 -0.33316656 1.40176256 +C -1.27639279 -0.74383027 0.03051185 +O -2.11855775 -0.49643175 -0.79163412 +H 0.93158072 1.93091398 -0.23353204 +H -0.57713995 1.61290939 -1.11835562 +H -0.62444315 1.94776119 0.62573670 +H 0.69687521 -0.50071620 -0.67159471 +H 1.45603316 0.06066659 1.47858584 +H -1.40723217 -1.53388627 0.80211181 +12 +Properties=species:S:1:pos:R:3 A=8.9331 B=4.19405 C=2.96046 mu=3.7163 alpha=44.42 epsHOMO=-0.2424 epsLUMO=0.0327 epsgap=0.2751 =441.85 zpve=0.10227 U0=-248.430371 U=-248.424309 H=-248.423365 G=-248.459383 Cv=19.918 pbc="F F F" +C -0.09776499 1.44917268 0.04561984 +N 0.01043053 0.00373818 -0.00070600 +C 0.05323028 -0.71432112 1.25264891 +C 0.06728716 -0.64067225 -1.20060778 +O 0.03636432 -0.11006515 -2.29109228 +H 0.75650660 1.88747109 0.57647421 +H -0.11648577 1.81632103 -0.98049172 +H -1.01641106 1.75521548 0.56151415 +H -0.85453556 -0.53657675 1.84390168 +H 0.13297722 -1.78696336 1.05851351 +H 0.91637348 -0.40737846 1.85761345 +H 0.14762240 -1.73801339 -1.06805139 +11 +Properties=species:S:1:pos:R:3 A=10.13982 B=3.81346 C=2.86965 mu=2.9514 alpha=39.33 epsHOMO=-0.2699 epsLUMO=-0.0262 epsgap=0.2437 =440.1727 zpve=0.088924 U0=-268.301176 U=-268.295084 H=-268.29414 G=-268.331307 Cv=19.824 pbc="F F F" +C -0.01708808 1.44281136 0.03225501 +C -0.05544542 -0.06560572 -0.02375999 +O -0.17379913 -0.76196233 0.96223129 +C 0.06068107 -0.73615179 -1.38825252 +O 0.00877771 -2.12574390 -1.27749486 +H 0.92062950 1.81463913 -0.39522215 +H -0.83050523 1.86542281 -0.56780350 +H -0.10911975 1.78049045 1.06493675 +H -0.75015067 -0.35235060 -2.03056074 +H 1.00312898 -0.40389029 -1.85592942 +H -0.09063255 -2.29450960 -0.32608019 +13 +Properties=species:S:1:pos:R:3 A=9.55216 B=3.57844 C=2.7341 mu=2.6168 alpha=46.19 epsHOMO=-0.2423 epsLUMO=-0.0072 epsgap=0.2351 =489.8518 zpve=0.112006 U0=-232.377706 U=-232.371073 H=-232.370129 G=-232.408256 Cv=21.716 pbc="F F F" +C -0.04384102 1.54099793 -0.03913814 +C 0.02765132 0.01885817 0.04778773 +C 0.76814644 -0.48842218 1.28000847 +C 0.87850208 -1.99746312 1.42393323 +O 1.24179142 0.26651340 2.09788702 +H 0.95796454 1.97690198 -0.06816012 +H -0.58447717 1.85356031 -0.93690369 +H -0.55173626 1.95737759 0.83440529 +H -0.97692824 -0.42709877 0.05353178 +H 0.51853486 -0.40806507 -0.83809409 +H 1.39924980 -2.42678188 0.56053177 +H 1.42001187 -2.24449367 2.33776426 +H -0.11750602 -2.45360651 1.45141965 +12 +Properties=species:S:1:pos:R:3 A=9.78802 B=3.78441 C=2.84416 mu=3.499 alpha=42.54 epsHOMO=-0.2438 epsLUMO=0.0355 epsgap=0.2793 =457.447 zpve=0.101847 U0=-248.448467 U=-248.441988 H=-248.441044 G=-248.478935 Cv=21.374 pbc="F F F" +C -0.00220144 1.53344329 -0.05960369 +C -0.03235837 0.01206167 0.06840265 +C 0.69666086 -0.48196462 1.31828322 +N 0.98104007 -1.82034162 1.31984698 +O 1.00392124 0.24606232 2.24136749 +H 1.02111190 1.89525169 -0.19182672 +H -0.59707204 1.86206427 -0.91645604 +H -0.39728839 1.99920712 0.84545201 +H -1.06936083 -0.34552935 0.13040806 +H 0.39947165 -0.46906818 -0.81777020 +H 0.63281951 -2.43917647 0.60874785 +H 1.35708014 -2.22217793 2.16303522 +12 +Properties=species:S:1:pos:R:3 A=10.01185 B=3.84195 C=2.87635 mu=3.5402 alpha=43.61 epsHOMO=-0.2418 epsLUMO=0.0387 epsgap=0.2805 =458.9803 zpve=0.101735 U0=-248.443503 U=-248.436899 H=-248.435955 G=-248.474272 Cv=20.967 pbc="F F F" +C 0.00619941 1.45933202 -0.03893859 +N -0.06411616 0.01422963 -0.12046901 +C -1.20595394 -0.68196796 0.16117120 +C -1.08982704 -2.19284899 0.02095775 +O -2.24482911 -0.14443077 0.49994418 +H 0.75053989 1.78372520 0.69715573 +H 0.25692001 1.90395263 -1.00876215 +H -0.97856026 1.81121438 0.27082194 +H 0.75820982 -0.49543660 -0.39464024 +H -0.09909703 -2.53543104 -0.28970003 +H -1.34097671 -2.65395851 0.97972885 +H -1.82903100 -2.53483935 -0.70813076 +11 +Properties=species:S:1:pos:R:3 A=10.44045 B=4.04692 C=2.98547 mu=3.5648 alpha=39.51 epsHOMO=-0.2436 epsLUMO=0.0599 epsgap=0.3034 =428.404 zpve=0.091713 U0=-264.504487 U=-264.498452 H=-264.497508 G=-264.533633 Cv=20.274 pbc="F F F" +C 0.00922630 1.45021814 -0.03194543 +N -0.01429488 -0.00054646 -0.01782667 +C -1.21576579 -0.67252403 0.04421896 +N -1.09733352 -2.04644898 -0.15997741 +O -2.28599299 -0.12893711 0.24196027 +H 0.89659038 1.81885022 0.49120949 +H 0.00233950 1.86388504 -1.04914155 +H -0.88562684 1.79943346 0.48353298 +H 0.75257305 -0.47506631 -0.46719265 +H -0.24882333 -2.48772194 0.16375654 +H -1.93419610 -2.53898393 0.11482880 +12 +Properties=species:S:1:pos:R:3 A=9.69683 B=4.1533 C=3.01703 mu=1.1876 alpha=43.57 epsHOMO=-0.2595 epsLUMO=0.0352 epsgap=0.2948 =443.1687 zpve=0.102062 U0=-248.416462 U=-248.410358 H=-248.409414 G=-248.445651 Cv=20.256 pbc="F F F" +C -0.09160695 1.37540046 -0.00729426 +O -0.06964534 -0.04831581 -0.11148812 +C 0.68114625 -0.56142673 -1.10732458 +C 0.60405298 -2.06701550 -1.08766892 +N 1.32645533 0.19266343 -1.89967731 +H 0.91463916 1.76843655 0.16257460 +H -0.48562826 1.82624615 -0.92235216 +H -0.73971430 1.60007423 0.84110605 +H -0.43430065 -2.39242162 -1.20325205 +H 1.20627110 -2.50049977 -1.88745640 +H 0.95651337 -2.44976670 -0.12501838 +H 1.83504115 -0.36104361 -2.58486055 +11 +Properties=species:S:1:pos:R:3 A=10.20392 B=4.16834 C=3.07281 mu=1.7569 alpha=39.33 epsHOMO=-0.2685 epsLUMO=0.0174 epsgap=0.2859 =427.6606 zpve=0.089436 U0=-268.32127 U=-268.315051 H=-268.314106 G=-268.351214 Cv=19.501 pbc="F F F" +C 0.07295254 1.38840919 0.06442046 +O 0.21049114 -0.03740028 0.04612736 +C 0.50102805 -0.57224288 -1.15975346 +C 0.62218715 -2.07378323 -1.06030684 +O 0.63922154 0.08224418 -2.16157325 +H 1.00029720 1.87062570 -0.25532064 +H -0.73338957 1.70782089 -0.60080454 +H -0.15751280 1.65154060 1.09692954 +H -0.31330001 -2.50202851 -0.68941906 +H 0.85646588 -2.48430275 -2.04157352 +H 1.40651685 -2.34055496 -0.34634301 +10 +Properties=species:S:1:pos:R:3 A=10.68182 B=4.39532 C=3.17969 mu=2.329 alpha=35.5 epsHOMO=-0.2669 epsLUMO=0.0575 epsgap=0.3244 =398.5908 zpve=0.079271 U0=-284.385189 U=-284.379361 H=-284.378417 G=-284.414085 Cv=19.084 pbc="F F F" +C -0.02645771 1.38305753 0.02085660 +O 0.04139566 -0.04439503 -0.00277452 +C 0.66775338 -0.56001625 -1.09000344 +N 0.72605063 -1.92219983 -0.98822954 +O 1.12952381 0.09648143 -1.99322180 +H 0.97529779 1.82044307 0.02387306 +H -0.57085698 1.76214621 -0.84790425 +H -0.55410722 1.63835731 0.94043594 +H 0.12662696 -2.38401590 -0.32566929 +H 0.97230070 -2.41798421 -1.82732573 +10 +Properties=species:S:1:pos:R:3 A=10.20696 B=3.93135 C=2.9219 mu=4.5676 alpha=35.87 epsHOMO=-0.246 epsLUMO=0.0273 epsgap=0.2733 =418.5967 zpve=0.078222 U0=-284.360325 U=-284.354148 H=-284.353204 G=-284.390143 Cv=20.089 pbc="F F F" +N -0.08115607 1.31807026 0.01276742 +C 0.05442259 -0.04477733 0.08155388 +O 1.12163074 -0.59783950 0.20940553 +C -1.28687786 -0.79011642 0.06178435 +O -1.04605114 -2.13954195 -0.23957158 +H 0.76623366 1.85991128 -0.04483425 +H -0.93021346 1.74567447 -0.31565615 +H -1.74559696 -0.65521334 1.05526700 +H -1.96026061 -0.31149222 -0.67026256 +H -1.85637296 -2.63017646 -0.07625478 +10 +Properties=species:S:1:pos:R:3 A=10.14389 B=4.0714 C=3.02223 mu=5.3004 alpha=35.19 epsHOMO=-0.2527 epsLUMO=0.0208 epsgap=0.2735 =408.0279 zpve=0.080317 U0=-284.372483 U=-284.367172 H=-284.366228 G=-284.40095 Cv=17.931 pbc="F F F" +N 0.01838100 0.93057357 0.04943058 +H -0.70674506 0.56116798 -0.55550134 +H 0.68248307 0.18430002 0.21487099 +H 0.21928084 2.79858558 -0.32670585 +C -0.54586914 1.44688585 1.30274586 +C -0.80466975 2.96030569 1.21052835 +O -0.21468822 3.53648884 0.15531687 +O -1.45295676 3.56338618 2.02143528 +H -1.47662579 0.96512183 1.62072169 +H 0.17781209 1.30324286 2.11157176 +13 +Properties=species:S:1:pos:R:3 A=6.6508 B=4.19317 C=3.39441 mu=2.2854 alpha=42.01 epsHOMO=-0.2597 epsLUMO=0.0631 epsgap=0.3228 =438.3028 zpve=0.11345 U0=-269.479234 U=-269.472993 H=-269.472049 G=-269.508213 Cv=22.072 pbc="F F F" +C -0.02775167 1.53421433 0.00651151 +C 0.08343246 0.01218277 0.05258986 +O 0.95856392 -0.48200710 -0.96694113 +C 0.69767574 -0.49516451 1.35948812 +O 2.02247062 -0.02842530 1.52222186 +H 0.94333763 1.98841434 0.21841589 +H -0.37320516 1.87354041 -0.97716908 +H -0.74773817 1.89256394 0.74926853 +H -0.91671848 -0.43697036 -0.06040528 +H 0.76726710 -0.01412177 -1.78502043 +H 0.11650002 -0.13351914 2.21392431 +H 0.65814685 -1.59563445 1.36274474 +H 2.46378566 -0.22762476 0.68698431 +17 +Properties=species:S:1:pos:R:3 A=7.26511 B=3.32874 C=2.54491 mu=0.0618 alpha=56.26 epsHOMO=-0.3085 epsLUMO=0.085 epsgap=0.3934 =565.8412 zpve=0.159632 U0=-197.629387 U=-197.622325 H=-197.621381 G=-197.659365 Cv=25.169 pbc="F F F" +C 0.05879035 1.56174169 -0.01945240 +C -0.01544024 0.03182306 0.00308332 +C -1.44103237 -0.54862028 0.05028629 +C -1.40777007 -2.07231030 -0.13012010 +C -2.18400529 -0.17659759 1.34114303 +H 1.09435638 1.90233607 -0.11900072 +H -0.50689566 1.97217319 -0.86388098 +H -0.34407951 2.00628082 0.89579522 +H 0.48926758 -0.36011214 -0.88953902 +H 0.55254938 -0.35139446 0.86269887 +H -2.00031575 -0.12153682 -0.79559684 +H -0.85501131 -2.55065810 0.68775438 +H -2.41787513 -2.49543628 -0.13720127 +H -0.91928762 -2.35363802 -1.06914014 +H -3.19100197 -0.60699559 1.35137051 +H -1.65197294 -0.56010584 2.22063481 +H -2.28793759 0.90575132 1.46079572 +15 +Properties=species:S:1:pos:R:3 A=8.11598 B=3.43678 C=2.66869 mu=1.3894 alpha=49.45 epsHOMO=-0.2617 epsLUMO=0.0733 epsgap=0.335 =521.8605 zpve=0.136091 U0=-233.555951 U=-233.549143 H=-233.548199 G=-233.585602 Cv=23.99 pbc="F F F" +C -0.03841227 1.55621396 0.00935837 +C 0.01907135 0.02716079 -0.02074203 +C 0.73141506 -0.52709328 -1.25454422 +C 0.83975868 -2.05206973 -1.22872655 +O -0.01226470 -0.08514394 -2.38973758 +H 0.96845664 1.98863424 0.03526234 +H -0.54377006 1.93824134 -0.88074407 +H -0.57652955 1.91383386 0.89256698 +H 0.53166867 -0.34991486 0.87240999 +H -0.99727186 -0.38638654 -0.00185289 +H 1.74909346 -0.09943025 -1.28826184 +H 1.34143968 -2.42487137 -2.12957624 +H 1.41822012 -2.39229108 -0.36340871 +H -0.15616765 -2.50443859 -1.18308875 +H 0.43090825 -0.40901616 -3.17979642 +15 +Properties=species:S:1:pos:R:3 A=7.55335 B=3.78323 C=2.80566 mu=1.0758 alpha=49.8 epsHOMO=-0.2476 epsLUMO=0.086 epsgap=0.3336 =507.9614 zpve=0.135681 U0=-233.545899 U=-233.539034 H=-233.53809 G=-233.57582 Cv=23.573 pbc="F F F" +C -0.00857605 1.38097833 0.09454670 +O -0.02255106 -0.00739461 -0.13062576 +C -1.30243341 -0.61997805 -0.02088309 +C -1.18687286 -1.97885751 -0.70303918 +C -1.75587824 -0.74662760 1.43714508 +H 1.00321550 1.73258087 -0.12443496 +H -0.71511468 1.90838275 -0.56674280 +H -0.24909065 1.64967237 1.13384681 +H -2.03737978 -0.00432241 -0.56884198 +H -0.44792779 -2.59945605 -0.18596870 +H -2.14751041 -2.50225525 -0.69102581 +H -0.86524212 -1.85860679 -1.74077836 +H -2.73321482 -1.23674908 1.49205117 +H -1.03621350 -1.34483828 2.00544671 +H -1.84898410 0.22935483 1.92198087 +15 +Properties=species:S:1:pos:R:3 A=6.14183 B=5.17679 C=3.79297 mu=0.1068 alpha=52.92 epsHOMO=-0.2633 epsLUMO=0.0893 epsgap=0.3526 =439.1643 zpve=0.137025 U0=-196.409349 U=-196.403398 H=-196.402453 G=-196.437676 Cv=22.258 pbc="F F F" +C -0.03913204 1.50456305 0.04978913 +C 0.00985856 -0.01113880 0.01248619 +C -1.24167981 -0.66663044 -0.53989979 +C 1.33803387 -0.66895748 -0.27400430 +C 0.75126442 -0.72221586 1.11862571 +H 0.88633040 1.92490040 0.45579358 +H -0.18183878 1.92045743 -0.95489291 +H -0.86832076 1.85815718 0.67439648 +H -1.43570890 -0.34344387 -1.56976411 +H -1.15326757 -1.75757800 -0.54433832 +H -2.12221416 -0.40570820 0.05951361 +H 1.34398374 -1.58235208 -0.86005084 +H 2.20196897 -0.03329084 -0.43929326 +H 1.21949576 -0.12246018 1.89246580 +H 0.36153319 -1.67153717 1.47171932 +13 +Properties=species:S:1:pos:R:3 A=6.49534 B=5.52329 C=3.9108 mu=1.8235 alpha=45.7 epsHOMO=-0.2596 epsLUMO=0.091 epsgap=0.3505 =405.521 zpve=0.112851 U0=-232.335768 U=-232.329981 H=-232.329037 G=-232.364091 Cv=20.719 pbc="F F F" +C -0.03970330 1.50954998 -0.04112233 +C -0.03023556 -0.00335329 -0.06498361 +C -1.24613189 -0.68356648 0.52531190 +C 0.78937201 -0.71129193 -1.06027559 +O 1.22546798 -0.60059036 0.29712283 +H 0.88992772 1.90948478 -0.45296558 +H -0.87952723 1.90350108 -0.62376642 +H -0.14448576 1.87590604 0.98604429 +H -2.15218727 -0.41012616 -0.02629495 +H -1.13412584 -1.77005431 0.49747032 +H -1.38586631 -0.38055453 1.56882757 +H 0.50712690 -1.71386874 -1.38085018 +H 1.37088104 -0.14369322 -1.78640853 +13 +Properties=species:S:1:pos:R:3 A=6.57648 B=5.31603 C=3.98453 mu=1.3822 alpha=45.85 epsHOMO=-0.2494 epsLUMO=0.0776 epsgap=0.327 =401.4888 zpve=0.113237 U0=-232.33341 U=-232.327734 H=-232.32679 G=-232.36147 Cv=21.17 pbc="F F F" +C -0.03714052 1.53215920 0.01332310 +C 0.01344335 0.02117019 0.01922005 +O -1.17476104 -0.59735284 -0.40931260 +C 1.30744798 -0.69902106 -0.27389650 +C 0.70844749 -0.71373504 1.12226318 +H 0.89026861 1.96240279 0.40276135 +H -0.17846151 1.91561262 -1.00518522 +H -0.87011232 1.88955201 0.62730953 +H -1.28352265 -0.40225391 -1.34690641 +H 1.23973813 -1.61642115 -0.84783774 +H 2.20543993 -0.11523820 -0.44744474 +H 1.19539159 -0.13889834 1.90240284 +H 0.24735439 -1.63670241 1.45378480 +10 +Properties=species:S:1:pos:R:3 A=12.18399 B=5.1384 C=3.78868 mu=2.5732 alpha=40.19 epsHOMO=-0.263 epsLUMO=0.0277 epsgap=0.2907 =352.0378 zpve=0.080558 U0=-247.201165 U=-247.196699 H=-247.195755 G=-247.22841 Cv=15.51 pbc="F F F" +N -0.04445152 1.25623533 0.31681915 +C -0.03914559 0.04913710 -0.01670558 +C -1.08749619 -1.04164446 -0.11488033 +C 0.09869688 -1.90754844 -0.58225894 +O 0.99647508 -0.76306629 -0.44028010 +H 0.88935980 1.66481350 0.24916893 +H -1.54324114 -1.32235812 0.83572172 +H -1.86910036 -0.85713839 -0.85325910 +H 0.39739753 -2.72227350 0.08158721 +H 0.06945080 -2.25418345 -1.61796084 +11 +Properties=species:S:1:pos:R:3 A=10.79571 B=4.78732 C=3.54292 mu=2.7119 alpha=42.77 epsHOMO=-0.2415 epsLUMO=-0.0194 epsgap=0.2222 =379.6371 zpve=0.090544 U0=-231.15578 U=-231.150804 H=-231.149859 G=-231.184401 Cv=17.076 pbc="F F F" +O -0.00101746 0.01846628 -0.20114742 +C -0.01405811 1.19658059 0.00205306 +C -1.13542911 2.22979148 0.21957980 +C -0.03500884 3.29097637 0.53052223 +C 1.08469664 2.25947795 0.18994331 +H -1.71642814 2.41772662 -0.68901078 +H -1.83392757 1.98415059 1.02444244 +H -0.05534741 4.17585264 -0.10670963 +H -0.02541477 3.61399048 1.57278917 +H 1.81074389 2.03288534 0.97578973 +H 1.63600801 2.46255380 -0.73376244 +10 +Properties=species:S:1:pos:R:3 A=12.23436 B=4.97895 C=3.70561 mu=3.6671 alpha=39.73 epsHOMO=-0.25 epsLUMO=0.038 epsgap=0.288 =355.0934 zpve=0.080442 U0=-247.225618 U=-247.220897 H=-247.219953 G=-247.253218 Cv=16.1 pbc="F F F" +O 0.03184134 -0.03461682 0.00849836 +C -0.02440334 1.16440533 0.02160811 +C -1.14541322 2.23712809 0.03012901 +C 0.00928805 3.27945878 0.04557758 +N 0.93843838 2.14652109 0.03554396 +H -1.77030529 2.23779730 -0.86428437 +H -1.77632809 2.21765386 0.92007934 +H 0.07170564 3.91863516 -0.84072193 +H 0.06622603 3.89826625 0.94657952 +H 1.94616840 2.09535713 0.03855945 +9 +Properties=species:S:1:pos:R:3 A=12.44643 B=5.23328 C=3.86599 mu=3.9339 alpha=35.4 epsHOMO=-0.2788 epsLUMO=0.0089 epsgap=0.2878 =336.0792 zpve=0.068574 U0=-267.106213 U=-267.101929 H=-267.100985 G=-267.13332 Cv=14.483 pbc="F F F" +O 0.04424004 0.03933243 -0.33071104 +C -0.04854479 1.17416230 0.00410076 +C -1.14211656 2.23611850 0.15338361 +C 0.02332538 3.12357010 0.61681625 +O 0.95663808 2.01190122 0.42603746 +H -1.62573755 2.51482212 -0.78401102 +H -1.90009213 1.99553386 0.90028663 +H 0.28368077 3.96172751 -0.03229287 +H 0.00782250 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"name": "Atoms.get_positions" + } + ], + "frame_ahead_trev": [ + { + "is_class": false, + "is_explicit": false, + "module": "ase.atoms", + "module_short": "ase", + "name": "Atoms" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Atoms" + } + ], + "frame_ahead_trev.get_momenta": [ + { + "is_class": false, + "is_explicit": false, + "module": "ase.atoms", + "module_short": "ase", + "name": "Atoms.get_momenta" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Atoms.get_momenta" + } + ], + "frame_ahead_trev.set_momenta": [ + { + "is_class": false, + "is_explicit": false, + "module": "ase.atoms", + "module_short": "ase", + "name": "Atoms.set_momenta" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Atoms.set_momenta" + } + ], + "frame_now": [ + { + "is_class": false, + "is_explicit": false, + "module": "ase.atoms", + "module_short": "ase", + "name": "Atoms" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Atoms" + } + ], + "frame_now_trev": [ + { + "is_class": false, + "is_explicit": false, + "module": "ase.atoms", + "module_short": "ase", + "name": "Atoms" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Atoms" + } + ], + "frame_now_trev.get_momenta": [ + { + "is_class": false, + "is_explicit": false, + "module": "ase.atoms", + "module_short": "ase", + "name": "Atoms.get_momenta" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Atoms.get_momenta" + } + ], + "frame_now_trev.set_momenta": [ + { + "is_class": false, + "is_explicit": false, + "module": "ase.atoms", + "module_short": "ase", + "name": "Atoms.set_momenta" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Atoms.set_momenta" + } + ], + "get_structure_for_dataset": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + } + ], + "i": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "int" + } + ], + "s": [ + { + "is_class": false, + "is_explicit": false, + "module": "ase.atoms", + "module_short": "ase", + "name": "Atoms" + }, + { + "is_class": false, + "is_explicit": false, + "module": "ase", + "module_short": "ase", + "name": "Atoms" + } + ], + "s.arrays": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "dict" + } + ], + "spacing": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "int" + } + ], + "store_trajectory": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + } + ], + "structures_for_dataset": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "list" + } + ], + "structures_for_dataset.append": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "builtin_function_or_method" + } + ], + "subprocess.run": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "subprocess", + "module_short": "subprocess", + "name": "run" + } + ], + "time_lag": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "int" + } + ], + "trajectory": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "list" + } + ], + "trajectory.append": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "builtin_function_or_method" + } + ] +} diff --git a/docs/src/examples/programmatic/flashmd/flashmd.ipynb b/docs/src/examples/programmatic/flashmd/flashmd.ipynb new file mode 100644 index 0000000000..6333cb2726 --- /dev/null +++ b/docs/src/examples/programmatic/flashmd/flashmd.ipynb @@ -0,0 +1,97 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Training a FlashMD model\n\nThis tutorial demonstrates how to train a FlashMD model for the direct prediction\nof molecular dynamics. This type of model affords faster MD simulations compared to\nMLIPs by a factor between 10 and 30 (https://arxiv.org/abs/2505.19350).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import copy\nimport subprocess\n\nimport ase\nimport ase.build\nimport ase.io\nimport ase.units\nfrom ase.calculators.emt import EMT\nfrom ase.md import VelocityVerlet\nfrom ase.md.langevin import Langevin\nfrom ase.md.velocitydistribution import MaxwellBoltzmannDistribution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data generation\n\nFlashMD models train on molecular dynamics trajectories in the NVE ensemble (i.e.,\nmost often with the velocity Verlet integrator). These trajectories can be generated\nwith almost any MD code (i-PI, LAMMPS, etc.). Here, for simplicity, we will use ASE\nand its built-in EMT potential. In reality, you might want to use a more accurate\nbaseline such as ab initio MD or a machine-learned interatomic potential (MLIP).\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# We start by creating a simple system (a small box of aluminum).\natoms = ase.build.bulk(\"Al\", \"fcc\", cubic=True) * (2, 2, 2)\n\n# We first equilibrate the system at 300K using a Langevin thermostat.\nMaxwellBoltzmannDistribution(atoms, temperature_K=300)\natoms.calc = EMT()\ndyn = Langevin(\n atoms, 2 * ase.units.fs, temperature_K=300, friction=1 / (100 * ase.units.fs)\n)\ndyn.run(1000) # 2 ps equilibration (around 10 ps is better in practice)\n\n# Then, we run a production simulation in the NVE ensemble.\ntrajectory = []\n\n\ndef store_trajectory():\n trajectory.append(copy.deepcopy(atoms))\n\n\ndyn = VelocityVerlet(atoms, 1 * ase.units.fs)\ndyn.attach(store_trajectory, interval=1)\ndyn.run(2000) # 2 ps NVE run" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data preparation\n\nNow, we need to generate the training data from the trajectory. FlashMD models\nrequire future positions and momenta as targets. We will save them in an `.xyz`\nfile under the `future_positions` and `future_momenta` keys.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# The FlashMD model will be trained to predict 30 steps into the future, i.e., 30 fs\n# since we ran the reference simulation with a time step of 1 fs. For this type\n# of system, FlashMD is expected to perform well up to around 60-80 fs.\ntime_lag = 30\n\n# We pick starting structures that are 200 steps apart. To avoid wasting training\n# structures, this should be set to be around the expected velocity-velocity\n# autocorrelation time for the system. This is the time scale that quantifies how long\n# it takes for the system to forget its original velocities.\nspacing = 200\n\n\ndef get_structure_for_dataset(frame_now, frame_ahead):\n s = copy.deepcopy(frame_now)\n s.arrays[\"future_positions\"] = frame_ahead.get_positions()\n s.arrays[\"future_momenta\"] = frame_ahead.get_momenta()\n return s\n\n\nstructures_for_dataset = []\nfor i in range(0, len(trajectory) - time_lag, spacing):\n frame_now = trajectory[i]\n frame_ahead = trajectory[i + time_lag]\n s = get_structure_for_dataset(frame_now, frame_ahead)\n structures_for_dataset.append(s)\n\n # Here, we also add the time-reversed pair (optional)\n frame_now_trev = copy.deepcopy(frame_now)\n frame_ahead_trev = copy.deepcopy(frame_ahead)\n frame_now_trev.set_momenta(-frame_now_trev.get_momenta())\n frame_ahead_trev.set_momenta(-frame_ahead_trev.get_momenta())\n s = get_structure_for_dataset(frame_ahead_trev, frame_now_trev)\n structures_for_dataset.append(s)\n\n# Write the structures to an xyz file\nase.io.write(\"flashmd.xyz\", structures_for_dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training the model\n\nThe dataset is now ready for training. You can now provide it to ``metatrain`` and\ntrain your FlashMD model!\n\nFor example, you can use the following options file:\n\n.. literalinclude:: options.yaml\n :language: yaml\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "subprocess.run([\"mtt\", \"train\", \"options.yaml\"])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/docs/src/examples/programmatic/flashmd/flashmd.py b/docs/src/examples/programmatic/flashmd/flashmd.py new file mode 100644 index 0000000000..970b523723 --- /dev/null +++ b/docs/src/examples/programmatic/flashmd/flashmd.py @@ -0,0 +1,120 @@ +""" +Training a FlashMD model +======================== + +This tutorial demonstrates how to train a FlashMD model for the direct prediction +of molecular dynamics. This type of model affords faster MD simulations compared to +MLIPs by a factor between 10 and 30 (https://arxiv.org/abs/2505.19350). +""" + +# %% +# + +import copy +import subprocess + +import ase +import ase.build +import ase.io +import ase.units +from ase.calculators.emt import EMT +from ase.md import VelocityVerlet +from ase.md.langevin import Langevin +from ase.md.velocitydistribution import MaxwellBoltzmannDistribution + + +# %% +# +# Data generation +# --------------- +# +# FlashMD models train on molecular dynamics trajectories in the NVE ensemble (i.e., +# most often with the velocity Verlet integrator). These trajectories can be generated +# with almost any MD code (i-PI, LAMMPS, etc.). Here, for simplicity, we will use ASE +# and its built-in EMT potential. In reality, you might want to use a more accurate +# baseline such as ab initio MD or a machine-learned interatomic potential (MLIP). + +# We start by creating a simple system (a small box of aluminum). +atoms = ase.build.bulk("Al", "fcc", cubic=True) * (2, 2, 2) + +# We first equilibrate the system at 300K using a Langevin thermostat. +MaxwellBoltzmannDistribution(atoms, temperature_K=300) +atoms.calc = EMT() +dyn = Langevin( + atoms, 2 * ase.units.fs, temperature_K=300, friction=1 / (100 * ase.units.fs) +) +dyn.run(1000) # 2 ps equilibration (around 10 ps is better in practice) + +# Then, we run a production simulation in the NVE ensemble. +trajectory = [] + + +def store_trajectory(): + trajectory.append(copy.deepcopy(atoms)) + + +dyn = VelocityVerlet(atoms, 1 * ase.units.fs) +dyn.attach(store_trajectory, interval=1) +dyn.run(2000) # 2 ps NVE run + + +# %% +# +# Data preparation +# ---------------- +# +# Now, we need to generate the training data from the trajectory. FlashMD models +# require future positions and momenta as targets. We will save them in an `.xyz` +# file under the `future_positions` and `future_momenta` keys. + +# The FlashMD model will be trained to predict 30 steps into the future, i.e., 30 fs +# since we ran the reference simulation with a time step of 1 fs. For this type +# of system, FlashMD is expected to perform well up to around 60-80 fs. +time_lag = 30 + +# We pick starting structures that are 200 steps apart. To avoid wasting training +# structures, this should be set to be around the expected velocity-velocity +# autocorrelation time for the system. This is the time scale that quantifies how long +# it takes for the system to forget its original velocities. +spacing = 200 + + +def get_structure_for_dataset(frame_now, frame_ahead): + s = copy.deepcopy(frame_now) + s.arrays["future_positions"] = frame_ahead.get_positions() + s.arrays["future_momenta"] = frame_ahead.get_momenta() + return s + + +structures_for_dataset = [] +for i in range(0, len(trajectory) - time_lag, spacing): + frame_now = trajectory[i] + frame_ahead = trajectory[i + time_lag] + s = get_structure_for_dataset(frame_now, frame_ahead) + structures_for_dataset.append(s) + + # Here, we also add the time-reversed pair (optional) + frame_now_trev = copy.deepcopy(frame_now) + frame_ahead_trev = copy.deepcopy(frame_ahead) + frame_now_trev.set_momenta(-frame_now_trev.get_momenta()) + frame_ahead_trev.set_momenta(-frame_ahead_trev.get_momenta()) + s = get_structure_for_dataset(frame_ahead_trev, frame_now_trev) + structures_for_dataset.append(s) + +# Write the structures to an xyz file +ase.io.write("flashmd.xyz", structures_for_dataset) + +# %% +# +# Training the model +# ------------------ +# +# The dataset is now ready for training. You can now provide it to ``metatrain`` and +# train your FlashMD model! +# +# For example, you can use the following options file: +# +# .. literalinclude:: options-flashmd.yaml +# :language: yaml + +subprocess.run(["mtt", "train", "options-flashmd.yaml"]) diff --git a/docs/src/examples/programmatic/flashmd/flashmd.py.md5 b/docs/src/examples/programmatic/flashmd/flashmd.py.md5 new file mode 100644 index 0000000000..09379633c1 --- /dev/null +++ b/docs/src/examples/programmatic/flashmd/flashmd.py.md5 @@ -0,0 +1 @@ +dc5c04be3dfe900edcf26c73a0ad6880 \ No newline at end of file diff --git a/docs/src/examples/programmatic/flashmd/flashmd.rst b/docs/src/examples/programmatic/flashmd/flashmd.rst new file mode 100644 index 0000000000..1e80ddd319 --- /dev/null +++ b/docs/src/examples/programmatic/flashmd/flashmd.rst @@ -0,0 +1,225 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "examples/programmatic/flashmd/flashmd.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code. + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_examples_programmatic_flashmd_flashmd.py: + + +Training a FlashMD model +======================== + +This tutorial demonstrates how to train a FlashMD model for the direct prediction +of molecular dynamics. This type of model affords faster MD simulations compared to +MLIPs by a factor between 10 and 30 (https://arxiv.org/abs/2505.19350). + +.. GENERATED FROM PYTHON SOURCE LINES 12-26 + +.. code-block:: Python + + + import copy + import subprocess + + import ase + import ase.build + import ase.io + import ase.units + from ase.calculators.emt import EMT + from ase.md import VelocityVerlet + from ase.md.langevin import Langevin + from ase.md.velocitydistribution import MaxwellBoltzmannDistribution + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 27-35 + +Data generation +--------------- + +FlashMD models train on molecular dynamics trajectories in the NVE ensemble (i.e., +most often with the velocity Verlet integrator). These trajectories can be generated +with almost any MD code (i-PI, LAMMPS, etc.). Here, for simplicity, we will use ASE +and its built-in EMT potential. In reality, you might want to use a more accurate +baseline such as ab initio MD or a machine-learned interatomic potential (MLIP). + +.. GENERATED FROM PYTHON SOURCE LINES 36-61 + +.. code-block:: Python + + + # We start by creating a simple system (a small box of aluminum). + atoms = ase.build.bulk("Al", "fcc", cubic=True) * (2, 2, 2) + + # We first equilibrate the system at 300K using a Langevin thermostat. + MaxwellBoltzmannDistribution(atoms, temperature_K=300) + atoms.calc = EMT() + dyn = Langevin( + atoms, 2 * ase.units.fs, temperature_K=300, friction=1 / (100 * ase.units.fs) + ) + dyn.run(1000) # 2 ps equilibration (around 10 ps is better in practice) + + # Then, we run a production simulation in the NVE ensemble. + trajectory = [] + + + def store_trajectory(): + trajectory.append(copy.deepcopy(atoms)) + + + dyn = VelocityVerlet(atoms, 1 * ase.units.fs) + dyn.attach(store_trajectory, interval=1) + dyn.run(2000) # 2 ps NVE run + + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + + True + + + +.. GENERATED FROM PYTHON SOURCE LINES 62-68 + +Data preparation +---------------- + +Now, we need to generate the training data from the trajectory. FlashMD models +require future positions and momenta as targets. We will save them in an `.xyz` +file under the `future_positions` and `future_momenta` keys. + +.. GENERATED FROM PYTHON SOURCE LINES 69-107 + +.. code-block:: Python + + + # The FlashMD model will be trained to predict 30 steps into the future, i.e., 30 fs + # since we ran the reference simulation with a time step of 1 fs. For this type + # of system, FlashMD is expected to perform well up to around 60-80 fs. + time_lag = 30 + + # We pick starting structures that are 200 steps apart. To avoid wasting training + # structures, this should be set to be around the expected velocity-velocity + # autocorrelation time for the system. This is the time scale that quantifies how long + # it takes for the system to forget its original velocities. + spacing = 200 + + + def get_structure_for_dataset(frame_now, frame_ahead): + s = copy.deepcopy(frame_now) + s.arrays["future_positions"] = frame_ahead.get_positions() + s.arrays["future_momenta"] = frame_ahead.get_momenta() + return s + + + structures_for_dataset = [] + for i in range(0, len(trajectory) - time_lag, spacing): + frame_now = trajectory[i] + frame_ahead = trajectory[i + time_lag] + s = get_structure_for_dataset(frame_now, frame_ahead) + structures_for_dataset.append(s) + + # Here, we also add the time-reversed pair (optional) + frame_now_trev = copy.deepcopy(frame_now) + frame_ahead_trev = copy.deepcopy(frame_ahead) + frame_now_trev.set_momenta(-frame_now_trev.get_momenta()) + frame_ahead_trev.set_momenta(-frame_ahead_trev.get_momenta()) + s = get_structure_for_dataset(frame_ahead_trev, frame_now_trev) + structures_for_dataset.append(s) + + # Write the structures to an xyz file + ase.io.write("flashmd.xyz", structures_for_dataset) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 108-118 + +Training the model +------------------ + +The dataset is now ready for training. You can now provide it to ``metatrain`` and +train your FlashMD model! + +For example, you can use the following options file: + +.. literalinclude:: options.yaml + :language: yaml + +.. GENERATED FROM PYTHON SOURCE LINES 119-121 + +.. code-block:: Python + + + subprocess.run(["mtt", "train", "options.yaml"]) + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + + CompletedProcess(args=['mtt', 'train', 'options.yaml'], returncode=0) + + + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** (0 minutes 16.440 seconds) + + +.. _sphx_glr_download_examples_programmatic_flashmd_flashmd.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: flashmd.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: flashmd.py ` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: flashmd.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/programmatic/flashmd/flashmd.xyz b/docs/src/examples/programmatic/flashmd/flashmd.xyz new file mode 100644 index 0000000000..7a9bb6e0ee --- /dev/null +++ b/docs/src/examples/programmatic/flashmd/flashmd.xyz @@ -0,0 +1,680 @@ +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=0.8948308788199846 free_energy=0.8948308788199846 stress="0.00582921114852916 0.000442369304973728 0.0011759981285591327 0.000442369304973728 0.004847050505392154 1.8183467986477127e-06 0.0011759981285591327 1.8183467986477127e-06 0.007675243103858247" pbc="T T T" +Al 0.16464029 0.00846807 0.08508833 -0.51435652 -1.29123021 -0.40577065 0.06123463 -0.13595049 0.07731856 -1.12368820 -1.14173022 0.32159375 0.02624888 -0.43303143 -0.16939770 0.17687361 +Al 0.04333576 2.12405991 2.14636843 0.32093878 -0.17299006 1.71382084 0.06562479 2.10857776 2.28688450 -0.03325533 -0.03348663 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-0.70948079 1.25639048 0.10789133 6.02790682 2.05765944 4.11081737 -0.39377453 1.39654577 0.50055116 0.02507895 -0.05684998 0.34628181 0.09833994 +Al 4.15353887 3.98323517 -0.00693228 0.07234339 -0.91889267 0.42736080 4.13333436 3.92650157 0.06146214 -0.32182670 -0.09386815 0.87097930 0.03166976 -0.23582388 0.21337205 0.08628749 +Al 4.13836479 6.07840665 1.93039247 -0.65108121 0.29673392 0.30647594 4.03241715 6.12659693 1.99383823 -1.21564110 0.47432752 0.76998567 0.02564584 -0.23677724 0.16515317 0.23204498 +Al 5.91356098 4.05332772 2.30407210 -0.11207184 -0.57732050 -0.43473224 6.00009513 3.99166004 2.18735879 1.52404557 -0.43452393 -1.67790947 0.04651277 0.65974572 -0.07548183 -0.39155185 +Al 6.04952230 6.03040117 0.00447251 -0.69510041 0.14983012 0.70220847 6.03184281 6.04602950 0.10751122 0.23359967 0.03041771 1.11755336 0.02607780 0.41601265 0.07073372 0.19255014 +Al 3.90492926 4.15165660 4.00534018 1.40576279 -0.78672061 0.03995367 4.05583797 4.06899126 4.06876345 1.18275263 -0.50712983 0.81502571 0.05153768 0.15003627 -0.14429056 0.51414408 +Al 4.07173410 6.18254887 6.19658769 -0.49226230 1.23361909 0.07352391 4.00532367 6.28118217 6.18870997 -0.47037602 0.49815600 -0.18710539 0.00176712 -0.22482859 -0.16464236 -0.11230117 +Al 6.02614131 4.07344579 5.98266586 -0.63584207 0.56407956 0.72323350 5.95129296 4.14512639 6.05337693 -0.64565669 0.77230350 0.62910140 -0.01167201 -0.08023362 0.05346028 -0.05969545 +Al 6.11856558 6.19884837 3.97257857 0.28150990 -0.47468254 -1.39776206 6.11209593 6.11582640 3.89617986 -0.39056651 -0.79178237 0.04705835 0.06848149 -0.20059260 -0.37868432 0.36157672 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=0.9775593220180188 free_energy=0.9775593220180188 stress="0.00561896524110683 0.00010955929300027058 0.0005917793502733804 0.00010955929300027058 0.004925564335948191 0.0001104654741423019 0.0005917793502733804 0.0001104654741423019 0.00593663920646172" pbc="T T T" +Al 0.06123463 -0.13595049 0.07731856 1.12368820 1.14173022 -0.32159375 0.16464029 0.00846807 0.08508833 0.51435652 1.29123021 0.40577065 0.02598423 0.06720061 0.25551433 0.28941604 +Al 0.06562479 2.10857776 2.28688450 0.03325533 0.03348663 -0.72445134 0.04333576 2.12405991 2.14636843 -0.32093878 0.17299006 -1.71382084 0.02256491 -0.22565110 0.11432577 -0.45364058 +Al 2.10411598 0.19875347 2.10572830 1.45828161 0.77836419 1.04256479 2.21922479 0.20052938 2.21911130 0.44642369 -0.68326702 0.86115870 0.03250304 -0.12232050 -0.53465264 0.10968291 +Al 2.07343613 2.03409454 0.21257465 -0.87860336 -0.18665112 -1.78096908 1.96155048 2.02748026 -0.04177685 -0.96270926 0.14683668 -2.36933351 0.03670059 -0.20199496 0.03991207 -0.65029019 +Al 0.06765827 0.10043852 4.11442519 0.00327411 0.12717917 1.13769447 0.05568839 0.05574736 4.18671791 -0.06245496 -0.81230955 0.20747444 0.03628151 -0.17114302 -0.41222499 -0.27086310 +Al -0.09891252 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0.57732050 0.43473224 0.03399237 0.33102920 0.15723171 -0.33119674 +Al 6.03184281 6.04602950 0.10751122 -0.23359967 -0.03041771 -1.11755336 6.04952230 6.03040117 0.00447251 0.69510041 -0.14983012 -0.70220847 0.00997002 0.14757047 -0.13376238 0.05685800 +Al 4.05583797 4.06899126 4.06876345 -1.18275263 0.50712983 -0.81502571 3.90492926 4.15165660 4.00534018 -1.40576279 0.78672061 -0.03995367 0.02750048 -0.21361793 0.30143009 -0.08016059 +Al 4.00532367 6.28118217 6.18870997 0.47037602 -0.49815600 0.18710539 4.07173410 6.18254887 6.19658769 0.49226230 -1.23361909 -0.07352391 0.02729147 0.27109491 -0.31810102 -0.05656443 +Al 5.95129296 4.14512639 6.05337693 0.64565669 -0.77230350 -0.62910140 6.02614131 4.07344579 5.98266586 0.63584207 -0.56407956 -0.72323350 0.01327066 0.09816123 0.09290604 0.05317744 +Al 6.11209593 6.11582640 3.89617986 0.39056651 0.79178237 -0.04705835 6.11856558 6.19884837 3.97257857 -0.28150990 0.47468254 1.39776206 0.02946727 -0.18687223 0.12201556 0.46460257 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=0.8431874507022643 free_energy=0.8431874507022643 stress="0.006244031443369388 0.0007165788141183207 -0.0001627289267088679 0.0007165788141183207 0.005467715397826384 0.00047222160456977303 -0.0001627289267088679 0.00047222160456977303 0.0065578157468693605" pbc="T T T" +Al 0.00264000 0.07844353 -0.12467518 -0.24833940 -0.96183864 0.49485711 -0.00613836 -0.01402459 -0.02917609 0.11464281 -0.61415357 1.04937503 0.02354378 0.07510104 -0.00840324 0.37378042 +Al 0.01973625 1.94453284 2.07781540 -1.14858181 0.43868461 0.26323310 -0.06034934 1.99671851 2.07559403 -0.06503116 0.39052762 -0.24704382 0.01148113 0.05369455 0.09880419 -0.20381966 +Al 2.12606659 -0.00311886 2.19972208 0.41302172 -0.56223045 -0.78333704 2.16448074 -0.04161635 2.08799427 0.27800815 0.11297422 -1.05153068 0.01817628 -0.04086683 -0.06843728 -0.30666921 +Al 2.08365382 1.99498282 0.09681029 0.06882021 0.63561182 0.23612025 2.06579896 2.08443873 0.08890775 -0.32866700 0.89063479 -0.32704702 0.00011097 -0.18451322 0.18586768 -0.22949960 +Al 0.12624551 0.03061316 4.28957870 -0.65159606 0.30279168 -0.14250778 0.01688464 0.02731380 4.25777021 -1.11169736 -0.18773383 -0.34981465 0.02738317 -0.41340684 -0.31632109 -0.16491883 +Al -0.12317083 1.98438986 6.08615311 -0.68033828 -1.02831320 0.81904355 -0.13183977 1.92300711 6.19473968 0.38176425 0.04409683 1.08051663 0.07402757 0.51180957 0.15316222 0.16612647 +Al 2.21175225 -0.05970562 6.17702698 1.71592151 -0.01437753 0.71618332 2.33255833 -0.03585943 6.20959721 0.16274206 0.29935664 -0.22485442 0.01697825 -0.15226446 0.25002542 -0.16014115 +Al 1.89525038 2.00666748 4.05656774 -0.19076047 1.70309258 0.77182852 1.93014534 2.15094689 4.16742316 0.67201897 0.56694128 1.13024239 0.03364249 0.44639722 0.06190913 0.26114939 +Al 0.06324146 4.13002584 0.21584986 -2.29720152 -0.73838022 0.28813726 -0.15219333 4.02591028 0.20765365 -1.23671285 -1.01266081 -0.62514988 0.01526475 -0.04390983 -0.24796852 -0.14860606 +Al -0.03568176 6.33257459 2.11146462 -0.42398127 1.76229272 0.33760784 -0.07002781 6.45271076 2.14810086 -0.14533080 0.17006376 0.39452288 -0.00237100 0.05110291 -0.25026777 -0.04388833 +Al 2.10471123 4.07299382 2.11509901 0.47122512 0.43019454 0.05486540 2.11829867 4.14627541 2.10388427 -0.29998206 0.77682088 -0.38295811 0.01438037 -0.14629987 0.20813554 0.00577124 +Al 1.97633693 6.14574144 0.04358988 -1.05480691 0.28916459 1.55234288 1.90811728 6.14999876 0.22260918 -0.17152022 -0.35306556 1.41560132 0.01932936 0.25472308 -0.06090382 0.23382698 +Al 0.05576764 4.07998668 4.01400335 0.57940655 -0.69956036 -0.77452503 0.08668458 4.01869212 3.92835072 -0.17503553 -0.25442022 -0.69464186 0.01526375 -0.04964464 -0.03080400 -0.05355566 +Al 0.02355291 5.97583815 6.21389775 1.29498290 -1.18774745 -1.11923094 0.15227294 5.91293447 6.07765261 0.83971435 0.19996515 -1.17915732 0.03685764 0.07105344 0.27335028 -0.22054127 +Al 2.03002194 4.09589495 5.96428476 -1.40614172 0.07190351 0.68383253 1.89204490 4.05702037 6.10583172 -0.78217195 -0.76552063 1.83558429 0.02500748 -0.18326424 -0.28465803 0.43550242 +Al 2.10294141 6.13517588 4.16052368 0.37528798 0.06546951 -1.32171616 2.12907650 6.12170770 4.01699769 0.07558778 -0.25067742 -1.03910889 0.02080381 -0.08017251 -0.15558951 -0.18798561 +Al 4.05154633 0.04180795 0.09587695 0.65312298 -0.02520858 -1.44851649 4.11018101 -0.00892605 -0.00370261 0.47925477 -0.82387382 -0.06336589 0.03791563 -0.11142811 -0.33346680 0.10579317 +Al 3.99463169 2.11178591 2.08298854 -0.76972578 0.43303785 1.45166045 3.92031111 2.12118979 2.21012431 -0.46042768 -0.30497609 0.66557543 0.04547036 -0.02623818 -0.15264150 0.00397004 +Al 6.06752037 0.16677762 2.13683865 0.78676613 0.52003149 -0.55703345 6.12683194 0.12611308 2.05526672 0.04974244 -1.11123483 -0.86262203 0.05797723 0.02567701 -0.60535123 -0.15552347 +Al 5.86666543 1.98359002 0.13390663 0.14656181 0.34355113 1.00219990 5.94749354 2.06926988 0.20344323 1.06654883 1.03497871 0.17235924 0.03670554 0.54268980 0.34223721 -0.17161289 +Al 4.16577409 0.00121745 4.10194437 1.27315080 0.00074655 -0.12617867 4.26403075 -0.00106685 4.08963455 0.44919701 -0.14322398 -0.23043618 0.01149277 -0.17588287 0.06046008 0.09255009 +Al 4.07582143 2.12841289 6.08245812 1.38235068 -2.27850567 -0.84858267 4.19026486 1.88563097 6.03156973 0.57677380 -1.58734033 -0.07971653 0.02925887 -0.11131245 -0.33460233 0.22692870 +Al 5.98796219 0.06005076 6.14967933 -0.30947349 0.38719007 1.15895734 5.98875373 0.07194356 6.22854943 0.59269769 -0.40128101 0.30465130 0.02411334 0.04497396 -0.01868090 -0.27046677 +Al 6.06100384 2.05139366 3.94813839 -0.49745219 -0.04888184 -0.80333862 6.06592325 2.06154953 3.94251448 0.55104434 0.29164259 0.63815221 0.05209771 0.33987961 0.02698787 0.48412612 +Al 3.98653190 4.16133100 -0.02674004 0.73984761 0.38361114 -0.54089747 4.07643916 4.17447757 -0.05548456 0.74131189 -0.16140478 -0.03102748 0.01030857 0.16089545 -0.14489250 0.22158812 +Al 4.20417461 5.98448795 2.23233022 -0.20346446 -0.31251847 0.04086883 4.17099734 5.99067130 2.19698932 -0.23762055 0.42943188 -0.68889399 0.00680334 -0.18787453 0.23188721 -0.23712622 +Al 6.10462315 4.08037294 2.06163275 0.38894279 0.29950384 -1.10535613 6.12924789 4.11675117 1.96135721 -0.17956907 0.39845727 -0.28799727 0.01783917 0.05689690 -0.00826779 -0.15578421 +Al 6.09358588 6.26490740 -0.03882978 0.91781049 -1.07153163 0.44265129 6.17717319 6.10261802 0.04548933 0.55468604 -1.63616333 0.85314067 0.02017304 -0.05987567 -0.42047706 0.36560357 +Al 4.10680770 3.82744588 4.14546261 -0.81027804 -0.44715914 0.36296012 4.02050430 3.89795647 4.17643776 -0.67074170 1.63304057 0.32819991 0.06887453 -0.05253364 0.72242265 -0.15967413 +Al 4.11682929 5.93532890 6.08322235 0.81699115 0.44743862 -0.34601489 4.19368248 6.01081999 6.04143619 0.35039176 0.86499219 -0.32412669 0.02209869 0.09430284 0.22566179 -0.06844949 +Al 6.30009733 3.91190767 6.18755949 -1.39862320 0.96217439 -1.09305327 6.09853461 4.03300185 6.08106214 -1.90717822 0.93747738 -0.80260399 0.04093503 -0.55378817 0.34079773 0.04585371 +Al 6.19758956 5.96463840 4.22287004 0.06655417 -0.10023746 0.33293893 6.18804712 5.99231814 4.22843248 -0.16444078 0.56632849 -0.37582460 0.01124382 -0.15592133 0.26002436 -0.08430789 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.199462930854991 free_energy=1.199462930854991 stress="0.004767208885128757 0.0018557943876572078 0.0006883010676196985 0.0018557943876572078 0.0037451877241799847 0.00010112084390507357 0.0006883010676196985 0.00010112084390507357 0.005853968447125383" pbc="T T T" +Al -0.00613836 -0.01402459 -0.02917609 -0.11464281 0.61415357 -1.04937503 0.00264000 0.07844353 -0.12467518 0.24833940 0.96183864 -0.49485711 0.03349547 0.13631418 0.22371231 -0.02665473 +Al -0.06034934 1.99671851 2.07559403 0.06503116 -0.39052762 0.24704382 0.01973625 1.94453284 2.07781540 1.14858181 -0.43868461 -0.26323310 0.03069566 0.54953215 -0.15485292 -0.09515438 +Al 2.16448074 -0.04161635 2.08799427 -0.27800815 -0.11297422 1.05153068 2.12606659 -0.00311886 2.19972208 -0.41302172 0.56223045 0.78333704 0.02903386 -0.07183026 0.43753749 0.11822710 +Al 2.06579896 2.08443873 0.08890775 0.32866700 -0.89063479 0.32704702 2.08365382 1.99498282 0.09681029 -0.06882021 -0.63561182 -0.23612025 -0.01261383 -0.05041681 -0.04377495 -0.12021375 +Al 0.01688464 0.02731380 4.25777021 1.11169736 0.18773383 0.34981465 0.12624551 0.03061316 4.28957870 0.65159606 -0.30279168 0.14250778 0.00959253 0.06685513 0.02610194 0.01406195 +Al -0.13183977 1.92300711 6.19473968 -0.38176425 -0.04409683 -1.08051663 -0.12317083 1.98438986 6.08615311 0.68033828 1.02831320 -0.81904355 0.02594094 0.22847308 0.43171417 -0.01594792 +Al 2.33255833 -0.03585943 6.20959721 -0.16274206 -0.29935664 0.22485442 2.21175225 -0.05970562 6.17702698 -1.71592151 0.01437753 -0.71618332 0.04715044 -0.80048541 -0.04283806 -0.36059252 +Al 1.93014534 2.15094689 4.16742316 -0.67201897 -0.56694128 -1.13024239 1.89525038 2.00666748 4.05656774 0.19076047 -1.70309258 -0.77182852 0.04547979 0.13494897 -0.66677570 -0.00006346 +Al -0.15219333 4.02591028 0.20765365 1.23671285 1.01266081 0.62514988 0.06324146 4.13002584 0.21584986 2.29720152 0.73838022 -0.28813726 0.05785902 0.75702091 0.06974274 -0.48877325 +Al -0.07002781 6.45271076 2.14810086 0.14533080 -0.17006376 -0.39452288 -0.03568176 6.33257459 2.11146462 0.42398127 -1.76229272 -0.33760784 0.06474253 0.17077224 -0.77802528 0.07261592 +Al 2.11829867 4.14627541 2.10388427 0.29998206 -0.77682088 0.38295811 2.10471123 4.07299382 2.11509901 -0.47122512 -0.43019454 -0.05486540 0.04325769 -0.29954835 -0.06494235 -0.23070870 +Al 1.90811728 6.14999876 0.22260918 0.17152022 0.35306556 -1.41560132 1.97633693 6.14574144 0.04358988 1.05480691 -0.28916459 -1.55234288 0.03400526 0.30380031 -0.33563679 -0.38137659 +Al 0.08668458 4.01869212 3.92835072 0.17503553 0.25442022 0.69464186 0.05576764 4.07998668 4.01400335 -0.57940655 0.69956036 0.77452503 0.03395830 -0.35545700 0.29317011 0.14656180 +Al 0.15227294 5.91293447 6.07765261 -0.83971435 -0.19996515 1.17915732 0.02355291 5.97583815 6.21389775 -1.29498290 1.18774745 1.11923094 0.05380499 -0.35515339 0.58618643 0.16363494 +Al 1.89204490 4.05702037 6.10583172 0.78217195 0.76552063 -1.83558429 2.03002194 4.09589495 5.96428476 1.40614172 -0.07190351 -0.68383253 0.05655014 0.48087682 -0.23330014 0.28346975 +Al 2.12907650 6.12170770 4.01699769 -0.07558778 0.25067742 1.03910889 2.10294141 6.13517588 4.16052368 -0.37528798 -0.06546951 1.32171616 0.01383164 -0.13423586 -0.02847313 0.33897934 +Al 4.11018101 -0.00892605 -0.00370261 -0.47925477 0.82387382 0.06336589 4.05154633 0.04180795 0.09587695 -0.65312298 0.02520858 1.44851649 0.05512610 -0.01100678 -0.17680588 0.69708319 +Al 3.92031111 2.12118979 2.21012431 0.46042768 0.30497609 -0.66557543 3.99463169 2.11178591 2.08298854 0.76972578 -0.43303785 -1.45166045 0.03966986 0.23246647 -0.23272805 -0.41295966 +Al 6.12683194 0.12611308 2.05526672 -0.04974244 1.11123483 0.86262203 6.06752037 0.16677762 2.13683865 -0.78676613 -0.52003149 0.55703345 0.06854007 -0.45842272 -0.30669064 -0.01343308 +Al 5.94749354 2.06926988 0.20344323 -1.06654883 -1.03497871 -0.17235924 5.86666543 1.98359002 0.13390663 -0.14656181 -0.34355113 -1.00219990 0.02575345 0.02044889 -0.02948428 -0.36560040 +Al 4.26403075 -0.00106685 4.08963455 -0.44919701 0.14322398 0.23043618 4.16577409 0.00121745 4.10194437 -1.27315080 -0.00074655 0.12617867 0.02971316 -0.33221050 -0.12872405 -0.15561704 +Al 4.19026486 1.88563097 6.03156973 -0.57677380 1.58734033 0.07971653 4.07582143 2.12841289 6.08245812 -1.38235068 2.27850567 0.84858267 0.07937888 -0.38169238 0.78551687 0.25347125 +Al 5.98875373 0.07194356 6.22854943 -0.59269769 0.40128101 -0.30465130 5.98796219 0.06005076 6.14967933 0.30947349 -0.38719007 -1.15895734 0.08191988 0.55016627 -0.46022891 -0.23925496 +Al 6.06592325 2.06154953 3.94251448 -0.55104434 -0.29164259 -0.63815221 6.06100384 2.05139366 3.94813839 0.49745219 0.04888184 0.80333862 0.03121993 0.27105175 0.13738104 0.36978353 +Al 4.07643916 4.17447757 -0.05548456 -0.74131189 0.16140478 0.03102748 3.98653190 4.16133100 -0.02674004 -0.73984761 -0.38361114 0.54089747 0.00022805 -0.16847597 -0.18531003 0.13206767 +Al 4.17099734 5.99067130 2.19698932 0.23762055 -0.42943188 0.68889399 4.20417461 5.98448795 2.23233022 0.20346446 0.31251847 -0.04086883 0.01073897 0.13320509 0.24022310 -0.23658737 +Al 6.12924789 4.11675117 1.96135721 0.17956907 -0.39845727 0.28799727 6.10462315 4.08037294 2.06163275 -0.38894279 -0.29950384 1.10535613 0.07379302 -0.39731176 0.04396371 0.70195297 +Al 6.17717319 6.10261802 0.04548933 -0.55468604 1.63616333 -0.85314067 6.09358588 6.26490740 -0.03882978 -0.91781049 1.07153163 -0.44265129 0.01294625 -0.17323240 0.10112039 -0.12388794 +Al 4.02050430 3.89795647 4.17643776 0.67074170 -1.63304057 -0.32819991 4.10680770 3.82744588 4.14546261 0.81027804 0.44715914 -0.36296012 0.04311384 0.13335415 0.51719125 0.05994098 +Al 4.19368248 6.01081999 6.04143619 -0.35039176 -0.86499219 0.32412669 4.11682929 5.93532890 6.08322235 -0.81699115 -0.44743862 0.34601489 0.04040675 -0.38230370 0.08651462 0.12154124 +Al 6.09853461 4.03300185 6.08106214 1.90717822 -0.93747738 0.80260399 6.30009733 3.91190767 6.18755949 1.39862320 -0.96217439 1.09305327 0.01528697 0.21304517 -0.28664949 0.15362757 +Al 6.18804712 5.99231814 4.22843248 0.16444078 -0.56632849 0.37582460 6.19758956 5.96463840 4.22287004 -0.06655417 0.10023746 -0.33293893 0.02484329 -0.01054830 0.17516447 -0.36019346 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=0.9065094691624092 free_energy=0.9065094691624092 stress="0.006507009423339605 0.0024200002914994603 -0.00022075970102456615 0.0024200002914994603 0.003678820038033757 0.0003131131401096385 -0.00022075970102456615 0.0003131131401096385 0.007312699442367703" pbc="T T T" +Al -0.05687315 -0.03388876 0.16287718 -0.62415401 0.48575283 -0.55836291 -0.09751901 0.03599860 0.09321066 -0.12394847 0.73523479 -0.62019847 0.01112616 0.16295799 0.13968044 -0.12882471 +Al -0.12234110 2.19231320 2.15387766 1.00861367 -1.57941151 0.66276345 0.01139616 1.96556019 2.19882218 1.19138721 -2.21416778 -0.02033057 0.03917191 0.35088781 -0.57624461 -0.01860402 +Al 2.09633081 -0.05920609 2.03506729 -0.21253948 0.87178322 -1.02489539 2.03574680 0.05555292 1.96041217 -0.75263694 0.89100467 -0.28676913 0.02203356 -0.28844433 0.33445779 0.17295760 +Al 1.92195757 2.08566514 0.09981783 -0.92722952 1.07135520 0.46979408 1.92061339 2.10189245 0.14542741 0.70217813 -0.55943463 0.26313512 0.05592186 0.63441651 -0.62812231 0.01798893 +Al -0.00741120 -0.03077141 4.04497130 -0.44031493 -0.32289174 1.47291583 -0.03049963 -0.05056105 4.19982037 0.11424348 -0.12324487 1.23577439 -0.00389129 0.08429529 0.14616256 0.05357573 +Al 0.06992523 2.04324862 6.11008585 -0.26161740 -0.57231641 -0.29198561 0.03932700 1.98467390 6.03866854 -0.21793746 -0.35217998 -0.75598771 0.03158290 -0.06447048 -0.05904760 -0.35260513 +Al 1.96694875 0.08177180 6.08827943 1.53776900 -0.23958866 -0.09184192 2.11906023 0.03117454 6.13345218 1.10025815 -0.70347641 0.86064749 0.01614244 0.01636473 -0.13935295 0.32015827 +Al 2.08259697 2.05876291 3.99972874 -0.71842131 -0.82714834 0.00275744 2.00973480 1.97622512 4.02010251 -0.48526775 -0.46694493 0.35788291 0.01335781 -0.05040502 -0.13893131 0.16019511 +Al 0.20340214 3.89848819 -0.04075709 -0.25118538 0.02070701 -0.38765410 0.09294391 4.03739570 -0.04416142 -1.63262323 2.16328731 0.41349061 0.09654046 -0.50035612 0.94298176 0.16323541 +Al 0.07689771 5.95830737 2.05970779 0.11743480 1.13969252 1.04743038 0.10625812 6.11052427 2.14867226 0.36136407 1.64129812 0.42440703 0.01154623 0.12392107 0.15510911 -0.02683190 +Al 2.19147068 3.95328790 2.10796172 -0.00514515 1.84588262 -0.70971788 2.16131747 4.15904964 2.03922631 -0.50961952 1.63928809 -0.41946845 0.02905612 -0.22188394 0.22307029 -0.06689831 +Al 2.03339315 6.16149670 0.04281063 0.52403967 -0.58476155 0.40192600 2.11644421 6.07746229 0.05635386 0.90003300 -0.72050319 -0.08069261 0.00581559 0.21754649 -0.25349589 -0.22580373 +Al 0.10294054 4.02601996 4.12311921 -1.36287959 0.33238141 -1.97616984 -0.06872219 4.07655930 3.97710345 -1.49994028 0.48060013 -0.63148491 0.02093027 -0.31100270 0.15198661 0.28935017 +Al -0.10075637 6.07907040 6.25758904 -0.71819774 1.03251371 0.50502251 -0.16586567 6.17207637 6.29087578 -0.33258390 0.53377023 0.07704100 -0.01041834 0.00855072 -0.01225639 -0.11767073 +Al 2.18238812 4.10976764 6.06159169 -0.71634565 -0.34096018 1.71826176 2.08821832 4.07619989 6.22921551 -1.02299598 -0.27678490 1.08482611 0.01440522 -0.08110527 0.00289577 0.09799612 +Al 1.95471271 6.07099717 4.20016064 0.75539272 -1.48482873 0.13063489 2.05265912 5.92594709 4.20224038 0.91757975 -1.07261277 0.00018278 0.02004944 0.17276229 0.02533458 -0.12964673 +Al 4.14520964 0.09029539 0.03096883 0.29927440 -0.41128145 0.24919694 4.12053850 0.05927389 0.02285378 -0.65678862 -0.16807727 -0.34884643 0.02387441 -0.36354715 0.05905165 -0.19197670 +Al 4.19095976 1.82325787 2.09487272 -0.44549341 -0.59107529 -0.26642180 4.11967879 1.85218666 2.05326459 -0.79192471 1.11950872 -0.41421409 0.03170229 -0.16143128 0.52091574 -0.12674294 +Al 6.10398953 -0.00041724 2.11202646 -1.21671586 -0.70773067 0.45664155 6.00074920 -0.06527471 2.19013045 -0.49063111 -0.32781240 0.85265293 0.04838621 0.02988905 -0.02665872 0.19671820 +Al 6.28235358 2.07527407 -0.03202213 -0.36282641 1.18302989 0.09069705 6.17846980 2.17374566 0.02863224 -1.34417167 0.41372027 1.02766412 0.03038438 -0.49310962 -0.03787292 0.26705480 +Al 3.95149447 0.02423752 4.27168672 0.02698226 -0.88889052 -0.35920435 3.99165084 -0.06423077 4.17176729 0.70901358 -0.55830115 -1.24128662 0.03865897 0.17595498 -0.06817318 -0.46785640 +Al 4.02852893 2.12907013 6.09892033 -0.58939947 0.32605456 -0.05051467 3.96240436 2.12229634 6.11590360 -0.46660715 -0.46612718 0.21238287 0.02675293 -0.11126647 -0.19710196 0.24401900 +Al 6.04747231 0.11921411 6.12828967 0.11896527 -0.01256561 0.14624619 6.07684964 0.09564256 6.14706748 0.15923475 -0.26946916 0.11189882 0.02629113 0.26802832 -0.25192606 0.08901300 +Al 5.98145593 1.97409250 4.18293057 1.06899451 1.18135986 -1.09935286 6.15374823 2.07830632 4.06222184 1.79928918 0.49655379 -0.99841919 0.04114691 0.49166094 0.06508637 -0.11671418 +Al 4.12069553 4.01088427 0.09583877 -0.10679640 -0.08411900 -0.57593156 4.08884167 4.01005571 0.06664839 -0.45825209 0.11176405 0.05765539 0.00141192 -0.12361624 0.01410260 0.14955568 +Al 3.91385716 6.07753167 2.15237661 1.47169633 0.34783071 1.20608420 4.08951615 6.12905060 2.27741939 1.40640378 0.54343716 0.89411612 0.02683878 0.31858513 0.07829785 0.05659930 +Al 6.23662182 4.14496687 2.09678581 -0.61185756 1.69440233 -1.32144143 6.13771079 4.27103656 1.92537324 -0.91341194 0.34631586 -1.61980826 0.05937909 -0.43106324 -0.11741197 -0.21663190 +Al 5.90523809 6.19847867 0.25338484 -0.45080405 0.48458824 0.63436761 5.92213432 6.19010676 0.23478642 0.72199407 -0.46862759 -1.06309747 0.05178327 0.38838352 -0.40836912 -0.42988073 +Al 4.29741270 4.00473024 3.96935420 1.84337850 -0.79392536 -0.34554586 4.38331856 3.95080738 3.95984944 -0.37118994 -0.18458946 0.17716336 0.04375586 -0.55020238 0.17184611 0.17495914 +Al 4.20614660 6.12307351 6.04186950 0.67167299 -0.92593602 -0.32961301 4.21571980 6.01739599 6.05861261 -0.37489047 -0.93957132 0.52387035 0.03886869 -0.44187052 -0.07436257 0.36902865 +Al 5.97036097 4.09193150 6.18669339 -0.33576846 -0.97460702 0.34933105 6.02210565 3.99831477 6.20417538 1.21472514 -0.62595645 -0.09808774 0.02905907 0.51335127 -0.02178934 -0.07992779 +Al 6.06685097 6.13854115 4.10618547 0.91347767 -0.67529603 -0.15541771 6.18968122 6.06604804 4.08890238 1.14771696 -0.61790175 0.02390026 0.01484524 0.23621866 -0.01986234 -0.12578925 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=0.8936910426021347 free_energy=0.8936910426021347 stress="0.006456601417350244 0.001245362542856234 0.000314848374501027 0.001245362542856234 0.004203273121418416 0.0004897180596170072 0.000314848374501027 0.0004897180596170072 0.006303993845666421" pbc="T T T" +Al -0.09751901 0.03599860 0.09321066 0.12394847 -0.73523479 0.62019847 -0.05687315 -0.03388876 0.16287718 0.62415401 -0.48575283 0.55836291 0.00654396 0.15740654 0.01938202 0.05850025 +Al 0.01139616 1.96556019 2.19882218 -1.19138721 2.21416778 0.02033057 -0.12234110 2.19231320 2.15387766 -1.00861367 1.57941151 -0.66276345 0.03104177 -0.16760058 0.15216819 -0.37220376 +Al 2.03574680 0.05555292 1.96041217 0.75263694 -0.89100467 0.28676913 2.09633081 -0.05920609 2.03506729 0.21253948 -0.87178322 1.02489539 0.02657263 0.00907289 -0.33967957 0.29360387 +Al 1.92061339 2.10189245 0.14542741 -0.70217813 0.55943463 -0.26313512 1.92195757 2.08566514 0.09981783 0.92722952 -1.07135520 -0.46979408 0.02608954 0.27583605 -0.24057319 -0.18386389 +Al -0.03049963 -0.05056105 4.19982037 -0.11424348 0.12324487 -1.23577439 -0.00741120 -0.03077141 4.04497130 0.44031493 0.32289174 -1.47291583 0.02126788 0.27900379 -0.02032248 -0.20400825 +Al 0.03932700 1.98467390 6.03866854 0.21793746 0.35217998 0.75598771 0.06992523 2.04324862 6.11008585 0.26161740 0.57231641 0.29198561 0.00833124 0.09559553 0.22599691 0.14420947 +Al 2.11906023 0.03117454 6.13345218 -1.10025815 0.70347641 -0.86064749 1.96694875 0.08177180 6.08827943 -1.53776900 0.23958866 0.09184192 0.01002900 -0.28461997 -0.16785895 0.20314519 +Al 2.00973480 1.97622512 4.02010251 0.48526775 0.46694493 -0.35788291 2.08259697 2.05876291 3.99972874 0.71842131 0.82714834 -0.00275744 0.01197306 0.20906580 0.29939441 0.12684890 +Al 0.09294391 4.03739570 -0.04416142 1.63262323 -2.16328731 -0.41349061 0.20340214 3.89848819 -0.04075709 0.25118538 -0.02070701 0.38765410 0.04140552 -0.24556934 0.27879586 0.34258475 +Al 0.10625812 6.11052427 2.14867226 -0.36136407 -1.64129812 -0.42440703 0.07689771 5.95830737 2.05970779 -0.11743480 -1.13969252 -1.04743038 0.01396109 0.00777312 0.13408010 -0.33941082 +Al 2.16131747 4.15904964 2.03922631 0.50961952 -1.63928809 0.41946845 2.19147068 3.95328790 2.10796172 0.00514515 -1.84588262 0.70971788 0.01707042 -0.14382861 -0.34434364 0.20024871 +Al 2.11644421 6.07746229 0.05635386 -0.90003300 0.72050319 0.08069261 2.03339315 6.16149670 0.04281063 -0.52403967 0.58476155 -0.40192600 0.00206081 0.02033281 0.20432512 -0.08595024 +Al -0.06872219 4.07655930 3.97710345 1.49994028 -0.48060013 0.63148491 0.10294054 4.02601996 4.12311921 1.36287959 -0.33238141 1.97616984 0.02819301 0.26157916 -0.06778076 0.43655768 +Al -0.16586567 6.17207637 6.29087578 0.33258390 -0.53377023 -0.07704100 -0.10075637 6.07907040 6.25758904 0.71819774 -1.03251371 -0.50502251 0.01598050 0.28840170 -0.28415746 -0.17346984 +Al 2.08821832 4.07619989 6.22921551 1.02299598 0.27678490 -1.08482611 2.18238812 4.10976764 6.06159169 0.71634565 0.34096018 -1.71826176 0.04921525 -0.11593535 -0.00429203 -0.43390088 +Al 2.05265912 5.92594709 4.20224038 -0.91757975 1.07261277 -0.00018278 1.95471271 6.07099717 4.20016064 -0.75539272 1.48482873 -0.13063489 0.01529379 -0.06989421 0.23106615 0.05048688 +Al 4.12053850 0.05927389 0.02285378 0.65678862 0.16807727 0.34884643 4.14520964 0.09029539 0.03096883 -0.29927440 0.41128145 -0.24919694 0.02351633 -0.17773931 0.04432774 -0.09262356 +Al 4.11967879 1.85218666 2.05326459 0.79192471 -1.11950872 0.41421409 4.19095976 1.82325787 2.09487272 0.44549341 0.59107529 0.26642180 0.02433009 -0.03320538 0.51507295 0.03712893 +Al 6.00074920 -0.06527471 2.19013045 0.49063111 0.32781240 -0.85265293 6.10398953 -0.00041724 2.11202646 1.21671586 0.70773067 -0.45664155 0.04017997 0.40328961 0.27513973 -0.04573773 +Al 6.17846980 2.17374566 0.02863224 1.34417167 -0.41372027 -1.02766412 6.28235358 2.07527407 -0.03202213 0.36282641 -1.18302989 -0.09069705 0.02467001 -0.11429253 -0.44737685 0.27739501 +Al 3.99165084 -0.06423077 4.17176729 -0.70901358 0.55830115 1.24128662 3.95149447 0.02423752 4.27168672 -0.02698226 0.88889052 0.35920435 0.02679854 0.17328928 0.28407592 -0.00929847 +Al 3.96240436 2.12229634 6.11590360 0.46660715 0.46612718 -0.21238287 4.02852893 2.12907013 6.09892033 0.58939947 -0.32605456 0.05051467 0.01460498 0.19503240 -0.22878397 -0.04784979 +Al 6.07684964 0.09564256 6.14706748 -0.15923475 0.26946916 -0.11189882 6.04747231 0.11921411 6.12828967 -0.11896527 0.01256561 -0.14624619 0.01093029 -0.24023966 0.04731775 -0.08321038 +Al 6.15374823 2.07830632 4.06222184 -1.79928918 -0.49655379 0.99841919 5.98145593 1.97409250 4.18293057 -1.06899451 -1.18135986 1.09935286 0.05435442 -0.08874101 -0.39336106 0.11115771 +Al 4.08884167 4.01005571 0.06664839 0.45825209 -0.11176405 -0.05765539 4.12069553 4.01088427 0.09583877 0.10679640 0.08411900 0.57593156 0.02565535 -0.08767682 0.09731989 0.18006274 +Al 4.08951615 6.12905060 2.27741939 -1.40640378 -0.54343716 -0.89411612 3.91385716 6.07753167 2.15237661 -1.47169633 -0.34783071 -1.20608420 0.04858553 -0.34151032 -0.03621993 -0.32699512 +Al 6.13771079 4.27103656 1.92537324 0.91341194 -0.34631586 1.61980826 6.23662182 4.14496687 2.09678581 0.61185756 -1.69440233 1.32144143 0.06559281 0.14105015 -0.65322859 0.17886841 +Al 5.92213432 6.19010676 0.23478642 -0.72199407 0.46862759 1.06309747 5.90523809 6.19847867 0.25338484 0.45080405 -0.48458824 -0.63436761 0.06961904 0.31176115 -0.08418942 -0.59340663 +Al 4.38331856 3.95080738 3.95984944 0.37118994 0.18458946 -0.17716336 4.29741270 4.00473024 3.96935420 -1.84337850 0.79392536 0.34554586 0.05705253 -0.75167478 0.18459187 0.18259713 +Al 4.21571980 6.01739599 6.05861261 0.37489047 0.93957132 -0.52387035 4.20614660 6.12307351 6.04186950 -0.67167299 0.92593602 0.32961301 0.00309027 -0.20696170 0.06265517 0.15023070 +Al 6.02210565 3.99831477 6.20417538 -1.21472514 0.62595645 0.09808774 5.97036097 4.09193150 6.18669339 0.33576846 0.97460702 -0.34933105 0.03698178 0.38061707 0.19963221 -0.21003717 +Al 6.18968122 6.06604804 4.08890238 -1.14771696 0.61790175 -0.02390026 6.06685097 6.13854115 4.10618547 -0.91347767 0.67529603 0.15541771 0.04269961 -0.13961749 0.05682592 0.22834020 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.0496213996973287 free_energy=1.0496213996973287 stress="0.0050412540679878135 0.0006477853945826963 -0.0001668343085574835 0.0006477853945826963 0.004431297660261729 -8.201800653474678e-05 -0.0001668343085574835 -8.201800653474678e-05 0.006065264190204926" pbc="T T T" +Al 0.10122258 -0.10966878 0.11583238 -0.65183463 0.62379972 1.47616753 -0.02789025 -0.03552281 0.22769330 -1.61899170 0.61514262 0.44175987 0.01627154 -0.33604521 0.15207386 -0.15394549 +Al 0.04298388 1.98852636 2.08224377 -0.65436195 -0.13546458 0.57742725 -0.04970584 2.06319289 2.12731842 -0.90325891 1.27448603 0.38532747 0.04248188 -0.16366281 0.59843150 -0.19576280 +Al 1.99206376 0.14503676 1.98033833 -1.79781972 0.55562620 -1.56759129 1.88811699 0.11538020 1.91465887 -0.00780193 -1.00060361 0.43777414 0.06728315 0.40458477 -0.52792677 0.49227841 +Al 2.10797560 1.99011134 0.11372571 -0.44475192 0.44312265 0.92397889 2.05613089 2.09040771 0.15826877 -0.41214089 1.17508447 -0.20829008 0.03413359 -0.10775604 0.37858706 -0.21800418 +Al -0.15936233 0.17057456 3.91544150 -1.29978743 -0.97098952 0.00587215 -0.21763631 0.00103324 3.96971247 0.20235474 -1.72504926 0.78135607 0.06272531 0.49123211 -0.60877838 0.43075244 +Al -0.01152506 2.00306105 6.12939722 0.94784953 0.11075907 -1.37244704 0.08819301 1.99481137 6.01982464 0.68219599 -0.23021869 -0.46865056 0.03775713 0.12358988 -0.11514859 0.09023538 +Al 2.14323951 0.20075913 6.10010622 -1.73812154 -0.17182853 0.29106492 1.90025233 0.11955926 6.13488825 -2.24051188 -1.22601727 0.33663713 0.06683578 -0.58901543 -0.43573075 0.04714049 +Al 1.97865080 2.04835628 4.18844391 0.45187924 0.63826775 -0.80148921 2.07250112 2.11931772 4.06044556 1.20307312 0.43090982 -1.37451993 0.01568506 0.28505701 0.16048250 -0.32379161 +Al -0.04370556 3.90728441 -0.10008633 0.43830490 0.25321690 -1.23832876 0.03923277 3.99019439 -0.18228154 0.83114663 1.06259081 -0.33475925 0.04327799 0.35962944 0.46916804 0.36505477 +Al -0.09389839 5.86400230 2.19930966 0.51305389 -1.33457864 0.55029550 -0.00117448 5.78115198 2.20513526 1.07547071 -0.23326776 -0.41136707 0.03528062 0.27999378 0.39776872 -0.32603095 +Al 2.22516603 4.07412600 2.04313126 0.21175610 -0.54153479 -0.14682908 2.18346539 3.97293698 2.04838010 -0.79429155 -1.02113711 0.29409029 0.03528168 -0.52255050 -0.40714140 0.08135634 +Al 1.96041189 5.99331499 0.12839923 0.58861329 -0.89940228 -0.19320009 2.01824176 5.94296639 0.11195992 0.43202325 0.09997826 -0.04108933 0.02267279 0.02547308 0.17486406 -0.02555815 +Al 0.01622504 4.03305362 4.24629539 -0.20897970 -0.43290867 0.94771227 0.02499006 3.99292013 4.31635930 0.28724972 -0.11688449 0.25301718 0.03019158 0.24590168 -0.08028449 -0.13735206 +Al -0.04717474 6.10258323 5.98345334 -0.19924822 1.18923225 -0.23774787 0.02741756 6.21451803 5.99028232 1.35933240 0.67652992 0.45769964 0.03764803 0.63139411 0.02387530 0.14450647 +Al 2.19535093 4.07950163 6.27092103 0.49669069 0.42004002 0.21328905 2.20978472 4.11524578 6.23947720 -0.05515350 0.15201378 -0.84314754 0.02159794 -0.36514127 0.00680721 -0.28237673 +Al 2.05921180 6.16495709 3.90030620 -0.69666048 -0.11172841 0.05873806 1.98920727 6.16778992 3.96357739 -0.43021369 0.08401848 1.03229593 0.00713044 -0.06691243 0.11796941 0.38115234 +Al 4.05202003 0.01070115 0.03065185 0.31142016 0.04184735 -0.76860160 4.10296519 0.03301753 0.00685599 0.37715428 0.37813873 0.31260723 0.03359944 0.23804609 0.06016911 0.32284858 +Al 4.16048556 2.01738399 2.11378911 1.71041171 -0.56244486 -0.40338931 4.28023528 1.98793431 2.08875939 0.24151183 0.00146091 -0.01257181 0.00854906 -0.20256318 0.18174341 0.08334349 +Al 5.97946595 0.04290261 2.07413626 -0.63565391 0.16744851 -0.20527034 5.96914732 0.03056657 2.00681229 0.59948520 -0.41408151 -0.81513480 0.03257702 0.23362178 -0.13418913 -0.35317077 +Al 6.14869392 2.13655303 0.04954117 0.02697863 0.07880540 0.72690466 6.14798601 2.07663662 0.09817659 0.10826231 -0.98203882 -0.12943480 0.02941124 -0.14807618 -0.51257652 0.02897729 +Al 4.13968993 0.01500317 4.13107062 0.36686831 -0.52119845 -0.50714151 4.12849918 -0.01625099 4.07786007 -0.54926543 -0.02627973 -0.33540446 0.02332059 -0.28133810 0.13256547 -0.08767421 +Al 4.12198454 2.06695547 6.18224982 0.23483739 -0.77006126 -0.42204804 4.14691602 1.97643788 6.13939562 0.04527294 -0.78914839 -0.26333537 0.02368015 0.11217059 -0.10695550 -0.06611671 +Al 6.25566481 -0.10128551 6.15264624 0.07767739 -0.66391012 1.34869573 6.19542255 -0.10985806 6.31740764 -1.02804911 0.47648802 1.45681029 0.02828435 -0.47408145 0.33607811 0.20598649 +Al 6.06172674 2.04814788 4.26981803 0.10951947 0.65028507 0.52808798 6.06452125 2.11571020 4.29040165 0.02683886 0.42280976 -0.19900004 0.03186952 -0.09483808 0.08096578 -0.18784736 +Al 4.21267789 4.01513856 0.03586771 0.53490136 0.11884854 1.02157862 4.23023799 4.03581066 0.14419383 -0.29683180 0.22521400 0.78710524 0.04025142 -0.18199172 0.08392670 0.10520734 +Al 4.17385429 5.90062313 2.02517111 0.17128695 1.71280142 0.48869317 4.15723009 6.14546482 2.06602523 -0.61260436 2.36001851 0.26488937 0.04729958 -0.11317749 0.60326078 -0.07991139 +Al 6.13144139 3.99610944 2.30979861 0.23302038 -0.55368936 -1.89995381 6.14445941 3.92170496 2.08885168 0.04995955 -0.54317511 -1.76785371 0.03477328 -0.09796674 -0.23400137 -0.33491051 +Al 6.16283748 5.95400700 0.06897947 0.23148447 -1.03266710 1.40056725 6.19340006 5.90029562 0.21703943 0.34282833 0.12252987 1.11075021 0.01300785 -0.00206709 0.29220819 0.09141552 +Al 4.01950371 4.14201927 4.18919487 0.92682378 -0.14722422 -0.61737093 4.10340797 4.11578306 4.11517003 0.47645258 -0.23647008 -0.85203587 0.01185028 -0.00287716 -0.12312126 0.02622525 +Al 3.92146091 6.31423008 6.24958566 0.24791905 0.66040367 0.55511546 3.98540207 6.29439016 6.25071365 0.76382995 -0.87797052 -0.53694638 0.04517418 0.31196126 -0.60089521 -0.30642207 +Al 6.03248216 4.21619905 6.06675612 -1.07580833 1.32665565 -0.63066453 5.92275141 4.32792490 6.02127062 -0.73867048 0.61925818 0.06170182 0.02368320 -0.06660190 -0.14018440 -0.04115811 +Al 6.00340549 6.19022467 4.05053520 0.57173114 -0.14152942 -0.10211507 6.07052178 6.13902154 4.06241672 0.58334284 -0.75432982 0.17971912 0.04603572 0.07400719 -0.22401144 0.22355249 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.0598670279781714 free_energy=1.0598670279781714 stress="0.005052295521701254 0.0003623609712633322 -0.00036006216669003474 0.0003623609712633322 0.005464424852972196 2.0063803643432364e-05 -0.00036006216669003474 2.0063803643432364e-05 0.0069520428514399865" pbc="T T T" +Al -0.02789025 -0.03552281 0.22769330 1.61899170 -0.61514262 -0.44175987 0.10122258 -0.10966878 0.11583238 0.65183463 -0.62379972 -1.47616753 0.04677184 -0.11585444 -0.08582853 -0.40591217 +Al -0.04970584 2.06319289 2.12731842 0.90325891 -1.27448603 -0.38532747 0.04298388 1.98852636 2.08224377 0.65436195 0.13546458 -0.57742725 0.01754470 0.11219607 0.16306876 0.05525110 +Al 1.88811699 0.11538020 1.91465887 0.00780193 1.00060361 -0.43777414 1.99206376 0.14503676 1.98033833 1.79781972 -0.55562620 1.56759129 0.06239616 0.59048502 -0.34073568 0.63061900 +Al 2.05613089 2.09040771 0.15826877 0.41214089 -1.17508447 0.20829008 2.10797560 1.99011134 0.11372571 0.44475192 -0.44312265 -0.92397889 0.03123103 0.07552269 -0.04896383 -0.42003529 +Al -0.21763631 0.00103324 3.96971247 -0.20235474 1.72504926 -0.78135607 -0.15936233 0.17057456 3.91544150 1.29978743 0.97098952 -0.00587215 0.02442416 0.43939881 0.18014228 0.03613343 +Al 0.08819301 1.99481137 6.01982464 -0.68219599 0.23021869 0.46865056 -0.01152506 2.00306105 6.12939722 -0.94784953 -0.11075907 1.37244704 0.07245426 -0.26654639 -0.05319396 0.41796345 +Al 1.90025233 0.11955926 6.13488825 2.24051188 1.22601727 -0.33663713 2.14323951 0.20075913 6.10010622 1.73812154 0.17182853 -0.29106492 0.03011933 0.34609724 -0.24095767 0.02867786 +Al 2.07250112 2.11931772 4.06044556 -1.20307312 -0.43090982 1.37451993 1.97865080 2.04835628 4.18844391 -0.45187924 -0.63826775 0.80148921 0.00901304 0.14949738 -0.29855848 0.01534920 +Al 0.03923277 3.99019439 -0.18228154 -0.83114663 -1.06259081 0.33475925 -0.04370556 3.90728441 -0.10008633 -0.43830490 -0.25321690 1.23832876 0.01462821 -0.12137574 0.07376550 0.23510663 +Al -0.00117448 5.78115198 2.20513526 -1.07547071 0.23326776 0.41136707 -0.09389839 5.86400230 2.19930966 -0.51305389 1.33457864 -0.55029550 0.03359128 0.07004072 0.31193338 -0.26943981 +Al 2.18346539 3.97293698 2.04838010 0.79429155 1.02113711 -0.29409029 2.22516603 4.07412600 2.04313126 -0.21175610 0.54153479 0.14682908 0.03338994 -0.16830166 0.16270713 0.17446616 +Al 2.01824176 5.94296639 0.11195992 -0.43202325 -0.09997826 0.04108933 1.96041189 5.99331499 0.12839923 -0.58861329 0.89940228 0.19320009 0.01583102 -0.05773059 0.41657068 0.09985010 +Al 0.02499006 3.99292013 4.31635930 -0.28724972 0.11688449 -0.25301718 0.01622504 4.03305362 4.24629539 0.20897970 0.43290867 -0.94771227 0.03385578 0.08442802 0.27496233 -0.29517719 +Al 0.02741756 6.21451803 5.99028232 -1.35933240 -0.67652992 -0.45769964 -0.04717474 6.10258323 5.98345334 0.19924822 -1.18923225 0.23774787 0.03586727 0.21394511 -0.35792691 0.33497121 +Al 2.20978472 4.11524578 6.23947720 0.05515350 -0.15201378 0.84314754 2.19535093 4.07950163 6.27092103 -0.49669069 -0.42004002 -0.21328905 0.01842292 -0.01942167 -0.15205282 -0.38517509 +Al 1.98920727 6.16778992 3.96357739 0.43021369 -0.08401848 -1.03229593 2.05921180 6.16495709 3.90030620 0.69666048 0.11172841 -0.05873806 0.00685140 0.24241490 -0.03414395 0.24087567 +Al 4.10296519 0.03301753 0.00685599 -0.37715428 -0.37813873 -0.31260723 4.05202003 0.01070115 0.03065185 -0.31142016 -0.04184735 0.76860160 0.04250339 -0.24205869 0.08233928 0.29312272 +Al 4.28023528 1.98793431 2.08875939 -0.24151183 -0.00146091 0.01257181 4.16048556 2.01738399 2.11378911 -1.71041171 0.56244486 0.40338931 0.04993652 -0.66461868 0.13158732 0.15889151 +Al 5.96914732 0.03056657 2.00681229 -0.59948520 0.41408151 0.81513480 5.97946595 0.04290261 2.07413626 0.63565391 -0.16744851 0.20527034 0.07777627 0.51622871 -0.16776067 0.06714457 +Al 6.14798601 2.07663662 0.09817659 -0.10826231 0.98203882 0.12943480 6.14869392 2.13655303 0.04954117 -0.02697863 -0.07880540 -0.72690466 0.02947094 0.13317446 -0.12164259 -0.53733238 +Al 4.12849918 -0.01625099 4.07786007 0.54926543 0.02627973 0.33540446 4.13968993 0.01500317 4.13107062 -0.36686831 0.52119845 0.50714151 0.01151456 -0.24044548 0.18451663 0.17698936 +Al 4.14691602 1.97643788 6.13939562 -0.04527294 0.78914839 0.26333537 4.12198454 2.06695547 6.18224982 -0.23483739 0.77006126 0.42204804 0.00773501 -0.23033708 0.09615680 0.12908107 +Al 6.19542255 -0.10985806 6.31740764 1.02804911 -0.47648802 -1.45681029 6.25566481 -0.10128551 6.15264624 -0.07767739 0.66391012 -1.34869573 0.01888789 -0.16980109 0.27931138 -0.22127630 +Al 6.06452125 2.11571020 4.29040165 -0.02683886 -0.42280976 0.19900004 6.06172674 2.04814788 4.26981803 -0.10951947 -0.65028507 -0.52808798 0.03039213 0.06230484 -0.23591972 -0.27487674 +Al 4.23023799 4.03581066 0.14419383 0.29683180 -0.22521400 -0.78710524 4.21267789 4.01513856 0.03586771 -0.53490136 -0.11884854 -1.02157862 0.02983579 -0.34345818 0.01392646 -0.23961528 +Al 4.15723009 6.14546482 2.06602523 0.61260436 -2.36001851 -0.26488937 4.17385429 5.90062313 2.02517111 -0.17128695 -1.71280142 -0.48869317 0.02107550 -0.37514881 -0.20604012 -0.07170910 +Al 6.14445941 3.92170496 2.08885168 -0.04995955 0.54317511 1.76785371 6.13144139 3.99610944 2.30979861 -0.23302038 0.55368936 1.89995381 0.06509629 -0.02235608 0.28176745 0.40562405 +Al 6.19340006 5.90029562 0.21703943 -0.34282833 -0.12252987 -1.11075021 6.16283748 5.95400700 0.06897947 -0.23148447 1.03266710 -1.40056725 0.03240190 0.01905578 0.43541944 -0.30060952 +Al 4.10340797 4.11578306 4.11517003 -0.47645258 0.23647008 0.85203587 4.01950371 4.14201927 4.18919487 -0.92682378 0.14722422 0.61737093 0.02111889 -0.25861542 0.06941220 -0.18311221 +Al 3.98540207 6.29439016 6.25071365 -0.76382995 0.87797052 0.53694638 3.92146091 6.31423008 6.24958566 -0.24791905 -0.66040367 -0.55511546 0.03988236 -0.00157099 -0.31690941 -0.31468515 +Al 5.92275141 4.32792490 6.02127062 0.73867048 -0.61925818 -0.06170182 6.03248216 4.21619905 6.06675612 1.07580833 -1.32665565 0.63066453 0.04791296 0.31466609 -0.35458918 0.46986949 +Al 6.07052178 6.13902154 4.06241672 -0.58334284 0.75432982 -0.17971912 6.00340549 6.19022467 4.05053520 -0.57173114 0.14152942 0.10211507 0.04793427 -0.07181483 -0.14236349 -0.05103034 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.1701265082332784 free_energy=1.1701265082332784 stress="0.004240275766441019 0.0004635646804701818 -0.00019295021039451258 0.0004635646804701818 0.004553247416723432 -7.657078983153338e-05 -0.00019295021039451258 -7.657078983153338e-05 0.007360803371220558" pbc="T T T" +Al 0.02344403 0.01551248 -0.19761095 -1.16323505 -0.26122577 -0.29488629 -0.02535890 -0.01666860 -0.15289106 0.28826462 -0.30466071 1.07928487 0.03848448 0.38635724 -0.01650272 0.45105419 +Al 0.03869236 1.83830745 2.13618222 -0.19190768 -0.80007077 0.53464521 0.08203055 1.76798180 2.14508712 0.89406061 -0.34609683 -0.51131534 0.01523279 0.38861915 0.03657230 -0.17717062 +Al 2.21696033 -0.00369555 2.08103589 0.49172392 -0.98175087 -1.06769837 2.19078758 -0.10086233 1.96301195 -0.84339155 -0.62867711 -0.85437542 0.03245089 -0.51758763 -0.04627487 -0.13487795 +Al 2.16865957 2.03296493 0.08130823 0.73865548 -0.13990468 -0.19515489 2.19124544 2.02189874 0.05478480 -0.28379326 -0.00199163 -0.20331497 0.00608690 -0.34940230 -0.03453984 -0.09684311 +Al 0.11565556 -0.08703035 4.32584055 -0.60495818 0.49127841 -1.39549901 0.04339555 0.01238779 4.16767894 -0.66737671 1.12465011 -1.24941081 0.01355355 -0.06584591 0.38837878 -0.23459667 +Al -0.09739854 2.11083217 6.25209748 -0.29516044 0.35653077 1.04581886 -0.08667627 2.14215956 6.30564995 0.50258723 0.07103308 -0.19853601 0.04747541 0.23084929 0.03537826 -0.24619136 +Al 2.35267551 0.00857491 6.05157987 -0.46205813 0.04785286 0.21264578 2.23590422 0.01790017 6.07751606 -1.72194435 0.13108337 0.20121051 0.02223262 -0.36276836 0.02793866 0.09346508 +Al 1.97634963 2.18784736 4.17164038 0.77580769 1.02425092 -0.82884860 2.02584481 2.21163753 4.13023792 0.08198376 -0.57431653 0.12525941 0.02439692 -0.11994031 -0.47622501 0.20545082 +Al 0.01137334 4.01510313 0.24021383 -0.93415439 0.83732410 -0.98372568 -0.08986066 4.09259345 0.16573580 -0.67317829 0.43937864 -0.21180730 0.01860832 -0.09398153 0.00980540 0.02322964 +Al -0.10751780 6.36589664 2.03998191 0.11822679 -0.54233461 0.26748073 -0.03820982 6.26443372 2.07001341 1.02046144 -1.22516014 0.30771802 0.00482065 0.40180929 -0.31689531 -0.00398226 +Al 2.09552761 4.14451928 2.18418504 0.28280458 -0.14393301 0.00424007 2.09234396 4.13172013 2.10658804 -0.31946865 -0.16413754 -1.41682083 0.03780661 -0.19702601 0.03480980 -0.40777609 +Al 1.99087608 6.03041168 -0.04696864 -0.14353749 0.00033405 0.83188883 2.01064771 6.07441477 0.09136432 0.41685005 0.58729020 1.39480062 0.02136328 0.24767567 0.35521372 0.42009104 +Al -0.06811528 4.04554951 3.88373122 0.19556418 -0.00422045 -0.49531416 -0.02250295 4.08105928 3.87934750 0.51574885 0.51481387 0.42592389 0.03209531 0.21748865 0.27722318 0.27892949 +Al -0.06436346 5.98382832 6.16365794 0.54787878 -0.04163019 -0.40358502 -0.01197730 6.03704335 6.09441764 0.50293963 1.09095144 -0.90914127 0.07055460 -0.07208947 0.22177421 -0.08190182 +Al 1.75291701 4.11946475 6.27009913 -0.75441855 0.62759591 0.88376122 1.78535119 4.11234955 6.29094723 1.31931167 -0.62635373 -0.37150565 0.07954511 0.64657652 -0.45730187 -0.48359978 +Al 2.00672533 6.04974713 4.10309527 -0.18108971 -0.70710692 -0.49781637 1.99809219 5.98659917 4.06941863 -0.01399102 -0.24475885 -0.13255745 0.00993975 0.09944542 -0.06148956 0.11360764 +Al 4.11539389 0.03520825 0.06083098 -0.37689914 -0.24152312 -0.46323548 4.07469952 -0.03498634 0.02684264 -0.19923830 -0.99129311 -0.08715584 0.04132444 -0.15383560 -0.24851788 0.03160766 +Al 3.96405453 2.17752042 2.18998955 0.19189540 0.58832274 1.30730034 4.01802925 2.19751151 2.27659620 0.70331049 -0.25955173 0.13583508 0.04210457 0.22735563 -0.22016853 -0.19501995 +Al 6.02419456 0.06907121 2.15899948 -0.86179089 -0.52327712 -0.55594996 5.95351650 -0.02605980 2.10710666 -0.35401754 -1.01180705 -0.56216711 0.10819101 0.09030648 -0.34026152 0.16686401 +Al 6.06682015 1.83842144 0.35078828 0.90247749 0.13493551 -0.43509367 6.15717909 1.94699849 0.23962681 0.61351328 1.60199540 -1.27106336 0.06957469 0.07509888 0.68263391 -0.61665581 +Al 4.09559994 -0.01213247 3.92601624 -0.26958396 0.72276936 -0.29079217 4.06700132 0.08203661 4.00185596 -0.31957091 0.83035284 1.49004461 0.06181064 0.05031530 0.19264484 0.70677658 +Al 4.29055238 2.04739453 6.18569088 0.58759882 -1.28668758 0.36965449 4.27448707 1.96363150 6.20005144 -0.96264502 0.09207187 -0.21098962 0.05947481 -0.40519337 0.03382496 -0.04825640 +Al 6.12561752 0.17978212 6.10323681 -1.04249764 0.97887128 1.72843701 6.00728087 0.17225198 6.22503583 -0.77944975 -1.27841362 0.36626718 0.04775849 -0.28276633 -0.50715965 -0.23480464 +Al 6.15462339 2.15209799 3.95509619 0.62962767 -0.16602841 -0.68791306 6.19023259 2.09200001 3.94830015 0.00623352 -0.80143549 0.68591550 0.02957361 -0.13902931 -0.34116219 0.27727649 +Al 4.07354781 4.00321974 0.02550575 1.77424394 -0.37405529 0.38363590 4.25264567 3.99322319 0.05213031 1.25414992 0.19130653 -0.01109703 0.00777593 0.07227276 0.18198188 0.00223878 +Al 4.14674599 6.05104520 2.10483322 0.03280139 0.08676754 -0.19676071 4.12920284 6.03270448 2.07159733 -0.32141806 -0.39517706 -0.36038710 0.01713185 -0.15750992 -0.16402530 -0.08583804 +Al 6.08991039 4.12647927 1.95742699 -0.62354249 -0.68569643 0.37027988 6.01740939 4.05582249 2.03816183 -0.50607760 -0.53380887 1.15359371 0.03803245 -0.15271479 -0.02539625 0.22190570 +Al 6.06431252 6.22291666 0.03871599 2.18534921 -1.54573041 0.31498948 6.25920008 6.04374176 0.05037981 1.05099556 -1.43098646 -0.05659016 0.00767646 -0.00159240 -0.23646063 -0.13268791 +Al 4.01942104 3.87517101 4.25704919 -0.88586858 0.93575525 0.82201085 3.96334456 4.04024212 4.31720590 0.00146938 1.77670670 0.27764780 0.07506267 0.09139631 0.56879153 -0.22611728 +Al 4.13837525 5.87134393 6.04631195 -1.03231625 0.54871167 -0.61389713 4.01524921 6.00221715 6.02907801 -1.03591986 1.75098043 0.43472914 0.02877591 -0.19312635 0.47347531 0.17717706 +Al 6.15013780 4.04861391 6.05201872 0.69980634 1.03198283 0.66840442 6.21496319 4.13313340 6.15742268 0.27083775 0.42638431 0.94623041 0.05145999 0.10110606 -0.08241841 0.38824867 +Al 6.11246211 6.07650590 4.14447109 -0.33144313 0.03189243 -0.33902251 6.06873209 6.08937632 4.09675090 -0.44123689 0.18962766 -0.40622548 0.00975180 -0.06226305 0.05435279 -0.15160318 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.070238329593166 free_energy=1.070238329593166 stress="0.005001714831958823 0.0014787882206239893 -8.696280675829449e-05 0.0014787882206239893 0.003844643493447092 -0.0001922692050899891 -8.696280675829449e-05 -0.0001922692050899891 0.0070443138583341485" pbc="T T T" +Al -0.02535890 -0.01666860 -0.15289106 -0.28826462 0.30466071 -1.07928487 0.02344403 0.01551248 -0.19761095 1.16323505 0.26122577 0.29488629 0.03722304 0.42582734 0.02702365 0.38695244 +Al 0.08203055 1.76798180 2.14508712 -0.89406061 0.34609683 0.51131534 0.03869236 1.83830745 2.13618222 0.19190768 0.80007077 -0.53464521 0.02672090 0.21143978 0.32090730 -0.44958622 +Al 2.19078758 -0.10086233 1.96301195 0.84339155 0.62867711 0.85437542 2.21696033 -0.00369555 2.08103589 -0.49172392 0.98175087 1.06769837 0.03911533 -0.27001058 0.29666503 0.33427476 +Al 2.19124544 2.02189874 0.05478480 0.28379326 0.00199163 0.20331497 2.16865957 2.03296493 0.08130823 -0.73865548 0.13990468 0.19515489 -0.00314954 -0.27327489 0.08254668 0.07618516 +Al 0.04339555 0.01238779 4.16767894 0.66737671 -1.12465011 1.24941081 0.11565556 -0.08703035 4.32584055 0.60495818 -0.49127841 1.39549901 0.00963083 0.03281695 -0.01581709 0.26729276 +Al -0.08667627 2.14215956 6.30564995 -0.50258723 -0.07103308 0.19853601 -0.09739854 2.11083217 6.25209748 0.29516044 -0.35653077 -1.04581886 0.04710413 0.25331477 -0.23971365 -0.50879010 +Al 2.23590422 0.01790017 6.07751606 1.72194435 -0.13108337 -0.20121051 2.35267551 0.00857491 6.05157987 0.46205813 -0.04785286 -0.21264578 0.01508188 -0.44633611 0.04535953 -0.02872732 +Al 2.02584481 2.21163753 4.13023792 -0.08198376 0.57431653 -0.12525941 1.97634963 2.18784736 4.17164038 -0.77580769 -1.02425092 0.82884860 0.03426526 -0.22181921 -0.44990337 0.31539808 +Al -0.08986066 4.09259345 0.16573580 0.67317829 -0.43937864 0.21180730 0.01137334 4.01510313 0.24021383 0.93415439 -0.83732410 0.98372568 0.04397739 0.39526212 -0.28067905 0.36081251 +Al -0.03820982 6.26443372 2.07001341 -1.02046144 1.22516014 -0.30771802 -0.10751780 6.36589664 2.03998191 -0.11822679 0.54233461 -0.26748073 0.00806430 0.13589729 -0.13224951 0.04267469 +Al 2.09234396 4.13172013 2.10658804 0.31946865 0.16413754 1.41682083 2.09552761 4.14451928 2.18418504 -0.28280458 0.14393301 -0.00424007 0.03078722 -0.15619484 -0.10972569 -0.38485851 +Al 2.01064771 6.07441477 0.09136432 -0.41685005 -0.58729020 -1.39480062 1.99087608 6.03041168 -0.04696864 0.14353749 -0.00033405 -0.83188883 0.01603801 0.07817414 -0.06739502 -0.18467174 +Al -0.02250295 4.08105928 3.87934750 -0.51574885 -0.51481387 -0.42592389 -0.06811528 4.04554951 3.88373122 -0.19556418 0.00422045 0.49531416 0.02086440 -0.01636202 0.00576295 0.30074486 +Al -0.01197730 6.03704335 6.09441764 -0.50293963 -1.09095144 0.90914127 -0.06436346 5.98382832 6.16365794 -0.54787878 0.04163019 0.40358502 0.05282273 0.08804989 0.36704810 -0.16508080 +Al 1.78535119 4.11234955 6.29094723 -1.31931167 0.62635373 0.37150565 1.75291701 4.11946475 6.27009913 0.75441855 -0.62759591 -0.88376122 0.05243553 0.59585008 -0.22017073 -0.24897694 +Al 1.99809219 5.98659917 4.06941863 0.01399102 0.24475885 0.13255745 2.00672533 6.04974713 4.10309527 0.18108971 0.70710692 0.49781637 0.01532469 0.03231636 0.33995106 0.08668348 +Al 4.07469952 -0.03498634 0.02684264 0.19923830 0.99129311 0.08715584 4.11539389 0.03520825 0.06083098 0.37689914 0.24152312 0.46323548 0.04074881 0.18382248 -0.14097405 0.16679055 +Al 4.01802925 2.19751151 2.27659620 -0.70331049 0.25955173 -0.13583508 3.96405453 2.17752042 2.18998955 -0.19189540 -0.58832274 -1.30730034 0.04682243 0.04302862 -0.29901108 -0.48121469 +Al 5.95351650 -0.02605980 2.10710666 0.35401754 1.01180705 0.56216711 6.02419456 0.06907121 2.15899948 0.86179089 0.52327712 0.55594996 0.03783924 0.24606853 0.05579295 -0.14373582 +Al 6.15717909 1.94699849 0.23962681 -0.61351328 -1.60199540 1.27106336 6.06682015 1.83842144 0.35078828 -0.90247749 -0.13493551 0.43509367 0.03135380 -0.20568383 0.18195181 0.01840027 +Al 4.06700132 0.08203661 4.00185596 0.31957091 -0.83035284 -1.49004461 4.09559994 -0.01213247 3.92601624 0.26958396 -0.72276936 0.29079217 0.02636238 -0.07034558 -0.14278764 0.34231774 +Al 4.27448707 1.96363150 6.20005144 0.96264502 -0.09207187 0.21098962 4.29055238 2.04739453 6.18569088 -0.58759882 1.28668758 -0.36965449 0.07827745 -0.53037369 0.67598726 -0.24720494 +Al 6.00728087 0.17225198 6.22503583 0.77944975 1.27841362 -0.36626718 6.12561752 0.17978212 6.10323681 1.04249764 -0.97887128 -1.72843701 0.08523737 0.37537492 -0.79728642 -0.50933463 +Al 6.19023259 2.09200001 3.94830015 -0.00623352 0.80143549 -0.68591550 6.15462339 2.15209799 3.95509619 -0.62962767 0.16602841 0.68791306 0.02275020 -0.18558137 -0.07633978 0.52265087 +Al 4.25264567 3.99322319 0.05213031 -1.25414992 -0.19130653 0.01109703 4.07354781 4.00321974 0.02550575 -1.77424394 0.37405529 -0.38363590 0.01350932 -0.42846414 0.18846228 -0.22125765 +Al 4.12920284 6.03270448 2.07159733 0.32141806 0.39517706 0.36038710 4.14674599 6.05104520 2.10483322 -0.03280139 -0.08676754 0.19676071 0.01800742 -0.09019933 -0.11484602 0.01389448 +Al 6.01740939 4.05582249 2.03816183 0.50607760 0.53380887 -1.15359371 6.08991039 4.12647927 1.95742699 0.62354249 0.68569643 -0.37027988 0.06125176 0.23028801 0.12380966 0.30482279 +Al 6.25920008 6.04374176 0.05037981 -1.05099556 1.43098646 0.05659016 6.06431252 6.22291666 0.03871599 -2.18534921 1.54573041 -0.31498948 0.04723209 -0.66841658 0.37808134 -0.04875717 +Al 3.96334456 4.04024212 4.31720590 -0.00146938 -1.77670670 -0.27764780 4.01942104 3.87517101 4.25704919 0.88586858 -0.93575525 -0.82201085 0.05314517 0.38205832 -0.04498728 -0.22280964 +Al 4.01524921 6.00221715 6.02907801 1.03591986 -1.75098043 -0.43472914 4.13837525 5.87134393 6.04631195 1.03231625 -0.54871167 0.61389713 0.02835011 0.17479331 0.27056336 0.44538917 +Al 6.21496319 4.13313340 6.15742268 -0.27083775 -0.42638431 -0.94623041 6.15013780 4.04861391 6.05201872 -0.69980634 -1.03198283 -0.66840442 0.03431130 -0.31711057 -0.26362013 -0.24207206 +Al 6.06873209 6.08937632 4.09675090 0.44123689 -0.18962766 0.40622548 6.11246211 6.07650590 4.14447109 0.33144313 -0.03189243 0.33902251 -0.00126660 -0.00421014 0.03559352 0.10179363 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=0.9510780384805617 free_energy=0.9510780384805617 stress="0.006018694982681886 0.000707532545302181 -0.00027031016091648506 0.000707532545302181 0.004672717481672366 -0.00033102340476843346 -0.00027031016091648506 -0.00033102340476843346 0.005535259343197416" pbc="T T T" +Al -0.01275222 0.05726802 0.18005727 -0.19927745 0.57237382 -0.71890947 -0.02605177 0.10230075 0.07012401 0.05724316 0.26450821 -1.29509040 0.01715142 -0.00947614 -0.09650966 -0.17147379 +Al -0.04999668 2.09243871 2.18464611 1.16756593 -1.12969892 -0.21790407 0.07843493 1.98449425 2.15335321 1.05132111 -0.77155548 -0.19687641 0.01395655 0.09345415 0.04660318 -0.17104505 +Al 2.03841892 0.08400047 1.95797645 -0.21404749 0.13441634 -0.50957714 2.03483420 0.10028453 1.98389770 0.16796648 -0.10216174 0.79790502 0.03647746 0.09405088 0.15662974 0.57829811 +Al 1.88149100 2.16983810 0.17118949 -0.37348322 0.82040314 0.79865440 1.90338778 2.19789253 0.20698188 0.73824318 -0.36892834 -0.24218248 0.03894211 0.37671562 -0.31266842 -0.22837753 +Al 0.14528137 0.00609861 4.16828852 0.51651896 0.04198044 1.30642404 0.13990167 -0.00336235 4.22699801 -0.60545575 -0.26572591 -0.30847152 0.01662716 -0.34466581 -0.04509918 -0.38810039 +Al 0.04182469 1.96808443 6.11673011 0.95765160 -0.31122890 -0.76605778 0.13966297 1.96766674 6.06165521 0.68463553 0.33608300 -0.02917049 0.01239413 0.07942294 0.15698910 -0.01756646 +Al 2.14379409 -0.12620265 6.05723504 0.29418097 -0.70748394 -0.21503271 2.15377844 -0.18480510 6.10440512 -0.05892927 -0.28482844 1.00274337 0.03794733 -0.19517314 0.08730603 0.42342000 +Al 2.15660798 1.93854607 4.19392680 -0.18126174 -0.96469831 0.84297807 2.11891387 1.88693950 4.21250084 -0.39286002 0.15025753 -0.44609361 0.01900670 -0.19472662 0.19664517 -0.42360793 +Al -0.06455924 3.99585417 -0.07151346 -0.69408874 -0.10696503 2.18650854 -0.10422580 4.04038397 0.15494813 0.02422449 0.85208347 1.72868698 0.02889958 0.16905584 0.34485614 0.12291162 +Al 0.10204672 5.90301118 2.08022324 -0.36262648 0.97833494 -0.55983239 0.01170307 6.08867921 2.02963497 -1.02291003 2.29986604 -0.33505130 0.05030673 -0.42298381 0.50281089 0.03691161 +Al 2.05870312 4.15893716 2.17240187 -1.16319169 1.06499491 -1.14366143 1.97633583 4.19644025 2.07173789 -0.38942544 -0.34491464 -0.51929877 0.03741481 0.25165522 -0.43786106 -0.00881342 +Al 2.03093237 6.16616343 0.22636209 1.10853852 -0.60495543 0.06030238 2.13028979 6.05720479 0.12437434 0.56192064 -1.10937502 -1.72467316 0.05134746 -0.02221241 -0.42220860 -0.72697251 +Al -0.03248695 4.00614451 4.07624522 -2.09309665 0.34674387 -0.80620611 -0.19927375 4.05760085 4.03833007 -0.53714714 0.63756118 0.00335450 0.01991890 0.07730724 0.05282723 0.36410710 +Al -0.09799495 6.12242326 6.12879692 -0.47834541 1.61534925 -0.08451546 -0.09511775 6.22080798 6.14893328 0.50769178 0.03447663 0.43884577 0.01447713 0.32358306 -0.30293348 0.14506716 +Al 2.18056963 4.02380800 6.07306091 0.40203843 -0.98284014 1.18021244 2.16192487 3.96734595 6.20660576 -0.81339916 0.00838017 1.00797297 0.00791789 -0.31024128 0.23116548 0.18355736 +Al 2.01765645 6.04958972 4.15201148 -0.01861823 -0.58504763 -0.46188455 2.04342734 5.99328302 4.09422797 0.34837466 -0.40386889 -0.49054059 0.02178846 0.24261821 0.03990691 -0.11911530 +Al 4.05950027 -0.06948805 -0.04315484 0.58650611 -0.92751197 -0.32352926 4.12873504 -0.09385544 -0.04070397 0.53032298 0.48212852 0.47057884 0.03338857 0.12557091 0.41969896 0.17389399 +Al 4.19843764 2.12498899 2.10447206 -1.23468406 0.15096150 0.05290245 4.00730035 2.08501061 2.10790045 -1.94036692 -0.69849121 0.07967466 0.04405164 -0.53520078 -0.43452428 -0.10314871 +Al 6.05319828 0.12654426 2.12190414 -0.08523618 -0.98627825 -0.69445973 6.09741148 0.00653072 2.03616757 0.75210300 -1.00224225 -0.63806698 0.03369725 0.38187535 -0.23784589 -0.21805822 +Al 6.08315617 2.13848904 0.03278364 0.34836385 0.78684029 1.80041807 6.11034052 2.14825483 0.21182502 0.08442768 -0.68640145 1.37842183 0.01696111 -0.01858395 -0.34007695 0.00341296 +Al 4.20171060 -0.00534617 4.08410867 0.05322740 0.18446773 -0.22596081 4.18230453 0.01957983 4.08632220 -0.32423008 0.24543437 0.13977754 0.02462109 -0.23015003 0.03204022 0.22542410 +Al 4.02856458 1.93877892 6.15737613 0.25549493 0.15341887 -0.05673810 4.07999080 1.95201866 6.17330241 0.55505176 0.05601350 0.30160711 0.01943686 0.18372586 0.04424184 0.13678912 +Al 6.21393996 0.00944117 6.24427663 -1.40626442 -0.40271162 -0.73926252 6.04080561 0.00917481 6.13038896 -1.45023888 0.50548269 -1.28920177 0.02606800 -0.29677378 0.11749940 -0.24596496 +Al 6.09409643 2.06035727 4.10435555 2.00132806 1.00508627 -0.45776236 6.27316329 2.12537318 4.07608664 0.87434549 -0.05772887 -0.25061144 0.02706454 0.06834237 -0.08794993 0.27737462 +Al 3.97908084 3.96675084 0.12495806 -0.25445326 -0.00480584 -0.31471881 3.95079123 4.02823658 0.05441541 -0.04630141 1.00228514 -0.79129964 0.03631462 -0.09855059 0.42483074 -0.30497350 +Al 4.09072487 6.31549215 2.04941714 0.27041855 -0.22481211 0.29090850 4.08969105 6.21746492 2.11108375 -0.31599317 -1.37128517 0.71912391 0.04687197 -0.15187606 -0.54152674 0.22824196 +Al 5.91610022 4.17335776 1.94905240 -1.06862616 0.85962761 -0.97767055 5.87327491 4.22164516 1.92540329 0.18556879 -0.01720883 0.49616957 0.06920451 0.49634203 -0.23608660 0.48410634 +Al 6.11551220 6.04983000 0.17353957 0.57098905 0.83736373 -0.40771347 6.16862108 6.16298195 0.07892599 0.26759031 0.99142965 -1.25033343 0.03249291 0.01854674 0.26723564 -0.32819190 +Al 4.13322622 3.97642393 4.10132135 0.45184882 1.00528488 0.89563720 4.14516662 4.11240863 4.16214297 -0.21441665 1.18576487 0.17789512 0.03625823 -0.19862484 0.35325575 -0.13373786 +Al 4.26643379 6.07914675 6.09461896 0.28178320 0.06450424 -0.18289899 4.23721508 6.08072290 6.08307472 -0.68653876 -0.21080882 -0.04560572 0.03582803 -0.44375770 0.10709073 0.07250139 +Al 5.91104231 4.07169010 6.11094541 -0.13442338 -1.78041860 0.70994746 5.97789206 3.87813881 6.20956249 1.29526782 -1.44949375 1.04090804 0.04048538 0.50676704 -0.22659101 0.13008584 +Al 6.15996987 6.04803280 4.01943776 0.69527018 -0.90269514 -0.26059786 6.21360121 5.99364994 4.00244441 0.11191382 0.09326383 0.06890248 0.00375953 -0.01603652 0.14024865 0.00304423 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.0918324809063336 free_energy=1.0918324809063336 stress="0.004911883159152096 0.00013220167420827304 0.0002369478789135862 0.00013220167420827304 0.003970602953314662 -0.0003438719444869235 0.0002369478789135862 -0.0003438719444869235 0.0055649138761324265" pbc="T T T" +Al -0.02605177 0.10230075 0.07012401 -0.05724316 -0.26450821 1.29509040 -0.01275222 0.05726802 0.18005727 0.19927745 -0.57237382 0.71890947 0.01089759 0.18593413 -0.07582203 -0.16820964 +Al 0.07843493 1.98449425 2.15335321 -1.05132111 0.77155548 0.19687641 -0.04999668 2.09243871 2.18464611 -1.16756593 1.12969892 0.21790407 0.04567581 -0.17172117 0.19717195 0.13080363 +Al 2.03483420 0.10028453 1.98389770 -0.16796648 0.10216174 -0.79790502 2.03841892 0.08400047 1.95797645 0.21404749 -0.13441634 0.50957714 0.01691608 0.13022594 -0.35664845 0.22282597 +Al 1.90338778 2.19789253 0.20698188 -0.73824318 0.36892834 0.24218248 1.88149100 2.16983810 0.17118949 0.37348322 -0.82040314 -0.79865440 0.06815795 0.31039774 -0.43757469 -0.42454519 +Al 0.13990167 -0.00336235 4.22699801 0.60545575 0.26572591 0.30847152 0.14528137 0.00609861 4.16828852 -0.51651896 -0.04198044 -1.30642404 0.04009873 -0.32360977 -0.13723595 -0.53365538 +Al 0.13966297 1.96766674 6.06165521 -0.68463553 -0.33608300 0.02917049 0.04182469 1.96808443 6.11673011 -0.95765160 0.31122890 0.76605778 0.02510051 -0.22070553 0.21686273 0.39568814 +Al 2.15377844 -0.18480510 6.10440512 0.05892927 0.28482844 -1.00274337 2.14379409 -0.12620265 6.05723504 -0.29418097 0.70748394 0.21503271 0.03250977 -0.08766551 0.24981086 0.27030230 +Al 2.11891387 1.88693950 4.21250084 0.39286002 -0.15025753 0.44609361 2.15660798 1.93854607 4.19392680 0.18126174 0.96469831 -0.84297807 0.04096731 0.02458988 0.46219317 -0.31579068 +Al -0.10422580 4.04038397 0.15494813 -0.02422449 -0.85208347 -1.72868698 -0.06455924 3.99585417 -0.07151346 0.69408874 0.10696503 -2.18650854 0.03328949 0.27590974 0.20745221 -0.34762655 +Al 0.01170307 6.08867921 2.02963497 1.02291003 -2.29986604 0.33505130 0.10204672 5.90301118 2.08022324 0.36262648 -0.97833494 0.55983239 0.02244153 0.09457648 0.25475448 0.09846705 +Al 1.97633583 4.19644025 2.07173789 0.38942544 0.34491464 0.51929877 2.05870312 4.15893716 2.17240187 1.16319169 -1.06499491 1.14366143 0.04186118 0.18711983 -0.38195184 0.35018185 +Al 2.13028979 6.05720479 0.12437434 -0.56192064 1.10937502 1.72467316 2.03093237 6.16616343 0.22636209 -1.10853852 0.60495543 -0.06030238 0.02477497 -0.32445294 0.12500193 -0.32464125 +Al -0.19927375 4.05760085 4.03833007 0.53714714 -0.63756118 -0.00335450 -0.03248695 4.00614451 4.07624522 2.09309665 -0.34674387 0.80620611 0.09022963 0.89309837 0.11954330 0.16250083 +Al -0.09511775 6.22080798 6.14893328 -0.50769178 -0.03447663 -0.43884577 -0.09799495 6.12242326 6.12879692 0.47834541 -1.61534925 0.08451546 0.03257992 0.28207578 -0.61847458 0.11533067 +Al 2.16192487 3.96734595 6.20660576 0.81339916 -0.00838017 -1.00797297 2.18056963 4.02380800 6.07306091 -0.40203843 0.98284014 -1.18021244 0.03489733 -0.44904038 0.35302437 -0.32493968 +Al 2.04342734 5.99328302 4.09422797 -0.34837466 0.40386889 0.49054059 2.01765645 6.04958972 4.15201148 0.01861823 0.58504763 0.46188455 0.00966126 -0.03890277 0.12400499 0.09699627 +Al 4.12873504 -0.09385544 -0.04070397 -0.53032298 -0.48212852 -0.47057884 4.05950027 -0.06948805 -0.04315484 -0.58650611 0.92751197 0.32352926 0.03556891 -0.16783018 0.42389581 0.36210099 +Al 4.00730035 2.08501061 2.10790045 1.94036692 0.69849121 -0.07967466 4.19843764 2.12498899 2.10447206 1.23468406 -0.15096150 -0.05290245 -0.00293970 0.11086241 -0.07470684 0.04269246 +Al 6.09741148 0.00653072 2.03616757 -0.75210300 1.00224225 0.63806698 6.05319828 0.12654426 2.12190414 0.08523618 0.98627825 0.69445973 0.01104747 0.09768037 0.18464649 0.24915921 +Al 6.11034052 2.14825483 0.21182502 -0.08442768 0.68640145 -1.37842183 6.08315617 2.13848904 0.03278364 -0.34836385 -0.78684029 -1.80041807 0.03573598 -0.14190429 -0.49806651 -0.21100660 +Al 4.18230453 0.01957983 4.08632220 0.32423008 -0.24543437 -0.13977754 4.20171060 -0.00534617 4.08410867 -0.05322740 -0.18446773 0.22596081 0.01371445 -0.06840396 -0.02096407 -0.02025451 +Al 4.07999080 1.95201866 6.17330241 -0.55505176 -0.05601350 -0.30160711 4.02856458 1.93877892 6.15737613 -0.25549493 -0.15341887 0.05673810 0.02841402 -0.07095965 -0.01594452 0.05239176 +Al 6.04080561 0.00917481 6.13038896 1.45023888 -0.50548269 1.28920177 6.21393996 0.00944117 6.24427663 1.40626442 0.40271162 0.73926252 0.03629277 0.32068290 0.33369400 -0.11607776 +Al 6.27316329 2.12537318 4.07608664 -0.87434549 0.05772887 0.25061144 6.09409643 2.06035727 4.10435555 -2.00132806 -1.00508627 0.45776236 0.05742928 -0.71649904 -0.54630815 -0.09325273 +Al 3.95079123 4.02823658 0.05441541 0.04630141 -1.00228514 0.79129964 3.97908084 3.96675084 0.12495806 0.25445326 0.00480584 0.31471881 0.05548307 0.31981913 0.16868008 0.05237449 +Al 4.08969105 6.21746492 2.11108375 0.31599317 1.37128517 -0.71912391 4.09072487 6.31549215 2.04941714 -0.27041855 0.22481211 -0.29090850 0.02844169 -0.20167795 -0.15027434 -0.00927075 +Al 5.87327491 4.22164516 1.92540329 -0.18556879 0.01720883 -0.49616957 5.91610022 4.17335776 1.94905240 1.06862616 -0.85962761 0.97767055 0.06559015 0.31393846 -0.32464927 0.39609546 +Al 6.16862108 6.16298195 0.07892599 -0.26759031 -0.99142965 1.25033343 6.11551220 6.04983000 0.17353957 -0.57098905 -0.83736373 0.40771347 0.04572929 -0.24487065 -0.20481584 -0.18512544 +Al 4.14516662 4.11240863 4.16214297 0.21441665 -1.18576487 -0.17789512 4.13322622 3.97642393 4.10132135 -0.45184882 -1.00528488 -0.89563720 0.00884758 -0.17041663 -0.24813674 -0.22303960 +Al 4.23721508 6.08072290 6.08307472 0.68653876 0.21080882 0.04560572 4.26643379 6.07914675 6.09461896 -0.28178320 -0.06450424 0.18289899 0.02204928 -0.18264197 -0.22128409 0.03875775 +Al 5.97789206 3.87813881 6.20956249 -1.29526782 1.44949375 -1.04090804 5.91104231 4.07169010 6.11094541 0.13442338 1.78041860 -0.70994746 0.02835153 0.38848951 0.39279792 0.02366620 +Al 6.21360121 5.99364994 4.00244441 -0.11191382 -0.09326383 -0.06890248 6.15996987 6.04803280 4.01943776 -0.69527018 0.90269514 0.26059786 0.05201764 -0.35409825 0.49932362 0.23710075 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.0130284021902485 free_energy=1.0130284021902485 stress="0.005128678845193628 8.697537281318916e-07 -0.00029998580829961133 8.697537281318916e-07 0.0044229703237791355 6.279173330563964e-05 -0.00029998580829961133 6.279173330563964e-05 0.005507960838444401" pbc="T T T" +Al 0.02213502 -0.08451190 0.25018385 -0.26435718 0.55167561 0.67885138 -0.00601513 -0.01338803 0.26538651 -0.12007549 0.66756936 -0.40063079 0.02408400 -0.06484044 0.12179066 -0.34869014 +Al -0.11538440 1.96031939 2.04291776 -1.43214907 1.27051127 0.75093292 -0.20701340 2.14730757 2.12562758 0.04706424 1.92342147 0.65133519 0.03202621 0.15205486 0.42208068 0.08403587 +Al 2.03849331 0.18810670 2.08207549 0.59865585 -0.11796993 0.07979404 2.11775516 0.08872301 2.09423589 0.61551726 -1.41529797 0.18435427 0.04260248 0.23273160 -0.66690936 -0.00670461 +Al 1.99624644 2.10310818 0.14973596 -0.11425410 -0.48290690 0.22861129 1.99121706 2.05443881 0.16027995 -0.06084396 -0.25244343 0.01634178 0.02138092 0.12577098 -0.11886664 -0.12707024 +Al -0.05837425 0.06203386 4.04433271 -0.79962697 -0.75040507 -0.85240723 -0.07338641 -0.02033140 3.99608904 0.62142255 -0.46085540 0.02651968 0.02673161 0.31441920 -0.18786780 0.19621972 +Al -0.00770457 2.06909293 6.10104693 0.70817611 -0.29592739 -0.03445779 0.09317355 1.97944054 6.12719561 0.95505116 -1.21265131 0.50688098 0.06705886 0.23824199 -0.38420829 0.13357847 +Al 1.97896566 0.12917286 6.23590763 -0.80309374 -0.52241192 1.76837785 1.92181187 0.05904736 6.36191356 -0.07616280 -0.80955649 0.46944109 0.01683225 0.07847420 -0.05961487 -0.31959155 +Al 2.05369233 2.06438910 3.96006411 1.16961714 0.61354318 -0.65646365 2.17885121 2.12222930 3.95703070 0.92327454 0.22511332 0.53560529 0.01971213 0.11191042 0.09528210 0.42521272 +Al 0.00698362 3.98154618 -0.17370970 -0.69730309 1.47085102 -0.97100251 -0.05014748 4.17053588 -0.21631364 -0.16757894 1.42047130 0.17073287 0.03988046 0.00149831 0.52149023 0.40411433 +Al -0.05288955 5.95606644 2.22626273 0.52308388 0.54286053 0.91214082 0.02455706 6.00411830 2.26357548 0.76346422 0.18179056 -0.39144355 0.02830709 0.22229435 0.07992085 -0.25793799 +Al 2.11289958 4.12089312 2.01835908 -1.00165741 -0.25236311 -0.09011107 1.98148873 4.04688871 2.00932370 -1.26842272 -0.98754574 -0.02296711 0.03429157 -0.21815781 -0.31953105 -0.00845476 +Al 2.07385933 6.02691843 0.11851837 -0.51781348 -0.76700998 0.31669745 2.00474070 5.99913554 0.16090479 -0.65142880 0.30661728 0.36585202 0.01347043 -0.14407845 0.26818065 0.10918844 +Al 0.07379181 3.95074811 4.16066022 0.28274843 -0.63740086 1.66182124 0.09728314 3.95784625 4.32313585 0.09514929 0.79912115 1.20054960 0.01885460 -0.01987759 0.37385512 -0.01594232 +Al 0.01970170 6.31508873 6.13616062 0.66174879 -0.13277108 0.33549058 0.09583308 6.22386576 6.15136584 0.67824314 -1.40931939 -0.13831786 0.01521367 0.06967147 -0.52011188 -0.07661197 +Al 2.20594642 4.04358694 6.26889780 0.08089119 -0.05692082 -0.31614389 2.17778868 4.03407371 6.16573066 -0.53269470 -0.06858254 -1.36773904 0.02939705 -0.26239430 -0.04485116 -0.51278746 +Al 2.04939216 6.02783801 4.00101857 -0.07919856 -0.15244839 -0.85845379 2.03830579 6.07919793 3.98217142 -0.00570732 0.94869788 0.57250429 0.02402397 -0.07222048 0.45815395 0.38046490 +Al 4.04865600 0.03497252 -0.07876810 0.10580979 1.34718334 0.49769567 4.06059397 0.14738452 0.07982591 0.20250051 0.45891526 1.92089753 0.05650232 -0.06961689 0.00056470 0.86917019 +Al 4.22026774 1.99102600 2.05061721 0.60510986 -0.84381460 0.06119475 4.25729372 1.92260931 2.06541232 -0.05954731 -0.32524965 0.24966477 -0.00166435 -0.09625805 0.08753587 0.04403594 +Al 6.07549009 -0.08385214 2.14347932 1.30988168 0.84804195 -1.06791337 6.18056567 0.02814837 1.99756354 0.46774409 0.99668802 -1.38919434 0.03859889 -0.07418075 0.24944452 -0.26409109 +Al 6.19186074 2.09740161 -0.02857643 -0.06702953 -0.24934370 0.00668792 6.15579182 2.02780824 -0.04953139 -0.50020516 -0.84999854 -0.51819493 0.02756169 -0.22344633 -0.33945515 -0.00589771 +Al 4.14299385 0.08645619 4.14261476 1.17104305 -0.15778604 0.15932594 4.21210948 0.04321468 4.15160924 0.09843822 -0.56420467 -0.03427003 0.00692053 -0.32967189 -0.20223724 -0.00630615 +Al 4.16366296 1.99290003 6.16679844 -0.66254727 -1.02372937 0.09626487 4.05934754 1.93621067 6.14446934 -1.09792379 -0.05101719 -0.44714627 0.03185297 -0.29924423 0.30695412 -0.20001791 +Al 5.94480474 -0.10034035 6.23393033 -0.04907645 -1.75398260 -0.61710366 6.04243363 -0.23671976 6.16832261 1.67840732 -0.52946212 -0.36547481 0.05717584 0.67699772 0.16576331 -0.15697046 +Al 6.21648228 2.01870097 4.25738983 0.90066960 0.19860041 0.07877886 6.21560078 2.05080104 4.22636022 -0.88568653 0.33096460 -0.42276020 0.04531873 -0.54938602 0.09238392 -0.38424506 +Al 4.14912259 4.01367788 0.05212177 -0.92324580 1.05232549 -0.45720373 4.04592202 4.11349092 0.03147643 -0.97211388 0.62728330 0.13266298 0.02480920 -0.01688396 0.04148863 0.11571892 +Al 4.21417820 6.02712474 2.15191252 -0.70303143 1.65229380 -0.07107904 4.09333020 6.24255705 2.08300373 -1.25278141 1.96456036 -1.02883463 0.05230762 -0.42375731 0.38880200 -0.44569508 +Al 6.08982307 4.00502052 2.00375293 -1.44048406 -1.11928088 -1.31327079 5.96676282 3.90728881 1.94460099 -0.82399861 -0.33750090 0.25827774 0.03384097 0.18680633 -0.09610198 0.42573264 +Al 6.17182647 5.99789598 0.17494200 0.73652825 -1.35023526 0.41123487 6.22025715 5.90599463 0.17281582 -0.00098980 -0.05324400 -0.29314965 0.01796102 -0.11751999 0.13384365 -0.36771480 +Al 4.01232337 4.19970871 4.04024162 -0.39263944 0.14090196 -0.38674701 4.00711671 4.13838430 4.02226635 0.38052503 -0.96741127 -0.06566993 0.05085593 0.13910101 -0.62654015 0.21938361 +Al 4.01156082 6.12397827 6.26155146 0.96586937 0.07250967 -0.16429524 4.07155007 6.13058473 6.19820112 -0.03834417 0.12849413 -0.84500948 0.04665229 -0.10336532 -0.06773895 -0.38038120 +Al 5.93181598 4.19017322 5.97824763 -0.93957766 0.01456575 0.82293726 5.89401831 4.16565552 6.09467449 0.23795735 -0.48981186 1.05748443 0.02535221 0.37888875 -0.14028154 0.34871659 +Al 6.06160705 6.11125177 4.12436328 1.06725222 0.89084394 -1.01018489 6.17529306 6.16395072 4.03832704 0.74974647 -0.19555552 -0.58830190 0.04511526 0.15603860 -0.03321890 0.12953815 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.1047037076242896 free_energy=1.1047037076242896 stress="0.004138521127263458 0.0009391501441625187 0.00011793231796938067 0.0009391501441625187 0.004119625289886451 -0.00018275874172973586 0.00011793231796938067 -0.00018275874172973586 0.007073958518883559" pbc="T T T" +Al -0.00601513 -0.01338803 0.26538651 0.12007549 -0.66756936 0.40063079 0.02213502 -0.08451190 0.25018385 0.26435718 -0.55167561 -0.67885138 0.03076914 0.19096507 -0.04593207 -0.34803027 +Al -0.20701340 2.14730757 2.12562758 -0.04706424 -1.92342147 -0.65133519 -0.11538440 1.96031939 2.04291776 1.43214907 -1.27051127 -0.75093292 0.04393056 0.71520172 -0.03680347 -0.13705089 +Al 2.11775516 0.08872301 2.09423589 -0.61551726 1.41529797 -0.18435427 2.03849331 0.18810670 2.08207549 -0.59865585 0.11796993 -0.07979404 0.02049922 -0.25179988 -0.09134542 0.06993599 +Al 1.99121706 2.05443881 0.16027995 0.06084396 0.25244343 -0.01634178 1.99624644 2.10310818 0.14973596 0.11425410 0.48290690 -0.22861129 0.01246022 -0.03595561 0.18666288 -0.02643292 +Al -0.07338641 -0.02033140 3.99608904 -0.62142255 0.46085540 -0.02651968 -0.05837425 0.06203386 4.04433271 0.79962697 0.75040507 0.85240723 0.03892371 0.50396552 0.38058985 0.30839175 +Al 0.09317355 1.97944054 6.12719561 -0.95505116 1.21265131 -0.50688098 -0.00770457 2.06909293 6.10104693 -0.70817611 0.29592739 0.03445779 0.04634591 -0.12009872 -0.12086814 0.11597975 +Al 1.92181187 0.05904736 6.36191356 0.07616280 0.80955649 -0.46944109 1.97896566 0.12917286 6.23590763 0.80309374 0.52241192 -1.76837785 0.05695456 0.40878577 -0.14799086 -0.47405682 +Al 2.17885121 2.12222930 3.95703070 -0.92327454 -0.22511332 -0.53560529 2.05369233 2.06438910 3.96006411 -1.16961714 -0.61354318 0.65646365 0.02718331 -0.28795978 -0.34411634 0.30539513 +Al -0.05014748 4.17053588 -0.21631364 0.16757894 -1.42047130 -0.17073287 0.00698362 3.98154618 -0.17370970 0.69730309 -1.47085102 0.97100251 0.05741352 0.35071467 -0.58712393 0.37469906 +Al 0.02455706 6.00411830 2.26357548 -0.76346422 -0.18179056 0.39144355 -0.05288955 5.95606644 2.22626273 -0.52308388 -0.54286053 -0.91214082 0.05477183 -0.04443676 -0.23061949 -0.55987018 +Al 1.98148873 4.04688871 2.00932370 1.26842272 0.98754574 0.02296711 2.11289958 4.12089312 2.01835908 1.00165741 0.25236311 0.09011107 0.03509882 0.06153255 -0.08999473 0.09253752 +Al 2.00474070 5.99913554 0.16090479 0.65142880 -0.30661728 -0.36585202 2.07385933 6.02691843 0.11851837 0.51781348 0.76700998 -0.31669745 0.01364835 0.05071369 0.36745917 -0.07874840 +Al 0.09728314 3.95784625 4.32313585 -0.09514929 -0.79912115 -1.20054960 0.07379181 3.95074811 4.16066022 -0.28274843 0.63740086 -1.66182124 0.02306465 -0.13254854 0.43200105 -0.25592987 +Al 0.09583308 6.22386576 6.15136584 -0.67824314 1.40931939 0.13831786 0.01970170 6.31508873 6.13616062 -0.66174879 0.13277108 -0.33549058 0.03722723 -0.04317109 -0.25700677 -0.23399412 +Al 2.17778868 4.03407371 6.16573066 0.53269470 0.06858254 1.36773904 2.20594642 4.04358694 6.26889780 -0.08089119 0.05692082 0.31614389 0.01701042 -0.12802143 0.04953544 -0.10675663 +Al 2.03830579 6.07919793 3.98217142 0.00570732 -0.94869788 -0.57250429 2.04939216 6.02783801 4.00101857 0.07919856 0.15244839 0.85845379 0.02706303 0.15199849 0.17959259 0.49733150 +Al 4.06059397 0.14738452 0.07982591 -0.20250051 -0.45891526 -1.92089753 4.04865600 0.03497252 -0.07876810 -0.10580979 -1.34718334 -0.49769567 0.03940503 0.09237554 -0.49490403 -0.08433439 +Al 4.25729372 1.92260931 2.06541232 0.05954731 0.32524965 -0.24966477 4.22026774 1.99102600 2.05061721 -0.60510986 0.84381460 -0.06119475 0.05047209 -0.35149253 0.26052588 0.11741839 +Al 6.18056567 0.02814837 1.99756354 -0.46774409 -0.99668802 1.38919434 6.07549009 -0.08385214 2.14347932 -1.30988168 -0.84804195 1.06791337 0.06588470 -0.35899921 -0.15016671 0.16723139 +Al 6.15579182 2.02780824 -0.04953139 0.50020516 0.84999854 0.51819493 6.19186074 2.09740161 -0.02857643 0.06702953 0.24934370 -0.00668792 0.00663271 -0.04669239 0.00974961 -0.25286891 +Al 4.21210948 0.04321468 4.15160924 -0.09843822 0.56420467 0.03427003 4.14299385 0.08645619 4.14261476 -1.17104305 0.15778604 -0.15932594 0.02100275 -0.32405759 -0.06506621 -0.08030997 +Al 4.05934754 1.93621067 6.14446934 1.09792379 0.05101719 0.44714627 4.16366296 1.99290003 6.16679844 0.66254727 1.02372937 -0.09626487 0.01455896 0.00164852 0.24458914 -0.08242188 +Al 6.04243363 -0.23671976 6.16832261 -1.67840732 0.52946212 0.36547481 5.94480474 -0.10034035 6.23393033 0.04907645 1.75398260 0.61710366 0.04211444 0.37058568 0.57817759 0.26001291 +Al 6.21560078 2.05080104 4.22636022 0.88568653 -0.33096460 0.42276020 6.21648228 2.01870097 4.25738983 -0.90066960 -0.19860041 -0.07877886 0.02772826 -0.49207051 -0.02178290 0.04977811 +Al 4.04592202 4.11349092 0.03147643 0.97211388 -0.62728330 -0.13266298 4.14912259 4.01367788 0.05212177 0.92324580 -1.05232549 0.45720373 0.02983322 -0.02166875 -0.25777803 0.21986734 +Al 4.09333020 6.24255705 2.08300373 1.25278141 -1.96456036 1.02883463 4.21417820 6.02712474 2.15191252 0.70303143 -1.65229380 0.07107904 0.00366273 0.09250872 -0.27189183 -0.12916503 +Al 5.96676282 3.90728881 1.94460099 0.82399861 0.33750090 -0.25827774 6.08982307 4.00502052 2.00375293 1.44048406 1.11928088 1.31327079 0.08337120 0.18539275 0.55661727 0.48285839 +Al 6.22025715 5.90599463 0.17281582 0.00098980 0.05324400 0.29314965 6.17182647 5.99789598 0.17494200 -0.73652825 1.35023526 -0.41123487 0.05662362 -0.41202555 0.67932921 -0.07057314 +Al 4.00711671 4.13838430 4.02226635 -0.38052503 0.96741127 0.06566993 4.01232337 4.19970871 4.04024162 0.39263944 -0.14090196 0.38674701 0.01472974 0.30459472 -0.05071633 -0.01069060 +Al 4.07155007 6.13058473 6.19820112 0.03834417 -0.12849413 0.84500948 4.01156082 6.12397827 6.26155146 -0.96586937 -0.07250967 0.16429524 0.03660563 -0.43194861 0.08406947 -0.08097677 +Al 5.89401831 4.16565552 6.09467449 -0.23795735 0.48981186 -1.05748443 5.93181598 4.19017322 5.97824763 0.93957766 -0.01456575 -0.82293726 0.02227255 0.35322095 -0.18358794 -0.14640537 +Al 6.17529306 6.16395072 4.03832704 -0.74974647 0.19555552 0.58830190 6.06160705 6.11125177 4.12436328 -1.06725222 -0.89084394 1.01018489 0.04744160 -0.35125741 -0.56120394 0.09717892 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.1705957239373603 free_energy=1.1705957239373603 stress="0.003845133113172732 0.00044194282378009364 -0.0003317322932143324 0.00044194282378009364 0.004823164759565864 -0.00029437895860849356 -0.0003317322932143324 -0.00029437895860849356 0.00632812513627653" pbc="T T T" +Al -0.07821786 0.12108144 -0.16766018 -0.31105836 0.08388703 -0.63141108 -0.05270062 0.06097786 -0.17412374 0.70497751 -1.21496841 0.70935559 0.05109044 0.36299030 -0.33364679 0.23888644 +Al 0.09085967 1.71555159 2.07646922 0.95086557 -0.57698993 -0.08295244 0.19853509 1.69386181 2.06649659 0.90014887 0.23915207 -0.25077749 0.00038390 0.09802415 0.22986456 0.09326055 +Al 2.19426707 -0.15809549 2.02373705 1.03647976 -1.03793313 -0.04976920 2.23658087 -0.19380258 2.03850145 -0.21319336 0.49623588 0.43911276 0.05712845 -0.40657940 0.36796046 0.04493915 +Al 2.09224055 1.98196121 -0.05771524 -0.13416708 0.33750053 -0.29578459 2.07887852 2.01921285 -0.06688840 -0.14288731 0.29808352 0.12765216 -0.00460580 0.01594345 0.04017677 0.13878125 +Al -0.08478475 -0.12847821 4.22978432 0.04816760 1.42413180 0.02858846 -0.08829754 0.07314862 4.22138691 -0.00453537 1.72391754 -0.16935684 0.04496107 -0.06362365 0.61444069 -0.08989657 +Al -0.11142129 2.19243089 6.10770098 -0.29508494 -1.74031306 0.04224444 -0.09678244 1.99392413 6.09074772 0.45594717 -1.43132773 -0.32804129 0.03419664 0.35177307 -0.37446681 -0.10103132 +Al 1.96624328 0.03494261 6.02228034 -0.64379813 0.26059213 1.01041857 1.94546271 0.07343064 6.15728247 0.28186001 0.37447223 1.24972324 0.02227456 0.27337636 0.10202422 0.27831770 +Al 2.06870533 2.22704702 4.07403457 -0.06714251 0.61567618 -0.46811984 2.03518431 2.23346279 4.05781615 -0.47171450 -0.43154372 0.14868740 0.02538211 -0.19260893 -0.40686164 0.21489186 +Al 0.18967762 4.08336422 0.21743947 -0.56962473 1.38747015 -0.09640643 0.03782485 4.17525923 0.16782586 -1.89001058 0.15668653 -0.68728980 0.05252502 -0.64896938 -0.24446278 -0.26072600 +Al 0.01222183 6.25978028 2.03920652 0.80500617 0.40527830 0.35732957 0.09526192 6.28916164 2.07937186 0.56667804 0.12459322 0.38512026 0.01442015 0.07565116 -0.11384181 0.00532914 +Al 2.00164369 3.99107963 1.99727438 -0.12767300 -1.11536353 -2.17565712 2.06495718 3.91150630 1.83616264 1.05294354 -0.25973113 -0.66956097 0.06160533 0.55238040 0.21839811 0.33217773 +Al 2.15614552 6.00964837 0.04723443 -0.36000976 0.40697371 0.78991870 2.10781449 6.08554736 0.11366897 -0.51514776 0.77564353 0.28362216 0.02468155 -0.08517049 0.31151973 -0.00470665 +Al -0.02563888 4.10402757 3.89647265 0.47653601 1.32818343 0.76603495 0.03241275 4.21604793 4.00576873 0.58030786 0.46142675 1.11029174 0.02265039 0.03234221 0.00109147 0.23913714 +Al -0.01618421 6.05639081 6.24826744 0.72018435 -0.07301222 -0.08632860 0.05203349 6.06872737 6.22529035 0.43790944 0.33572790 -0.40137470 0.04648780 0.03031433 0.07432401 -0.05158156 +Al 1.94771696 4.13587640 6.16413404 -0.03963949 0.07061201 -0.48028331 1.97983812 4.10452836 6.11160637 0.57463206 -0.61689164 -0.28871923 0.02109376 0.24079029 -0.22729036 -0.13619823 +Al 1.86084557 6.09813012 4.16931790 0.03314649 -0.25956277 0.59576932 1.94573769 6.05014443 4.19139282 1.41026781 -0.37083548 -0.24236961 0.03404173 0.53960505 -0.28615455 -0.21038931 +Al 4.07882182 0.00677076 0.10893728 0.18806050 -0.25676481 0.97901444 4.07076995 -0.02019098 0.15472006 -0.27146475 -0.15993975 -0.19035691 0.04430474 -0.16355851 -0.05056738 -0.27788435 +Al 4.09698915 2.04241337 2.13967434 0.67562370 -0.07071069 0.19576034 4.11409461 2.04203469 2.13012541 -0.31081755 0.02257674 -0.39023380 0.02657714 -0.33227946 0.06828139 -0.14414582 +Al 5.93247690 -0.07858327 2.09892254 -0.89349890 -0.46046371 -0.84058752 5.93331369 -0.08370514 2.07968937 0.83191229 0.22731745 0.45711485 0.08222741 0.54708726 0.31663741 0.38228639 +Al 6.10748533 1.87416468 0.29330146 0.59206701 -0.84707729 -0.79441788 6.16150442 1.89159061 0.16804059 0.33829256 1.17602453 -1.23748826 0.06435485 -0.00997542 0.54848301 -0.43998100 +Al 3.92045497 0.06163327 4.07252328 -0.91223561 0.47767476 0.79626074 3.87722111 0.10313340 4.18873592 0.15217493 0.27151528 1.14257570 0.02482290 0.29862544 -0.04882112 0.27752877 +Al 4.21848770 2.13036532 6.14141824 -0.41114079 0.10240664 0.23353929 4.11356789 2.15132489 6.16104690 -1.24434655 0.21610760 -0.00815013 0.07155155 -0.51189100 0.06379645 0.07858018 +Al 5.83816546 0.19180441 6.18106669 -0.41228672 -0.38450265 1.11629972 5.88112726 0.07078982 6.23781975 1.00302767 -1.67088185 -0.14582247 0.06566150 0.64779487 -0.54878397 -0.29055007 +Al 6.13965224 2.17354209 3.95807566 0.59199442 -0.20549863 -1.31688692 6.20761334 2.07960042 3.88674503 0.51467342 -1.28628043 0.13875943 0.03296307 0.06960998 -0.52944362 0.28892537 +Al 4.21364971 4.02582056 0.04027400 0.63015129 0.22185216 0.01522223 4.23271569 4.05833408 0.04915904 -0.29191187 0.35947221 0.13961150 0.00859065 -0.28835755 0.05580523 0.04202125 +Al 4.22484888 6.13119549 2.12010265 0.09500864 -0.04043648 -0.08435994 4.15892190 6.01897958 2.13387411 -1.21149386 -1.69843909 0.27506941 0.05828532 -0.47979900 -0.73308901 0.13153562 +Al 6.09355870 4.00713122 1.99412437 -0.42926307 0.18877520 1.56403723 6.08490501 4.06392335 2.14781591 0.36585391 0.69472622 0.98088513 0.04450934 0.16143462 0.29058982 0.11129000 +Al 6.22427957 6.11304095 0.15661615 0.13062756 -1.01140828 0.52108241 6.18973320 6.01762233 0.15892707 -0.73797405 -0.52137499 -0.37754255 0.02995663 -0.33164617 -0.02589610 -0.36225408 +Al 4.17757328 4.00448317 4.26864029 0.21320757 0.73021792 -1.51694048 4.15864148 4.12972052 4.07699427 -0.64123928 1.20563015 -1.63272963 0.04009722 -0.21789368 0.47546617 -0.39690543 +Al 4.12732324 6.06330384 6.15537448 -1.55587482 1.46599085 -0.20539925 3.95865176 6.21074962 6.13660947 -1.37174879 0.99341933 -0.10773843 0.00159240 -0.09847691 0.08233686 0.01224113 +Al 6.25680380 4.06583361 6.25279967 0.05038495 0.04702882 0.57699152 6.22342951 4.07849676 6.29671196 -0.58779115 0.04362041 0.00625911 0.04900440 -0.29861961 0.14390298 0.01326880 +Al 6.12933970 6.08283506 4.22722171 -0.07501366 -1.47421443 -0.46320732 6.10527835 5.95295029 4.16772913 -0.26533035 -0.53413487 -0.46628833 0.01777950 -0.16829377 -0.08177340 -0.15714810 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.1319334923518758 free_energy=1.1319334923518758 stress="0.004724185153377603 7.628423560682366e-05 -0.0002545923994040662 7.628423560682366e-05 0.005218669922821519 -0.0007487346003771602 -0.0002545923994040662 -0.0007487346003771602 0.005293740127255423" pbc="T T T" +Al -0.05270062 0.06097786 -0.17412374 -0.70497751 1.21496841 -0.70935559 -0.07821786 0.12108144 -0.16766018 0.31105836 -0.08388703 0.63141108 0.07696546 0.21245686 -0.37979575 0.61031702 +Al 0.19853509 1.69386181 2.06649659 -0.90014887 -0.23915207 0.25077749 0.09085967 1.71555159 2.07646922 -0.95086557 0.57698993 0.08295244 0.02353866 -0.15155912 0.34707640 -0.19713105 +Al 2.23658087 -0.19380258 2.03850145 0.21319336 -0.49623588 -0.43911276 2.19426707 -0.15809549 2.02373705 -1.03647976 1.03793313 0.04976920 0.09072832 -0.31324147 0.58453916 0.26290110 +Al 2.07887852 2.01921285 -0.06688840 0.14288731 -0.29808352 -0.12765216 2.09224055 1.98196121 -0.05771524 0.13416708 -0.33750053 0.29578459 0.02375263 -0.04589477 -0.05241661 0.14080337 +Al -0.08829754 0.07314862 4.22138691 0.00453537 -1.72391754 0.16935684 -0.08478475 -0.12847821 4.22978432 -0.04816760 -1.42413180 -0.02858846 0.03241941 0.14300888 -0.46633462 -0.07246492 +Al -0.09678244 1.99392413 6.09074772 -0.45594717 1.43132773 0.32804129 -0.11142129 2.19243089 6.10770098 0.29508494 1.74031306 -0.04224444 0.04259439 0.14716773 0.52984534 -0.03711024 +Al 1.94546271 0.07343064 6.15728247 -0.28186001 -0.37447223 -1.24972324 1.96624328 0.03494261 6.02228034 0.64379813 -0.26059213 -1.01041857 0.03894151 0.30674188 -0.02846450 -0.13871674 +Al 2.03518431 2.23346279 4.05781615 0.47171450 0.43154372 -0.14868740 2.06870533 2.22704702 4.07403457 0.06714251 -0.61567618 0.46811984 0.01766624 -0.04441021 -0.27918251 0.17114627 +Al 0.03782485 4.17525923 0.16782586 1.89001058 -0.15668653 0.68728980 0.18967762 4.08336422 0.21743947 0.56962473 -1.38747015 0.09640643 0.04639039 -0.03141727 -0.52073599 -0.03208457 +Al 0.09526192 6.28916164 2.07937186 -0.56667804 -0.12459322 -0.38512026 0.01222183 6.25978028 2.03920652 -0.80500617 -0.40527830 -0.35732957 0.00748749 -0.21630445 -0.13170005 0.02047973 +Al 2.06495718 3.91150630 1.83616264 -1.05294354 0.25973113 0.66956097 2.00164369 3.99107963 1.99727438 0.12767300 1.11536353 2.17565712 0.05023971 0.09226620 0.38100607 0.56053859 +Al 2.10781449 6.08554736 0.11366897 0.51514776 -0.77564353 -0.28362216 2.15614552 6.00964837 0.04723443 0.36000976 -0.40697371 -0.78991870 0.02690310 -0.05944763 -0.09206261 -0.28167760 +Al 0.03241275 4.21604793 4.00576873 -0.58030786 -0.46142675 -1.11029174 -0.02563888 4.10402757 3.89647265 -0.47653601 -1.32818343 -0.76603495 0.04407131 0.01865042 -0.50807453 -0.00717840 +Al 0.05203349 6.06872737 6.22529035 -0.43790944 -0.33572790 0.40137470 -0.01618421 6.05639081 6.24826744 -0.72018435 0.07301222 0.08632860 0.03391349 -0.15246141 0.14326701 -0.17910228 +Al 1.97983812 4.10452836 6.11160637 -0.57463206 0.61689164 0.28871923 1.94771696 4.13587640 6.16413404 0.03963949 -0.07061201 0.48028331 0.01173459 0.13290160 -0.16687558 0.24062884 +Al 1.94573769 6.05014443 4.19139282 -1.41026781 0.37083548 0.24236961 1.86084557 6.09813012 4.16931790 -0.03314649 0.25956277 -0.59576932 0.03391715 0.31065990 0.18796839 -0.31160774 +Al 4.07076995 -0.02019098 0.15472006 0.27146475 0.15993975 0.19035691 4.07882182 0.00677076 0.10893728 -0.18806050 0.25676481 -0.97901444 0.05183028 -0.04269523 0.10126469 -0.37168808 +Al 4.11409461 2.04203469 2.13012541 0.31081755 -0.02257674 0.39023380 4.09698915 2.04241337 2.13967434 -0.67562370 0.07071069 -0.19576034 0.01849459 -0.23017099 -0.01318411 -0.18292420 +Al 5.93331369 -0.08370514 2.07968937 -0.83191229 -0.22731745 -0.45711485 5.93247690 -0.07858327 2.09892254 0.89349890 0.46046371 0.84058752 0.03547313 0.41018630 0.06012462 0.32619196 +Al 6.16150442 1.89159061 0.16804059 -0.33829256 -1.17602453 1.23748826 6.10748533 1.87416468 0.29330146 -0.59206701 0.84707729 0.79441788 0.02401281 -0.12497142 0.56777704 0.07678397 +Al 3.87722111 0.10313340 4.18873592 -0.15217493 -0.27151528 -1.14257570 3.92045497 0.06163327 4.07252328 0.91223561 -0.47767476 -0.79626074 0.02068391 0.36984144 -0.07058954 -0.09454685 +Al 4.11356789 2.15132489 6.16104690 1.24434655 -0.21610760 0.00815013 4.21848770 2.13036532 6.14141824 0.41114079 -0.10240664 -0.23353929 0.01320282 0.00686839 -0.04902407 -0.18646135 +Al 5.88112726 0.07078982 6.23781975 -1.00302767 1.67088185 0.14582247 5.83816546 0.19180441 6.18106669 0.41228672 0.38450265 -1.11629972 0.03781755 0.27659027 -0.23868751 -0.42541641 +Al 6.20761334 2.07960042 3.88674503 -0.51467342 1.28628043 -0.13875943 6.13965224 2.17354209 3.95807566 -0.59199442 0.20549863 1.31688692 0.04506028 -0.20682894 -0.07030927 0.54440327 +Al 4.23271569 4.05833408 0.04915904 0.29191187 -0.35947221 -0.13961150 4.21364971 4.02582056 0.04027400 -0.63015129 -0.22185216 -0.01522223 0.01553884 -0.30541546 0.02468783 0.01859276 +Al 4.15892190 6.01897958 2.13387411 1.21149386 1.69843909 -0.27506941 4.22484888 6.13119549 2.12010265 -0.09500864 0.04043648 0.08435994 0.04838400 -0.30999667 -0.11359591 0.00080967 +Al 6.08490501 4.06392335 2.14781591 -0.36585391 -0.69472622 -0.98088513 6.09355870 4.00713122 1.99412437 0.42926307 -0.18877520 -1.56403723 0.06658670 0.35172883 -0.02878338 -0.42193113 +Al 6.18973320 6.01762233 0.15892707 0.73797405 0.52137499 0.37754255 6.22427957 6.11304095 0.15661615 -0.13062756 1.01140828 -0.52108241 0.03404906 -0.27780295 0.39168594 -0.17430482 +Al 4.15864148 4.12972052 4.07699427 0.64123928 -1.20563015 1.63272963 4.17757328 4.00448317 4.26864029 -0.21320757 -0.73021792 1.51694048 0.03561012 -0.36476290 -0.23650006 0.32937773 +Al 3.95865176 6.21074962 6.13660947 1.37174879 -0.99341933 0.10773843 4.12732324 6.06330384 6.15537448 1.55587482 -1.46599085 0.20539925 0.01435656 0.22762121 -0.39890259 0.07316909 +Al 6.22342951 4.07849676 6.29671196 0.58779115 -0.04362041 -0.00625911 6.25680380 4.06583361 6.25279967 -0.05038495 -0.04702882 -0.57699152 0.03400607 -0.15967021 -0.12221934 -0.42208732 +Al 6.10527835 5.95295029 4.16772913 0.26533035 0.53413487 0.46628833 6.12933970 6.08283506 4.22722171 0.07501366 1.47421443 0.46320732 0.03556289 0.03036121 0.64819608 0.16029033 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=0.8951962562931346 free_energy=0.8951962562931346 stress="0.006762199627886309 0.0004169919220461864 -0.0008031643384668323 0.0004169919220461864 0.005747662582846801 -0.000428147773379447 -0.0008031643384668323 -0.000428147773379447 0.004994554919625807" pbc="T T T" +Al 0.04030615 0.09171277 0.07398912 0.38636985 -0.26785991 -1.18800449 0.06581519 0.04879040 -0.01555996 0.01961857 -0.34441577 -0.41055198 0.02629904 -0.04086984 -0.20727226 0.19639264 +Al -0.12171355 2.17383175 2.12351050 1.74966288 -0.20615385 -0.60071966 0.08038219 2.12948842 2.07436373 1.67722418 -0.80460017 -0.11102183 0.02141505 0.26516093 0.02322641 -0.07555855 +Al 2.03765560 -0.01002666 1.97078838 -0.62320780 -0.94937909 0.88574523 1.95879126 -0.08023619 2.10366340 -0.68772421 -0.33659151 1.23440452 0.02093106 -0.13340948 0.20230105 0.42010620 +Al 2.00171901 2.14455269 0.15523686 0.00321539 0.51057209 -0.48895757 2.04587205 2.12931995 0.08137624 0.87260899 -0.88441054 -0.86187432 0.02624709 0.19260231 -0.31137041 -0.12425624 +Al -0.09804956 0.06403908 3.98361968 -0.68929423 0.25759542 0.04306192 -0.12152908 0.05559522 4.00783576 0.10720760 -0.27312751 0.40484185 0.02128379 0.40046616 -0.30232589 0.12058408 +Al 0.15539836 1.97759483 6.24745943 0.87733745 0.17902510 -0.97164565 0.19943780 2.01780899 6.10701190 -0.20382960 0.54310498 -1.41613739 0.02505161 -0.18748584 0.14341215 -0.30518330 +Al 1.98624033 -0.15814989 6.19826227 -0.60773897 0.07461010 0.32927300 1.96694088 -0.10344309 6.19559125 0.40128717 0.84846884 -0.41404884 0.02540262 0.14631467 0.32192273 -0.18883617 +Al 2.04642751 1.89297675 4.15788683 -0.62869663 -0.13074701 -0.76423065 1.96736042 1.91065810 4.06338249 -0.63139454 0.52782311 -0.79878893 0.01199395 -0.15239640 0.13096791 -0.16119262 +Al -0.01747146 4.10007167 0.08539584 0.90325506 -1.61264617 1.64213071 0.12587224 3.97546132 0.23412357 1.39754803 -0.36322689 0.82907806 0.04763147 0.47137546 0.01176526 -0.01225831 +Al -0.10141215 5.99272522 2.07955022 0.61617945 1.31755784 -0.56483227 0.00340532 6.12824527 2.05016579 1.05375268 0.99523202 -0.05685841 0.01357684 0.37229650 0.05506781 0.22206913 +Al 2.12914650 3.94779452 2.14985644 0.86537330 -0.37586189 -0.99826326 2.20524110 3.96277242 2.08137541 0.63507760 0.61405245 -0.14791065 0.00930486 -0.18502388 0.32459501 0.12354929 +Al 2.10070277 6.13114012 0.16554730 0.10690922 -0.45122988 -0.00453352 2.06587039 6.02516082 0.11880134 -0.65364368 -1.17912109 -0.79129445 0.03197627 -0.32541784 -0.50132716 -0.30043325 +Al -0.00868798 3.98356535 4.22366706 -0.05772660 1.19852707 -0.83847878 -0.00235878 4.11774520 4.10616391 0.07879199 1.08551819 -1.21673047 0.02067019 0.14274806 0.13289078 -0.23181123 +Al -0.00632027 6.12440168 6.00449636 -0.28596208 1.22293318 0.12824325 -0.00767530 6.20129015 6.04712119 0.22000838 0.02427421 0.69871558 0.02763927 0.20998319 -0.17951572 0.13515265 +Al 2.16945475 3.98020268 6.04987816 0.57012647 0.10567414 -0.00825937 2.17804046 4.05003176 6.08039845 -0.50270234 1.13340743 0.36505972 0.02098777 -0.26108484 0.34398379 0.31495971 +Al 2.16814667 6.02303491 4.08164440 -1.14321789 -0.40270175 -0.79903788 2.01239751 6.01421621 3.99371947 -1.47880632 0.19508605 -0.58124010 0.00548880 -0.33213446 0.23488200 -0.14431356 +Al 4.08252758 -0.04479429 0.01262739 0.31174153 -0.51720945 0.16647834 4.11405658 -0.02918545 0.03730931 0.03406929 0.72011542 0.32169577 0.02708811 0.13870641 0.44236477 -0.00466326 +Al 4.11235350 1.97171785 2.18742279 -1.32958999 -1.52447359 0.18497931 3.96856818 1.83732052 2.16108047 -1.08780885 -0.76436510 -0.62167924 0.02836949 -0.14933564 0.02861238 -0.28262655 +Al 6.08381770 0.12544549 1.99268556 -0.44145584 -1.12110738 -0.60654371 6.05551529 -0.01294167 1.94669499 -0.02640194 -1.16919255 -0.05117513 0.02299619 0.06752537 -0.27914362 0.01791927 +Al 6.16610854 2.17394690 0.06744149 0.51495135 1.00235602 0.82543738 6.16218986 2.19148373 0.16019636 -0.53621336 -0.89440095 0.86565878 0.02532385 -0.35542614 -0.33718829 -0.00116998 +Al 4.07349123 0.06111168 4.08523602 -0.52503638 0.25400097 0.79263136 4.03559140 0.03489630 4.18004288 -0.24119216 -0.71501090 0.79505778 0.01948209 0.15015112 -0.28683814 0.13270393 +Al 4.04105891 1.96200610 6.13931792 -0.23991478 0.83969869 -0.56219122 4.03459946 2.03956322 6.10684676 0.01169608 0.46076989 0.00278711 -0.00287159 0.16588633 0.01577100 0.12521895 +Al 6.19833383 0.00456750 6.17281595 -1.73641053 -1.22728472 -0.44627987 6.02227943 -0.05614444 6.11849488 -1.14910064 0.42125459 -0.47054757 0.01187952 -0.15250100 0.17639050 -0.07632223 +Al 6.17546528 2.00658843 4.16939598 -0.89303301 0.41757900 0.13320522 6.04807583 2.08194629 4.18477719 -1.15524930 0.79987472 -0.03870507 0.02492388 -0.37832236 0.26108141 0.13908683 +Al 4.02300101 4.05460594 0.10407229 -0.08466468 0.88580504 -1.05547603 4.01649545 4.15694469 -0.05446419 0.09875659 0.87810922 -1.46360084 0.03982559 -0.07081709 0.10336648 -0.47897970 +Al 4.13349863 6.23926263 2.00253602 -0.32231191 -1.31722110 0.64706809 4.06992697 6.11066867 2.09916276 -0.71747734 -0.99336096 1.03186059 0.06579938 -0.22099494 0.01163989 0.19237468 +Al 5.67385897 4.18246137 2.07058895 0.51978493 -0.13990880 -0.51579553 5.87648240 4.10437225 2.02638211 2.79126132 -1.08325026 -0.21472952 0.12375417 1.11265999 -0.44058548 0.01588787 +Al 6.27963843 6.07074260 0.02249205 -0.14107743 0.95290867 0.00736737 6.20537212 6.16858980 0.02929378 -1.00616679 0.50341561 0.12020389 0.03831953 -0.51261400 0.21842855 0.03203579 +Al 4.07926200 4.08278615 4.13191192 1.64531526 1.38040234 1.10297444 4.24425510 4.18878201 4.27058686 1.15037549 0.54752722 1.18204432 0.05376507 0.06859666 -0.23184758 0.27856857 +Al 4.18633985 6.20421370 6.11489840 0.55329629 1.07419882 0.39805994 4.20374882 6.23617210 6.15914449 -0.48551820 -0.60066098 0.37044394 0.00953607 -0.07119094 -0.39193268 0.05899364 +Al 6.17803387 4.05672476 6.25146659 -0.38203238 -1.59931754 1.76792776 6.11338408 3.91725158 6.36368277 -0.45984604 -0.68749219 0.23439178 0.02796470 -0.36416804 -0.01186680 -0.40726781 +Al 6.07589854 6.00963866 4.02135256 0.50785270 0.16965766 1.35866614 6.12982596 6.06786846 4.17828535 0.47379136 0.79519342 1.21065105 0.02314055 -0.01128042 0.29854411 0.26926950 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=0.9510229816315583 free_energy=0.9510229816315583 stress="0.005355766511518642 -0.00015898575467537403 0.0002944926131876724 -0.00015898575467537403 0.005196658301976934 0.0003584193039226458 0.0002944926131876724 0.0003584193039226458 0.005599291602127005" pbc="T T T" +Al 0.06581519 0.04879040 -0.01555996 -0.01961857 0.34441577 0.41055198 0.04030615 0.09171277 0.07398912 -0.38636985 0.26785991 1.18800449 0.01576916 -0.15968619 0.13283827 0.27661730 +Al 0.08038219 2.12948842 2.07436373 -1.67722418 0.80460017 0.11102183 -0.12171355 2.17383175 2.12351050 -1.74966288 0.20615385 0.60071966 0.04460707 -0.28255525 -0.35259785 0.28011178 +Al 1.95879126 -0.08023619 2.10366340 0.68772421 0.33659151 -1.23440452 2.03765560 -0.01002666 1.97078838 0.62320780 0.94937909 -0.88574523 0.02359368 0.13992990 0.21201345 -0.19557014 +Al 2.04587205 2.12931995 0.08137624 -0.87260899 0.88441054 0.86187432 2.00171901 2.14455269 0.15523686 -0.00321539 -0.51057209 0.48895757 0.03279676 0.30898664 -0.48741908 -0.11215886 +Al -0.12152908 0.05559522 4.00783576 -0.10720760 0.27312751 -0.40484185 -0.09804956 0.06403908 3.98361968 0.68929423 -0.25759542 -0.04306192 0.01347739 0.10973139 -0.03677510 0.13421175 +Al 0.19943780 2.01780899 6.10701190 0.20382960 -0.54310498 1.41613739 0.15539836 1.97759483 6.24745943 -0.87733745 -0.17902510 0.97164565 0.01909485 -0.45547710 0.11054971 0.05347960 +Al 1.96694088 -0.10344309 6.19559125 -0.40128717 -0.84846884 0.41404884 1.98624033 -0.15814989 6.19826227 0.60773897 -0.07461010 -0.32927300 0.02848132 0.42244280 0.16448257 -0.25993558 +Al 1.96736042 1.91065810 4.06338249 0.63139454 -0.52782311 0.79878893 2.04642751 1.89297675 4.15788683 0.62869663 0.13074701 0.76423065 0.03727213 0.21379297 0.27802780 0.17032791 +Al 0.12587224 3.97546132 0.23412357 -1.39754803 0.36322689 -0.82907806 -0.01747146 4.10007167 0.08539584 -0.90325506 1.61264617 -1.64213071 0.06199020 -0.16357235 0.60377604 -0.50190637 +Al 0.00340532 6.12824527 2.05016579 -1.05375268 -0.99523202 0.05685841 -0.10141215 5.99272522 2.07955022 -0.61617945 -1.31755784 0.56483227 0.02054842 -0.12371944 -0.27899698 0.05954010 +Al 2.20524110 3.96277242 2.08137541 -0.63507760 -0.61405245 0.14791065 2.12914650 3.94779452 2.14985644 -0.86537330 0.37586189 0.99826326 0.02316608 0.01740835 0.24796754 0.33990223 +Al 2.06587039 6.02516082 0.11880134 0.65364368 1.17912109 0.79129445 2.10070277 6.13114012 0.16554730 -0.10690922 0.45122988 0.00453352 0.00840819 -0.14770860 0.10625320 -0.18062693 +Al -0.00235878 4.11774520 4.10616391 -0.07879199 -1.08551819 1.21673047 -0.00868798 3.98356535 4.22366706 0.05772660 -1.19852707 0.83847878 0.00081534 -0.04712535 -0.21330453 -0.03399417 +Al -0.00767530 6.20129015 6.04712119 -0.22000838 -0.02427421 -0.69871558 -0.00632027 6.12440168 6.00449636 0.28596208 -1.22293318 -0.12824325 0.03753049 0.12671702 -0.50446786 0.22636876 +Al 2.17804046 4.05003176 6.08039845 0.50270234 -1.13340743 -0.36505972 2.16945475 3.98020268 6.04987816 -0.57012647 -0.10567414 0.00825937 0.02336355 -0.43951671 0.25837907 -0.08081847 +Al 2.01239751 6.01421621 3.99371947 1.47880632 -0.19508605 0.58124010 2.16814667 6.02303491 4.08164440 1.14321789 0.40270175 0.79903788 0.01082547 0.12696809 0.14945724 0.31764973 +Al 4.11405658 -0.02918545 0.03730931 -0.03406929 -0.72011542 -0.32169577 4.08252758 -0.04479429 0.01262739 -0.31174153 0.51720945 -0.16647834 0.01906301 -0.30466960 0.27693186 0.05877250 +Al 3.96856818 1.83732052 2.16108047 1.08780885 0.76436510 0.62167924 4.11235350 1.97171785 2.18742279 1.32958999 1.52447359 -0.18497931 0.03263914 0.27954480 0.38528529 -0.18614564 +Al 6.05551529 -0.01294167 1.94669499 0.02640194 1.16919255 0.05117513 6.08381770 0.12544549 1.99268556 0.44145584 1.12110738 0.60654371 0.02332039 0.16683356 0.20183902 0.37917529 +Al 6.16218986 2.19148373 0.16019636 0.53621336 0.89440095 -0.86565878 6.16610854 2.17394690 0.06744149 -0.51495135 -1.00235602 -0.82543738 0.06315257 -0.25916765 -0.74877786 -0.03497495 +Al 4.03559140 0.03489630 4.18004288 0.24119216 0.71501090 -0.79505778 4.07349123 0.06111168 4.08523602 0.52503638 -0.25400097 -0.79263136 0.02223663 0.01150428 -0.25588228 -0.16690466 +Al 4.03459946 2.03956322 6.10684676 -0.01169608 -0.46076989 -0.00278711 4.04105891 1.96200610 6.13931792 0.23991478 -0.83969869 0.56219122 0.01165805 -0.04428177 -0.21964557 0.19817130 +Al 6.02227943 -0.05614444 6.11849488 1.14910064 -0.42125459 0.47054757 6.19833383 0.00456750 6.17281595 1.73641053 1.22728472 0.44627987 0.06512900 0.49711281 0.76615985 0.09028909 +Al 6.04807583 2.08194629 4.18477719 1.15524930 -0.79987472 0.03870507 6.17546528 2.00658843 4.16939598 0.89303301 -0.41757900 -0.13320522 0.00438215 0.18170041 -0.05711852 -0.23361038 +Al 4.01649545 4.15694469 -0.05446419 -0.09875659 -0.87810922 1.46360084 4.02300101 4.05460594 0.10407229 0.08466468 -0.88580504 1.05547603 0.04556192 0.18355018 -0.12843955 0.27279422 +Al 4.06992697 6.11066867 2.09916276 0.71747734 0.99336096 -1.03186059 4.13349863 6.23926263 2.00253602 0.32231191 1.31722110 -0.64706809 0.01208245 0.02307459 0.12557888 0.01444798 +Al 5.87648240 4.10437225 2.02638211 -2.79126132 1.08325026 0.21472952 5.67385897 4.18246137 2.07058895 -0.51978493 0.13990880 0.51579553 0.01720789 0.34241871 -0.05139472 0.17649261 +Al 6.20537212 6.16858980 0.02929378 1.00616679 -0.50341561 -0.12020389 6.27963843 6.07074260 0.02249205 0.14107743 -0.95290867 -0.00736737 0.02524995 -0.09127068 -0.43879120 0.03037808 +Al 4.24425510 4.18878201 4.27058686 -1.15037549 -0.54752722 -1.18204432 4.07926200 4.08278615 4.13191192 -1.64531526 -1.38040234 -1.10297444 0.04393131 -0.38433958 -0.26739960 -0.22946120 +Al 4.20374882 6.23617210 6.15914449 0.48551820 0.60066098 -0.37044394 4.18633985 6.20421370 6.11489840 -0.55329629 -1.07419882 -0.39805994 0.07028986 -0.53670625 -0.58233609 -0.02857253 +Al 6.11338408 3.91725158 6.36368277 0.45984604 0.68749219 -0.23439178 6.17803387 4.05672476 6.25146659 0.38203238 1.59931754 -1.76792776 0.05020167 0.31411432 0.50908613 -0.50588602 +Al 6.12982596 6.06786846 4.17828535 -0.47379136 -0.79519342 -1.21065105 6.07589854 6.00963866 4.02135256 -0.50785270 -0.16965766 -1.35866614 0.04317689 -0.02603431 0.09472086 -0.32816435 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.1675676068749237 free_energy=1.1675676068749237 stress="0.004908787662223326 -0.00033893401826187074 -0.00032429419235869704 -0.00033893401826187074 0.003913777206277226 -0.0007783761478527335 -0.00032429419235869704 -0.0007783761478527335 0.005075587681380582" pbc="T T T" +Al -0.00185302 0.02426664 0.16434278 0.12720369 -0.18378306 0.97776962 0.04317956 -0.00732802 0.21327814 0.53704185 -0.35326694 -0.12768044 0.02478353 0.23577860 -0.08203303 -0.27762373 +Al 0.01515044 2.07047049 2.12761419 -0.54305909 1.08927945 -0.02476894 -0.07414190 2.16810837 2.11370144 -0.92590854 0.60312353 -0.16969529 0.02646097 -0.24940757 -0.06215032 -0.10220150 +Al 2.12911798 0.06413381 2.11670377 -0.63193583 -0.88833950 -0.73418424 2.05568077 -0.06543782 2.01649404 -0.71430213 -1.06142925 -0.85744623 0.03271342 -0.03776546 -0.44659353 -0.28475965 +Al 2.04692238 2.04879653 0.23296122 0.82191423 -0.39734617 0.89358241 2.10467880 2.02652171 0.26734181 0.14311315 0.07710744 -0.32285886 0.03616315 -0.10735262 0.04534988 -0.31003431 +Al 0.07292698 0.00331094 4.01254402 -0.59531092 -0.61308493 -1.01977183 -0.00335281 -0.04100425 3.96620129 -0.66947288 -0.06458096 0.13750223 0.03282747 -0.14161936 0.01479325 0.37205277 +Al -0.05651734 1.90310706 6.22378422 0.46654326 -0.63291996 0.22779528 0.04412518 1.85090503 6.24204019 1.27846986 -0.29405123 0.12375454 0.01548789 0.32372064 0.09764366 -0.05639423 +Al 2.18769487 0.12011645 6.37437933 1.15993144 -0.78009484 0.96318082 2.25691278 0.00348286 6.41013064 -0.11834806 -1.25356898 -0.34525565 0.01688474 -0.17966655 -0.22714995 -0.40023312 +Al 2.00415971 1.90910936 4.04193885 -1.17122376 0.07582396 0.31190970 1.91023702 1.97021005 4.12805772 -0.49516514 0.73356827 1.00317123 0.03434012 0.14255335 0.51860156 0.46947175 +Al -0.25239450 4.10335980 0.02242564 -0.69683066 1.62570703 -0.32339798 -0.18710041 4.23784600 -0.01968754 1.85059825 0.64667348 -0.33199462 0.05784604 0.77381162 -0.08468665 -0.13149140 +Al -0.01215844 6.16217913 2.15758673 0.11013689 0.10018702 0.73447951 0.02853716 6.11675336 2.17225547 0.55742211 -0.97767944 -0.48468735 0.04879425 0.17788003 -0.27367699 -0.31312362 +Al 1.77529919 4.10550461 1.97620131 0.41382126 -0.67263535 0.28007888 1.88149467 4.03509934 2.04652599 1.43232492 -0.49658820 0.90008700 0.04690850 0.43369480 -0.07990822 0.31127974 +Al 2.02788973 5.98343987 0.07453357 -0.26863157 -0.55294163 -0.51026941 1.96741618 6.01044886 0.03400322 -0.73176476 0.76760756 -0.19601635 0.04568437 -0.18122048 0.63224725 0.04079831 +Al 0.03581419 4.00324193 4.22211570 0.27420213 0.71951715 1.26050839 0.08433936 4.08900385 4.35250833 0.42766594 0.60688305 0.84216730 0.00970041 0.19683984 0.18607845 0.09661851 +Al 0.10862960 6.18459276 6.09922979 1.58546667 -0.98017218 -1.38608442 0.26764130 6.08208888 5.96728535 1.12358099 -0.59757810 -0.83462249 -0.00100301 0.04895198 -0.15932505 -0.00060835 +Al 2.17990164 4.02680262 6.31606666 0.44723311 0.08849840 -0.26741732 2.13417363 3.98587652 6.18938034 -0.98552785 -0.77306952 -1.60734689 0.06492956 -0.68840023 -0.28622209 -0.78400965 +Al 2.07995963 6.02822313 4.03364142 0.57025551 -0.22930274 -0.65458184 2.13760259 6.04368598 4.00481849 0.47447187 0.48658019 0.09740398 0.01668477 -0.01513304 0.24503464 0.25468046 +Al 4.27054899 0.22143854 0.01660427 0.06118528 1.70818804 0.13362958 4.24464168 0.28650627 0.05614728 -0.40179527 -0.61986983 0.47823966 0.05659592 -0.30523991 -0.59143380 0.19369338 +Al 4.07664153 1.98663560 2.14441130 0.06417520 -0.76922333 -0.53474677 4.11325620 1.96361281 2.10187921 0.46184229 0.36275948 -0.04651572 0.02175900 0.25101132 0.33421525 -0.07072516 +Al 6.17171796 -0.01228142 1.97264767 -0.38881147 0.59572477 -0.81452230 6.08468826 0.06627537 1.96578331 -0.97797324 0.72849580 0.64438343 0.03532097 -0.35863836 0.14988671 0.50180839 +Al 6.15807277 2.09675177 0.05468414 0.40063793 0.73764000 -0.44812065 6.16710095 2.16376533 0.02441337 -0.18280135 0.46448263 -0.16731328 0.05807746 -0.23274447 -0.07558895 0.13259220 +Al 4.16097172 0.13497395 4.19020531 -0.02362303 -0.48826234 0.37275462 4.12378863 0.02963705 4.16655131 -0.61938066 -1.25530625 -0.76329818 0.04012127 -0.20597200 -0.41513788 -0.36259576 +Al 4.21794216 2.04681036 6.13715320 -1.16793728 -1.46010669 0.07390457 4.05627445 1.94029903 6.12659085 -1.50241383 -0.26633184 -0.27493638 0.06002539 -0.41023425 0.07874366 -0.09253041 +Al 5.99547231 -0.24515960 6.00903860 0.56026203 -0.68877577 -0.27697202 6.13481543 -0.24111003 6.02398672 1.78596149 0.69732803 0.53149961 0.07150722 0.55520675 0.52240333 0.25974533 +Al 6.23570967 2.11009799 4.24484450 -0.19907115 0.19874853 0.94092219 6.16936911 2.08792428 4.27794876 -0.88830542 -0.54928839 -0.21758544 0.03992958 -0.34185769 -0.29049410 -0.46468112 +Al 3.93007466 4.10046340 0.03181401 0.03098557 1.49654007 -0.50491144 4.04497018 4.19949967 0.07129031 1.72663821 0.25895382 0.99190441 0.07235608 0.79722560 -0.29198894 0.64980201 +Al 4.06922429 6.09154755 2.01019928 -1.54673877 1.27925131 -0.40728082 3.94069600 6.24228568 1.96361284 -0.55756108 1.18992474 -0.39526222 0.00296661 0.05598775 0.25053795 -0.04749189 +Al 6.06034779 4.09099328 2.11504827 0.20450501 -0.15112673 1.10530144 6.08149030 4.05429204 2.20193983 0.09331740 -0.40100700 0.42272420 0.01420419 0.07474584 -0.18139013 -0.13994017 +Al 6.17281603 6.07611340 -0.01198680 0.45516323 -0.25690963 -0.20399511 6.14803655 6.09298262 -0.00285753 -0.87385972 0.65027837 0.40420258 0.03562887 -0.40138833 0.13992465 0.16507872 +Al 4.02008785 4.12273212 3.94214357 -0.67106962 -0.77308791 0.43758277 3.94465049 3.99777711 4.02421544 -0.55242949 -1.33540912 0.96216734 0.02024145 -0.11776071 -0.34871473 0.28078714 +Al 4.08970161 6.00429343 6.11167176 -0.43365585 0.45554041 0.02111044 4.01401383 6.08616142 6.10372337 -0.73265454 0.85275676 -0.13980305 0.04281139 -0.31083308 0.28390454 -0.04738626 +Al 5.90646120 4.04704461 6.11929009 0.40417930 -0.09248174 0.71659416 5.99282082 4.07623325 6.16536055 0.94135989 0.57743653 -0.02512404 0.05472461 0.45436064 0.27419845 -0.06612105 +Al 6.16789697 6.00738288 4.01321235 0.18009723 0.43994835 -1.34007928 6.13219381 6.06809034 3.92213019 -0.90414425 0.59506537 -0.23176503 0.03209141 -0.23653464 0.12293115 0.22354268 +32 +Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.0779681848667755 free_energy=1.0779681848667755 stress="0.005570476181737316 0.0008721194749470843 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+ +.. only:: html + + .. image:: /examples/programmatic/flashmd/images/thumb/sphx_glr_flashmd_thumb.png + :alt: + + :ref:`sphx_glr_examples_programmatic_flashmd_flashmd.py` + +.. raw:: html + +
Training a FlashMD model
+
+ + +.. thumbnail-parent-div-close + +.. raw:: html + + + + +.. toctree:: + :hidden: + + /examples/programmatic/flashmd/flashmd + + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-gallery + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download all examples in Python source code: flashmd_python.zip ` + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download all examples in Jupyter notebooks: flashmd_jupyter.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/programmatic/flashmd/options.yaml b/docs/src/examples/programmatic/flashmd/options.yaml new file mode 100644 index 0000000000..cc73011ea5 --- /dev/null +++ b/docs/src/examples/programmatic/flashmd/options.yaml @@ -0,0 +1,43 @@ +seed: 42 + +architecture: + name: experimental.flashmd + training: + timestep: 30 # in this case 30 (time lag) * 1 fs (timestep of reference MD) + batch_size: 2 # to be increased in a production scenario + num_epochs: 5 # to be increased (at least 1000-10000) in a production scenario + log_interval: 1 + loss: + positions: + type: mse + weight: 1.0 + reduction: mean + momenta: + type: mse + weight: 1.0 + reduction: mean + +training_set: + systems: + read_from: flashmd.xyz + length_unit: A + targets: + positions: + key: future_positions + quantity: length + unit: A + type: + cartesian: + rank: 1 + per_atom: true + momenta: + key: future_momenta + quantity: momentum + unit: (eV*u)^1/2 + type: + cartesian: + rank: 1 + per_atom: true + +validation_set: 0.1 +test_set: 0.1 diff --git a/docs/src/examples/programmatic/use_architectures_outside/images/thumb/sphx_glr_use_outside_thumb.png b/docs/src/examples/programmatic/use_architectures_outside/images/thumb/sphx_glr_use_outside_thumb.png new file mode 100644 index 0000000000000000000000000000000000000000..5f3d70d019fb5eec5b6eede318f689c9f410323b GIT binary patch literal 41946 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+ +.. only:: html + + .. image:: /examples/programmatic/use_architectures_outside/images/thumb/sphx_glr_use_outside_thumb.png + :alt: + + :ref:`sphx_glr_examples_programmatic_use_architectures_outside_use_outside.py` + +.. raw:: html + +
Using metatrain architectures outside of metatrain
+
+ + +.. thumbnail-parent-div-close + +.. raw:: html + + + + +.. toctree:: + :hidden: + + /examples/programmatic/use_architectures_outside/use_outside + + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-gallery + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download all examples in Python source code: use_architectures_outside_python.zip ` + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download all examples in Jupyter notebooks: use_architectures_outside_jupyter.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/programmatic/use_architectures_outside/qm9_reduced_100.xyz b/docs/src/examples/programmatic/use_architectures_outside/qm9_reduced_100.xyz new file mode 100644 index 0000000000..14b2a0416e --- /dev/null +++ b/docs/src/examples/programmatic/use_architectures_outside/qm9_reduced_100.xyz @@ -0,0 +1,1201 @@ +5 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G=-76.422349 Cv=6.002 pbc="F F F" +O -0.03436050 0.97753957 0.00760159 +H 0.06476649 0.02057220 0.00153463 +H 0.87179037 1.30079240 0.00069313 +4 +Properties=species:S:1:pos:R:3 A=0 B=35.6100361 C=35.6100361 mu=0 alpha=16.28 epsHOMO=-0.2845 epsLUMO=0.0506 epsgap=0.3351 =59.5248 zpve=0.026841 U0=-77.308427 U=-77.305527 H=-77.304583 G=-77.327429 Cv=8.574 pbc="F F F" +C 0.59953949 0.00000000 1.00000000 +C -0.59953949 0.00000000 1.00000000 +H -1.66163859 0.00000000 1.00000000 +H 1.66163859 0.00000000 1.00000000 +3 +Properties=species:S:1:pos:R:3 A=0 B=44.593883 C=44.593883 mu=2.8937 alpha=12.99 epsHOMO=-0.3604 epsLUMO=0.0191 epsgap=0.3796 =48.7476 zpve=0.016601 U0=-93.411888 U=-93.40937 H=-93.408425 G=-93.431246 Cv=6.278 pbc="F F F" +C -0.01332393 1.13246572 0.00827589 +N 0.00231072 -0.01915859 0.00192873 +H -0.02780270 2.19894930 0.01415379 +4 +Properties=species:S:1:pos:R:3 A=285.48839 B=38.9823 C=34.29892 mu=2.1089 alpha=14.18 epsHOMO=-0.267 epsLUMO=-0.0406 epsgap=0.2263 =59.9891 zpve=0.026603 U0=-114.483613 U=-114.480746 H=-114.479802 G=-114.505268 Cv=6.413 pbc="F F F" +C -0.01397770 1.18021143 0.00775250 +O 0.00231399 -0.01966366 0.00216107 +H 0.91496029 1.78951117 0.00395656 +H -0.95910972 1.76401798 0.01718284 +8 +Properties=species:S:1:pos:R:3 A=80.46225 B=19.90649 C=19.90633 mu=0 alpha=23.95 epsHOMO=-0.3385 epsLUMO=0.1041 epsgap=0.4426 =109.5031 zpve=0.074542 U0=-79.764152 U=-79.760666 H=-79.759722 G=-79.787269 Cv=10.098 pbc="F F F" +C -0.01870400 1.52558201 0.01043281 +C 0.00210374 -0.00388191 0.00199882 +H 0.99487275 1.93974324 0.00294120 +H -0.54207611 1.92361063 -0.86511735 +H -0.52524112 1.91417308 0.90002399 +H 0.52548654 -0.40190784 0.87754395 +H -1.01147651 -0.41803380 0.00950849 +H 0.50862619 -0.39247040 -0.88760117 +6 +Properties=species:S:1:pos:R:3 A=127.83497 B=24.85872 C=23.97872 mu=1.5258 alpha=16.97 epsHOMO=-0.2653 epsLUMO=0.0784 epsgap=0.3437 =83.794 zpve=0.051208 U0=-115.679136 U=-115.675816 H=-115.674872 G=-115.701876 Cv=8.751 pbc="F F F" +C -0.00828817 1.39046978 -0.00560069 +O -0.00797038 -0.02504537 0.02030606 +H 1.00658338 1.81556366 0.00348335 +H -0.54657475 1.79916975 -0.87390126 +H -0.52288871 1.72555240 0.89907326 +H 0.44142019 -0.33354425 -0.77152059 +7 +Properties=species:S:1:pos:R:3 A=160.28041 B=8.59323 C=8.59321 mu=0.7156 alpha=28.78 epsHOMO=-0.2609 epsLUMO=0.0613 epsgap=0.3222 =177.1963 zpve=0.05541 U0=-116.609549 U=-116.60555 H=-116.604606 G=-116.633775 Cv=12.482 pbc="F F F" +C -0.01782102 1.46435788 0.01009397 +C 0.00208816 0.00950777 0.00201200 +C 0.01834085 -1.19180518 -0.00450508 +H 0.99782210 1.87425349 0.00260606 +H -0.54220434 1.85801178 -0.86721192 +H -0.52533306 1.84834356 0.90148138 +H 0.03231658 -2.25314823 -0.01025999 +6 +Properties=species:S:1:pos:R:3 A=159.03567 B=9.22327 C=9.22324 mu=3.8266 alpha=24.45 epsHOMO=-0.3264 epsLUMO=0.0376 epsgap=0.364 =160.7223 zpve=0.045286 U0=-132.71815 U=-132.714563 H=-132.713619 G=-132.742149 Cv=10.287 pbc="F F F" +C -0.01788629 1.46712787 0.01011311 +C 0.00173819 0.01035260 0.00207588 +N 0.01765235 -1.14452926 -0.00420445 +H 1.00202941 1.86089927 0.00245401 +H -0.54398848 1.84479901 -0.87075460 +H -0.52707783 1.83518166 0.90486329 +7 +Properties=species:S:1:pos:R:3 A=57.22434 B=10.11122 C=9.07368 mu=2.5682 alpha=25.11 epsHOMO=-0.254 epsLUMO=-0.0198 epsgap=0.2342 =166.9728 zpve=0.055355 U0=-153.787612 U=-153.783728 H=-153.782784 G=-153.812518 Cv=11.219 pbc="F F F" +C -0.00294482 1.50991366 0.00867278 +C 0.02608284 0.00327563 -0.03745912 +O 0.94228801 -0.65507035 -0.45682576 +H 0.92278802 1.92634242 -0.39146557 +H -0.86201540 1.87852481 -0.56479538 +H -0.15050638 1.84393383 1.04289100 +H -0.89443009 -0.48643408 0.35774865 +6 +Properties=species:S:1:pos:R:3 A=73.8472 B=11.34793 C=9.83639 mu=3.7286 alpha=21.57 epsHOMO=-0.2543 epsLUMO=0.0302 epsgap=0.2845 =145.3078 zpve=0.045279 U0=-169.860788 U=-169.856903 H=-169.855958 G=-169.885594 Cv=10.89 pbc="F F F" +N -0.02589986 1.34614561 0.00889391 +C 0.04646728 -0.01174341 0.00120425 +O 1.07183520 -0.65258782 -0.01113313 +H 0.82535497 1.88504948 0.00373769 +H -0.90837680 1.82679638 0.01891992 +H -0.96144100 -0.47500376 0.00807391 +11 +Properties=species:S:1:pos:R:3 A=29.45018 B=8.37701 C=7.42076 mu=0.0597 alpha=34.75 epsHOMO=-0.323 epsLUMO=0.0949 epsgap=0.4179 =227.1361 zpve=0.103182 U0=-119.052475 U=-119.047927 H=-119.046983 G=-119.078157 Cv=14.84 pbc="F F F" +C -0.03113825 1.54081582 0.03192126 +C 0.01215347 0.01092235 -0.01603259 +C 0.72169129 -0.52583353 -1.26230570 +H 0.97955987 1.96459116 0.03098367 +H -0.55840223 1.94831192 -0.83816075 +H -0.54252252 1.90153531 0.93005671 +H 0.51522791 -0.36840234 0.88231134 +H -1.01070641 -0.38456999 0.02051783 +H 1.75851210 -0.17376585 -1.30871516 +H 0.74087192 -1.62024959 -1.27516511 +H 0.22023351 -0.19051179 -2.17729020 +9 +Properties=species:S:1:pos:R:3 A=35.09545 B=9.3686 C=8.1497 mu=1.4131 alpha=27.87 epsHOMO=-0.2619 epsLUMO=0.0798 epsgap=0.3417 =193.1659 zpve=0.079754 U0=-154.972731 U=-154.968412 H=-154.967467 G=-154.998148 Cv=13.546 pbc="F F F" +C -0.00860504 1.50203829 -0.00681217 +C 0.01099310 -0.01764877 -0.01377035 +O 0.68088841 -0.44041803 -1.19313210 +H 1.01153334 1.89662030 -0.01920155 +H -0.53159862 1.88076109 -0.88974659 +H -0.51674572 1.87611771 0.88710739 +H 0.52377121 -0.38912295 0.88824082 +H -1.02027544 -0.40507261 0.01690670 +H 0.69529573 -1.40179568 -1.20148495 +9 +Properties=species:S:1:pos:R:3 A=39.37691 B=10.04033 C=8.90353 mu=1.1502 alpha=28.13 epsHOMO=-0.2525 epsLUMO=0.091 epsgap=0.3435 =187.1015 zpve=0.079534 U0=-154.960361 U=-154.956045 H=-154.9551 G=-154.985747 Cv=12.934 pbc="F F F" +C -0.01482147 1.39241234 0.00567104 +O -0.00471537 -0.01360660 0.01459658 +C 0.63794893 -0.55329676 -1.11358232 +H 1.00528358 1.81015807 0.00465631 +H -0.54689560 1.79343530 -0.87251051 +H -0.53002895 1.72291969 0.91101696 +H 0.13993792 -0.25599333 -2.05098375 +H 1.69265275 -0.23868382 -1.17477743 +H 0.59959435 -1.64180161 -1.02407616 +9 +Properties=species:S:1:pos:R:3 A=20.15852 B=20.15302 C=12.5891 mu=0.0005 alpha=30.82 epsHOMO=-0.2888 epsLUMO=0.1042 epsgap=0.393 =155.8145 zpve=0.081231 U0=-117.824798 U=-117.821426 H=-117.820482 G=-117.849087 Cv=11.041 pbc="F F F" +C -0.01193280 1.51433198 0.01031700 +C 1.30299118 0.77886562 -0.00617842 +C 0.00867174 0.00767042 0.00201032 +H -0.30541464 2.01702117 0.92533239 +H -0.32275498 2.02680156 -0.89347839 +H 1.88493396 0.79145020 -0.92119393 +H 1.90226631 0.78165391 0.89766373 +H -0.27088043 -0.51289125 0.91137474 +H -0.28821983 -0.50310409 -0.90740824 +7 +Properties=species:S:1:pos:R:3 A=25.77385 B=22.12109 C=14.18066 mu=1.7675 alpha=24.04 epsHOMO=-0.2682 epsLUMO=0.1042 epsgap=0.3724 =129.891 zpve=0.057289 U0=-153.742562 U=-153.73941 H=-153.738466 G=-153.766642 Cv=9.176 pbc="F F F" +C 0.01537720 1.41764380 0.00956287 +C 1.26478161 0.64923984 -0.00655044 +O -0.00024018 -0.00771006 0.00204055 +H -0.31763326 1.88586598 0.93475751 +H -0.33527583 1.89576673 -0.90397578 +H 1.83246682 0.56256848 -0.93193290 +H 1.85008258 0.55267938 0.90680147 +10 +Properties=species:S:1:pos:R:3 A=10.12193 B=8.49011 C=4.89615 mu=2.7362 alpha=35.53 epsHOMO=-0.2431 epsLUMO=-0.0087 epsgap=0.2344 =292.4367 zpve=0.083382 U0=-193.08834 U=-193.082969 H=-193.082024 G=-193.116476 Cv=16.893 pbc="F F F" +C -0.00310189 1.48050328 -0.17259867 +C -0.04527202 -0.02887059 0.00139493 +C 1.29705800 -0.72194415 0.16889042 +O -1.08643172 -0.64273343 0.00630393 +H 0.59403529 1.74668579 -1.05225043 +H -1.01540407 1.87034023 -0.28382265 +H 0.48099600 1.95070605 0.69099376 +H 1.82512144 -0.32470731 1.04328658 +H 1.14892507 -1.79597689 0.28554393 +H 1.93682635 -0.52949692 -0.69995403 +9 +Properties=species:S:1:pos:R:3 A=10.78886 B=9.27509 C=5.14977 mu=3.6367 alpha=31.83 epsHOMO=-0.2436 epsLUMO=0.0347 epsgap=0.2783 =267.6148 zpve=0.07319 U0=-209.159302 U=-209.15402 H=-209.153076 G=-209.187468 Cv=16.561 pbc="F F F" +C 0.00679889 1.49969736 -0.02567030 +C -0.02740385 -0.01752051 -0.14894811 +N 1.17826667 -0.63324298 0.05691721 +O -1.03721965 -0.63980649 -0.40727572 +H 0.91390280 1.92923755 -0.46100067 +H -0.87080896 1.91124186 -0.52345176 +H -0.02826124 1.78517271 1.03149125 +H 1.98405139 -0.13175861 0.38674906 +H 1.19049421 -1.63919563 0.10006209 +8 +Properties=species:S:1:pos:R:3 A=11.1475 B=10.36388 C=5.42091 mu=3.4869 alpha=28.07 epsHOMO=-0.2495 epsLUMO=0.0556 epsgap=0.3051 =244.2308 zpve=0.063824 U0=-225.221461 U=-225.217075 H=-225.216131 G=-225.247724 Cv=15.292 pbc="F F F" +N 0.03605270 1.36077873 -0.12416403 +C -0.02591138 -0.02076560 0.00200645 +N 1.21968479 -0.62334220 0.11963222 +O -1.06822866 -0.64174597 0.00865573 +H 0.80749439 1.83455148 0.32144915 +H -0.85825775 1.80373500 0.02243770 +H 1.99927148 -0.17303054 -0.33602496 +H 1.18204327 -1.62147901 -0.02109234 +14 +Properties=species:S:1:pos:R:3 A=7.75166 B=7.74847 C=4.48668 mu=0.0897 alpha=45.46 epsHOMO=-0.3167 epsLUMO=0.0843 epsgap=0.401 =355.0621 zpve=0.131146 U0=-158.342346 U=-158.336603 H=-158.335658 G=-158.370016 Cv=20.273 pbc="F F F" +C -0.03215888 1.54021598 0.01074456 +C 0.03381741 0.00745852 0.00180716 +C 0.71375572 -0.50856404 -1.27296949 +C 0.73848999 -0.52220886 1.25750907 +H 0.97522736 1.97414341 0.00528362 +H -0.56196769 1.92232326 -0.86835418 +H -0.54817239 1.91268414 0.90208039 +H -0.99752525 -0.37310578 0.00977297 +H 1.75070021 -0.15586784 -1.33269631 +H 0.73446691 -1.60328288 -1.29939696 +H 0.19367011 -0.16085589 -2.17195508 +H 0.23611990 -0.18382017 2.17005260 +H 0.75964083 -1.61713153 1.27226101 +H 1.77635866 -0.16985318 1.30053019 +12 +Properties=species:S:1:pos:R:3 A=8.67553 B=8.00568 C=4.75542 mu=1.4259 alpha=38.58 epsHOMO=-0.2612 epsLUMO=0.074 epsgap=0.3351 =318.3721 zpve=0.107673 U0=-194.267232 U=-194.261748 H=-194.260804 G=-194.294663 Cv=19.052 pbc="F F F" +C -0.03315871 1.54782566 -0.00438815 +C -0.01108534 0.01859095 0.01676117 +C 0.70938384 -0.53596711 1.23978444 +O -1.33219841 -0.51712554 0.05236057 +H 0.98215692 1.95715900 -0.03182225 +H -0.56388100 1.92087623 -0.88845865 +H -0.54082548 1.93340945 0.88575489 +H 0.51042122 -0.33767241 -0.88819176 +H 0.22240492 -0.18785860 2.15650245 +H 0.68242024 -1.62887638 1.23305141 +H 1.75434301 -0.21246950 1.25524188 +H -1.81818681 -0.15577435 -0.69553962 +6 +Properties=species:S:1:pos:R:3 A=0 B=4.4259726 C=4.4259726 mu=0 alpha=38.52 epsHOMO=-0.2599 epsLUMO=-0.0214 epsgap=0.2386 =278.6264 zpve=0.037354 U0=-153.459846 U=-153.455442 H=-153.454498 G=-153.482621 Cv=15.312 pbc="F F F" +C 0.68098021 0.00000000 0.00000000 +C -0.68098021 0.00000000 0.00000000 +C -1.88766603 0.00000000 0.00000000 +C 1.88766603 0.00000000 0.00000000 +H -2.94960000 0.00000000 0.00000000 +H 2.94960000 0.00000000 0.00000000 +5 +Properties=species:S:1:pos:R:3 A=0 B=4.5793222 C=4.5793222 mu=3.792 alpha=32.66 epsHOMO=-0.3102 epsLUMO=-0.0543 epsgap=0.2559 =260.1896 zpve=0.027259 U0=-169.557758 U=-169.553764 H=-169.55282 G=-169.581024 Cv=12.93 pbc="F F F" +C 0.01510390 0.00000000 1.00000000 +C 1.38233149 0.00000000 1.00000000 +C -1.18862525 0.00000000 1.00000000 +H -2.25177201 0.00000000 1.00000000 +N 2.54296187 0.00000000 1.00000000 +4 +Properties=species:S:1:pos:R:3 A=0 B=4.73269 C=4.73269 mu=0.0023 alpha=27.7 epsHOMO=-0.3696 epsLUMO=-0.0926 epsgap=0.277 =242.9308 zpve=0.015951 U0=-185.648533 U=-185.644825 H=-185.64388 G=-185.667652 Cv=10.398 pbc="F F F" +N 0.01745734 -1.16134217 -0.00415342 +C 0.00253212 -0.00344278 0.00179939 +C -0.01611419 1.37220987 0.00934873 +N -0.03264302 2.53008276 0.01609090 +6 +Properties=species:S:1:pos:R:3 A=68.5171 B=4.8345 C=4.51586 mu=2.7824 alpha=31.14 epsHOMO=-0.2777 epsLUMO=-0.0735 epsgap=0.2042 =268.3921 zpve=0.037208 U0=-190.624631 U=-190.620363 H=-190.619419 G=-190.650543 Cv=13.049 pbc="F F F" +O -0.06776120 -0.04233838 0.00282489 +C -0.00562945 1.16382879 0.00743862 +C 1.22985272 1.91339997 0.00028781 +C 2.23625322 2.57430970 -0.00529277 +H -0.91670701 1.79467546 0.01781352 +H 3.13429662 3.14370267 -0.01037656 +5 +Properties=species:S:1:pos:R:3 A=67.88408 B=5.00823 C=4.66413 mu=2.3112 alpha=26.25 epsHOMO=-0.3166 epsLUMO=-0.11 epsgap=0.2066 =251.0007 zpve=0.02654 U0=-206.721858 U=-206.717875 H=-206.716931 G=-206.747625 Cv=11.329 pbc="F F F" +O -0.04055978 -0.03799144 0.00266513 +C -0.01768870 1.16193251 0.00746008 +C 1.23315534 1.93381336 0.00043371 +N 2.19857378 2.57055097 -0.00483320 +H -0.92627534 1.78959196 0.01764859 +6 +Properties=species:S:1:pos:R:3 A=56.25048 B=4.77441 C=4.40088 mu=0.002 alpha=26.12 epsHOMO=-0.2668 epsLUMO=-0.1113 epsgap=0.1555 =266.8164 zpve=0.036943 U0=-227.798785 U=-227.79457 H=-227.793626 G=-227.825074 Cv=12.147 pbc="F F F" +O 0.00337122 -0.03146586 0.00157206 +C -0.01713174 1.17081409 0.00676444 +C 1.26555783 1.99719410 -0.00007703 +O 1.24507069 3.19946589 0.00681275 +H -0.94690483 1.77678142 0.01852668 +H 2.19534410 1.39121374 -0.01006739 +10 +Properties=species:S:1:pos:R:3 A=80.28766 B=3.3688 C=3.3688 mu=0 alpha=42.32 epsHOMO=-0.2412 epsLUMO=0.0684 epsgap=0.3096 =400.2236 zpve=0.083896 U0=-155.908941 U=-155.90318 H=-155.902236 G=-155.937641 Cv=17.447 pbc="F F F" +C -0.01787959 1.46676893 0.01011180 +C 0.00186644 0.01072913 0.00211109 +C 0.01819704 -1.19272861 -0.00450723 +C 0.03795170 -2.64876793 -0.01251193 +H 0.86460312 1.87589979 0.51479719 +H -0.03217624 1.87210581 -1.00791002 +H -0.90233115 1.85185524 0.53003953 +H -0.83477011 -3.06339007 0.50451436 +H 0.03276422 -3.04315564 -1.03491223 +H 0.93213486 -3.03931642 0.48626606 +10 +Properties=species:S:1:pos:R:3 A=27.39459 B=4.53005 C=4.08342 mu=0.7067 alpha=40.09 epsHOMO=-0.2592 epsLUMO=0.0566 epsgap=0.3157 =333.9589 zpve=0.084338 U0=-155.897345 U=-155.892291 H=-155.891347 G=-155.924226 Cv=17.13 pbc="F F F" +C -0.03095840 1.54775025 0.03167945 +C 0.01485375 0.00962471 -0.02082154 +C 0.69099073 -0.49954575 -1.20857624 +C 1.25222275 -0.89514467 -2.19527879 +H 0.97899300 1.96621609 0.03373834 +H -0.56082354 1.95002902 -0.83580745 +H -0.54576708 1.88225519 0.93709062 +H 0.51920135 -0.37425398 0.87496224 +H -1.00644802 -0.39030838 0.01340632 +H 1.74594541 -1.25604187 -3.06299415 +9 +Properties=species:S:1:pos:R:3 A=28.0016 B=4.67752 C=4.21765 mu=3.9233 alpha=35.38 epsHOMO=-0.3213 epsLUMO=0.034 epsgap=0.3553 =314.5335 zpve=0.07419 U0=-172.006141 U=-172.001467 H=-172.000523 G=-172.032826 Cv=14.988 pbc="F F F" +C -0.02522005 1.54773054 0.02133370 +C 0.01183939 0.00999278 -0.01556702 +C 0.68915114 -0.50836846 -1.20331488 +N 1.22565733 -0.90360846 -2.14737482 +H 0.98571620 1.96212990 0.02370153 +H -0.55522192 1.94599662 -0.84711771 +H -0.53962556 1.88294354 0.92536604 +H 0.52481910 -0.38226048 0.86933355 +H -1.00453542 -0.39832109 0.00530071 +8 +Properties=species:S:1:pos:R:3 A=32.19893 B=4.76299 C=4.30696 mu=4.4361 alpha=31.81 epsHOMO=-0.2683 epsLUMO=0.0173 epsgap=0.2855 =295.6635 zpve=0.063305 U0=-188.042067 U=-188.037478 H=-188.036534 G=-188.06863 Cv=14.488 pbc="F F F" +N -0.03608099 1.48762533 0.03678072 +C -0.01039471 0.02517605 -0.01506608 +C -1.37180956 -0.52108920 0.02739272 +N -2.44026695 -0.95956444 0.03370519 +H 0.91729752 1.83547547 0.05202682 +H -0.47162178 1.85677205 -0.80275569 +H 0.48946161 -0.40880401 -0.89715168 +H 0.52827150 -0.34533338 0.86469547 +8 +Properties=species:S:1:pos:R:3 A=35.11615 B=4.66117 C=4.22354 mu=1.7211 alpha=33.1 epsHOMO=-0.2595 epsLUMO=0.0277 epsgap=0.2872 =300.0993 zpve=0.060632 U0=-191.810916 U=-191.806025 H=-191.805081 G=-191.837634 Cv=15.855 pbc="F F F" +O -0.02013197 1.45738137 0.01010195 +C 0.00168478 0.03579980 0.00224736 +C -1.36366691 -0.47481101 0.01255056 +C -2.47902835 -0.91970202 0.02082451 +H 0.89446911 1.75736553 0.00327363 +H 0.51979028 -0.35175413 -0.88980302 +H 0.53670458 -0.36127661 0.88000894 +H -3.46773502 -1.30737009 0.02818120 +7 +Properties=species:S:1:pos:R:3 A=35.93172 B=4.83443 C=4.37827 mu=4.6788 alpha=28.56 epsHOMO=-0.3018 epsLUMO=0.0022 epsgap=0.3039 =280.6659 zpve=0.050262 U0=-207.916786 U=-207.912215 H=-207.911271 G=-207.943384 Cv=13.845 pbc="F F F" +O -0.03738221 1.44547841 0.01010769 +C 0.01292906 0.03441661 0.00209165 +C -1.35747452 -0.48874523 0.01239738 +N -2.42135277 -0.93685184 0.02016053 +H 0.86898057 1.76972949 0.00417577 +H 0.51692713 -0.36329790 -0.89215814 +H 0.53387616 -0.37278749 0.88223921 +10 +Properties=species:S:1:pos:R:3 A=26.14564 B=4.2893 C=4.12773 mu=2.6741 alpha=35.83 epsHOMO=-0.25 epsLUMO=-0.0205 epsgap=0.2295 =333.3276 zpve=0.084175 U0=-193.075202 U=-193.070116 H=-193.069171 G=-193.102798 Cv=15.954 pbc="F F F" +C 0.16879340 1.52513042 -0.15747056 +C -0.18737625 0.06194763 0.14679374 +C 0.50917645 -0.43996215 1.39125850 +O 1.18191079 -1.43565585 1.45816384 +H 1.23126511 1.63137254 -0.39632693 +H -0.40744668 1.89431681 -1.00969246 +H -0.04931034 2.17266260 0.69857432 +H -1.26905160 -0.01668067 0.32854433 +H 0.06279062 -0.60250479 -0.68474036 +H 0.35384841 0.20663370 2.28871052 +9 +Properties=species:S:1:pos:R:3 A=19.98749 B=6.11589 C=4.82584 mu=3.7071 alpha=32.78 epsHOMO=-0.2516 epsLUMO=0.0335 epsgap=0.2851 =279.7863 zpve=0.074166 U0=-209.144909 U=-209.139976 H=-209.139032 G=-209.172305 Cv=15.058 pbc="F F F" +C 0.01474916 1.47888256 -0.03509326 +N 0.18708077 0.03831317 -0.02906851 +C 0.45703661 -0.66858895 1.10069001 +O 0.58457996 -0.19876202 2.21119705 +H 0.76025289 1.96736104 -0.67198126 +H -0.98462423 1.75984483 -0.38485103 +H 0.14269849 1.82387718 0.99149397 +H 0.10386349 -0.46937726 -0.89461379 +H 0.55047568 -1.75253452 0.88868242 +8 +Properties=species:S:1:pos:R:3 A=48.62351 B=4.67298 C=4.38187 mu=3.92 alpha=29.47 epsHOMO=-0.2814 epsLUMO=0.0074 epsgap=0.2888 =293.917 zpve=0.061327 U0=-229.013797 U=-229.009003 H=-229.008059 G=-229.041086 Cv=13.885 pbc="F F F" +C 0.10653781 1.43512049 0.03336100 +O 0.42686564 0.04716929 0.09309945 +C 0.50714862 -0.63652665 -1.06318297 +O 0.76927014 -1.79955017 -1.11010475 +H 0.92349251 2.00385139 0.48465728 +H -0.03757815 1.76927183 -1.00057425 +H -0.81269569 1.60801916 0.59873287 +H 0.30458460 -0.00231902 -1.94965553 +8 +Properties=species:S:1:pos:R:3 A=36.49282 B=4.31175 C=4.04493 mu=1.7341 alpha=28.53 epsHOMO=-0.2537 epsLUMO=-0.0341 epsgap=0.2196 =303.8129 zpve=0.060508 U0=-228.992613 U=-228.987769 H=-228.986825 G=-229.019918 Cv=14.78 pbc="F F F" +O 0.19375856 1.37170995 0.31398297 +C 0.04992637 0.01405481 -0.04526239 +C -1.40888025 -0.39635680 -0.20601574 +O -1.77729462 -1.37233860 -0.80361689 +H -0.01723186 1.91269132 -0.45337758 +H 0.60440478 -0.26519766 -0.95205725 +H 0.46740406 -0.57521679 0.78176270 +H -2.11835055 0.30075962 0.29870515 +14 +Properties=species:S:1:pos:R:3 A=23.44662 B=3.60588 C=3.38683 mu=0 alpha=45.71 epsHOMO=-0.317 epsLUMO=0.0937 epsgap=0.4107 =426.2996 zpve=0.131708 U0=-158.340943 U=-158.33517 H=-158.334226 G=-158.36894 Cv=19.668 pbc="F F F" +C -0.03007624 1.55804912 0.02980774 +C 0.00707277 0.02834877 -0.00712529 +C 0.71320792 -0.52989340 -1.24765581 +C 0.75036895 -2.05959366 -1.28458957 +H 0.98195330 1.97845113 0.02800797 +H -0.55582433 1.96212343 -0.84270157 +H -0.54083762 1.92692755 0.92493697 +H 0.50928672 -0.34909049 0.89361319 +H -1.01733481 -0.36542299 0.03207393 +H 0.21098948 -0.15245770 -2.14839302 +H 1.73761331 -0.13611625 -1.28685672 +H 1.27616854 -2.46366410 -0.41210980 +H -0.26165699 -2.48000447 -1.28273552 +H 1.26108491 -2.42846444 -2.17974803 +12 +Properties=species:S:1:pos:R:3 A=26.59789 B=3.79186 C=3.53779 mu=1.3402 alpha=38.61 epsHOMO=-0.2619 epsLUMO=0.081 epsgap=0.3429 =382.8628 zpve=0.108241 U0=-194.261089 U=-194.255495 H=-194.254551 G=-194.28893 Cv=18.431 pbc="F F F" +C -0.00379012 1.52196790 0.01027996 +C -0.03589372 -0.00778254 0.00225671 +C -1.45219016 -0.56653711 0.01284386 +O -1.36799785 -1.98394604 0.00453606 +H 1.02477576 1.89411231 0.00239793 +H -0.51286674 1.93764614 -0.86653888 +H -0.49584080 1.92801344 0.90121721 +H 0.48140784 -0.39530552 -0.88316064 +H 0.49828589 -0.40486256 0.87330348 +H -1.98734220 -0.20399494 0.90666062 +H -2.00431485 -0.19459473 -0.86666460 +H -2.26171819 -2.33824298 0.01034748 +12 +Properties=species:S:1:pos:R:3 A=28.29567 B=4.14901 C=3.88349 mu=1.0363 alpha=39.34 epsHOMO=-0.2503 epsLUMO=0.0925 epsgap=0.3428 =368.9331 zpve=0.107895 U0=-194.254127 U=-194.248585 H=-194.247641 G=-194.281899 Cv=17.888 pbc="F F F" +C -0.00519551 1.50868224 0.03308760 +C 0.01316580 -0.01037819 0.01988643 +O 0.69758944 -0.46344140 1.16868979 +C 0.76688491 -1.86508183 1.24602669 +H 1.01445476 1.90423799 0.02996018 +H -0.52990866 1.89184059 -0.84726911 +H -0.51263765 1.87805843 0.92875424 +H -1.01599507 -0.40998729 0.00714282 +H 0.51093143 -0.38369435 -0.89219395 +H -0.23415841 -2.32451489 1.28977780 +H 1.30129276 -2.29853663 0.38486095 +H 1.30990287 -2.11811998 2.16013206 +10 +Properties=species:S:1:pos:R:3 A=14.79671 B=5.6956 C=4.58846 mu=0.0075 alpha=31.42 epsHOMO=-0.2594 epsLUMO=0.0584 epsgap=0.3179 =297.8398 zpve=0.085172 U0=-230.183076 U=-230.177723 H=-230.176779 G=-230.211195 Cv=16.837 pbc="F F F" +O -0.01418735 1.42642242 -0.05423651 +C -0.00391839 0.00717714 0.03496062 +C -1.42317019 -0.55560600 -0.01256639 +O -2.11229413 -0.10563875 -1.17228313 +H -0.18987882 1.64154705 -0.97555148 +H 0.59659504 -0.43315430 -0.77409095 +H 0.47311116 -0.24903733 0.98747216 +H -1.39494859 -1.64975279 -0.06283188 +H -1.96497417 -0.27216915 0.90131972 +H -2.35925991 0.80944396 -1.00614597 +12 +Properties=species:S:1:pos:R:3 A=15.61472 B=6.31363 C=5.54514 mu=0.1136 alpha=41.96 epsHOMO=-0.2727 epsLUMO=0.1012 epsgap=0.3738 =298.6061 zpve=0.109284 U0=-157.116735 U=-157.11209 H=-157.111146 G=-157.143262 Cv=16.49 pbc="F F F" +C -0.03602245 1.52092949 0.01569161 +C 0.01938487 0.01004403 0.04814953 +C 1.30891671 -0.71251995 -0.24920416 +C 0.75347423 -0.70267714 1.15564676 +H 0.90105691 1.95389291 0.38283338 +H -0.19640365 1.89437369 -1.00208569 +H -0.84685273 1.90588930 0.64451322 +H -0.88416809 -0.48001889 -0.30566335 +H 1.26900901 -1.63903068 -0.81150371 +H 2.19010738 -0.11131209 -0.44927513 +H 1.26248862 -0.09487455 1.89690028 +H 0.33540836 -1.62248918 1.54980695 +10 +Properties=species:S:1:pos:R:3 A=18.21931 B=6.63877 C=5.92459 mu=1.812 alpha=35.01 epsHOMO=-0.2633 epsLUMO=0.1052 epsgap=0.3685 =267.2979 zpve=0.085275 U0=-193.039603 U=-193.035186 H=-193.034242 G=-193.065979 Cv=14.764 pbc="F F F" +C -0.01646343 1.51830705 0.00771445 +C -0.02743227 0.01657694 -0.10889741 +C 0.77296553 -0.70131886 -1.10863115 +O 1.17212293 -0.66702969 0.26278829 +H 0.91865980 1.92914217 -0.38285880 +H -0.85158014 1.95456506 -0.55109460 +H -0.11487702 1.82596470 1.05397777 +H -0.93582598 -0.47350600 0.24682793 +H 0.44443922 -1.67201495 -1.47810622 +H 1.40538284 -0.13600339 -1.79271491 +11 +Properties=species:S:1:pos:R:3 A=16.58914 B=7.18798 C=6.11415 mu=1.1353 alpha=39.02 epsHOMO=-0.2304 epsLUMO=0.0968 epsgap=0.3271 =270.5508 zpve=0.097671 U0=-173.147782 U=-173.143343 H=-173.142399 G=-173.174073 Cv=15.298 pbc="F F F" +C -0.05275541 1.47419574 0.00999108 +N 0.01940516 0.02189947 0.06334866 +C 1.32511048 -0.56446850 -0.21420725 +C 0.78051759 -0.54057993 1.17242760 +H 0.86784202 1.97176076 0.36297285 +H -0.23845155 1.79095005 -1.02142866 +H -0.88683448 1.81938959 0.62950398 +H 1.31992909 -1.48186782 -0.79602038 +H 2.15202820 0.11020204 -0.43285248 +H 1.24563570 0.14996098 1.87499245 +H 0.37013358 -1.44020447 1.62233365 +10 +Properties=species:S:1:pos:R:3 A=16.79477 B=6.71015 C=5.86781 mu=1.3894 alpha=34.64 epsHOMO=-0.239 epsLUMO=0.0775 epsgap=0.3166 =263.966 zpve=0.085106 U0=-193.034988 U=-193.030356 H=-193.029411 G=-193.061689 Cv=15.679 pbc="F F F" +O 0.21577583 1.35386859 -0.10695107 +C 0.01628099 -0.02832718 -0.02915694 +C -0.39081465 -0.71497009 1.24315669 +C -1.35118138 -0.64280380 0.06273589 +H -0.43642338 1.79062849 0.45006761 +H 0.74981167 -0.52404050 -0.65624767 +H 0.05959375 -1.66840483 1.49729237 +H -0.61369353 -0.08848006 2.10170640 +H -2.19821932 0.03058430 0.15411451 +H -1.56395912 -1.54640426 -0.49826515 +12 +Properties=species:S:1:pos:R:3 A=10.66107 B=10.66107 C=6.30452 mu=0 alpha=41.83 epsHOMO=-0.2982 epsLUMO=0.0956 epsgap=0.3938 =268.4432 zpve=0.110511 U0=-157.115484 U=-157.111322 H=-157.110378 G=-157.141657 Cv=14.696 pbc="F F F" +C -0.02542957 1.54032578 -0.04215944 +C 1.52564399 1.50419989 0.04121276 +C 1.42146727 0.06146790 0.60844885 +C -0.02638833 -0.01070956 0.04935088 +H -0.46591372 1.99639894 0.84962894 +H -0.47900239 2.00028207 -0.92365389 +H 1.98248278 1.51375334 -0.95303183 +H 2.02191365 2.25986579 0.65522129 +H 2.14291679 -0.67956358 0.25531480 +H 1.43105853 0.05751479 1.70261879 +H -0.79053440 -0.48530296 0.66996573 +H -0.05233245 -0.47238446 -0.94236394 +10 +Properties=species:S:1:pos:R:3 A=12.06545 B=11.77731 C=6.71997 mu=1.7978 alpha=34.56 epsHOMO=-0.2424 epsLUMO=0.0859 epsgap=0.3283 =236.9759 zpve=0.086675 U0=-193.034094 U=-193.029968 H=-193.029024 G=-193.060777 Cv=12.915 pbc="F F F" +C -0.03489789 1.55259455 -0.08507691 +C 1.49349183 1.45459700 0.10114677 +O 1.42725884 0.02348990 0.27254886 +C -0.00634758 0.02236494 0.10944630 +H -0.56579210 2.11301721 0.68591527 +H -0.36628352 1.89393380 -1.06692327 +H 2.10031724 1.73525720 -0.76898349 +H 1.90023090 1.95506611 0.98896748 +H -0.51987849 -0.35595179 1.00235948 +H -0.31970062 -0.57567421 -0.75559275 +12 +Properties=species:S:1:pos:R:3 A=8.63483 B=4.11977 C=2.88825 mu=0.6875 alpha=45.37 epsHOMO=-0.2392 epsLUMO=0.0192 epsgap=0.2584 =452.5112 zpve=0.100501 U0=-248.375248 U=-248.368823 H=-248.367879 G=-248.405354 Cv=21.616 pbc="F F F" +C -0.10255370 1.49907127 -0.00133642 +C -0.00519774 -0.00343114 -0.02723470 +C -0.00688536 -0.76890740 1.26434744 +N 0.08186799 -0.69202791 -1.10087306 +O 0.07439067 0.13215394 -2.24195258 +H 0.78342093 1.94690862 -0.46354050 +H -0.96150430 1.83614082 -0.59021965 +H -0.19854173 1.87462989 1.01931607 +H -0.92620012 -0.57297946 1.82982479 +H 0.07256154 -1.84064782 1.07516863 +H 0.82937178 -0.45553340 1.90133089 +H 0.13975924 -0.51254299 -2.95389260 +10 +Properties=species:S:1:pos:R:3 A=9.17136 B=9.04195 C=4.5531 mu=1.8689 alpha=43.14 epsHOMO=-0.2029 epsLUMO=0.0499 epsgap=0.2528 =303.9808 zpve=0.082433 U0=-210.101789 U=-210.097816 H=-210.096872 G=-210.12818 Cv=14.821 pbc="F F F" +N -0.00829694 1.35362838 0.00995971 +C 1.28033254 1.82457421 -0.00022459 +C 2.12216639 0.73698898 -0.01445600 +C 1.30849904 -0.43109580 -0.01284154 +C -0.00342782 -0.01836884 0.00235736 +H -0.83243234 1.92772668 0.02122995 +H 1.48471820 2.88312630 0.00354313 +H 3.20066918 0.77343077 -0.02488416 +H 1.64810599 -1.45539683 -0.02180394 +H -0.92551647 -0.57697054 0.00839402 +9 +Properties=species:S:1:pos:R:3 A=9.77329 B=9.41192 C=4.7946 mu=3.6193 alpha=39.13 epsHOMO=-0.2253 epsLUMO=0.0332 epsgap=0.2585 =283.6817 zpve=0.071145 U0=-226.160842 U=-226.157088 H=-226.156144 G=-226.187104 Cv=13.371 pbc="F F F" +N -0.00996090 1.35636172 0.01000855 +C 1.29535422 1.79575578 -0.00047017 +C 2.05550519 0.65637438 -0.01422927 +N 1.25385745 -0.46275132 -0.01260894 +C 0.02301170 -0.00706382 0.00200146 +H -0.83195718 1.93618436 0.02132575 +H 1.54553961 2.84344998 0.00289054 +H 3.13177434 0.57569766 -0.02524155 +H -0.87667864 -0.60414531 0.00750794 +9 +Properties=species:S:1:pos:R:3 A=9.50228 B=9.29497 C=4.69874 mu=0.5571 alpha=39.2 epsHOMO=-0.2246 epsLUMO=0.0199 epsgap=0.2445 =289.005 zpve=0.069883 U0=-229.969129 U=-229.965414 H=-229.96447 G=-229.995393 Cv=13.358 pbc="F F F" +O -0.03255819 1.35065130 0.00967376 +C 1.25319237 1.79167710 -0.00030351 +C 2.11664681 0.74375623 -0.01441494 +C 1.30397796 -0.43852387 -0.01283316 +C 0.01629568 -0.00777185 0.00191294 +H 1.37951089 2.86161135 0.00427433 +H 3.19376406 0.79865501 -0.02458604 +H 1.63858893 -1.46383271 -0.02156949 +H -0.93736295 -0.50899925 0.00843891 +8 +Properties=species:S:1:pos:R:3 A=10.10926 B=9.69372 C=4.94857 mu=1.5081 alpha=35.17 epsHOMO=-0.2509 epsLUMO=0.001 epsgap=0.2519 =269.2444 zpve=0.058593 U0=-246.02915 U=-246.025614 H=-246.024669 G=-246.055309 Cv=12.04 pbc="F F F" +O -0.03808298 1.35643965 0.00975193 +C 1.26496982 1.76855319 -0.00061744 +C 2.04955750 0.66630033 -0.01406047 +N 1.24227075 -0.46732066 -0.01237541 +C 0.04019820 0.00561577 0.00183569 +H 1.43513907 2.83095265 0.00340823 +H 3.12532898 0.59783015 -0.02480757 +H -0.89387707 -0.53342154 0.00796935 +17 +Properties=species:S:1:pos:R:3 A=4.4178 B=4.41753 C=4.41717 mu=0.0003 alpha=56.01 epsHOMO=-0.3145 epsLUMO=0.0737 epsgap=0.3882 =486.2719 zpve=0.158836 U0=-197.632222 U=-197.625241 H=-197.624297 G=-197.661411 Cv=26.084 pbc="F F F" +C -0.01859029 1.53486266 0.01039904 +C 0.00210320 -0.00413672 0.00196980 +C 0.74643954 -0.51390760 1.24887638 +C -1.44190166 -0.53673615 0.01311457 +C 0.72234971 -0.50049606 -1.26445632 +H 0.99776928 1.94420529 0.00435474 +H -0.54181883 1.92841415 -0.86819911 +H -0.52758402 1.91888376 0.90148530 +H 0.25549576 -0.17602728 2.16833067 +H 0.77815389 -1.60892372 1.27088904 +H 1.77964786 -0.14975834 1.26944035 +H -1.99871895 -0.18888794 -0.86408066 +H -1.45956909 -1.63227235 0.00649916 +H -1.98088827 -0.19979639 0.90561580 +H 1.75482473 -0.13554018 -1.30118083 +H 0.75396989 -1.59524036 -1.29822280 +H 0.21358221 -0.15343900 -2.17067181 +15 +Properties=species:S:1:pos:R:3 A=4.69474 B=4.66938 C=4.47909 mu=1.384 alpha=49.04 epsHOMO=-0.2601 epsLUMO=0.0664 epsgap=0.3265 =449.0573 zpve=0.134977 U0=-233.560626 U=-233.553779 H=-233.552834 G=-233.589759 Cv=25.128 pbc="F F F" +C -0.00855999 1.54270147 0.00152717 +C 0.00506780 0.00702635 0.01856160 +C 0.76137731 -0.51839196 1.24766443 +C -1.42012774 -0.54933183 -0.02116008 +O 0.62590477 -0.48363745 -1.17657057 +H 1.01361406 1.94020776 0.00281128 +H -0.51459669 1.90702466 -0.89705781 +H -0.52214815 1.94882514 0.87918344 +H 0.28130613 -0.20165548 2.17943598 +H 0.79965506 -1.61131746 1.23022549 +H 1.79096916 -0.14072935 1.26083003 +H -1.93854829 -0.19910724 -0.91828281 +H -1.39937632 -1.64259062 -0.04563833 +H -1.98850725 -0.23059656 0.85726520 +H 1.53111188 -0.15488046 -1.19210222 +9 +Properties=species:S:1:pos:R:3 A=10.21876 B=4.04324 C=2.94978 mu=2.8579 alpha=42.02 epsHOMO=-0.2654 epsLUMO=-0.0575 epsgap=0.2079 =416.7799 zpve=0.065175 U0=-229.927277 U=-229.921622 H=-229.920677 G=-229.955843 Cv=18.723 pbc="F F F" +C -0.00693851 1.49559567 -0.01063358 +C -0.02986170 -0.01594623 0.06248673 +O -0.08815239 -0.63215062 1.10334812 +C 0.02336981 -0.70256747 -1.22062379 +C 0.06738132 -1.26058341 -2.28623129 +H 0.90429806 1.83281887 -0.51655384 +H -0.85338524 1.85556926 -0.60552819 +H -0.05237764 1.91099807 0.99695949 +H 0.10466868 -1.77036047 -3.21832617 +8 +Properties=species:S:1:pos:R:3 A=10.16063 B=4.15847 C=3.0056 mu=3.3351 alpha=37.08 epsHOMO=-0.3007 epsLUMO=-0.0889 epsgap=0.2118 =397.7757 zpve=0.05451 U0=-246.027383 U=-246.022024 H=-246.02108 G=-246.055769 Cv=16.963 pbc="F F F" +C -0.01171097 1.49973776 -0.01213268 +C -0.02919740 -0.00338745 0.07151218 +O -0.09164028 -0.64592927 1.08732241 +C 0.03831104 -0.69690258 -1.23748557 +N 0.09136286 -1.22982650 -2.26228068 +H 0.90093132 1.83370670 -0.51814928 +H -0.85663933 1.84864400 -0.61618135 +H -0.06416540 1.92131542 0.99203818 +8 +Properties=species:S:1:pos:R:3 A=10.68423 B=4.3198 C=3.07993 mu=5.1815 alpha=38.31 epsHOMO=-0.2739 epsLUMO=-0.0438 epsgap=0.2301 =384.5574 zpve=0.056619 U0=-242.19573 U=-242.190591 H=-242.189646 G=-242.223513 Cv=17.465 pbc="F F F" +N -0.05232108 1.34991769 0.15535538 +C 0.08549378 -0.01476951 0.02668890 +N 1.14555215 -0.71993906 -0.03966037 +C -1.19426556 -0.71152088 0.01802151 +N -2.22747610 -1.22790543 0.00132148 +H 0.75474469 1.91998737 -0.04411334 +H -0.94458527 1.76600473 -0.05932293 +H 1.98312429 -0.13415500 -0.02878551 +9 +Properties=species:S:1:pos:R:3 A=9.08854 B=4.42607 C=3.03169 mu=0.9461 alpha=36.51 epsHOMO=-0.2538 epsLUMO=-0.0964 epsgap=0.1574 =399.222 zpve=0.064992 U0=-267.10335 U=-267.097658 H=-267.096714 G=-267.132534 Cv=17.806 pbc="F F F" +C -0.43126066 1.30586664 -0.12758585 +C 0.25285715 -0.01026988 0.13138867 +O 0.69872099 -0.36890333 1.19604690 +C 0.38978943 -0.96773680 -1.06226208 +O -0.01611268 -0.71564293 -2.16583718 +H 0.10006565 1.85324490 -0.91332597 +H -1.44270433 1.13069361 -0.50923371 +H -0.46757554 1.89548158 0.78836479 +H 0.90329886 -1.91558845 -0.79531972 +9 +Properties=species:S:1:pos:R:3 A=10.08927 B=4.54115 C=3.13487 mu=2.7707 alpha=38.09 epsHOMO=-0.2561 epsLUMO=-0.064 epsgap=0.192 =384.4827 zpve=0.067668 U0=-263.278851 U=-263.273589 H=-263.272645 G=-263.306835 Cv=17.972 pbc="F F F" +N -0.16032929 1.08528962 -0.45398652 +C 0.18649127 -0.08762327 0.14758021 +N 1.28979627 -0.46427423 0.67927030 +C -0.94204384 -1.08440264 0.20423621 +O -2.01802205 -0.89007687 -0.30583149 +H 0.54574437 1.73837930 -0.74689246 +H -1.04684920 1.09570857 -0.93607796 +H 2.01738195 0.24991828 0.59881811 +H -0.67740148 -2.01003995 0.74746441 +13 +Properties=species:S:1:pos:R:3 A=7.92874 B=3.8272 C=2.82581 mu=0.6578 alpha=51.2 epsHOMO=-0.2589 epsLUMO=0.0571 epsgap=0.316 =481.9854 zpve=0.112471 U0=-195.186772 U=-195.180446 H=-195.179502 G=-195.215658 Cv=22.569 pbc="F F F" +C -0.01763434 1.54859468 -0.00253584 +C -0.01719180 0.00735789 0.03269604 +C 0.75818463 -0.52820155 1.25278862 +C -1.38213303 -0.51768438 0.00775516 +C -2.50796392 -0.94005629 0.00478552 +H 1.00944396 1.92639613 -0.02632830 +H -0.54514614 1.92217052 -0.88393459 +H -0.51243000 1.95339892 0.88560642 +H 0.49366179 -0.34904155 -0.87265890 +H 0.28968458 -0.19345372 2.18334679 +H 0.77833392 -1.62102506 1.25783727 +H 1.78952492 -0.16181727 1.23574954 +H -3.49998244 -1.31810165 -0.00723572 +12 +Properties=species:S:1:pos:R:3 A=7.90009 B=3.95466 C=2.88643 mu=3.9512 alpha=46.23 epsHOMO=-0.318 epsLUMO=0.0365 epsgap=0.3545 =460.694 zpve=0.102281 U0=-211.295796 U=-211.289821 H=-211.288877 G=-211.324525 Cv=20.467 pbc="F F F" +C -0.00242350 1.55035837 0.01056187 +C -0.01856600 0.01004352 -0.02261770 +C -1.45045318 -0.55766178 0.01293347 +C 0.70125749 -0.48583168 -1.20172764 +N 1.26190946 -0.87126766 -2.13619740 +H 1.01959496 1.93673215 0.00349668 +H -0.52972844 1.96141639 -0.85498386 +H -0.50134251 1.90346839 0.91755862 +H 0.52481877 -0.36222456 0.85525462 +H -2.02427903 -0.21428716 -0.85251869 +H -1.44438296 -1.65026927 0.00754107 +H -1.95805494 -0.21725882 0.91996760 +11 +Properties=species:S:1:pos:R:3 A=8.30095 B=3.97615 C=2.93764 mu=2.7429 alpha=42.87 epsHOMO=-0.2704 epsLUMO=0.0278 epsgap=0.2983 =440.0738 zpve=0.091554 U0=-227.338075 U=-227.332253 H=-227.331309 G=-227.366638 Cv=19.946 pbc="F F F" +C -0.01064295 1.54128691 0.00722087 +C -0.00203811 0.00756015 0.03863463 +N 0.76268677 -0.47101822 1.19250856 +C -1.38541954 -0.52099256 -0.00194097 +N -2.45680847 -0.95636072 0.00694969 +H 1.02043523 1.90106589 0.00392365 +H -0.52873161 1.91193452 -0.88031480 +H -0.51849536 1.94150431 0.89013197 +H 0.50418828 -0.35805500 -0.86276072 +H 0.30070681 -0.18364934 2.05155305 +H 0.78148992 -1.48670299 1.20428183 +11 +Properties=species:S:1:pos:R:3 A=8.55919 B=3.89952 C=2.91869 mu=1.3582 alpha=44.03 epsHOMO=-0.2665 epsLUMO=0.0336 epsgap=0.3001 =444.6452 zpve=0.088908 U0=-231.108368 U=-231.102292 H=-231.101348 G=-231.137061 Cv=21.306 pbc="F F F" +C -0.00754278 1.55566144 0.08650633 +C 0.01207541 0.02096553 0.04935916 +O -1.29150251 -0.52785128 0.22126386 +C 0.65093362 -0.47718651 -1.17430507 +C 1.13902673 -0.86577343 -2.20205518 +H 1.00824243 1.95969458 0.04929733 +H -0.56272245 1.95192010 -0.76978085 +H -0.49328161 1.88929438 1.00704123 +H 0.57811967 -0.34464907 0.91458864 +H -1.78516532 -0.35903262 -0.58909400 +H 1.58078877 -1.22365060 -3.09919447 +10 +Properties=species:S:1:pos:R:3 A=8.55908 B=4.01671 C=2.97882 mu=3.269 alpha=39.28 epsHOMO=-0.3051 epsLUMO=0.0115 epsgap=0.3166 =424.3395 zpve=0.078602 U0=-247.214861 U=-247.209162 H=-247.208218 G=-247.243338 Cv=19.252 pbc="F F F" +C -0.00992462 1.54951908 0.00732200 +C 0.00740485 0.01722020 0.05031270 +O 0.72085722 -0.49630479 1.15864583 +C -1.36995095 -0.52355929 0.00894432 +N -2.45081572 -0.93233138 0.00716912 +H 1.01892314 1.91630447 0.02724149 +H -0.50261490 1.90969362 -0.89997951 +H -0.54800426 1.95377611 0.87053664 +H 0.53602846 -0.36494195 -0.82970945 +H 0.27702643 -0.20644534 1.96421773 +13 +Properties=species:S:1:pos:R:3 A=7.66289 B=3.70925 C=2.79937 mu=2.6921 alpha=46.58 epsHOMO=-0.2469 epsLUMO=-0.0188 epsgap=0.2281 =482.0475 zpve=0.112328 U0=-232.364952 U=-232.358577 H=-232.357633 G=-232.394589 Cv=21.434 pbc="F F F" +C -0.02629647 1.53877116 -0.00783747 +C 0.01468904 0.00095310 0.00166037 +C 0.74737862 -0.53388859 1.24420471 +C -1.40211264 -0.53859257 -0.01557820 +O -1.87620455 -1.23582351 -0.87399559 +H 0.98823696 1.94625080 0.03705522 +H -0.50725090 1.92153422 -0.91285565 +H -0.57756514 1.92360883 0.85796386 +H 0.50584680 -0.36421091 -0.90619124 +H 0.22958380 -0.23909403 2.16431052 +H 0.81692736 -1.62559499 1.22973438 +H 1.76267153 -0.12846566 1.29050755 +H -2.00861510 -0.22792742 0.87121666 +11 +Properties=species:S:1:pos:R:3 A=8.31096 B=4.07963 C=2.92918 mu=2.8354 alpha=39.16 epsHOMO=-0.255 epsLUMO=-0.0279 epsgap=0.2271 =432.1489 zpve=0.088443 U0=-268.287661 U=-268.281505 H=-268.280561 G=-268.316982 Cv=20.344 pbc="F F F" +C -0.05483118 1.46032843 -0.17068647 +C 0.07105714 -0.03524485 0.11225841 +O 0.58204486 -0.33316656 1.40176256 +C -1.27639279 -0.74383027 0.03051185 +O -2.11855775 -0.49643175 -0.79163412 +H 0.93158072 1.93091398 -0.23353204 +H -0.57713995 1.61290939 -1.11835562 +H -0.62444315 1.94776119 0.62573670 +H 0.69687521 -0.50071620 -0.67159471 +H 1.45603316 0.06066659 1.47858584 +H -1.40723217 -1.53388627 0.80211181 +12 +Properties=species:S:1:pos:R:3 A=8.9331 B=4.19405 C=2.96046 mu=3.7163 alpha=44.42 epsHOMO=-0.2424 epsLUMO=0.0327 epsgap=0.2751 =441.85 zpve=0.10227 U0=-248.430371 U=-248.424309 H=-248.423365 G=-248.459383 Cv=19.918 pbc="F F F" +C -0.09776499 1.44917268 0.04561984 +N 0.01043053 0.00373818 -0.00070600 +C 0.05323028 -0.71432112 1.25264891 +C 0.06728716 -0.64067225 -1.20060778 +O 0.03636432 -0.11006515 -2.29109228 +H 0.75650660 1.88747109 0.57647421 +H -0.11648577 1.81632103 -0.98049172 +H -1.01641106 1.75521548 0.56151415 +H -0.85453556 -0.53657675 1.84390168 +H 0.13297722 -1.78696336 1.05851351 +H 0.91637348 -0.40737846 1.85761345 +H 0.14762240 -1.73801339 -1.06805139 +11 +Properties=species:S:1:pos:R:3 A=10.13982 B=3.81346 C=2.86965 mu=2.9514 alpha=39.33 epsHOMO=-0.2699 epsLUMO=-0.0262 epsgap=0.2437 =440.1727 zpve=0.088924 U0=-268.301176 U=-268.295084 H=-268.29414 G=-268.331307 Cv=19.824 pbc="F F F" +C -0.01708808 1.44281136 0.03225501 +C -0.05544542 -0.06560572 -0.02375999 +O -0.17379913 -0.76196233 0.96223129 +C 0.06068107 -0.73615179 -1.38825252 +O 0.00877771 -2.12574390 -1.27749486 +H 0.92062950 1.81463913 -0.39522215 +H -0.83050523 1.86542281 -0.56780350 +H -0.10911975 1.78049045 1.06493675 +H -0.75015067 -0.35235060 -2.03056074 +H 1.00312898 -0.40389029 -1.85592942 +H -0.09063255 -2.29450960 -0.32608019 +13 +Properties=species:S:1:pos:R:3 A=9.55216 B=3.57844 C=2.7341 mu=2.6168 alpha=46.19 epsHOMO=-0.2423 epsLUMO=-0.0072 epsgap=0.2351 =489.8518 zpve=0.112006 U0=-232.377706 U=-232.371073 H=-232.370129 G=-232.408256 Cv=21.716 pbc="F F F" +C -0.04384102 1.54099793 -0.03913814 +C 0.02765132 0.01885817 0.04778773 +C 0.76814644 -0.48842218 1.28000847 +C 0.87850208 -1.99746312 1.42393323 +O 1.24179142 0.26651340 2.09788702 +H 0.95796454 1.97690198 -0.06816012 +H -0.58447717 1.85356031 -0.93690369 +H -0.55173626 1.95737759 0.83440529 +H -0.97692824 -0.42709877 0.05353178 +H 0.51853486 -0.40806507 -0.83809409 +H 1.39924980 -2.42678188 0.56053177 +H 1.42001187 -2.24449367 2.33776426 +H -0.11750602 -2.45360651 1.45141965 +12 +Properties=species:S:1:pos:R:3 A=9.78802 B=3.78441 C=2.84416 mu=3.499 alpha=42.54 epsHOMO=-0.2438 epsLUMO=0.0355 epsgap=0.2793 =457.447 zpve=0.101847 U0=-248.448467 U=-248.441988 H=-248.441044 G=-248.478935 Cv=21.374 pbc="F F F" +C -0.00220144 1.53344329 -0.05960369 +C -0.03235837 0.01206167 0.06840265 +C 0.69666086 -0.48196462 1.31828322 +N 0.98104007 -1.82034162 1.31984698 +O 1.00392124 0.24606232 2.24136749 +H 1.02111190 1.89525169 -0.19182672 +H -0.59707204 1.86206427 -0.91645604 +H -0.39728839 1.99920712 0.84545201 +H -1.06936083 -0.34552935 0.13040806 +H 0.39947165 -0.46906818 -0.81777020 +H 0.63281951 -2.43917647 0.60874785 +H 1.35708014 -2.22217793 2.16303522 +12 +Properties=species:S:1:pos:R:3 A=10.01185 B=3.84195 C=2.87635 mu=3.5402 alpha=43.61 epsHOMO=-0.2418 epsLUMO=0.0387 epsgap=0.2805 =458.9803 zpve=0.101735 U0=-248.443503 U=-248.436899 H=-248.435955 G=-248.474272 Cv=20.967 pbc="F F F" +C 0.00619941 1.45933202 -0.03893859 +N -0.06411616 0.01422963 -0.12046901 +C -1.20595394 -0.68196796 0.16117120 +C -1.08982704 -2.19284899 0.02095775 +O -2.24482911 -0.14443077 0.49994418 +H 0.75053989 1.78372520 0.69715573 +H 0.25692001 1.90395263 -1.00876215 +H -0.97856026 1.81121438 0.27082194 +H 0.75820982 -0.49543660 -0.39464024 +H -0.09909703 -2.53543104 -0.28970003 +H -1.34097671 -2.65395851 0.97972885 +H -1.82903100 -2.53483935 -0.70813076 +11 +Properties=species:S:1:pos:R:3 A=10.44045 B=4.04692 C=2.98547 mu=3.5648 alpha=39.51 epsHOMO=-0.2436 epsLUMO=0.0599 epsgap=0.3034 =428.404 zpve=0.091713 U0=-264.504487 U=-264.498452 H=-264.497508 G=-264.533633 Cv=20.274 pbc="F F F" +C 0.00922630 1.45021814 -0.03194543 +N -0.01429488 -0.00054646 -0.01782667 +C -1.21576579 -0.67252403 0.04421896 +N -1.09733352 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epsgap=0.2735 =408.0279 zpve=0.080317 U0=-284.372483 U=-284.367172 H=-284.366228 G=-284.40095 Cv=17.931 pbc="F F F" +N 0.01838100 0.93057357 0.04943058 +H -0.70674506 0.56116798 -0.55550134 +H 0.68248307 0.18430002 0.21487099 +H 0.21928084 2.79858558 -0.32670585 +C -0.54586914 1.44688585 1.30274586 +C -0.80466975 2.96030569 1.21052835 +O -0.21468822 3.53648884 0.15531687 +O -1.45295676 3.56338618 2.02143528 +H -1.47662579 0.96512183 1.62072169 +H 0.17781209 1.30324286 2.11157176 +13 +Properties=species:S:1:pos:R:3 A=6.6508 B=4.19317 C=3.39441 mu=2.2854 alpha=42.01 epsHOMO=-0.2597 epsLUMO=0.0631 epsgap=0.3228 =438.3028 zpve=0.11345 U0=-269.479234 U=-269.472993 H=-269.472049 G=-269.508213 Cv=22.072 pbc="F F F" +C -0.02775167 1.53421433 0.00651151 +C 0.08343246 0.01218277 0.05258986 +O 0.95856392 -0.48200710 -0.96694113 +C 0.69767574 -0.49516451 1.35948812 +O 2.02247062 -0.02842530 1.52222186 +H 0.94333763 1.98841434 0.21841589 +H -0.37320516 1.87354041 -0.97716908 +H -0.74773817 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-0.60699559 1.35137051 +H -1.65197294 -0.56010584 2.22063481 +H -2.28793759 0.90575132 1.46079572 +15 +Properties=species:S:1:pos:R:3 A=8.11598 B=3.43678 C=2.66869 mu=1.3894 alpha=49.45 epsHOMO=-0.2617 epsLUMO=0.0733 epsgap=0.335 =521.8605 zpve=0.136091 U0=-233.555951 U=-233.549143 H=-233.548199 G=-233.585602 Cv=23.99 pbc="F F F" +C -0.03841227 1.55621396 0.00935837 +C 0.01907135 0.02716079 -0.02074203 +C 0.73141506 -0.52709328 -1.25454422 +C 0.83975868 -2.05206973 -1.22872655 +O -0.01226470 -0.08514394 -2.38973758 +H 0.96845664 1.98863424 0.03526234 +H -0.54377006 1.93824134 -0.88074407 +H -0.57652955 1.91383386 0.89256698 +H 0.53166867 -0.34991486 0.87240999 +H -0.99727186 -0.38638654 -0.00185289 +H 1.74909346 -0.09943025 -1.28826184 +H 1.34143968 -2.42487137 -2.12957624 +H 1.41822012 -2.39229108 -0.36340871 +H -0.15616765 -2.50443859 -1.18308875 +H 0.43090825 -0.40901616 -3.17979642 +15 +Properties=species:S:1:pos:R:3 A=7.55335 B=3.78323 C=2.80566 mu=1.0758 alpha=49.8 epsHOMO=-0.2476 epsLUMO=0.086 epsgap=0.3336 =507.9614 zpve=0.135681 U0=-233.545899 U=-233.539034 H=-233.53809 G=-233.57582 Cv=23.573 pbc="F F F" +C -0.00857605 1.38097833 0.09454670 +O -0.02255106 -0.00739461 -0.13062576 +C -1.30243341 -0.61997805 -0.02088309 +C -1.18687286 -1.97885751 -0.70303918 +C -1.75587824 -0.74662760 1.43714508 +H 1.00321550 1.73258087 -0.12443496 +H -0.71511468 1.90838275 -0.56674280 +H -0.24909065 1.64967237 1.13384681 +H -2.03737978 -0.00432241 -0.56884198 +H -0.44792779 -2.59945605 -0.18596870 +H -2.14751041 -2.50225525 -0.69102581 +H -0.86524212 -1.85860679 -1.74077836 +H -2.73321482 -1.23674908 1.49205117 +H -1.03621350 -1.34483828 2.00544671 +H -1.84898410 0.22935483 1.92198087 +15 +Properties=species:S:1:pos:R:3 A=6.14183 B=5.17679 C=3.79297 mu=0.1068 alpha=52.92 epsHOMO=-0.2633 epsLUMO=0.0893 epsgap=0.3526 =439.1643 zpve=0.137025 U0=-196.409349 U=-196.403398 H=-196.402453 G=-196.437676 Cv=22.258 pbc="F F F" +C -0.03913204 1.50456305 0.04978913 +C 0.00985856 -0.01113880 0.01248619 +C -1.24167981 -0.66663044 -0.53989979 +C 1.33803387 -0.66895748 -0.27400430 +C 0.75126442 -0.72221586 1.11862571 +H 0.88633040 1.92490040 0.45579358 +H -0.18183878 1.92045743 -0.95489291 +H -0.86832076 1.85815718 0.67439648 +H -1.43570890 -0.34344387 -1.56976411 +H -1.15326757 -1.75757800 -0.54433832 +H -2.12221416 -0.40570820 0.05951361 +H 1.34398374 -1.58235208 -0.86005084 +H 2.20196897 -0.03329084 -0.43929326 +H 1.21949576 -0.12246018 1.89246580 +H 0.36153319 -1.67153717 1.47171932 +13 +Properties=species:S:1:pos:R:3 A=6.49534 B=5.52329 C=3.9108 mu=1.8235 alpha=45.7 epsHOMO=-0.2596 epsLUMO=0.091 epsgap=0.3505 =405.521 zpve=0.112851 U0=-232.335768 U=-232.329981 H=-232.329037 G=-232.364091 Cv=20.719 pbc="F F F" +C -0.03970330 1.50954998 -0.04112233 +C -0.03023556 -0.00335329 -0.06498361 +C -1.24613189 -0.68356648 0.52531190 +C 0.78937201 -0.71129193 -1.06027559 +O 1.22546798 -0.60059036 0.29712283 +H 0.88992772 1.90948478 -0.45296558 +H -0.87952723 1.90350108 -0.62376642 +H -0.14448576 1.87590604 0.98604429 +H -2.15218727 -0.41012616 -0.02629495 +H -1.13412584 -1.77005431 0.49747032 +H -1.38586631 -0.38055453 1.56882757 +H 0.50712690 -1.71386874 -1.38085018 +H 1.37088104 -0.14369322 -1.78640853 +13 +Properties=species:S:1:pos:R:3 A=6.57648 B=5.31603 C=3.98453 mu=1.3822 alpha=45.85 epsHOMO=-0.2494 epsLUMO=0.0776 epsgap=0.327 =401.4888 zpve=0.113237 U0=-232.33341 U=-232.327734 H=-232.32679 G=-232.36147 Cv=21.17 pbc="F F F" +C -0.03714052 1.53215920 0.01332310 +C 0.01344335 0.02117019 0.01922005 +O -1.17476104 -0.59735284 -0.40931260 +C 1.30744798 -0.69902106 -0.27389650 +C 0.70844749 -0.71373504 1.12226318 +H 0.89026861 1.96240279 0.40276135 +H -0.17846151 1.91561262 -1.00518522 +H -0.87011232 1.88955201 0.62730953 +H -1.28352265 -0.40225391 -1.34690641 +H 1.23973813 -1.61642115 -0.84783774 +H 2.20543993 -0.11523820 -0.44744474 +H 1.19539159 -0.13889834 1.90240284 +H 0.24735439 -1.63670241 1.45378480 +10 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1.08469664 2.25947795 0.18994331 +H -1.71642814 2.41772662 -0.68901078 +H -1.83392757 1.98415059 1.02444244 +H -0.05534741 4.17585264 -0.10670963 +H -0.02541477 3.61399048 1.57278917 +H 1.81074389 2.03288534 0.97578973 +H 1.63600801 2.46255380 -0.73376244 +10 +Properties=species:S:1:pos:R:3 A=12.23436 B=4.97895 C=3.70561 mu=3.6671 alpha=39.73 epsHOMO=-0.25 epsLUMO=0.038 epsgap=0.288 =355.0934 zpve=0.080442 U0=-247.225618 U=-247.220897 H=-247.219953 G=-247.253218 Cv=16.1 pbc="F F F" +O 0.03184134 -0.03461682 0.00849836 +C -0.02440334 1.16440533 0.02160811 +C -1.14541322 2.23712809 0.03012901 +C 0.00928805 3.27945878 0.04557758 +N 0.93843838 2.14652109 0.03554396 +H -1.77030529 2.23779730 -0.86428437 +H -1.77632809 2.21765386 0.92007934 +H 0.07170564 3.91863516 -0.84072193 +H 0.06622603 3.89826625 0.94657952 +H 1.94616840 2.09535713 0.03855945 +9 +Properties=species:S:1:pos:R:3 A=12.44643 B=5.23328 C=3.86599 mu=3.9339 alpha=35.4 epsHOMO=-0.2788 epsLUMO=0.0089 epsgap=0.2878 =336.0792 zpve=0.068574 U0=-267.106213 U=-267.101929 H=-267.100985 G=-267.13332 Cv=14.483 pbc="F F F" +O 0.04424004 0.03933243 -0.33071104 +C -0.04854479 1.17416230 0.00410076 +C -1.14211656 2.23611850 0.15338361 +C 0.02332538 3.12357010 0.61681625 +O 0.95663808 2.01190122 0.42603746 +H -1.62573755 2.51482212 -0.78401102 +H -1.90009213 1.99553386 0.90028663 +H 0.28368077 3.96172751 -0.03229287 +H 0.00782250 3.43942655 1.66162725 +10 +Properties=species:S:1:pos:R:3 A=11.54185 B=4.90368 C=3.63309 mu=2.5257 alpha=39.34 epsHOMO=-0.2437 epsLUMO=-0.0258 epsgap=0.2179 =359.152 zpve=0.07965 U0=-247.190194 U=-247.185543 H=-247.184599 G=-247.217688 Cv=16.083 pbc="F F F" +O 0.00053891 0.00665475 -0.09743049 +C -0.01371990 1.19213881 0.03139936 +C -1.10668681 2.26612284 0.17247913 +N -0.03543445 3.25123475 0.48533693 +C 1.05328615 2.29521506 0.14383904 +H -1.65047970 2.42796175 -0.76886054 +H -1.83606786 2.11370530 0.97567410 +H -0.05434600 4.07193609 -0.10727051 +H 1.80754206 2.16277968 0.92736250 +H 1.56737937 2.47130277 -0.81152688 +9 +Properties=species:S:1:pos:R:3 A=12.18264 B=4.94228 C=3.68162 mu=0.8477 alpha=35.94 epsHOMO=-0.2647 epsLUMO=-0.0352 epsgap=0.2295 =344.5473 zpve=0.066989 U0=-267.068488 U=-267.063976 H=-267.063032 G=-267.095887 Cv=15.115 pbc="F F F" +O -0.00031692 0.00676321 -0.17139774 +C -0.01417098 1.18672865 -0.00980279 +C -1.08283120 2.27653381 0.15574094 +O -0.03856115 3.26460423 0.27513986 +C 1.02857270 2.30515693 0.12774311 +H -1.72923855 2.43647507 -0.71671274 +H -1.70219898 2.19346725 1.05800754 +H 1.67366781 2.23923293 1.01324179 +H 1.64705068 2.48224548 -0.76148355 +15 +Properties=species:S:1:pos:R:3 A=9.63416 B=4.10254 C=3.28665 mu=0.095 alpha=52.94 epsHOMO=-0.2896 epsLUMO=0.0927 epsgap=0.3823 =455.0215 zpve=0.138424 U0=-196.407957 U=-196.40245 H=-196.401505 G=-196.436159 Cv=20.299 pbc="F F F" +C -0.47005934 1.44206547 -0.06684548 +C 0.14287306 0.05485144 0.03747984 +C 1.37254693 -0.30295273 -0.84679168 +C 1.95947994 -1.09364561 0.35326039 +C 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This will be done by taking internal representations of a +NanoPET model (as an example) and using them inside a user-defined torch ``Module``. + +Only architectures which can output internal representations ("features" output) can +be used in this way. + +.. GENERATED FROM PYTHON SOURCE LINES 15-28 + +.. code-block:: Python + + + import torch + from metatomic.torch import ModelOutput + + from metatrain.pet import PET + from metatrain.utils.architectures import get_default_hypers + from metatrain.utils.data import DatasetInfo, read_systems + from metatrain.utils.neighbor_lists import ( + get_requested_neighbor_lists, + get_system_with_neighbor_lists, + ) + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 29-31 + +Read some sample systems. Metatrain always reads systems in float64, while torch +uses float32 by default. We will convert the systems to float32. + +.. GENERATED FROM PYTHON SOURCE LINES 32-37 + +.. code-block:: Python + + + systems = read_systems("qm9_reduced_100.xyz") + systems = [s.to(torch.float32) for s in systems] + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 38-41 + +Define the custom model using the PET architecture as a building block. +The dummy architecture here adds a linear layer and a tanh activation function +on top of the PET model. + +.. GENERATED FROM PYTHON SOURCE LINES 42-68 + +.. code-block:: Python + + + + class PETWithTanh(torch.nn.Module): + def __init__(self): + super().__init__() + self.pet = PET( + get_default_hypers("pet")["model"], + DatasetInfo( + length_unit="angstrom", + atomic_types=[1, 6, 7, 8, 9], + targets={}, + ), + ) + self.linear = torch.nn.Linear(384, 1) + self.tanh = torch.nn.Tanh() + + def forward(self, systems): + model_outputs = self.pet( + systems, + {"features": ModelOutput()}, + # ModelOutput(per_atom=True) would give per-atom features + ) + features = model_outputs["features"].block().values + return self.tanh(self.linear(features)) + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 69-71 + +Now we can train the custom model. Here is one training step executed with +some random targets. + +.. GENERATED FROM PYTHON SOURCE LINES 72-91 + +.. code-block:: Python + + my_targets = torch.randn(100, 1) + + # instantiate the model + model = PETWithTanh() + + # all metatrain models require neighbor lists to be present in the input systems + systems = [ + get_system_with_neighbor_lists(sys, get_requested_neighbor_lists(model)) + for sys in systems + ] + + # define an optimizer + optimizer = torch.optim.SGD(model.parameters(), lr=1e-3) + + # this is one training step + predictions = model(systems) + loss = torch.nn.functional.mse_loss(predictions, my_targets) + loss.backward() + optimizer.step() + + + + + + + + +.. _sphx_glr_download_examples_programmatic_use_architectures_outside_use_outside.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: use_outside.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: use_outside.py ` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: use_outside.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/programmatic/use_architectures_outside/use_outside.zip b/docs/src/examples/programmatic/use_architectures_outside/use_outside.zip new file mode 100644 index 0000000000000000000000000000000000000000..6a075f505bc2699e4a479fa393b86291801c082d GIT binary patch literal 7022 zcmdT}+iv8<77dpJl93QcLP9!EB}WKb>M;`6-#@B#bE zvbkUhF`RFh;TNQ33ND4h`Gr_PJ22%Yg&=e$cRuGTKmPE9WqBeb^SEYQG4Yg>06sn^ z5Rtf;rO3dFY{)o?UEE2;B2^F&>cva;=+S3+QcCgYQQ$aVD7hI5&eqEmR57?ig;il(KvosDW zDQgOdD&!Yd4yQ**u@I~TWUCm#FyqzIq*4dNc&Vw+g^6GjUP==!H!D%--J26`cyq!7 zeAdEzq89lC4&q6qH`<77&sHVU^W{7*BAIGq!-Vwp9EOXIFT~?gV1E(~DJLyO#Xef6 zX1PVyTQ}V zcdp-~KGpEQXG0*Ux%q677YsI3%~5B4^&i;awvxo9HWW%WL)vU$lr{Z0V3a#-UZygk 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+ +.. only:: html + + .. image:: /examples/train_from_scratch/images/thumb/sphx_glr_train_from_scratch_thumb.png + :alt: + + :ref:`sphx_glr_examples_train_from_scratch_train_from_scratch.py` + +.. raw:: html + +
Training a model from scratch
+
+ + +.. thumbnail-parent-div-close + +.. raw:: html + + + + +.. toctree:: + :hidden: + + /examples/train_from_scratch/train_from_scratch + + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-gallery + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download all examples in Python source code: train_from_scratch_python.zip ` + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download all examples in Jupyter notebooks: train_from_scratch_jupyter.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/train_from_scratch/options.yaml b/docs/src/examples/train_from_scratch/options.yaml new file mode 100644 index 0000000000..ceef2551ed --- /dev/null +++ b/docs/src/examples/train_from_scratch/options.yaml @@ -0,0 +1,22 @@ +architecture: + name: pet # the architecture name for PET, or the name for other architectures, if you want to train other models + model: + cutoff: 4.5 # the cutoff + training: + num_epochs: 10 # this is for a reasonable time of a tutorial, for a good model, consider increasing the number + batch_size: 10 # the size of the training data feed to the model per batch, determining the GPU memory usage during the training + log_interval: 1 + checkpoint_interval: 10 # it saves checkpoints of the model every 10 epochs + +# this needs specifying based on the specific dataset +training_set: + systems: + read_from: ./ethanol_reduced_100.xyz + length_unit: Angstrom + targets: + energy: + key: energy # name of the target value + unit: eV # unit of the target value + +test_set: 0.1 # 10 % of the training_set are randomly split and taken for test set +validation_set: 0.1 # 10 % of the training_set are randomly split and for validation set diff --git a/docs/src/examples/train_from_scratch/train_from_scratch.ipynb b/docs/src/examples/train_from_scratch/train_from_scratch.ipynb new file mode 100644 index 0000000000..d0f0dadee6 --- /dev/null +++ b/docs/src/examples/train_from_scratch/train_from_scratch.ipynb @@ -0,0 +1,32 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n\n# Training a model from scratch\nThis tutorial explains how to train a model with ``metatrain`` from scratch and evaluate\nit. [This dataset](./ethanol_reduced_100.xyz) is used here as an example of the\npreferred dataset format. If you have your own dataset, you can simply replace the\ndataset file name with yours.\n\n## Train the model\n\n### Configure the ``options.yaml`` and run the training\nBelow is an example ``options.yaml`` for training a PET model. In order to train other\nmodels, simply replace the architecture name with other models' architecture name. For\nthe supported models, please check `Available Architectures`_ .\n\n\n.. literalinclude:: ./options.yaml\n :language: yaml\n :linenos:\n\nStart the training by running\n\n```bash\nmtt train options.yaml\n```\nIt will start training. ``metatrain`` will automatically read the atomic forces from the\ntraining set, if they are stored in it and named as \"forces\". The model can also be\ntrained to learn other properties through transfer learning. For this, please refer to\nthis `transfer learning tutorial`_.\n\n\nOnce the training is started, a folder named ``outputs`` will be created automatically\nunder the folder where you run the command. Under this ``outputs`` folder, there is a\nfolder with the timestamp. Below is a normal structure of that folder of a successful\ntraining run.\n\n```bash\noutputs/2025-10-07/17-08-25/\n\u251c\u2500\u2500 indices # the results of dataset-spliting\n\u2502 \u251c\u2500\u2500 test.txt\n\u2502 \u251c\u2500\u2500 training.txt\n\u2502 \u2514\u2500\u2500 validation.txt\n\u251c\u2500\u2500 model_0.ckpt # the intermediate model saved at the 0th training step\n\u251c\u2500\u2500 model.ckpt # the final model\n\u251c\u2500\u2500 model.pt # the final model , usable directly by ASE and LAMMPS\n\u251c\u2500\u2500 options_restart.yaml # an expanded options file\n\u251c\u2500\u2500 train.csv # structured log of training metrics (loss, MAE, RMSE,...)\n\u2514\u2500\u2500 train.log # a human-friendly output\n```\nThe ``train.log`` provides information of the training procedure. For example, by\nchecking the following:\n\n```bash\n[2025-10-07 17:08:25][INFO] - Setting up training set [2025-10-07 17:08:25][INFO] -\nForces found in section 'energy', we will use this gradient to train the model\n[2025-10-07 17:08:25][WARNING] - No stress found in section 'energy'.\n```\nYou can know that the forces are identified by ``metatrain`` and are used during the\ntraining, and it fails to find stress. The following provides some statistical of the\ntraining, validation, and the test set\n\n```bash\n[2025-10-07 17:08:25][INFO] - Training dataset:\n Dataset containing 80 structures Mean and standard deviation of targets: -\n energy:\n - mean -9.708e+04 eV\n - std 3.97 eV\n[2025-10-07 17:08:25][INFO] - Validation dataset:\n Dataset containing 10 structures Mean and standard deviation of targets: -\n energy:\n - mean -9.708e+04 eV\n - std 3.73 eV\n[2025-10-07 17:08:25][INFO] - Test dataset:\n Dataset containing 10 structures Mean and standard deviation of targets: -\n energy:\n - mean -9.708e+04 eV\n - std 3.535 eV\n```\nThe training metrics are outputted every epoch, like\n\n```bash\n[2025-10-07 17:08:28][INFO] - Epoch: 0 | learning rate: 0.000e+00 | training\nloss: 6.305e+03 | training energy RMSE (per atom): 884.08 meV | training energy MAE\n(per atom): 773.44 meV | training forces RMSE: 28059.9 meV/A | training forces MAE:\n20581.1 meV/A | validation loss: 7.725e+02 | validation energy RMSE (per atom):\n877.08 meV | validation energy MAE (per atom): 772.04 meV | validation forces RMSE:\n27779.2 meV/A | validation forces MAE: 20201.9 meV/A\n```\nThese metrics are also outputted into ``train.csv`` in a formatted way, which can be\nused for plotting graph like loss curve.\n\nIt is easy to restart the training from the last step, by running\n\n```bash\nmtt train options.yaml --restart model.ckpt\n```\n## Evaluate the trained model\nIn order to evaluate the model on the test set, we can use the mtt eval sub-command.\nFirst, create the input file ``eval.yaml`` with the following options:\n\n.. literalinclude:: ./eval.yaml\n :language: yaml\n :linenos:\n\nand run\n\n```bash\nmtt eval PATH_TO_YOUR_MODEL/model.pt eval.yaml # be sure to replace the path\n```\nAfter this, a file named ``output.xyz`` will be created, with the atom positions and the\npredicted forces recorded in it. Also, you should see these statistical on your screen\n\n```bash\n[2025-10-07 17:11:47][INFO] - energy RMSE (per atom): 436.50 meV | energy MAE (per\natom): 341.32 meV | forces RMSE: 27823.1 meV/A | forces MAE: 20392.7 meV/A\n[2025-10-07 17:11:47][INFO] - Evaluation time: 1.10 s [1.2185 \u00b1 1.2768 ms per atom]\n```\nFurther analysis can be performed now that the model is trained. We provide a `Python\nscript`_ that can be used to generate a parity plot of the target vs predicted energies,\nbut otherwise leave this open-ended.\n\n\nTo run the script, download it from the repository, modify the paths as necessary\n(indicated with a #TODO), and run. This will generate a plot saved at parity_plot.png.\n\n\n## Use the model\nWith the trained model, you can run molecular dynamics. Please refer to these two\ntutorials for `ASE`_ and `LAMMPS`_ to see how to do that.\n\n\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/docs/src/examples/train_from_scratch/train_from_scratch.py b/docs/src/examples/train_from_scratch/train_from_scratch.py new file mode 100644 index 0000000000..a639da7f55 --- /dev/null +++ b/docs/src/examples/train_from_scratch/train_from_scratch.py @@ -0,0 +1,151 @@ +""" +Training a model from scratch +============================= + +This tutorial explains how to train a model with ``metatrain`` from scratch and evaluate +it. :download:`This dataset ` is used here as an example of the +preferred dataset format. If you have your own dataset, you can simply replace the +dataset file name with yours. + +Train the model +--------------- + +Configure the ``options.yaml`` and run the training +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Below is an example ``options.yaml`` for training a PET model. In order to train other +models, simply replace the architecture name with other models' architecture name. For +the supported models, please check `Available Architectures`_ . + +.. _`Available Architectures`: https://metatensor.github.io/metatrain/latest/architectures/index.html + +.. literalinclude:: options-scratch.yaml + :language: yaml + :linenos: + +Start the training by running + +.. code-block:: bash + + mtt train options-scratch.yaml + +It will start training. ``metatrain`` will automatically read the atomic forces from the +training set, if they are stored in it and named as "forces". The model can also be +trained to learn other properties through transfer learning. For this, please refer to +this `transfer learning tutorial`_. + +.. _`transfer learning tutorial`: https://metatensor.github.io/metatrain/latest/advanced-concepts/transfer-learning.html + +Once the training is started, a folder named ``outputs`` will be created automatically +under the folder where you run the command. Under this ``outputs`` folder, there is a +folder with the timestamp. Below is a normal structure of that folder of a successful +training run. + +.. code-block:: bash + + outputs/2025-10-07/17-08-25/ + ├── indices # the results of dataset-spliting + │ ├── test.txt + │ ├── training.txt + │ └── validation.txt + ├── model_0.ckpt # the intermediate model saved at the 0th training step + ├── model.ckpt # the final model + ├── model.pt # the final model , usable directly by ASE and LAMMPS + ├── options_restart.yaml # an expanded options file + ├── train.csv # structured log of training metrics (loss, MAE, RMSE,...) + └── train.log # a human-friendly output + +The ``train.log`` provides information of the training procedure. For example, by +checking the following: + +.. code-block:: bash + + [2025-10-07 17:08:25][INFO] - Setting up training set [2025-10-07 17:08:25][INFO] - + Forces found in section 'energy', we will use this gradient to train the model + [2025-10-07 17:08:25][WARNING] - No stress found in section 'energy'. + +You can know that the forces are identified by ``metatrain`` and are used during the +training, and it fails to find stress. The following provides some statistical of the +training, validation, and the test set + +.. code-block:: bash + + [2025-10-07 17:08:25][INFO] - Training dataset: + Dataset containing 80 structures Mean and standard deviation of targets: - + energy: + - mean -9.708e+04 eV + - std 3.97 eV + [2025-10-07 17:08:25][INFO] - Validation dataset: + Dataset containing 10 structures Mean and standard deviation of targets: - + energy: + - mean -9.708e+04 eV + - std 3.73 eV + [2025-10-07 17:08:25][INFO] - Test dataset: + Dataset containing 10 structures Mean and standard deviation of targets: - + energy: + - mean -9.708e+04 eV + - std 3.535 eV + +The training metrics are outputted every epoch, like + +.. code-block:: bash + + [2025-10-07 17:08:28][INFO] - Epoch: 0 | learning rate: 0.000e+00 | training + loss: 6.305e+03 | training energy RMSE (per atom): 884.08 meV | training energy MAE + (per atom): 773.44 meV | training forces RMSE: 28059.9 meV/A | training forces MAE: + 20581.1 meV/A | validation loss: 7.725e+02 | validation energy RMSE (per atom): + 877.08 meV | validation energy MAE (per atom): 772.04 meV | validation forces RMSE: + 27779.2 meV/A | validation forces MAE: 20201.9 meV/A + +These metrics are also outputted into ``train.csv`` in a formatted way, which can be +used for plotting graph like loss curve. + +It is easy to restart the training from the last step, by running + +.. code-block:: bash + + mtt train options-scratch.yaml --restart model.ckpt + +Evaluate the trained model +-------------------------- + +In order to evaluate the model on the test set, we can use the mtt eval sub-command. +First, create the input file ``eval-scratch.yaml`` with the following options: + +.. literalinclude:: ./eval-scratch.yaml + :language: yaml + :linenos: + +and run (be sure to replace the path to the ``model.pt``) + +.. code-block:: bash + + mtt eval model.pt eval-scratch.yaml + +After this, a file named ``output.xyz`` will be created, with the atom positions and the +predicted forces recorded in it. Also, you should see these statistical on your screen + +.. code-block:: bash + + [2025-10-07 17:11:47][INFO] - energy RMSE (per atom): 436.50 meV | energy MAE (per + atom): 341.32 meV | forces RMSE: 27823.1 meV/A | forces MAE: 20392.7 meV/A + [2025-10-07 17:11:47][INFO] - Evaluation time: 1.10 s [1.2185 ± 1.2768 ms per atom] + +Further analysis can be performed now that the model is trained. We provide a `Python +script`_ that can be used to generate a parity plot of the target vs predicted energies, +but otherwise leave this open-ended. + +.. _`Python script`: https://raw.githubusercontent.com/metatensor/Workshop-spring-2025/refs/heads/main/training-custom-models/part-1-gap/parity_plot.py + +To run the script, download it from the repository, modify the paths as necessary +(indicated with a #TODO), and run. This will generate a plot saved at parity_plot.png. + +Use the model +------------- + +With the trained model, you can run molecular dynamics. Please refer to these two +tutorials for `ASE`_ and `LAMMPS`_ to see how to do that. + +.. _`ASE`: https://docs.metatensor.org/metatomic/latest/examples/2-running-ase-md.html + +.. _`LAMMPS`: https://atomistic-cookbook.org/examples/pet-mad-nc/pet-mad-nc.html#running-lammps-on-gpus-with-kokkos +""" diff --git a/docs/src/examples/train_from_scratch/train_from_scratch.py.md5 b/docs/src/examples/train_from_scratch/train_from_scratch.py.md5 new file mode 100644 index 0000000000..d77b561efc --- /dev/null +++ b/docs/src/examples/train_from_scratch/train_from_scratch.py.md5 @@ -0,0 +1 @@ +6dabc07a5102263b98df5d6e7b15c10a \ No newline at end of file diff --git a/docs/src/examples/train_from_scratch/train_from_scratch.rst b/docs/src/examples/train_from_scratch/train_from_scratch.rst new file mode 100644 index 0000000000..c57f518ceb --- /dev/null +++ b/docs/src/examples/train_from_scratch/train_from_scratch.rst @@ -0,0 +1,195 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "examples/train_from_scratch/train_from_scratch.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code. + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_examples_train_from_scratch_train_from_scratch.py: + + +.. _train-from-scratch: + +Training a model from scratch +############################# +This tutorial explains how to train a model with ``metatrain`` from scratch and evaluate +it. `This dataset <./ethanol_reduced_100.xyz>`_ is used here as an example of the +preferred dataset format. If you have your own dataset, you can simply replace the +dataset file name with yours. + +Train the model +--------------- + +Configure the ``options.yaml`` and run the training +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Below is an example ``options.yaml`` for training a PET model. In order to train other +models, simply replace the architecture name with other models' architecture name. For +the supported models, please check `Available Architectures`_ . + +.. _`Available Architectures`: https://metatensor.github.io/metatrain/latest/architectures/index.html + +.. literalinclude:: ./options.yaml + :language: yaml + :linenos: + +Start the training by running + +.. code-block:: bash + + mtt train options.yaml + +It will start training. ``metatrain`` will automatically read the atomic forces from the +training set, if they are stored in it and named as "forces". The model can also be +trained to learn other properties through transfer learning. For this, please refer to +this `transfer learning tutorial`_. + +.. _`transfer learning tutorial`: https://metatensor.github.io/metatrain/latest/advanced-concepts/transfer-learning.html + +Once the training is started, a folder named ``outputs`` will be created automatically +under the folder where you run the command. Under this ``outputs`` folder, there is a +folder with the timestamp. Below is a normal structure of that folder of a successful +training run. + +.. code-block:: bash + + outputs/2025-10-07/17-08-25/ + ├── indices # the results of dataset-spliting + │ ├── test.txt + │ ├── training.txt + │ └── validation.txt + ├── model_0.ckpt # the intermediate model saved at the 0th training step + ├── model.ckpt # the final model + ├── model.pt # the final model , usable directly by ASE and LAMMPS + ├── options_restart.yaml # an expanded options file + ├── train.csv # structured log of training metrics (loss, MAE, RMSE,...) + └── train.log # a human-friendly output + +The ``train.log`` provides information of the training procedure. For example, by +checking the following: + +.. code-block:: bash + + [2025-10-07 17:08:25][INFO] - Setting up training set [2025-10-07 17:08:25][INFO] - + Forces found in section 'energy', we will use this gradient to train the model + [2025-10-07 17:08:25][WARNING] - No stress found in section 'energy'. + +You can know that the forces are identified by ``metatrain`` and are used during the +training, and it fails to find stress. The following provides some statistical of the +training, validation, and the test set + +.. code-block:: bash + + [2025-10-07 17:08:25][INFO] - Training dataset: + Dataset containing 80 structures Mean and standard deviation of targets: - + energy: + - mean -9.708e+04 eV + - std 3.97 eV + [2025-10-07 17:08:25][INFO] - Validation dataset: + Dataset containing 10 structures Mean and standard deviation of targets: - + energy: + - mean -9.708e+04 eV + - std 3.73 eV + [2025-10-07 17:08:25][INFO] - Test dataset: + Dataset containing 10 structures Mean and standard deviation of targets: - + energy: + - mean -9.708e+04 eV + - std 3.535 eV + +The training metrics are outputted every epoch, like + +.. code-block:: bash + + [2025-10-07 17:08:28][INFO] - Epoch: 0 | learning rate: 0.000e+00 | training + loss: 6.305e+03 | training energy RMSE (per atom): 884.08 meV | training energy MAE + (per atom): 773.44 meV | training forces RMSE: 28059.9 meV/A | training forces MAE: + 20581.1 meV/A | validation loss: 7.725e+02 | validation energy RMSE (per atom): + 877.08 meV | validation energy MAE (per atom): 772.04 meV | validation forces RMSE: + 27779.2 meV/A | validation forces MAE: 20201.9 meV/A + +These metrics are also outputted into ``train.csv`` in a formatted way, which can be +used for plotting graph like loss curve. + +It is easy to restart the training from the last step, by running + +.. code-block:: bash + + mtt train options.yaml --restart model.ckpt + +Evaluate the trained model +-------------------------- +In order to evaluate the model on the test set, we can use the mtt eval sub-command. +First, create the input file ``eval.yaml`` with the following options: + +.. literalinclude:: ./eval.yaml + :language: yaml + :linenos: + +and run + +.. code-block:: bash + + mtt eval PATH_TO_YOUR_MODEL/model.pt eval.yaml # be sure to replace the path + +After this, a file named ``output.xyz`` will be created, with the atom positions and the +predicted forces recorded in it. Also, you should see these statistical on your screen + +.. code-block:: bash + + [2025-10-07 17:11:47][INFO] - energy RMSE (per atom): 436.50 meV | energy MAE (per + atom): 341.32 meV | forces RMSE: 27823.1 meV/A | forces MAE: 20392.7 meV/A + [2025-10-07 17:11:47][INFO] - Evaluation time: 1.10 s [1.2185 ± 1.2768 ms per atom] + +Further analysis can be performed now that the model is trained. We provide a `Python +script`_ that can be used to generate a parity plot of the target vs predicted energies, +but otherwise leave this open-ended. + +.. _`Python script`: https://raw.githubusercontent.com/metatensor/Workshop-spring-2025/refs/heads/main/training-custom-models/part-1-gap/parity_plot.py + +To run the script, download it from the repository, modify the paths as necessary +(indicated with a #TODO), and run. This will generate a plot saved at parity_plot.png. + + +Use the model +------------------------- +With the trained model, you can run molecular dynamics. Please refer to these two +tutorials for `ASE`_ and `LAMMPS`_ to see how to do that. + +.. _`ASE`: https://docs.metatensor.org/metatomic/latest/examples/2-running-ase-md.html + +.. _`LAMMPS`: https://atomistic-cookbook.org/examples/pet-mad-nc/pet-mad-nc.html#running-lammps-on-gpus-with-kokkos + + +.. _sphx_glr_download_examples_train_from_scratch_train_from_scratch.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: train_from_scratch.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: train_from_scratch.py ` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: train_from_scratch.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git 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+ +.. only:: html + + .. image:: /examples/validation/images/thumb/sphx_glr_parity_plot_thumb.png + :alt: + + :ref:`sphx_glr_examples_validation_parity_plot.py` + +.. raw:: html + +
Model validation with parity plots for energies and forces
+
+ + +.. thumbnail-parent-div-close + +.. raw:: html + + + + +.. toctree:: + :hidden: + + /examples/validation/parity_plot + + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-gallery + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download all examples in Python source code: validation_python.zip ` + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download all examples in Jupyter notebooks: validation_jupyter.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/validation/output.xyz b/docs/src/examples/validation/output.xyz new file mode 100644 index 0000000000..a07f3fddcc --- /dev/null +++ b/docs/src/examples/validation/output.xyz @@ -0,0 +1,1100 @@ +9 +Properties=species:S:1:pos:R:3:forces:R:3 energy=-97079.8125 pbc="F F F" +H 0.16712676 -1.19985032 0.84958249 -7.49372673 12.43364716 -17.29284668 +H -0.93496192 -0.92263609 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"module_short": "numpy", + "name": "_ArrayFunctionDispatcher" + }, + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "min" + } + ], + "np.sqrt": [ + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "ufunc" + }, + { + "is_class": false, + "is_explicit": false, + "module": "numpy", + "module_short": "numpy", + "name": "sqrt" + } + ], + "plt.show": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib.pyplot", + "module_short": "matplotlib.pyplot", + "name": "show" + } + ], + "plt.subplots": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib.pyplot", + "module_short": "matplotlib.pyplot", + "name": "subplots" + } + ], + "plt.tight_layout": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib.pyplot", + "module_short": "matplotlib.pyplot", + "name": "tight_layout" + } + ], + "predictions": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "list" + } + ], + "targets": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "list" + } + ] +} diff --git a/docs/src/examples/validation/parity_plot.ipynb b/docs/src/examples/validation/parity_plot.ipynb new file mode 100644 index 0000000000..7b3b971834 --- /dev/null +++ b/docs/src/examples/validation/parity_plot.ipynb @@ -0,0 +1,154 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Model validation with parity plots for energies and forces\n\nThis tutorial shows how to visualise your model output using parity plots. In the\n`train-from-scratch` we learned how to evaluate a trained model on a test set and\nsave the results to an output file. Here we will show how to create parity plots from\nthese results.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Import necessary libraries\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import ase.io\nimport chemiscope\nimport matplotlib.pyplot as plt\nimport numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the target and predicted data\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "targets = ase.io.read(\n \"../train_from_scratch/ethanol_reduced_100.xyz\", \":\"\n) # reference data (ground truth)\npredictions = ase.io.read(\"output.xyz\", \":\") # predicted data from the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Extract the energies from the loaded frames\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "e_targets = np.array(\n [frame.get_total_energy() / len(frame) for frame in targets]\n) # target energies\ne_predictions = np.array(\n [frame.get_total_energy() / len(frame) for frame in predictions]\n) # predicted energies\nf_targets = np.array(\n [frame.get_forces().flatten() for frame in targets]\n).flatten() # target forces\nf_predictions = np.array(\n [frame.get_forces().flatten() for frame in predictions]\n).flatten() # predicted forces" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create parity plots to compare predicted vs. target energies and forces\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "fig, axs = plt.subplots(1, 2, figsize=(12, 5))\n\n# Parity plot for energies\naxs[0].scatter(e_targets, e_predictions)\naxs[0].axline((np.min(e_targets), np.min(e_targets)), slope=1, ls=\"--\", color=\"red\")\naxs[0].set_xlabel(\"Target energy / kcal\")\naxs[0].set_ylabel(\"Predicted energy / kcal\")\nmin_e = np.min(np.array([e_targets, e_predictions])) - 2\nmax_e = np.max(np.array([e_targets, e_predictions])) + 2\naxs[0].set_xlim([min_e, max_e])\naxs[0].set_ylim([min_e, max_e])\naxs[0].set_title(\"Energy Parity Plot\")\n\n# Parity plot for forces\naxs[1].scatter(f_targets, f_predictions, alpha=0.5)\naxs[1].axline((np.min(f_targets), np.min(f_targets)), slope=1, ls=\"--\", color=\"red\")\naxs[1].set_xlabel(\"Target force / kcal/\u00c5\")\naxs[1].set_ylabel(\"Predicted force / kcal/\u00c5\")\nmin_f = np.min(np.array([f_targets, f_predictions])) - 2\nmax_f = np.max(np.array([f_targets, f_predictions])) + 2\naxs[1].set_xlim([min_f, max_f])\naxs[1].set_ylim([min_f, max_f])\naxs[1].set_title(\"Force Parity Plot\")\n\nplt.tight_layout()\nplt.show()\n\nprint(\n \"RMSE energy (per atom):\",\n np.sqrt(np.mean((e_targets - e_predictions) ** 2)),\n \"kcal\",\n)\nprint(\"RMSE forces:\", np.sqrt(np.mean((f_targets - f_predictions) ** 2)), \"kcal/\u00c5 \")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The results are a bit poor here because the model was not trained well enough and\nwas created only for demonstration purposes. In the case of a well-trained model, the\npoints should be closer to the diagonal line.\n\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Check outliers with ``Chemiscope``\nWith the approach above, you can inspect the whole dataset, but it might be difficult\nto identify outliers. `Chemiscope ` is a\nvisualisation tool, allowing you to explore the dataset interactively. The following\nexample shows how to use it to check the structure of probable outliers and the atomic\nforces.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "for frame in targets + predictions:\n frame.arrays[\"forces\"] = frame.get_forces()\n# a workaround, because the chemiscope interface for getting forces is broken with ASE\n# 3.23" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot the energy parity plot with Chemiscope. This can be rendered as a widget in a\nJupyter notebook.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "cs = chemiscope.show(\n targets, # reading structures from the dataset\n properties={\n \"Target energy\": {\"values\": e_targets, \"target\": \"structure\", \"units\": \"kcal\"},\n \"Predicted energy\": {\n \"values\": e_predictions,\n \"target\": \"structure\",\n \"units\": \"kcal\",\n },\n }, # plotting the energy parity plot\n mode=\"default\",\n shapes={\n \"target_forces\": chemiscope.ase_vectors_to_arrows(\n targets, key=\"forces\", scale=0.05, radius=0.15\n ),\n \"predicted_forces\": chemiscope.ase_vectors_to_arrows(\n predictions, key=\"forces\", scale=0.05, radius=0.15\n ),\n }, # plotting the atomic forces\n settings=chemiscope.quick_settings(\n trajectory=True,\n map_settings={\"joinPoints\": False},\n structure_settings={\n \"unitCell\": True,\n \"environments\": {\"activated\": False},\n \"shape\": \"predicted_forces\", # show predicted forces by defalut\n },\n ),\n)\ncs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can check the structures by clicking the red dots on the parity plot, or\ndragging the bar in the bottom right corner. Currently, plotting the diagonal line is\nnot supported in chemiscope, so please check the parity plot above to see the\noutliers.\n\nThe atomic forces are shown in arrows. The predicted forces are shown here. The target\nforces can be toggled by clicking the \"target_forces\" option in the menu in the upper\nright corner of the right panel.\n\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/docs/src/examples/validation/parity_plot.py b/docs/src/examples/validation/parity_plot.py new file mode 100644 index 0000000000..d5d23cf12e --- /dev/null +++ b/docs/src/examples/validation/parity_plot.py @@ -0,0 +1,147 @@ +""" +Model validation with parity plots +================================== + +This tutorial shows how to visualise your model output using parity plots. In the +:ref:`sphx_glr_generated_examples_0-beginner_03-train_from_scratch.py` we learned how to +evaluate a trained model on a test set and save the results to an output file. Here we +will show how to create parity plots from these results. + +""" + +# %% +# Import necessary libraries +import ase.io +import chemiscope +import matplotlib.pyplot as plt +import numpy as np + + +# %% +# Load the reference data (ground truth) +targets = ase.io.read("ethanol_reduced_100.xyz", ":") + +# %% +# predicted data from a model +predictions = ase.io.read("ethanol_reduced_100_predicted.xyz", ":") + +# %% +# Extract the energies from the loaded frames +e_targets = np.array( + [frame.get_total_energy() / len(frame) for frame in targets] +) # target energies +e_predictions = np.array( + [frame.get_total_energy() / len(frame) for frame in predictions] +) # predicted energies +f_targets = np.array( + [frame.get_forces().flatten() for frame in targets] +).flatten() # target forces +f_predictions = np.array( + [frame.get_forces().flatten() for frame in predictions] +).flatten() # predicted forces + + +# %% +# Create parity plots to compare predicted vs. target energies and forces +# ----------------------------------------------------------------------- + +fig, axs = plt.subplots(1, 2, figsize=(12, 5)) + +# Parity plot for energies +axs[0].scatter(e_targets, e_predictions) +axs[0].axline((np.min(e_targets), np.min(e_targets)), slope=1, ls="--", color="red") +axs[0].set_xlabel("Target energy / kcal") +axs[0].set_ylabel("Predicted energy / kcal") +min_e = np.min(np.array([e_targets, e_predictions])) - 2 +max_e = np.max(np.array([e_targets, e_predictions])) + 2 +axs[0].set_xlim([min_e, max_e]) +axs[0].set_ylim([min_e, max_e]) +axs[0].set_title("Energy Parity Plot") + +# Parity plot for forces +axs[1].scatter(f_targets, f_predictions, alpha=0.5) +axs[1].axline((np.min(f_targets), np.min(f_targets)), slope=1, ls="--", color="red") +axs[1].set_xlabel("Target force / kcal/Ã…") +axs[1].set_ylabel("Predicted force / kcal/Ã…") +min_f = np.min(np.array([f_targets, f_predictions])) - 2 +max_f = np.max(np.array([f_targets, f_predictions])) + 2 +axs[1].set_xlim([min_f, max_f]) +axs[1].set_ylim([min_f, max_f]) +axs[1].set_title("Force Parity Plot") + +fig.tight_layout() +plt.show() + +# %% +# We can also compute and print RMSE for energies and forces + +print( + "RMSE energy (per atom):", + np.sqrt(np.mean((e_targets - e_predictions) ** 2)), + "kcal", +) +print("RMSE forces:", np.sqrt(np.mean((f_targets - f_predictions) ** 2)), "kcal/Ã… ") + +# %% +# +# The results are a bit poor here because the model was not trained well enough and +# was created only for demonstration purposes. In the case of a well-trained model, the +# points should be closer to the diagonal line. +# +# Check outliers with ``Chemiscope`` +# ---------------------------------- +# With the approach above, you can inspect the whole dataset, but it might be difficult +# to identify outliers. `Chemiscope ` is a +# visualisation tool, allowing you to explore the dataset interactively. The following +# example shows how to use it to check the structure of probable outliers and the atomic +# forces. + +for frame in targets + predictions: + frame.arrays["forces"] = frame.get_forces() + +# a workaround, because the chemiscope interface for getting forces is broken with ASE +# 3.23 + +# %% +# Plot the energy parity plot with Chemiscope. This can be rendered as a widget in a +# Jupyter notebook. + +chemiscope.show( + targets, # reading structures from the dataset + properties={ + "Target energy": {"values": e_targets, "target": "structure", "units": "kcal"}, + "Predicted energy": { + "values": e_predictions, + "target": "structure", + "units": "kcal", + }, + }, # plotting the energy parity plot + mode="default", + shapes={ + "target_forces": chemiscope.ase_vectors_to_arrows( + targets, key="forces", scale=0.05, radius=0.15 + ), + "predicted_forces": chemiscope.ase_vectors_to_arrows( + predictions, key="forces", scale=0.05, radius=0.15 + ), + }, # plotting the atomic forces + settings=chemiscope.quick_settings( + trajectory=True, + map_settings={"joinPoints": False}, + structure_settings={ + "unitCell": True, + "environments": {"activated": False}, + "shape": "predicted_forces", # show predicted forces by defalut + }, + ), +) + +# %% +# You can check the structures by clicking the red dots on the parity plot, or +# dragging the bar in the bottom right corner. Currently, plotting the diagonal line is +# not supported in chemiscope, so please check the parity plot above to see the +# outliers. +# +# The atomic forces are shown in arrows. The predicted forces are shown here. The target +# forces can be toggled by clicking the "target_forces" option in the menu in the upper +# right corner of the right panel. diff --git a/docs/src/examples/validation/parity_plot.py.md5 b/docs/src/examples/validation/parity_plot.py.md5 new file mode 100644 index 0000000000..c1a3ea37a7 --- /dev/null +++ b/docs/src/examples/validation/parity_plot.py.md5 @@ -0,0 +1 @@ +4381fe7e0c0bc39cf97ff93007ea6114 \ No newline at end of file diff --git a/docs/src/examples/validation/parity_plot.rst b/docs/src/examples/validation/parity_plot.rst new file mode 100644 index 0000000000..de747cd147 --- /dev/null +++ b/docs/src/examples/validation/parity_plot.rst @@ -0,0 +1,281 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "examples/validation/parity_plot.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code. + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_examples_validation_parity_plot.py: + + +Model validation with parity plots for energies and forces +========================================================== + +This tutorial shows how to visualise your model output using parity plots. In the +:ref:`train-from-scratch` we learned how to evaluate a trained model on a test set and +save the results to an output file. Here we will show how to create parity plots from +these results. + +.. GENERATED FROM PYTHON SOURCE LINES 13-14 + +Import necessary libraries + +.. GENERATED FROM PYTHON SOURCE LINES 14-20 + +.. code-block:: Python + + import ase.io + import chemiscope + import matplotlib.pyplot as plt + import numpy as np + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 21-22 + +Load the target and predicted data + +.. GENERATED FROM PYTHON SOURCE LINES 22-28 + +.. code-block:: Python + + targets = ase.io.read( + "../train_from_scratch/ethanol_reduced_100.xyz", ":" + ) # reference data (ground truth) + predictions = ase.io.read("output.xyz", ":") # predicted data from the model + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 29-30 + +Extract the energies from the loaded frames + +.. GENERATED FROM PYTHON SOURCE LINES 30-44 + +.. code-block:: Python + + e_targets = np.array( + [frame.get_total_energy() / len(frame) for frame in targets] + ) # target energies + e_predictions = np.array( + [frame.get_total_energy() / len(frame) for frame in predictions] + ) # predicted energies + f_targets = np.array( + [frame.get_forces().flatten() for frame in targets] + ).flatten() # target forces + f_predictions = np.array( + [frame.get_forces().flatten() for frame in predictions] + ).flatten() # predicted forces + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 45-47 + +Create parity plots to compare predicted vs. target energies and forces +----------------------------------------------------------------------- + +.. GENERATED FROM PYTHON SOURCE LINES 47-81 + +.. code-block:: Python + + + fig, axs = plt.subplots(1, 2, figsize=(12, 5)) + + # Parity plot for energies + axs[0].scatter(e_targets, e_predictions) + axs[0].axline((np.min(e_targets), np.min(e_targets)), slope=1, ls="--", color="red") + axs[0].set_xlabel("Target energy / kcal") + axs[0].set_ylabel("Predicted energy / kcal") + min_e = np.min(np.array([e_targets, e_predictions])) - 2 + max_e = np.max(np.array([e_targets, e_predictions])) + 2 + axs[0].set_xlim([min_e, max_e]) + axs[0].set_ylim([min_e, max_e]) + axs[0].set_title("Energy Parity Plot") + + # Parity plot for forces + axs[1].scatter(f_targets, f_predictions, alpha=0.5) + axs[1].axline((np.min(f_targets), np.min(f_targets)), slope=1, ls="--", color="red") + axs[1].set_xlabel("Target force / kcal/Ã…") + axs[1].set_ylabel("Predicted force / kcal/Ã…") + min_f = np.min(np.array([f_targets, f_predictions])) - 2 + max_f = np.max(np.array([f_targets, f_predictions])) + 2 + axs[1].set_xlim([min_f, max_f]) + axs[1].set_ylim([min_f, max_f]) + axs[1].set_title("Force Parity Plot") + + plt.tight_layout() + plt.show() + + print( + "RMSE energy (per atom):", + np.sqrt(np.mean((e_targets - e_predictions) ** 2)), + "kcal", + ) + print("RMSE forces:", np.sqrt(np.mean((f_targets - f_predictions) ** 2)), "kcal/Ã… ") + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + RMSE energy (per atom): 0.3662913918764794 kcal + RMSE forces: 15.042111936421486 kcal/Ã… + + + + +.. GENERATED FROM PYTHON SOURCE LINES 82-85 + +The results are a bit poor here because the model was not trained well enough and +was created only for demonstration purposes. In the case of a well-trained model, the +points should be closer to the diagonal line. + +.. GENERATED FROM PYTHON SOURCE LINES 87-94 + +Check outliers with ``Chemiscope`` +---------------------------------- +With the approach above, you can inspect the whole dataset, but it might be difficult +to identify outliers. `Chemiscope ` is a +visualisation tool, allowing you to explore the dataset interactively. The following +example shows how to use it to check the structure of probable outliers and the atomic +forces. + +.. GENERATED FROM PYTHON SOURCE LINES 94-100 + +.. code-block:: Python + + + for frame in targets + predictions: + frame.arrays["forces"] = frame.get_forces() + # a workaround, because the chemiscope interface for getting forces is broken with ASE + # 3.23 + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 101-103 + +Plot the energy parity plot with Chemiscope. This can be rendered as a widget in a +Jupyter notebook. + +.. GENERATED FROM PYTHON SOURCE LINES 103-134 + +.. code-block:: Python + + cs = chemiscope.show( + targets, # reading structures from the dataset + properties={ + "Target energy": {"values": e_targets, "target": "structure", "units": "kcal"}, + "Predicted energy": { + "values": e_predictions, + "target": "structure", + "units": "kcal", + }, + }, # plotting the energy parity plot + mode="default", + shapes={ + "target_forces": chemiscope.ase_vectors_to_arrows( + targets, key="forces", scale=0.05, radius=0.15 + ), + "predicted_forces": chemiscope.ase_vectors_to_arrows( + predictions, key="forces", scale=0.05, radius=0.15 + ), + }, # plotting the atomic forces + settings=chemiscope.quick_settings( + trajectory=True, + map_settings={"joinPoints": False}, + structure_settings={ + "unitCell": True, + "environments": {"activated": False}, + "shape": "predicted_forces", # show predicted forces by defalut + }, + ), + ) + cs + + + +.. chemiscope:: _datasets/fig_parity_plot_001.json.gz + :mode: default + :warning_timeout: 2000 + + + +.. raw:: html + +
+ +
+
+
+ +.. GENERATED FROM PYTHON SOURCE LINES 135-143 + +You can check the structures by clicking the red dots on the parity plot, or +dragging the bar in the bottom right corner. Currently, plotting the diagonal line is +not supported in chemiscope, so please check the parity plot above to see the +outliers. + +The atomic forces are shown in arrows. The predicted forces are shown here. The target +forces can be toggled by clicking the "target_forces" option in the menu in the upper +right corner of the right panel. + + +.. _sphx_glr_download_examples_validation_parity_plot.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: parity_plot.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: parity_plot.py ` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: parity_plot.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/validation/parity_plot.zip b/docs/src/examples/validation/parity_plot.zip new file mode 100644 index 0000000000000000000000000000000000000000..f3d9dbd83d2f478cceb628d85b1850ddb7b94f50 GIT binary patch literal 12844 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}, + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib.pyplot", + "module_short": "matplotlib.pyplot", + "name": "title" + } + ], + "plt.xlabel": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib.pyplot", + "module_short": "matplotlib.pyplot", + "name": "xlabel" + } + ], + "plt.ylabel": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "matplotlib.pyplot", + "module_short": "matplotlib.pyplot", + "name": "ylabel" + } + ], + "structures": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "list" + } + ], + "structures.append": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "builtin_function_or_method" + } + ], + "structures_for_pair": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "list" + } + ], + "torch.jit.optimized_execution": [ + { + "is_class": false, + "is_explicit": false, + "module": "builtins", + "module_short": "builtins", + "name": "function" + }, + { + "is_class": false, + "is_explicit": false, + "module": "torch.jit", + "module_short": "torch.jit", + "name": "optimized_execution" + } + ] +} diff --git a/docs/src/examples/zbl/dimers.ipynb b/docs/src/examples/zbl/dimers.ipynb new file mode 100644 index 0000000000..b241500ab9 --- /dev/null +++ b/docs/src/examples/zbl/dimers.ipynb @@ -0,0 +1,140 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Training a model with ZBL corrections\n\nThis tutorial demonstrates how to train a model with ZBL corrections.\n\nThe training set for this example consists of a\nsubset of the ethanol moleculs from the [rMD17 dataset](https://iopscience.iop.org/article/10.1088/2632-2153/abba6f/meta).\n\nThe models are trained using the following training options, respectively:\n\n.. literalinclude:: options_no_zbl.yaml\n :language: yaml\n\n.. literalinclude:: options_zbl.yaml\n :language: yaml\n\nAs you can see, they are identical, except for the ``zbl`` key in the\n``model`` section.\nYou can train the same models yourself with\n\n.. literalinclude:: train.sh\n :language: bash\n\nA detailed step-by-step introduction on how to train a model is provided in\nthe `label_basic_usage` tutorial.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we start by importing the necessary libraries, including the integration of ASE\ncalculators for metatensor atomistic models.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import ase\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\nfrom metatomic.torch.ase_calculator import MetatomicCalculator" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting up the dimers\n\nWe set up a series of dimers with different atom pairs and distances. We will\ncalculate the energies of these dimers using the models trained with and without ZBL\ncorrections.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "distances = np.linspace(0.5, 6.0, 200)\npairs = {}\nfor pair in [(\"H\", \"H\"), (\"H\", \"C\"), (\"C\", \"C\"), (\"C\", \"O\"), (\"O\", \"O\"), (\"H\", \"O\")]:\n structures = []\n for distance in distances:\n atoms = ase.Atoms(\n symbols=[pair[0], pair[1]],\n positions=[[0, 0, 0], [0, 0, distance]],\n )\n structures.append(atoms)\n pairs[pair] = structures" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now load the two exported models, one with and one without ZBL corrections\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "calc_no_zbl = MetatomicCalculator(\"model_no_zbl.pt\", extensions_directory=\"extensions/\")\ncalc_zbl = MetatomicCalculator(\"model_zbl.pt\", extensions_directory=\"extensions/\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculate and plot energies without ZBL\n\nWe calculate the energies of the dimer curves for each pair of atoms and\nplot the results, using the non-ZBL-corrected model.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "for pair, structures_for_pair in pairs.items():\n energies = []\n for atoms in structures_for_pair:\n atoms.set_calculator(calc_no_zbl)\n with torch.jit.optimized_execution(False):\n energies.append(atoms.get_potential_energy())\n energies = np.array(energies) - energies[-1]\n plt.plot(distances, energies, label=f\"{pair[0]}-{pair[1]}\")\nplt.title(\"Dimer curves - no ZBL\")\nplt.xlabel(\"Distance (\u00c5)\")\nplt.ylabel(\"Energy (eV)\")\nplt.legend()\nplt.tight_layout()\nplt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculate and plot energies from the ZBL-corrected model\n\nWe repeat the same procedure as above, but this time with the ZBL-corrected model.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "for pair, structures_for_pair in pairs.items():\n energies = []\n for atoms in structures_for_pair:\n atoms.set_calculator(calc_zbl)\n with torch.jit.optimized_execution(False):\n energies.append(atoms.get_potential_energy())\n energies = np.array(energies) - energies[-1]\n plt.plot(distances, energies, label=f\"{pair[0]}-{pair[1]}\")\nplt.title(\"Dimer curves - with ZBL\")\nplt.xlabel(\"Distance (\u00c5)\")\nplt.ylabel(\"Energy (eV)\")\nplt.legend()\nplt.tight_layout()\nplt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It can be seen that all the dimer curves include a strong repulsion\nat short distances, which is due to the ZBL contribution. Even the H-H dimer,\nwhose ZBL correction is very weak due to the small covalent radii of hydrogen,\nwould show a strong repulsion closer to the origin (here, we only plotted\nstarting from a distance of 0.5 \u00c5). Let's zoom in on the H-H dimer to see\nthis effect more clearly.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "new_distances = np.linspace(0.1, 2.0, 200)\n\nstructures = []\nfor distance in new_distances:\n atoms = ase.Atoms(\n symbols=[\"H\", \"H\"],\n positions=[[0, 0, 0], [0, 0, distance]],\n )\n structures.append(atoms)\n\nfor atoms in structures:\n atoms.set_calculator(calc_zbl)\nwith torch.jit.optimized_execution(False):\n energies = [atoms.get_potential_energy() for atoms in structures]\nenergies = np.array(energies) - energies[-1]\nplt.plot(new_distances, energies, label=\"H-H\")\nplt.title(\"Dimer curve - H-H with ZBL\")\nplt.xlabel(\"Distance (\u00c5)\")\nplt.ylabel(\"Energy (eV)\")\nplt.legend()\nplt.tight_layout()\nplt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/docs/src/examples/zbl/dimers.py b/docs/src/examples/zbl/dimers.py new file mode 100644 index 0000000000..3fa6b5acdc --- /dev/null +++ b/docs/src/examples/zbl/dimers.py @@ -0,0 +1,147 @@ +""" +Training a model with ZBL corrections +===================================== + +This tutorial demonstrates how to train a model with ZBL corrections. + +The training set for this example consists of a +subset of the ethanol moleculs from the `rMD17 dataset +`_. + +The models are trained using the following training options, respectively: + +.. literalinclude:: options-no-zbl.yaml + :language: yaml + +.. literalinclude:: options_zbl.yaml + :language: yaml + +As you can see, they are identical, except for the ``zbl`` key in the ``model`` section. +A detailed step-by-step introduction on how to train a model is provided in the +:ref:`label_basic_usage` tutorial. +""" + +# %% +# +# First, we start by importing the necessary libraries, including the integration of ASE +# calculators for metatensor atomistic models. + +import subprocess + +import ase +import matplotlib.pyplot as plt +import numpy as np +import torch +from metatomic.torch.ase_calculator import MetatomicCalculator + + +# %% + +subprocess.run(["mtt", "train", "options-no-zbl.yaml", "-o", "model_no_zbl.pt"]) +subprocess.run(["mtt", "train", "options_zbl.yaml", "-o", "model_zbl.pt"]) + +# %% +# +# Setting up the dimers +# --------------------- +# +# We set up a series of dimers with different atom pairs and distances. We will +# calculate the energies of these dimers using the models trained with and without ZBL +# corrections. + +distances = np.linspace(0.5, 6.0, 200) +pairs = {} +for pair in [("H", "H"), ("H", "C"), ("C", "C"), ("C", "O"), ("O", "O"), ("H", "O")]: + structures = [] + for distance in distances: + atoms = ase.Atoms( + symbols=[pair[0], pair[1]], + positions=[[0, 0, 0], [0, 0, distance]], + ) + structures.append(atoms) + pairs[pair] = structures + +# %% +# +# We now load the two exported models, one with and one without ZBL corrections + +calc_no_zbl = MetatomicCalculator("model_no_zbl.pt", extensions_directory="extensions/") +calc_zbl = MetatomicCalculator("model_zbl.pt", extensions_directory="extensions/") + + +# %% +# +# Calculate and plot energies without ZBL +# --------------------------------------- +# +# We calculate the energies of the dimer curves for each pair of atoms and +# plot the results, using the non-ZBL-corrected model. + +for pair, structures_for_pair in pairs.items(): + energies = [] + for atoms in structures_for_pair: + atoms.set_calculator(calc_no_zbl) + with torch.jit.optimized_execution(False): + energies.append(atoms.get_potential_energy()) + energies = np.array(energies) - energies[-1] + plt.plot(distances, energies, label=f"{pair[0]}-{pair[1]}") +plt.title("Dimer curves - no ZBL") +plt.xlabel("Distance (Ã…)") +plt.ylabel("Energy (eV)") +plt.legend() +plt.tight_layout() +plt.show() + +# %% +# +# Calculate and plot energies from the ZBL-corrected model +# -------------------------------------------------------- +# +# We repeat the same procedure as above, but this time with the ZBL-corrected model. + +for pair, structures_for_pair in pairs.items(): + energies = [] + for atoms in structures_for_pair: + atoms.set_calculator(calc_zbl) + with torch.jit.optimized_execution(False): + energies.append(atoms.get_potential_energy()) + energies = np.array(energies) - energies[-1] + plt.plot(distances, energies, label=f"{pair[0]}-{pair[1]}") +plt.title("Dimer curves - with ZBL") +plt.xlabel("Distance (Ã…)") +plt.ylabel("Energy (eV)") +plt.legend() +plt.tight_layout() +plt.show() + +# %% +# +# It can be seen that all the dimer curves include a strong repulsion +# at short distances, which is due to the ZBL contribution. Even the H-H dimer, +# whose ZBL correction is very weak due to the small covalent radii of hydrogen, +# would show a strong repulsion closer to the origin (here, we only plotted +# starting from a distance of 0.5 Ã…). Let's zoom in on the H-H dimer to see +# this effect more clearly. + +new_distances = np.linspace(0.1, 2.0, 200) + +structures = [] +for distance in new_distances: + atoms = ase.Atoms( + symbols=["H", "H"], + positions=[[0, 0, 0], [0, 0, distance]], + ) + structures.append(atoms) + +for atoms in structures: + atoms.set_calculator(calc_zbl) +with torch.jit.optimized_execution(False): + energies = [atoms.get_potential_energy() for atoms in structures] +energies = np.array(energies) - energies[-1] +plt.plot(new_distances, energies, label="H-H") +plt.title("Dimer curve - H-H with ZBL") +plt.xlabel("Distance (Ã…)") +plt.ylabel("Energy (eV)") +plt.legend() +plt.tight_layout() +plt.show() diff --git a/docs/src/examples/zbl/dimers.py.md5 b/docs/src/examples/zbl/dimers.py.md5 new file mode 100644 index 0000000000..2d0c746956 --- /dev/null +++ b/docs/src/examples/zbl/dimers.py.md5 @@ -0,0 +1 @@ +fc55a8030c89ecfae84a5d1c4dce758f \ No newline at end of file diff --git a/docs/src/examples/zbl/dimers.rst b/docs/src/examples/zbl/dimers.rst new file mode 100644 index 0000000000..c529edc435 --- /dev/null +++ b/docs/src/examples/zbl/dimers.rst @@ -0,0 +1,287 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "examples/zbl/dimers.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code. + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_examples_zbl_dimers.py: + + +Training a model with ZBL corrections +===================================== + +This tutorial demonstrates how to train a model with ZBL corrections. + +The training set for this example consists of a +subset of the ethanol moleculs from the `rMD17 dataset +`_. + +The models are trained using the following training options, respectively: + +.. literalinclude:: options_no_zbl.yaml + :language: yaml + +.. literalinclude:: options_zbl.yaml + :language: yaml + +As you can see, they are identical, except for the ``zbl`` key in the +``model`` section. +You can train the same models yourself with + +.. literalinclude:: train.sh + :language: bash + +A detailed step-by-step introduction on how to train a model is provided in +the :ref:`label_basic_usage` tutorial. + +.. GENERATED FROM PYTHON SOURCE LINES 31-33 + +First, we start by importing the necessary libraries, including the integration of ASE +calculators for metatensor atomistic models. + +.. GENERATED FROM PYTHON SOURCE LINES 34-42 + +.. code-block:: Python + + + import ase + import matplotlib.pyplot as plt + import numpy as np + import torch + from metatomic.torch.ase_calculator import MetatomicCalculator + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 43-49 + +Setting up the dimers +--------------------- + +We set up a series of dimers with different atom pairs and distances. We will +calculate the energies of these dimers using the models trained with and without ZBL +corrections. + +.. GENERATED FROM PYTHON SOURCE LINES 50-63 + +.. code-block:: Python + + + distances = np.linspace(0.5, 6.0, 200) + pairs = {} + for pair in [("H", "H"), ("H", "C"), ("C", "C"), ("C", "O"), ("O", "O"), ("H", "O")]: + structures = [] + for distance in distances: + atoms = ase.Atoms( + symbols=[pair[0], pair[1]], + positions=[[0, 0, 0], [0, 0, distance]], + ) + structures.append(atoms) + pairs[pair] = structures + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 64-65 + +We now load the two exported models, one with and one without ZBL corrections + +.. GENERATED FROM PYTHON SOURCE LINES 66-71 + +.. code-block:: Python + + + calc_no_zbl = MetatomicCalculator("model_no_zbl.pt", extensions_directory="extensions/") + calc_zbl = MetatomicCalculator("model_zbl.pt", extensions_directory="extensions/") + + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 72-77 + +Calculate and plot energies without ZBL +--------------------------------------- + +We calculate the energies of the dimer curves for each pair of atoms and +plot the results, using the non-ZBL-corrected model. + +.. GENERATED FROM PYTHON SOURCE LINES 78-94 + +.. code-block:: Python + + + for pair, structures_for_pair in pairs.items(): + energies = [] + for atoms in structures_for_pair: + atoms.set_calculator(calc_no_zbl) + with torch.jit.optimized_execution(False): + energies.append(atoms.get_potential_energy()) + energies = np.array(energies) - energies[-1] + plt.plot(distances, energies, label=f"{pair[0]}-{pair[1]}") + plt.title("Dimer curves - no ZBL") + plt.xlabel("Distance (Ã…)") + plt.ylabel("Energy (eV)") + plt.legend() + plt.tight_layout() + plt.show() + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + /Users/philiploche/repos/lab-cosmo/metatrain/examples/zbl/dimers.py:82: FutureWarning: Please use atoms.calc = calc + atoms.set_calculator(calc_no_zbl) + + + + +.. GENERATED FROM PYTHON SOURCE LINES 95-99 + +Calculate and plot energies from the ZBL-corrected model +-------------------------------------------------------- + +We repeat the same procedure as above, but this time with the ZBL-corrected model. + +.. GENERATED FROM PYTHON SOURCE LINES 100-116 + +.. code-block:: Python + + + for pair, structures_for_pair in pairs.items(): + energies = [] + for atoms in structures_for_pair: + atoms.set_calculator(calc_zbl) + with torch.jit.optimized_execution(False): + energies.append(atoms.get_potential_energy()) + energies = np.array(energies) - energies[-1] + plt.plot(distances, energies, label=f"{pair[0]}-{pair[1]}") + plt.title("Dimer curves - with ZBL") + plt.xlabel("Distance (Ã…)") + plt.ylabel("Energy (eV)") + plt.legend() + plt.tight_layout() + plt.show() + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + /Users/philiploche/repos/lab-cosmo/metatrain/examples/zbl/dimers.py:104: FutureWarning: Please use atoms.calc = calc + atoms.set_calculator(calc_zbl) + + + + +.. GENERATED FROM PYTHON SOURCE LINES 117-123 + +It can be seen that all the dimer curves include a strong repulsion +at short distances, which is due to the ZBL contribution. Even the H-H dimer, +whose ZBL correction is very weak due to the small covalent radii of hydrogen, +would show a strong repulsion closer to the origin (here, we only plotted +starting from a distance of 0.5 Ã…). Let's zoom in on the H-H dimer to see +this effect more clearly. + +.. GENERATED FROM PYTHON SOURCE LINES 124-147 + +.. code-block:: Python + + + new_distances = np.linspace(0.1, 2.0, 200) + + structures = [] + for distance in new_distances: + atoms = ase.Atoms( + symbols=["H", "H"], + positions=[[0, 0, 0], [0, 0, distance]], + ) + structures.append(atoms) + + for atoms in structures: + atoms.set_calculator(calc_zbl) + with torch.jit.optimized_execution(False): + energies = [atoms.get_potential_energy() for atoms in structures] + energies = np.array(energies) - energies[-1] + plt.plot(new_distances, energies, label="H-H") + plt.title("Dimer curve - H-H with ZBL") + plt.xlabel("Distance (Ã…)") + plt.ylabel("Energy (eV)") + plt.legend() + plt.tight_layout() + plt.show() + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + /Users/philiploche/repos/lab-cosmo/metatrain/examples/zbl/dimers.py:136: FutureWarning: Please use atoms.calc = calc + atoms.set_calculator(calc_zbl) + + + + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** (0 minutes 15.025 seconds) + + +.. _sphx_glr_download_examples_zbl_dimers.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: dimers.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: dimers.py ` + + .. container:: sphx-glr-download sphx-glr-download-zip + + :download:`Download zipped: dimers.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/zbl/dimers.zip b/docs/src/examples/zbl/dimers.zip new file mode 100644 index 0000000000000000000000000000000000000000..6fc0c82974246cb822013bf756b95c1fb876a0d3 GIT binary patch literal 11424 zcmeHNTW{OQ6;3y8fwJvGfqiKoiUEVg%78@Mag%O^xH{CL^J)Fy#GiT0izTwkHH^2M-4SH_= z{*Qwne;$1Jx1Zd&K|eo0-6WlhqKxuY7>2>Kf~P7~r;M|CHW897Q$1sU{_SIyWJMto zoo1>Gj<+LazEj5JgO;S`=KS 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+ +.. only:: html + + .. image:: /examples/zbl/images/thumb/sphx_glr_dimers_thumb.png + :alt: + + :ref:`sphx_glr_examples_zbl_dimers.py` + +.. raw:: html + +
Training a model with ZBL corrections
+
+ + +.. thumbnail-parent-div-close + +.. raw:: html + + + + +.. toctree:: + :hidden: + + /examples/zbl/dimers + + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-gallery + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download all examples as source code: zbl.zip ` + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download all examples in Jupyter notebooks: zbl_jupyter.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/zbl/options_no_zbl.yaml b/docs/src/examples/zbl/options_no_zbl.yaml new file mode 100644 index 0000000000..efc0bf57ce --- /dev/null +++ b/docs/src/examples/zbl/options_no_zbl.yaml @@ -0,0 +1,22 @@ +seed: 42 + +architecture: + name: soap_bpnn + model: + zbl: false + training: + num_epochs: 10 + batch_size: 10 + +# training set section +training_set: + systems: + read_from: ethanol_reduced_100.xyz + length_unit: angstrom + targets: + energy: + key: energy + unit: eV # very important to run simulations + +validation_set: 0.1 +test_set: 0.0 diff --git a/docs/src/examples/zbl/options_zbl.yaml b/docs/src/examples/zbl/options_zbl.yaml new file mode 100644 index 0000000000..f7969f9d90 --- /dev/null +++ b/docs/src/examples/zbl/options_zbl.yaml @@ -0,0 +1,22 @@ +seed: 42 + +architecture: + name: soap_bpnn + model: + zbl: true + training: + num_epochs: 10 + batch_size: 10 + +# training set section +training_set: + systems: + read_from: ethanol_reduced_100.xyz + length_unit: angstrom + targets: + energy: + key: energy + unit: eV # very important to run simulations + +validation_set: 0.1 +test_set: 0.0 diff --git a/docs/src/examples/zbl/train.sh b/docs/src/examples/zbl/train.sh new file mode 100644 index 0000000000..03b6baab24 --- /dev/null +++ b/docs/src/examples/zbl/train.sh @@ -0,0 +1,4 @@ +#!/bin/bash + +mtt train options_no_zbl.yaml -o model_no_zbl.pt +mtt train options_zbl.yaml -o model_zbl.pt diff --git a/docs/src/examples/zbl/zbl.zip b/docs/src/examples/zbl/zbl.zip new file mode 100644 index 0000000000000000000000000000000000000000..1e804b005e3441c902c5f04b4b933aeec40abd00 GIT binary patch literal 4789 zcmd^D-;WbF5WbeSYPaeG73#Aagh-@hb9V@eqF$vdKSTwHt3c2;Iwj7olel=-tF`xX zf%3pZpZibnpY$K=H}>vk!z~nEP^AWPalH1-c;@?N#yPrs?vo2U^!oDWU&c?)e{)G< zOz+R}wmFw+XqFeh9nNGroN==Zg2YFT3qZNiN^@>?tp7?FLLJ}kVLbA&U%jP;~mSl@l zo@7eEw$w;t4byYR15?as6~-1jg<{+$u3j1P*^FPC4-;Ye57S9$ zt&5dmJTDCwF)IvNPX^7kQhMdSRn9I$-eBUQv=G-`uK zz!)1To-PW$5F$Bg!zK)VEv0&#lP?TUga?|_nH!=W3mNDYRVOn`hDm0kn@y-o&9SHpzcOlb?HU^$KO zkY&nN&(k8w5Mjg8teS$ZXc;)FIBn1pg>Ev0;K|0K@`&s{*+i>u)W!x4-#ir7xuM7$ z2Yk-K(FScV=glW@BUp?L90T$dImB=DSt*wDxyS)Y=LME=_@D7K#v%j&^fe)al$BJf z;Y3i9vO+5IMJW^Cji@AVcv_O!VGgKpiIAkzKo^#hB8lCkMfkqf%nsnp5VSQJk3@Gj zyxM2i!reaG-`(v6-qZv3{6#?Vq_0Gqakq1)(`We8>$7rv!;f#!d~^K3&p)8~a_shE zd^~czMydtq3#1enGd^~6q^+`%G*)Bdw@^BuDAEpuIXt9s*X^W7^fK#Yrj{vm`j55cuu|LH)T_5|3=eshi8SsyoBRUjI;Z>?MsAii zu!fkYC@)I$*u~neH1ZV@5n=RE?IRHoGEmznq}7))tj(YX0b#wYyU^Hz^KQop-PC5p zM4S>qT^)}l)lHqR4?3IKVW;N>BX7Q4s;}YiD6-*&iig6T(6PP-{WgT#DA+Prv9jWR zE0H{nSXAUEz#YL&@M!4~PQ}@Q3oZw9oJqt*Ahb{xsc^%+RHp;*Yf!SQiW?YK@ulC0 z{RESfibM_|A*v>j+w-ul&%MP1ZwpAU0Z}dJH9CaIM2(!+Z9rlJTZbl}B7c`Qq?(?{ zXCj`6Q#2mLh3;*xj3|xrbYgWT+QNFcfWk}zLl&*m#2sMW?RjO5;~>ZIJm>3fHP>T< zTH<)H=UIg%Nc4u(M=j=Y@t(F>{5S-DdfF$Zf zpl4kP{cwbCf-=xP(ukaA2Kr>-Tmm}3+LYan-PAeaj#b|>7%*_H+QxDwBz^e e?7VvU7l!`sge&0D-A_NGM<3%~g3tZ0>DxaLdIuN) literal 0 HcmV?d00001 diff --git a/docs/src/examples/zbl/zbl_jupyter.zip b/docs/src/examples/zbl/zbl_jupyter.zip new file mode 100644 index 0000000000000000000000000000000000000000..a9dbfb39d4d3302871790d89a169d8616113ede9 GIT binary patch literal 6854 zcmeHMTaO#J72Y&yAJRVNsSgDqKU51yBgtF4$pZV}CJtQKK^-LMO%k-!3?;GVkOPvl z+Ogq>{+Ira{G|Mbeuw0Yq+QwVB1K>qkuT!)@bK`Q?_Bi!>4zVEazdY9e*M?!cfaX> z|LoT%C-m=E7%OBctjlE8=;f=A8SCXjDc3t^Q*;=kA^L>jg>Nc>sb0zLi^6PmZ_rMc z!t;WAj@ehQyHRIqo70X+ED)OMd+b}wrIvclI4eyd6x&L_VSoR_GnN}`Mee21Zl-7Y z+l_S0*WOsk6)QxEac{X7j&01AdBf0?`mJ=bNJzw`bgC1ct&C-!sEcb}R!ZPx?WFUL znHA$R?dl~hL&t9f6Mn;+chZLwpfFE4{QFzTXuKumv2bfXH8R2(lw)TnT(bwaIzZoNqgVQc0#H?O|rG&a+Z zpmUx}1=%|}ejplOg@@ZSYHJogc6*B<7)&YzaY^m4SXH_0l32&T-p7ah8@_Dck^eAmwS7 zKm=k9Q-rCKl|1_9@l4}2VI3Ui3JZaOkwSZ+9hw*{VO}y%?}N+U>AE>^Vh1qfwG5K^Sa?S56dIzT(P>G_}JkQ~Rp+0sZD3v11?>rNv(^4$VmmlagsFyu3uqFA1tH~G7|lR@u@h)|AodlBlvaJ- z$@)i9ntS)yH^K)MsjC1&(Ra{8{so!@w8S_^gVcp|BF;x}FXU<^EKELV0;@Pwka1mL z5p)nR=`sc?Td9CU?X`h?1io6>bs~wr6D{E#944#`<_v2K?u3X_1CL6s1ypyw<^9&a zPGsx?%9H`sUBz?JA7>8+>|r(@u>0fj=}bo7o--? 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This can be rendered as a widget in a # Jupyter notebook. +<<<<<<< HEAD chemiscope.show( +======= +cs = chemiscope.show( +>>>>>>> a77e21e8 (Reorganized example files) targets, # reading structures from the dataset properties={ "Target energy": {"values": e_targets, "target": "structure", "units": "kcal"}, @@ -135,6 +142,10 @@ }, ), ) +<<<<<<< HEAD +======= +cs +>>>>>>> a77e21e8 (Reorganized example files) # %% # You can check the structures by clicking the red dots on the parity plot, or diff --git a/examples/0-beginner/data.xyz b/examples/0-beginner/data.xyz new file mode 100644 index 0000000000..4f7575ee81 --- /dev/null +++ b/examples/0-beginner/data.xyz @@ -0,0 +1,1201 @@ +5 +Properties=species:S:1:pos:R:3:forces:R:3:my_tensor:R:9 A=157.7118 B=157.70997 C=157.70699 mu=0 alpha=13.21 epsHOMO=-0.3877 epsLUMO=0.1171 epsgap=0.5048 =35.3641 zpve=0.044749 U0=-100.0 U=-40.476062 H=-40.475117 G=-40.498597 Cv=6.469 pbc="F F F" +C -0.01269814 1.08580416 0.00800100 0.00000000 0.00000000 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This approach allows the user to reduce the time it takes to train models. +<<<<<<< HEAD To know if the model supports multi-GPU training, please check :ref:`available-architectures` and see if the default hyperparameters have the ``distributed`` option. @@ -23,6 +28,18 @@ for the training. Let's take the :ref:`sphx_glr_generated_examples_0-beginner_03-train_from_scratch.py` example and adjust the ``options.yaml`` file. +======= +To know if the model supports multi-GPU training, please check `Available Architectures +<../../docs/src/architectures/index.rst>`_ and see if the default hyperparameters have +the ``distributed`` option. + +Input file +---------- +To do this, you only need to switch on the ``distributed`` option in the ``.yaml`` file +for the training. Let's take `this tutorial +<../beginner_tutorials/train-from-scratch.rst>`_ as an example. Now, the +``options.yaml`` is +>>>>>>> a77e21e8 (Reorganized example files) .. literalinclude:: options-distributed.yaml :language: yaml @@ -35,8 +52,13 @@ configurations vary from clusters to clusters, so you have to modify it. Different scheduler will require similar options. ``metatrain`` will automatically use all the GPUs that you have asked for. You should make a single GPU visible for each process +<<<<<<< HEAD (setting ``--gpus-per-node`` equal to the number of GPUs, or setting ``--gpus-per-task=1``, depending on your cluster configuration). +======= +(setting `--gpus-per-node` equal to the number of GPUs, or setting `--gpus-per-task=1`, +depending on your cluster configuration). +>>>>>>> a77e21e8 (Reorganized example files) .. code-block:: bash @@ -84,8 +106,14 @@ Multi-GPU fine-tuning --------------------- +<<<<<<< HEAD You can use multi-GPU for fine-tuning too, by writing ``distributed: True`` in the ``.yaml`` input. For information about fine-tuning, please refer to the :ref:`sphx_glr_generated_examples_0-beginner_02-fine-tuning.py` example. +======= +You can use multi-GPU for fine-tuning too, by writing ``distributed: True`` in the +``.yaml`` input. For information about fine-tuning, please refer to `this tutorial on +fine-tuning <../../getting-started/finetuning-example.rst>`_. +>>>>>>> a77e21e8 (Reorganized example files) """ diff --git a/tox.ini b/tox.ini index a5395a98c4..5b8eca68f9 100644 --- a/tox.ini +++ b/tox.ini @@ -223,7 +223,11 @@ extras = # these architectures are used in the documentation flashmd soap-bpnn commands = +<<<<<<< HEAD uv pip install --no-build-isolation sphericart torch-spex>=0.1,<0.2 wigners +======= + {env:ENV_INSTALLER:python -m pip} install --no-build-isolation sphericart torch-spex>=0.1,<0.2 wigners +>>>>>>> a77e21e8 (Reorganized example files) # Run all .sh files in the example folder (including subfolders) bash -c "set -e && for f in $(find examples -name '*.sh'); do cd $(dirname $f); bash $(basename $f); cd -; done" From c581746965d08acc9abbe1247e00f9a4e985694e Mon Sep 17 00:00:00 2001 From: Philip Loche Date: Mon, 27 Oct 2025 12:06:34 +0100 Subject: [PATCH 5/7] Suggestions from code review --- 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- -.. only:: html - - .. image:: /examples/ase/images/thumb/sphx_glr_run_ase_thumb.png - :alt: - - :ref:`sphx_glr_examples_ase_run_ase.py` - -.. raw:: html - -
Running molecular dynamics with ASE
-
- - -.. thumbnail-parent-div-close - -.. raw:: html - - - - -.. toctree:: - :hidden: - - /examples/ase/run_ase - - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-gallery - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download all examples as source code: ase.zip ` - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download all examples in Jupyter notebooks: ase_jupyter.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/ase/options.yaml b/docs/src/examples/ase/options.yaml deleted file mode 100644 index 87300b9263..0000000000 --- a/docs/src/examples/ase/options.yaml +++ /dev/null @@ -1,15 +0,0 @@ -seed: 42 - -architecture: - name: gap - -# training set section -training_set: - systems: ethanol_reduced_100.xyz - targets: - energy: - key: energy - unit: kcal/mol # very important to run simulations - -validation_set: 0.1 -test_set: 0.0 diff --git a/docs/src/examples/ase/run_ase.codeobj.json b/docs/src/examples/ase/run_ase.codeobj.json deleted file mode 100644 index 44011ab7cd..0000000000 --- a/docs/src/examples/ase/run_ase.codeobj.json +++ /dev/null @@ -1,926 +0,0 @@ -{ - "Analysis": [ - { - "is_class": true, - "is_explicit": false, - "module": "ase.geometry.analysis", - "module_short": "ase.geometry.analysis", - "name": "Analysis" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.geometry", - "module_short": "ase.geometry", - "name": "Analysis" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Analysis" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.geometry.analysis", - "module_short": "ase.geometry.analysis", - "name": "Analysis" - } - ], - "MetatomicCalculator": [ - { - "is_class": true, - "is_explicit": false, - "module": "metatomic.torch.ase_calculator", - "module_short": "metatomic.torch.ase_calculator", - "name": "MetatomicCalculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "metatomic.torch", - "module_short": "metatomic.torch", - "name": "MetatomicCalculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "metatomic", - "module_short": "metatomic", - "name": "MetatomicCalculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.calculators.calculator", - "module_short": "ase.calculators.calculator", - "name": "Calculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.calculators", - "module_short": "ase.calculators", - "name": "Calculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Calculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.calculators.calculator", - "module_short": "ase.calculators.calculator", - "name": "BaseCalculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.calculators", - "module_short": "ase.calculators", - "name": "BaseCalculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "BaseCalculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.calculators.abc", - "module_short": "ase.calculators.abc", - "name": "GetPropertiesMixin" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.calculators", - "module_short": "ase.calculators", - "name": "GetPropertiesMixin" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "GetPropertiesMixin" - }, - { - "is_class": true, - "is_explicit": false, - "module": "abc", - "module_short": "abc", - "name": "ABC" - }, - { - "is_class": false, - "is_explicit": false, - "module": "metatomic.torch.ase_calculator", - "module_short": "metatomic.torch.ase_calculator", - "name": "MetatomicCalculator" - } - ], - "ana_train": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.geometry.analysis", - "module_short": "ase.geometry.analysis", - "name": "Analysis" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.geometry", - "module_short": "ase.geometry", - "name": "Analysis" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Analysis" - } - ], - "ana_train.get_rdf": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.geometry.analysis", - "module_short": "ase.geometry.analysis", - "name": "Analysis.get_rdf" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.geometry", - "module_short": "ase.geometry", - "name": "Analysis.get_rdf" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Analysis.get_rdf" - } - ], - "ana_traj": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.geometry.analysis", - "module_short": "ase.geometry.analysis", - "name": "Analysis" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.geometry", - "module_short": "ase.geometry", - "name": "Analysis" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Analysis" - } - ], - "ana_traj.get_rdf": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.geometry.analysis", - "module_short": "ase.geometry.analysis", - "name": "Analysis.get_rdf" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.geometry", - "module_short": "ase.geometry", - "name": "Analysis.get_rdf" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Analysis.get_rdf" - } - ], - "animation": [ - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.animation", - "module_short": "matplotlib.animation", - "name": "FuncAnimation" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib", - "module_short": "matplotlib", - "name": "FuncAnimation" - } - ], - "ase.geometry.analysis.Analysis": [ - { - "is_class": false, - "is_explicit": true, - "module": "ase.geometry.analysis", - "module_short": "ase.geometry.analysis", - "name": "Analysis" - } - ], - "ase.geometry.analysis.Analysis.get_rdf": [ - { - "is_class": false, - "is_explicit": true, - "module": "ase.geometry.analysis.Analysis", - "module_short": "ase.geometry.analysis.Analysis", - "name": "get_rdf" - } - ], - "ase.io.read": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - } - ], - "ase.md.VelocityVerlet": [ - { - "is_class": true, - "is_explicit": false, - "module": "ase.md.verlet", - "module_short": "ase.md", - "name": "VelocityVerlet" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.md", - "module_short": "ase.md", - "name": "VelocityVerlet" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "VelocityVerlet" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.md.md", - "module_short": "ase.md.md", - "name": "MolecularDynamics" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.md", - "module_short": "ase.md", - "name": "MolecularDynamics" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "MolecularDynamics" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.optimize.optimize", - "module_short": "ase.optimize.optimize", - "name": "Dynamics" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.optimize", - "module_short": "ase.optimize", - "name": "Dynamics" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Dynamics" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.utils", - "module_short": "ase.utils", - "name": "IOContext" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "IOContext" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.md", - "module_short": "ase.md", - "name": "VelocityVerlet" - } - ], - "ase.md.velocitydistribution.MaxwellBoltzmannDistribution": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.md.velocitydistribution", - "module_short": "ase.md.velocitydistribution", - "name": "MaxwellBoltzmannDistribution" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.md.velocitydistribution", - "module_short": "ase.md.velocitydistribution", - "name": "MaxwellBoltzmannDistribution" - } - ], - "ase.units.fs": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "float" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.units", - "module_short": "ase.units", - "name": "fs" - } - ], - "ase.visualize.plot.animate": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.visualize.plot", - "module_short": "ase.visualize.plot", - "name": "animate" - 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}, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "VelocityVerlet" - } - ], - "integrator.run": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.md.verlet", - "module_short": "ase.md", - "name": "VelocityVerlet.run" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.md", - "module_short": "ase.md", - "name": "VelocityVerlet.run" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "VelocityVerlet.run" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.md.md", - "module_short": "ase.md.md", - "name": "MolecularDynamics.run" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.md", - "module_short": "ase.md", - "name": "MolecularDynamics.run" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "MolecularDynamics.run" - }, - { - "is_class": false, - 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{ - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "int" - } - ], - "np.mean": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "_ArrayFunctionDispatcher" - }, - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "mean" - } - ], - "np.ones": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ones" - } - ], - "np.zeros": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "builtin_function_or_method" - }, - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "zeros" - } - ], - "plt.legend": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot", - "module_short": "matplotlib.pyplot", - "name": "legend" - } - ], - "plt.plot": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot", - "module_short": "matplotlib.pyplot", - "name": "plot" - } - ], - "plt.show": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot", - "module_short": "matplotlib.pyplot", - "name": "show" - } - ], - "plt.xlabel": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot", - "module_short": "matplotlib.pyplot", - "name": "xlabel" - } - ], - "plt.ylabel": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot", - "module_short": "matplotlib.pyplot", - "name": "ylabel" - } - ], - "potential_energy": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray" - } - ], - "potential_energy.mean": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "builtin_function_or_method" - } - ], - "rdf_train": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "list" - } - ], - "rdf_train_mean": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray" - } - ], - "rdf_traj": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "list" - } - ], - "rdf_traj_mean": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray" - } - ], - "step": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "int" - } - ], - "total_energy": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray" - } - ], - "total_energy.mean": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "builtin_function_or_method" - } - ], - "train_frames": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "list" - } - ], - "trajectory": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "list" - } - ], - "trajectory.append": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "builtin_function_or_method" - } - ] -} diff --git a/docs/src/examples/ase/run_ase.ipynb b/docs/src/examples/ase/run_ase.ipynb deleted file mode 100644 index 61d76e3cb0..0000000000 --- a/docs/src/examples/ase/run_ase.ipynb +++ /dev/null @@ -1,266 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# Running molecular dynamics with ASE\n\nThis tutorial demonstrates how to use an already trained and exported model to run an\nASE simulation of a single ethanol molecule in vacuum. We use a model that was trained\nusing the `architecture-soap-bpnn` architecture on 100 ethanol systems\ncontaining energies and forces. You can obtain the :download:`dataset file\n` used in this example from our website. The dataset is a\nsubset of the [rMD17 dataset](https://iopscience.iop.org/article/10.1088/2632-2153/abba6f/meta).\n\nThe model was trained using the following training options.\n\n.. literalinclude:: options.yaml\n :language: yaml\n\nYou can train the same model yourself with\n\n.. literalinclude:: train.sh\n :language: bash\n\nA detailed step-by-step introduction on how to train a model is provided in\nthe `label_basic_usage` tutorial.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, we start by importing the necessary libraries, including the integration of ASE\ncalculators for metatensor atomistic models.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import ase.md\nimport ase.md.velocitydistribution\nimport ase.units\nimport ase.visualize.plot\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom ase.geometry.analysis import Analysis\nfrom metatomic.torch.ase_calculator import MetatomicCalculator" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setting up the simulation\n\nNext, we initialize the simulation by extracting the initial positions from the\ndataset file which we initially trained the model on.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "train_frames = ase.io.read(\"ethanol_reduced_100.xyz\", \":\")\natoms = train_frames[0].copy()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below we show the initial configuration of a single ethanol molecule in vacuum.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "ase.visualize.plot.plot_atoms(atoms)\n\nplt.xlabel(\"\u00c5\")\nplt.ylabel(\"\u00c5\")\n\nplt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our initial coordinates do not include velocities. We initialize the velocities\naccording to a Maxwell-Boltzmann Distribution at 300 K.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "ase.md.velocitydistribution.MaxwellBoltzmannDistribution(atoms, temperature_K=300)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now register our exported model as the energy calculator to obtain energies and\nforces.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "atoms.calc = MetatomicCalculator(\"model.pt\", extensions_directory=\"extensions/\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we define the integrator which we use to obtain new positions and velocities\nbased on our energy calculator. We use a common timestep of 0.5 fs.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "integrator = ase.md.VelocityVerlet(atoms, timestep=0.5 * ase.units.fs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run the simulation\n\nWe now have everything ready to run the MD simulation at constant energy (NVE). To\nkeep the execution time of this tutorial small we run the simulations only for 100\nsteps. If you want to run a longer simulation you can increase the ``n_steps``\nvariable.\n\nDuring the simulation loop we collect data about the simulation for later analysis.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n_steps = 100\n\npotential_energy = np.zeros(n_steps)\nkinetic_energy = np.zeros(n_steps)\ntotal_energy = np.zeros(n_steps)\ntrajectory = []\n\nfor step in range(n_steps):\n # run a single simulation step\n integrator.run(1)\n\n trajectory.append(atoms.copy())\n potential_energy[step] = atoms.get_potential_energy()\n kinetic_energy[step] = atoms.get_kinetic_energy()\n total_energy[step] = atoms.get_total_energy()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Analyse the results\n\n### Energy conservation\n\nFor a first analysis, we plot the evolution of the mean of the kinetic, potential, and\ntotal energy which is an important measure for the stability of a simulation.\n\nAs shown below we see that both the kinetic, potential, and total energy\nfluctuate but the total energy is conserved over the length of the simulation.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "plt.plot(potential_energy - potential_energy.mean(), label=\"potential energy\")\nplt.plot(kinetic_energy - kinetic_energy.mean(), label=\"kinetic energy\")\nplt.plot(total_energy - total_energy.mean(), label=\"total energy\")\n\nplt.xlabel(\"step\")\nplt.ylabel(\"energy / kcal/mol\")\nplt.legend()\n\nplt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Inspect the systems\n\nEven though the total energy is conserved, we also have to verify that the ethanol\nmolecule is stable and the bonds did not break.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "animation = ase.visualize.plot.animate(trajectory, interval=100, save_count=None)\nplt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Carbon-hydrogen radial distribution function\n\nAs a final analysis we also calculate and plot the carbon-hydrogen radial distribution\nfunction (RDF) from the trajectory and compare this to the RDF from the training set.\n\nTo use the RDF code from ase we first have to define a unit cell for our systems.\nWe choose a cubic one with a side length of 10 \u00c5.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "for atoms in train_frames:\n atoms.cell = 10 * np.ones(3)\n atoms.pbc = True\n\nfor atoms in trajectory:\n atoms.cell = 10 * np.ones(3)\n atoms.pbc = True" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now can initilize the :py:class:`ase.geometry.analysis.Analysis` objects and\ncompute the the RDF using the :py:meth:`ase.geometry.analysis.Analysis.get_rdf`\nmethod.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "ana_traj = Analysis(trajectory)\nana_train = Analysis(train_frames)\n\nrdf_traj = ana_traj.get_rdf(rmax=5, nbins=50, elements=[\"C\", \"H\"], return_dists=True)\nrdf_train = ana_train.get_rdf(rmax=5, nbins=50, elements=[\"C\", \"H\"], return_dists=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We extract the bin positions from the returned values and and averege the RDF over the\nwhole trajectory and dataset, respectively.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "bins = rdf_traj[0][1]\nrdf_traj_mean = np.mean([rdf_traj[i][0] for i in range(n_steps)], axis=0)\nrdf_train_mean = np.mean([rdf_train[i][0] for i in range(n_steps)], axis=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plotting the RDF verifies that the hydrogen bonds are stable, confirming that we\nperformed an energy-conserving and stable simulation.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "plt.plot(bins, rdf_traj_mean, label=\"trajectory\")\nplt.plot(bins, rdf_train_mean, label=\"training set\")\n\nplt.legend()\nplt.xlabel(\"r / \u00c5\")\nplt.ylabel(\"radial distribution function\")\n\nplt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/src/examples/ase/run_ase.py b/docs/src/examples/ase/run_ase.py deleted file mode 100644 index 9310d95b25..0000000000 --- a/docs/src/examples/ase/run_ase.py +++ /dev/null @@ -1,207 +0,0 @@ -""" -Running molecular dynamics with ASE -=================================== - -This tutorial demonstrates how to use an already trained and exported model to run an -ASE simulation of a single ethanol molecule in vacuum. We use a model that was trained -using the :ref:`architecture-soap-bpnn` architecture on 100 ethanol systems -containing energies and forces. You can obtain the :download:`dataset file -` used in this example from our website. The dataset is a -subset of the `rMD17 dataset -`_. - -The model was trained using the following training options. - -.. literalinclude:: options-ase.yaml - :language: yaml - -We first train the model same model but and before import the necessary libraries and -run the training process and the integration of ASE. - -""" -# %% -# - -import subprocess - -import ase.md -import ase.md.velocitydistribution -import ase.units -import ase.visualize.plot -import matplotlib.pyplot as plt -import numpy as np -from ase.geometry.analysis import Analysis -from metatomic.torch.ase_calculator import MetatomicCalculator - - -# %% -# - -subprocess.run(["mtt", "train", "options-ase.yaml", "--output", "model-md.pt"]) - -# %% -# -# A detailed step-by-step introduction on how to train a model is provided in the -# :ref:`label_basic_usage` tutorial. -# -# Setting up the simulation -# ------------------------- -# -# Next, we initialize the simulation by extracting the initial positions from the -# dataset file which we initially trained the model on. - -train_frames = ase.io.read("ethanol_reduced_100.xyz", ":") -atoms = train_frames[0].copy() - -# %% -# -# Below we show the initial configuration of a single ethanol molecule in vacuum. - -ase.visualize.plot.plot_atoms(atoms) - -plt.xlabel("Ã…") -plt.ylabel("Ã…") - -plt.show() - - -# %% -# -# Our initial coordinates do not include velocities. We initialize the velocities -# according to a Maxwell-Boltzmann Distribution at 300 K. - -ase.md.velocitydistribution.MaxwellBoltzmannDistribution(atoms, temperature_K=300) - -# %% -# -# We now register our exported model as the energy calculator to obtain energies and -# forces. - -atoms.calc = MetatomicCalculator("model-md.pt", extensions_directory="extensions/") - -# %% -# -# Finally, we define the integrator which we use to obtain new positions and velocities -# based on our energy calculator. We use a common timestep of 0.5 fs. - -integrator = ase.md.VelocityVerlet(atoms, timestep=0.5 * ase.units.fs) - - -# %% -# -# Run the simulation -# ------------------ -# -# We now have everything ready to run the MD simulation at constant energy (NVE). To -# keep the execution time of this tutorial small we run the simulations only for 100 -# steps. If you want to run a longer simulation you can increase the ``n_steps`` -# variable. -# -# During the simulation loop we collect data about the simulation for later analysis. - - -n_steps = 100 - -potential_energy = np.zeros(n_steps) -kinetic_energy = np.zeros(n_steps) -total_energy = np.zeros(n_steps) -trajectory = [] - -for step in range(n_steps): - # run a single simulation step - integrator.run(1) - - trajectory.append(atoms.copy()) - potential_energy[step] = atoms.get_potential_energy() - kinetic_energy[step] = atoms.get_kinetic_energy() - total_energy[step] = atoms.get_total_energy() - -# %% -# -# Analyse the results -# ------------------- -# -# Energy conservation -# ################### -# -# For a first analysis, we plot the evolution of the mean of the kinetic, potential, and -# total energy which is an important measure for the stability of a simulation. -# -# As shown below we see that both the kinetic, potential, and total energy -# fluctuate but the total energy is conserved over the length of the simulation. - - -plt.plot(potential_energy - potential_energy.mean(), label="potential energy") -plt.plot(kinetic_energy - kinetic_energy.mean(), label="kinetic energy") -plt.plot(total_energy - total_energy.mean(), label="total energy") - -plt.xlabel("step") -plt.ylabel("energy / kcal/mol") -plt.legend() - -plt.show() - -# %% -# -# Inspect the systems -# ################### -# -# Even though the total energy is conserved, we also have to verify that the ethanol -# molecule is stable and the bonds did not break. - -animation = ase.visualize.plot.animate(trajectory, interval=100, save_count=None) -plt.show() - -# %% -# -# Carbon-hydrogen radial distribution function -# ############################################ -# -# As a final analysis we also calculate and plot the carbon-hydrogen radial distribution -# function (RDF) from the trajectory and compare this to the RDF from the training set. -# -# To use the RDF code from ase we first have to define a unit cell for our systems. -# We choose a cubic one with a side length of 10 Ã…. - -for atoms in train_frames: - atoms.cell = 10 * np.ones(3) - atoms.pbc = True - -for atoms in trajectory: - atoms.cell = 10 * np.ones(3) - atoms.pbc = True - -# %% -# -# We now can initilize the :py:class:`ase.geometry.analysis.Analysis` objects and -# compute the the RDF using the :py:meth:`ase.geometry.analysis.Analysis.get_rdf` -# method. - -ana_traj = Analysis(trajectory) -ana_train = Analysis(train_frames) - -rdf_traj = ana_traj.get_rdf(rmax=5, nbins=50, elements=["C", "H"], return_dists=True) -rdf_train = ana_train.get_rdf(rmax=5, nbins=50, elements=["C", "H"], return_dists=True) - -# %% -# -# We extract the bin positions from the returned values and and averege the RDF over the -# whole trajectory and dataset, respectively. - -bins = rdf_traj[0][1] -rdf_traj_mean = np.mean([rdf_traj[i][0] for i in range(n_steps)], axis=0) -rdf_train_mean = np.mean([rdf_train[i][0] for i in range(n_steps)], axis=0) - -# %% -# -# Plotting the RDF verifies that the hydrogen bonds are stable, confirming that we -# performed an energy-conserving and stable simulation. - -plt.plot(bins, rdf_traj_mean, label="trajectory") -plt.plot(bins, rdf_train_mean, label="training set") - -plt.legend() -plt.xlabel("r / Ã…") -plt.ylabel("radial distribution function") - -plt.show() diff --git a/docs/src/examples/ase/run_ase.py.md5 b/docs/src/examples/ase/run_ase.py.md5 deleted file mode 100644 index 31f5391e73..0000000000 --- a/docs/src/examples/ase/run_ase.py.md5 +++ /dev/null @@ -1 +0,0 @@ -ac05d2993cd2bb401fd7750e31d970c6 \ No newline at end of file diff --git a/docs/src/examples/ase/run_ase.rst b/docs/src/examples/ase/run_ase.rst deleted file mode 100644 index b1401d1108..0000000000 --- a/docs/src/examples/ase/run_ase.rst +++ /dev/null @@ -1,403 +0,0 @@ - -.. DO NOT EDIT. -.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. -.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: -.. "examples/ase/run_ase.py" -.. LINE NUMBERS ARE GIVEN BELOW. - -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - :ref:`Go to the end ` - to download the full example code. - -.. rst-class:: sphx-glr-example-title - -.. _sphx_glr_examples_ase_run_ase.py: - - -Running molecular dynamics with ASE -=================================== - -This tutorial demonstrates how to use an already trained and exported model to run an -ASE simulation of a single ethanol molecule in vacuum. We use a model that was trained -using the :ref:`architecture-soap-bpnn` architecture on 100 ethanol systems -containing energies and forces. You can obtain the :download:`dataset file -` used in this example from our website. The dataset is a -subset of the `rMD17 dataset -`_. - -The model was trained using the following training options. - -.. literalinclude:: options.yaml - :language: yaml - -You can train the same model yourself with - -.. literalinclude:: train.sh - :language: bash - -A detailed step-by-step introduction on how to train a model is provided in -the :ref:`label_basic_usage` tutorial. - -.. GENERATED FROM PYTHON SOURCE LINES 28-30 - -First, we start by importing the necessary libraries, including the integration of ASE -calculators for metatensor atomistic models. - -.. GENERATED FROM PYTHON SOURCE LINES 31-42 - -.. code-block:: Python - - - import ase.md - import ase.md.velocitydistribution - import ase.units - import ase.visualize.plot - import matplotlib.pyplot as plt - import numpy as np - from ase.geometry.analysis import Analysis - from metatomic.torch.ase_calculator import MetatomicCalculator - - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 43-48 - -Setting up the simulation -------------------------- - -Next, we initialize the simulation by extracting the initial positions from the -dataset file which we initially trained the model on. - -.. GENERATED FROM PYTHON SOURCE LINES 49-53 - -.. code-block:: Python - - - train_frames = ase.io.read("ethanol_reduced_100.xyz", ":") - atoms = train_frames[0].copy() - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 54-55 - -Below we show the initial configuration of a single ethanol molecule in vacuum. - -.. GENERATED FROM PYTHON SOURCE LINES 56-65 - -.. code-block:: Python - - - ase.visualize.plot.plot_atoms(atoms) - - plt.xlabel("Ã…") - plt.ylabel("Ã…") - - plt.show() - - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 66-68 - -Our initial coordinates do not include velocities. We initialize the velocities -according to a Maxwell-Boltzmann Distribution at 300 K. - -.. GENERATED FROM PYTHON SOURCE LINES 69-72 - -.. code-block:: Python - - - ase.md.velocitydistribution.MaxwellBoltzmannDistribution(atoms, temperature_K=300) - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 73-75 - -We now register our exported model as the energy calculator to obtain energies and -forces. - -.. GENERATED FROM PYTHON SOURCE LINES 76-79 - -.. code-block:: Python - - - atoms.calc = MetatomicCalculator("model.pt", extensions_directory="extensions/") - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 80-82 - -Finally, we define the integrator which we use to obtain new positions and velocities -based on our energy calculator. We use a common timestep of 0.5 fs. - -.. GENERATED FROM PYTHON SOURCE LINES 83-87 - -.. code-block:: Python - - - integrator = ase.md.VelocityVerlet(atoms, timestep=0.5 * ase.units.fs) - - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 88-97 - -Run the simulation ------------------- - -We now have everything ready to run the MD simulation at constant energy (NVE). To -keep the execution time of this tutorial small we run the simulations only for 100 -steps. If you want to run a longer simulation you can increase the ``n_steps`` -variable. - -During the simulation loop we collect data about the simulation for later analysis. - -.. GENERATED FROM PYTHON SOURCE LINES 98-116 - -.. code-block:: Python - - - - n_steps = 100 - - potential_energy = np.zeros(n_steps) - kinetic_energy = np.zeros(n_steps) - total_energy = np.zeros(n_steps) - trajectory = [] - - for step in range(n_steps): - # run a single simulation step - integrator.run(1) - - trajectory.append(atoms.copy()) - potential_energy[step] = atoms.get_potential_energy() - kinetic_energy[step] = atoms.get_kinetic_energy() - total_energy[step] = atoms.get_total_energy() - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 117-128 - -Analyse the results -------------------- - -Energy conservation -################### - -For a first analysis, we plot the evolution of the mean of the kinetic, potential, and -total energy which is an important measure for the stability of a simulation. - -As shown below we see that both the kinetic, potential, and total energy -fluctuate but the total energy is conserved over the length of the simulation. - -.. GENERATED FROM PYTHON SOURCE LINES 129-141 - -.. code-block:: Python - - - - plt.plot(potential_energy - potential_energy.mean(), label="potential energy") - plt.plot(kinetic_energy - kinetic_energy.mean(), label="kinetic energy") - plt.plot(total_energy - total_energy.mean(), label="total energy") - - plt.xlabel("step") - plt.ylabel("energy / kcal/mol") - plt.legend() - - plt.show() - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 142-147 - -Inspect the systems -################### - -Even though the total energy is conserved, we also have to verify that the ethanol -molecule is stable and the bonds did not break. - -.. GENERATED FROM PYTHON SOURCE LINES 148-152 - -.. code-block:: Python - - - animation = ase.visualize.plot.animate(trajectory, interval=100, save_count=None) - plt.show() - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 153-161 - -Carbon-hydrogen radial distribution function -############################################ - -As a final analysis we also calculate and plot the carbon-hydrogen radial distribution -function (RDF) from the trajectory and compare this to the RDF from the training set. - -To use the RDF code from ase we first have to define a unit cell for our systems. -We choose a cubic one with a side length of 10 Ã…. - -.. GENERATED FROM PYTHON SOURCE LINES 162-171 - -.. code-block:: Python - - - for atoms in train_frames: - atoms.cell = 10 * np.ones(3) - atoms.pbc = True - - for atoms in trajectory: - atoms.cell = 10 * np.ones(3) - atoms.pbc = True - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 172-175 - -We now can initilize the :py:class:`ase.geometry.analysis.Analysis` objects and -compute the the RDF using the :py:meth:`ase.geometry.analysis.Analysis.get_rdf` -method. - -.. GENERATED FROM PYTHON SOURCE LINES 176-183 - -.. code-block:: Python - - - ana_traj = Analysis(trajectory) - ana_train = Analysis(train_frames) - - rdf_traj = ana_traj.get_rdf(rmax=5, nbins=50, elements=["C", "H"], return_dists=True) - rdf_train = ana_train.get_rdf(rmax=5, nbins=50, elements=["C", "H"], return_dists=True) - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 184-186 - -We extract the bin positions from the returned values and and averege the RDF over the -whole trajectory and dataset, respectively. - -.. GENERATED FROM PYTHON SOURCE LINES 187-192 - -.. code-block:: Python - - - bins = rdf_traj[0][1] - rdf_traj_mean = np.mean([rdf_traj[i][0] for i in range(n_steps)], axis=0) - rdf_train_mean = np.mean([rdf_train[i][0] for i in range(n_steps)], axis=0) - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 193-195 - -Plotting the RDF verifies that the hydrogen bonds are stable, confirming that we -performed an energy-conserving and stable simulation. - -.. GENERATED FROM PYTHON SOURCE LINES 196-205 - -.. code-block:: Python - - - plt.plot(bins, rdf_traj_mean, label="trajectory") - plt.plot(bins, rdf_train_mean, label="training set") - - plt.legend() - plt.xlabel("r / Ã…") - plt.ylabel("radial distribution function") - - plt.show() - - - - - - - - -.. _sphx_glr_download_examples_ase_run_ase.py: - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-example - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: run_ase.ipynb ` - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: run_ase.py ` - - .. container:: sphx-glr-download sphx-glr-download-zip - - :download:`Download zipped: run_ase.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/ase/run_ase.zip b/docs/src/examples/ase/run_ase.zip deleted file mode 100644 index 074a0cf90b7fc095beb556e85cee490919d5a9a0..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 16212 zcmdU0+iv5=8FrE$00O-W(AxoxpmJc-cHC`KAr7$F0}?EDyG<5p3)7HCF=%0s&MJ-3X6hS`sec$`KmNJo3mZ?-kb;=7C&n3^2sAOkJHDeFIdEy;j z{o{GBW=YAax>7~LQx=O%$+9YVB}z6^XRK1JE(PO~@w5;;o-@2mq=@k-X5u_oMTHNU zibYC`fhFE}IG&YB2E?jFNv0-@+3qFGXjsHzLGQ!1YCjB_bZSob-@`TKi&?%Z--Rw66CNXZH} zA%H@PVw&K_bf<|bB2fnHuc~Gd?ybhO%p4l4GnpzL4^LuVff*H>B&qN|vv(R5BCaD5 zk8oaaK7aH130)_q8&*VSan7?GB$yN`W2!FLnHZPAKVYwLr3NGxa<8n%1RjK=+nyB9 zAMgLZS>b&)tE#*l?(QZkFQY`rNCfyZP{nkY7gZ9aVt0Qp*x&o~)7?*gcks#1C;JZ$ zcKLYBe>d69M8!Wp83n|8VF}%#F)(guJW*+?&h#guKmDO{;%AA?LBLX=Uhp)LQCi1h zICO94JWD;s*f8aCTJxzGGX2PFXsY?CZ&vcGL1GSylp>vI0bYH8-WQa!1>j?jN8Uq7 zFIbg=;NUPsVZKAZLH(*w;ERz&*@(5lfm;fI=y{<|lUTFMYeg*OW08(gVd!7>sXw@S}8?VEPZ$zc3Ugt*2 zoGAh|cP{$TOnf2E4G$sk6-*+oz5s&wibVy7L|JQO*Roteok>BAibINUUZdA!D%ja9 ziDo1sc1zkCRFXMkN0kH{wS0~y1r(%Yhnk&<3djZz{0r=jzs>xizvU5SXipc!(cWX7LRh{Kr{SZ(WpKywpV$4N4+TSA}4FKtCyg6rl?mS`GUnY-Wq0DxfynQ^kr&2%)t^%t|@bOS=@iqv2%x7Jzva zX)y3A3O?#NKR**`y7Prft2Y^!l0EKd3bc(Kz?D1$dyIOmRH(p0aX`8-3=y^&d{7SS zNuF!;>=019e1RX7U`!#VI9(LlS1kBi@`~V>c3tz1g`j&_m(_J^xT<@Fw7K5KmwNT+zA{al_7#*RRXOc;|~YA7d&7SZ5XX>1r|o1 zvN-tG8pLl!k&4PO$8JBQy&r4c)0Q!qls$Pw6t-X-K#FDaq794X>5QL(Yp0@^BP4+N zutAlHoQR{(A9o`sNE}oV{zO*Iy$3J8eX@m+LE)^|0(aDMaE>rgdkwm}i5p;HU0_#c z;0tLw1!%OPjm}EY6=sMW3*r_4CBi{-zM3$&WW--Mx{2&qs^k;`+3wbBC{BX%;V#5f zKtDN=BMs){1OQLr>i9SnfhNS`x@hcX2TZC|PWVL#Iv{1*YBD}nH4g8gs}Y4W1Qv}W zfjWBjJaCpS;pGZqOZIzYrR5O5D0m|ZRSxXZE$=nt6Rz{JC6%f!hXEV=ozV&$eRS-3 zL`N-pi24e|Kf;|iq_Bl;ni#nWhdU&oU3y8YE&aLR7Xcxy}A6OS3d;@I3jaIV@^c0MbeRq)|OM18f~!wb|xs&H^H{O=Qiw- zm7#4)3~->nO{0d!kWsJ>+1FtnDGC6UFzH%IwcJ*GoFGu16Lj*SO-^KV;bFKlI%eOD*f;HMqVUv7P3E7l5+MR{-h?7r=BsF5=TA z1v#v41VS3J5QA8ovCCe=OYS1aw(nChCAG5vn)t+pCtt}jC)=eNZ_~_+#q`OkAXBXB zso`@MN7iMRmR+8fifQKoTMYIk6GV`jIpjabfc?DEp?K%TD6yz-9FJe4@DxCNA%%Tp1g^sT9Z8(QaB?J zU5yf{l|_mTzJC04%fW+xyD?x5%WX8p83iINLXfI$48=pXq5-H>K#68bQN@Nu^%!X(SYj$dWPET!tMU7L z?57_g0pzf>cK~C1DTDD>Hd>?u$+sdLf+Gdga&TZAhgp!1DVls$)M6Q;b&xlP=M)Ur zqqx|F8_2X=b~eoC!zkrtiJH)=R5NID%o7C1gpRKDfMCS88a9)-&~T!yfDl;ZqqD1^ zBDbwZMLeNkht{iDYZf1o+JG$0=^ZsfmL~Ok5WT-3kv8jtnE}iOhF$($bJn29`1#?3 zZ6?QwEDs+*6-6pCL_OuLgaAtF-Xqy6LN_TP=P)7NpK_FhMBU2=@2TD=m|iGCCZlJfJUJVe4~H-KRmfJ)@i z+rnyT_YywWRTjvGXlqa9NoVh!88tSY439DP+vMb7I5HDq!#$i)2?CiWv^)c)5v&;J zwqv6KT1Ygv&Y~9rkeAdhn+OT&T4B_;b*{ek3|%MR+hGaR-QsLvzp{yr+||JeWgzh3-iV}t(vs#8%<^0^#;k0975vs(H?cBCU>dPG0^ z@u-^T0~A-(q4AtV5O2T#!96Wunj_j_fN4OX++@fZud3XKGL*Mcmk;H` zTPe(kvbCA-Qj`xRq;gGF`B1LEEFa2#UH+(7mp>l7Yh8XTFsyG1f!D6f)Ap+>^Pz++ zE|lhJ-`f}GFFFOLAdx7~FP=d4dD^qa3VkTaft*}coe!mNHS}2jwyL>)t-hVQKXk#B zwy!Sx6M3(3T?#f^116f{k%{ABvt*s7&XRTQo24ijMHFgyjM5osNEp8 z()RS&0b6AjdmW1q2?;e#bkz+NI$H`#WOk zt@K($xrSEjy_Sf!`G(RF+5a1LiSI}T5YsL9po#oe-`Ch}4(0lr&B&+M+G`G_Yc&%Q zZlTkBuSL7pBd43VHeJ)%ThS#4->+PmUG+MmZf%od*(tIA<}mK7WO3z}_RO0C$`eLc7ar7}>uRz?VAtAXC)}>#C;q=`Hm*0Ys2=FVFFso6 z#)nJe0WMd%!GJ*M_a_AUu)6AW`HVp5>$O(!G?azBj!czkL?! zQc!TO#hs!wQ=!Ehb6FT==Mpq6OIl*}M=^_Ve9pgoJBvb1iT|!`VlgXufEmtnF z>Wxqtd1@x8kgK?Aic2?^C$$#luJkT9c-6^p+C+s8x8KYPeVV?ysF!uw5LO5B%9IC# z0(u_6Um7g%UyDMDl!o|dn95ER_?TVHQ$8Qz3(k53>m|NBgMsCPc7mb#qA7OyxqjQe zYj&CE2Syd_l!D)E93_ZIx@S_hoh3z~iV=Mqs`Co74-H66ju9sAEWE<79(`L1>kpO& zBIp}{BiAb+TdU&$^A`r@j5KGpM~ zqZAV#dWF3}hmtM+{z0&R5d6W?3zyL}^W_+!8%8N`z;6k4_h5vfLq$;cydPdZd++@Z zKe+hI?B-kU8yo-p8~ueZx!A9^clWIt*=zTGjjzQw`}6?c%V$6T1-*VB|CRXt@6YMe Fe*q$4Rrvq_ diff --git a/docs/src/examples/ase/train.sh b/docs/src/examples/ase/train.sh deleted file mode 100644 index f73b048e61..0000000000 --- a/docs/src/examples/ase/train.sh +++ /dev/null @@ -1,3 +0,0 @@ -#!/bin/bash - -mtt train options.yaml diff --git a/docs/src/examples/basic_usage/basic_usage.zip b/docs/src/examples/basic_usage/basic_usage.zip deleted file mode 100644 index 29173744cf9d766480cb629534615cf1c3358a9b..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 4718 zcmb7IO^+ML5tZX42=?Z{m)r^x3`oLqsN@j9jAKB89oP_7Sx0LmMh-j8nI@; 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- -.. only:: html - - .. image:: /examples/basic_usage/images/thumb/sphx_glr_usage_thumb.png - :alt: - - :ref:`sphx_glr_examples_basic_usage_usage.sh` - -.. raw:: html - -
Basic Usage
-
- - -.. thumbnail-parent-div-close - -.. raw:: html - - - - -.. toctree:: - :hidden: - - /examples/basic_usage/usage - - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-gallery - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download all examples as source code: basic_usage.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/basic_usage/options.yaml b/docs/src/examples/basic_usage/options.yaml deleted file mode 100644 index c0c0ddf7bf..0000000000 --- a/docs/src/examples/basic_usage/options.yaml +++ /dev/null @@ -1,18 +0,0 @@ -# architecture used to train the model -architecture: - name: soap_bpnn - training: - num_epochs: 5 # a very short training run - batch_size: 10 - -# Mandatory section defining the parameters for system and target data of the -# training set -training_set: - systems: qm9_reduced_100.xyz # file where the positions are stored - targets: - energy: - key: U0 # name of the target value - unit: eV # unit of the target value - -test_set: 0.1 # 10 % of the training_set are randomly split and taken for test set -validation_set: 0.1 # 10 % of the training_set are randomly split and for validation diff --git a/docs/src/examples/basic_usage/output.xyz b/docs/src/examples/basic_usage/output.xyz deleted file mode 100644 index 37cb4a2653..0000000000 --- a/docs/src/examples/basic_usage/output.xyz +++ /dev/null @@ -1,1201 +0,0 @@ -5 -Properties=species:S:1:pos:R:3 energy=-40.47401479185843 pbc="F F F" -C -0.01269814 1.08580416 0.00800100 -H 0.00215042 -0.00603132 0.00197612 -H 1.01173084 1.46375116 0.00027657 -H -0.54081507 1.44752661 -0.87664372 -H -0.52381363 1.43793264 0.90639729 -4 -Properties=species:S:1:pos:R:3 energy=-56.53954696998065 pbc="F F F" -N -0.04042605 1.02410775 0.06256380 -H 0.01725746 0.01254521 -0.02737716 -H 0.91578937 1.35874519 -0.02875776 -H -0.52027774 1.34353213 -0.77554261 -3 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0.00807391 -11 -Properties=species:S:1:pos:R:3 A=29.45018 B=8.37701 C=7.42076 mu=0.0597 alpha=34.75 epsHOMO=-0.323 epsLUMO=0.0949 epsgap=0.4179 =227.1361 zpve=0.103182 U0=-119.052475 U=-119.047927 H=-119.046983 G=-119.078157 Cv=14.84 pbc="F F F" -C -0.03113825 1.54081582 0.03192126 -C 0.01215347 0.01092235 -0.01603259 -C 0.72169129 -0.52583353 -1.26230570 -H 0.97955987 1.96459116 0.03098367 -H -0.55840223 1.94831192 -0.83816075 -H -0.54252252 1.90153531 0.93005671 -H 0.51522791 -0.36840234 0.88231134 -H -1.01070641 -0.38456999 0.02051783 -H 1.75851210 -0.17376585 -1.30871516 -H 0.74087192 -1.62024959 -1.27516511 -H 0.22023351 -0.19051179 -2.17729020 -9 -Properties=species:S:1:pos:R:3 A=35.09545 B=9.3686 C=8.1497 mu=1.4131 alpha=27.87 epsHOMO=-0.2619 epsLUMO=0.0798 epsgap=0.3417 =193.1659 zpve=0.079754 U0=-154.972731 U=-154.968412 H=-154.967467 G=-154.998148 Cv=13.546 pbc="F F F" -C -0.00860504 1.50203829 -0.00681217 -C 0.01099310 -0.01764877 -0.01377035 -O 0.68088841 -0.44041803 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H=-117.820482 G=-117.849087 Cv=11.041 pbc="F F F" -C -0.01193280 1.51433198 0.01031700 -C 1.30299118 0.77886562 -0.00617842 -C 0.00867174 0.00767042 0.00201032 -H -0.30541464 2.01702117 0.92533239 -H -0.32275498 2.02680156 -0.89347839 -H 1.88493396 0.79145020 -0.92119393 -H 1.90226631 0.78165391 0.89766373 -H -0.27088043 -0.51289125 0.91137474 -H -0.28821983 -0.50310409 -0.90740824 -7 -Properties=species:S:1:pos:R:3 A=25.77385 B=22.12109 C=14.18066 mu=1.7675 alpha=24.04 epsHOMO=-0.2682 epsLUMO=0.1042 epsgap=0.3724 =129.891 zpve=0.057289 U0=-153.742562 U=-153.73941 H=-153.738466 G=-153.766642 Cv=9.176 pbc="F F F" -C 0.01537720 1.41764380 0.00956287 -C 1.26478161 0.64923984 -0.00655044 -O -0.00024018 -0.00771006 0.00204055 -H -0.31763326 1.88586598 0.93475751 -H -0.33527583 1.89576673 -0.90397578 -H 1.83246682 0.56256848 -0.93193290 -H 1.85008258 0.55267938 0.90680147 -10 -Properties=species:S:1:pos:R:3 A=10.12193 B=8.49011 C=4.89615 mu=2.7362 alpha=35.53 epsHOMO=-0.2431 epsLUMO=-0.0087 epsgap=0.2344 =292.4367 zpve=0.083382 U0=-193.08834 U=-193.082969 H=-193.082024 G=-193.116476 Cv=16.893 pbc="F F F" -C -0.00310189 1.48050328 -0.17259867 -C -0.04527202 -0.02887059 0.00139493 -C 1.29705800 -0.72194415 0.16889042 -O -1.08643172 -0.64273343 0.00630393 -H 0.59403529 1.74668579 -1.05225043 -H -1.01540407 1.87034023 -0.28382265 -H 0.48099600 1.95070605 0.69099376 -H 1.82512144 -0.32470731 1.04328658 -H 1.14892507 -1.79597689 0.28554393 -H 1.93682635 -0.52949692 -0.69995403 -9 -Properties=species:S:1:pos:R:3 A=10.78886 B=9.27509 C=5.14977 mu=3.6367 alpha=31.83 epsHOMO=-0.2436 epsLUMO=0.0347 epsgap=0.2783 =267.6148 zpve=0.07319 U0=-209.159302 U=-209.15402 H=-209.153076 G=-209.187468 Cv=16.561 pbc="F F F" -C 0.00679889 1.49969736 -0.02567030 -C -0.02740385 -0.01752051 -0.14894811 -N 1.17826667 -0.63324298 0.05691721 -O -1.03721965 -0.63980649 -0.40727572 -H 0.91390280 1.92923755 -0.46100067 -H -0.87080896 1.91124186 -0.52345176 -H -0.02826124 1.78517271 1.03149125 -H 1.98405139 -0.13175861 0.38674906 -H 1.19049421 -1.63919563 0.10006209 -8 -Properties=species:S:1:pos:R:3 A=11.1475 B=10.36388 C=5.42091 mu=3.4869 alpha=28.07 epsHOMO=-0.2495 epsLUMO=0.0556 epsgap=0.3051 =244.2308 zpve=0.063824 U0=-225.221461 U=-225.217075 H=-225.216131 G=-225.247724 Cv=15.292 pbc="F F F" -N 0.03605270 1.36077873 -0.12416403 -C -0.02591138 -0.02076560 0.00200645 -N 1.21968479 -0.62334220 0.11963222 -O -1.06822866 -0.64174597 0.00865573 -H 0.80749439 1.83455148 0.32144915 -H -0.85825775 1.80373500 0.02243770 -H 1.99927148 -0.17303054 -0.33602496 -H 1.18204327 -1.62147901 -0.02109234 -14 -Properties=species:S:1:pos:R:3 A=7.75166 B=7.74847 C=4.48668 mu=0.0897 alpha=45.46 epsHOMO=-0.3167 epsLUMO=0.0843 epsgap=0.401 =355.0621 zpve=0.131146 U0=-158.342346 U=-158.336603 H=-158.335658 G=-158.370016 Cv=20.273 pbc="F F F" -C -0.03215888 1.54021598 0.01074456 -C 0.03381741 0.00745852 0.00180716 -C 0.71375572 -0.50856404 -1.27296949 -C 0.73848999 -0.52220886 1.25750907 -H 0.97522736 1.97414341 0.00528362 -H -0.56196769 1.92232326 -0.86835418 -H -0.54817239 1.91268414 0.90208039 -H -0.99752525 -0.37310578 0.00977297 -H 1.75070021 -0.15586784 -1.33269631 -H 0.73446691 -1.60328288 -1.29939696 -H 0.19367011 -0.16085589 -2.17195508 -H 0.23611990 -0.18382017 2.17005260 -H 0.75964083 -1.61713153 1.27226101 -H 1.77635866 -0.16985318 1.30053019 -12 -Properties=species:S:1:pos:R:3 A=8.67553 B=8.00568 C=4.75542 mu=1.4259 alpha=38.58 epsHOMO=-0.2612 epsLUMO=0.074 epsgap=0.3351 =318.3721 zpve=0.107673 U0=-194.267232 U=-194.261748 H=-194.260804 G=-194.294663 Cv=19.052 pbc="F F F" -C -0.03315871 1.54782566 -0.00438815 -C -0.01108534 0.01859095 0.01676117 -C 0.70938384 -0.53596711 1.23978444 -O -1.33219841 -0.51712554 0.05236057 -H 0.98215692 1.95715900 -0.03182225 -H -0.56388100 1.92087623 -0.88845865 -H -0.54082548 1.93340945 0.88575489 -H 0.51042122 -0.33767241 -0.88819176 -H 0.22240492 -0.18785860 2.15650245 -H 0.68242024 -1.62887638 1.23305141 -H 1.75434301 -0.21246950 1.25524188 -H -1.81818681 -0.15577435 -0.69553962 -6 -Properties=species:S:1:pos:R:3 A=0 B=4.4259726 C=4.4259726 mu=0 alpha=38.52 epsHOMO=-0.2599 epsLUMO=-0.0214 epsgap=0.2386 =278.6264 zpve=0.037354 U0=-153.459846 U=-153.455442 H=-153.454498 G=-153.482621 Cv=15.312 pbc="F F F" -C 0.68098021 0.00000000 0.00000000 -C -0.68098021 0.00000000 0.00000000 -C -1.88766603 0.00000000 0.00000000 -C 1.88766603 0.00000000 0.00000000 -H -2.94960000 0.00000000 0.00000000 -H 2.94960000 0.00000000 0.00000000 -5 -Properties=species:S:1:pos:R:3 A=0 B=4.5793222 C=4.5793222 mu=3.792 alpha=32.66 epsHOMO=-0.3102 epsLUMO=-0.0543 epsgap=0.2559 =260.1896 zpve=0.027259 U0=-169.557758 U=-169.553764 H=-169.55282 G=-169.581024 Cv=12.93 pbc="F F F" -C 0.01510390 0.00000000 1.00000000 -C 1.38233149 0.00000000 1.00000000 -C -1.18862525 0.00000000 1.00000000 -H -2.25177201 0.00000000 1.00000000 -N 2.54296187 0.00000000 1.00000000 -4 -Properties=species:S:1:pos:R:3 A=0 B=4.73269 C=4.73269 mu=0.0023 alpha=27.7 epsHOMO=-0.3696 epsLUMO=-0.0926 epsgap=0.277 =242.9308 zpve=0.015951 U0=-185.648533 U=-185.644825 H=-185.64388 G=-185.667652 Cv=10.398 pbc="F F F" -N 0.01745734 -1.16134217 -0.00415342 -C 0.00253212 -0.00344278 0.00179939 -C -0.01611419 1.37220987 0.00934873 -N -0.03264302 2.53008276 0.01609090 -6 -Properties=species:S:1:pos:R:3 A=68.5171 B=4.8345 C=4.51586 mu=2.7824 alpha=31.14 epsHOMO=-0.2777 epsLUMO=-0.0735 epsgap=0.2042 =268.3921 zpve=0.037208 U0=-190.624631 U=-190.620363 H=-190.619419 G=-190.650543 Cv=13.049 pbc="F F F" -O -0.06776120 -0.04233838 0.00282489 -C -0.00562945 1.16382879 0.00743862 -C 1.22985272 1.91339997 0.00028781 -C 2.23625322 2.57430970 -0.00529277 -H -0.91670701 1.79467546 0.01781352 -H 3.13429662 3.14370267 -0.01037656 -5 -Properties=species:S:1:pos:R:3 A=67.88408 B=5.00823 C=4.66413 mu=2.3112 alpha=26.25 epsHOMO=-0.3166 epsLUMO=-0.11 epsgap=0.2066 =251.0007 zpve=0.02654 U0=-206.721858 U=-206.717875 H=-206.716931 G=-206.747625 Cv=11.329 pbc="F F F" -O -0.04055978 -0.03799144 0.00266513 -C -0.01768870 1.16193251 0.00746008 -C 1.23315534 1.93381336 0.00043371 -N 2.19857378 2.57055097 -0.00483320 -H -0.92627534 1.78959196 0.01764859 -6 -Properties=species:S:1:pos:R:3 A=56.25048 B=4.77441 C=4.40088 mu=0.002 alpha=26.12 epsHOMO=-0.2668 epsLUMO=-0.1113 epsgap=0.1555 =266.8164 zpve=0.036943 U0=-227.798785 U=-227.79457 H=-227.793626 G=-227.825074 Cv=12.147 pbc="F F F" -O 0.00337122 -0.03146586 0.00157206 -C -0.01713174 1.17081409 0.00676444 -C 1.26555783 1.99719410 -0.00007703 -O 1.24507069 3.19946589 0.00681275 -H -0.94690483 1.77678142 0.01852668 -H 2.19534410 1.39121374 -0.01006739 -10 -Properties=species:S:1:pos:R:3 A=80.28766 B=3.3688 C=3.3688 mu=0 alpha=42.32 epsHOMO=-0.2412 epsLUMO=0.0684 epsgap=0.3096 =400.2236 zpve=0.083896 U0=-155.908941 U=-155.90318 H=-155.902236 G=-155.937641 Cv=17.447 pbc="F F F" -C -0.01787959 1.46676893 0.01011180 -C 0.00186644 0.01072913 0.00211109 -C 0.01819704 -1.19272861 -0.00450723 -C 0.03795170 -2.64876793 -0.01251193 -H 0.86460312 1.87589979 0.51479719 -H -0.03217624 1.87210581 -1.00791002 -H -0.90233115 1.85185524 0.53003953 -H -0.83477011 -3.06339007 0.50451436 -H 0.03276422 -3.04315564 -1.03491223 -H 0.93213486 -3.03931642 0.48626606 -10 -Properties=species:S:1:pos:R:3 A=27.39459 B=4.53005 C=4.08342 mu=0.7067 alpha=40.09 epsHOMO=-0.2592 epsLUMO=0.0566 epsgap=0.3157 =333.9589 zpve=0.084338 U0=-155.897345 U=-155.892291 H=-155.891347 G=-155.924226 Cv=17.13 pbc="F F F" -C -0.03095840 1.54775025 0.03167945 -C 0.01485375 0.00962471 -0.02082154 -C 0.69099073 -0.49954575 -1.20857624 -C 1.25222275 -0.89514467 -2.19527879 -H 0.97899300 1.96621609 0.03373834 -H -0.56082354 1.95002902 -0.83580745 -H -0.54576708 1.88225519 0.93709062 -H 0.51920135 -0.37425398 0.87496224 -H -1.00644802 -0.39030838 0.01340632 -H 1.74594541 -1.25604187 -3.06299415 -9 -Properties=species:S:1:pos:R:3 A=28.0016 B=4.67752 C=4.21765 mu=3.9233 alpha=35.38 epsHOMO=-0.3213 epsLUMO=0.034 epsgap=0.3553 =314.5335 zpve=0.07419 U0=-172.006141 U=-172.001467 H=-172.000523 G=-172.032826 Cv=14.988 pbc="F F F" -C -0.02522005 1.54773054 0.02133370 -C 0.01183939 0.00999278 -0.01556702 -C 0.68915114 -0.50836846 -1.20331488 -N 1.22565733 -0.90360846 -2.14737482 -H 0.98571620 1.96212990 0.02370153 -H -0.55522192 1.94599662 -0.84711771 -H -0.53962556 1.88294354 0.92536604 -H 0.52481910 -0.38226048 0.86933355 -H -1.00453542 -0.39832109 0.00530071 -8 -Properties=species:S:1:pos:R:3 A=32.19893 B=4.76299 C=4.30696 mu=4.4361 alpha=31.81 epsHOMO=-0.2683 epsLUMO=0.0173 epsgap=0.2855 =295.6635 zpve=0.063305 U0=-188.042067 U=-188.037478 H=-188.036534 G=-188.06863 Cv=14.488 pbc="F F F" -N -0.03608099 1.48762533 0.03678072 -C -0.01039471 0.02517605 -0.01506608 -C -1.37180956 -0.52108920 0.02739272 -N -2.44026695 -0.95956444 0.03370519 -H 0.91729752 1.83547547 0.05202682 -H -0.47162178 1.85677205 -0.80275569 -H 0.48946161 -0.40880401 -0.89715168 -H 0.52827150 -0.34533338 0.86469547 -8 -Properties=species:S:1:pos:R:3 A=35.11615 B=4.66117 C=4.22354 mu=1.7211 alpha=33.1 epsHOMO=-0.2595 epsLUMO=0.0277 epsgap=0.2872 =300.0993 zpve=0.060632 U0=-191.810916 U=-191.806025 H=-191.805081 G=-191.837634 Cv=15.855 pbc="F F F" -O -0.02013197 1.45738137 0.01010195 -C 0.00168478 0.03579980 0.00224736 -C -1.36366691 -0.47481101 0.01255056 -C -2.47902835 -0.91970202 0.02082451 -H 0.89446911 1.75736553 0.00327363 -H 0.51979028 -0.35175413 -0.88980302 -H 0.53670458 -0.36127661 0.88000894 -H -3.46773502 -1.30737009 0.02818120 -7 -Properties=species:S:1:pos:R:3 A=35.93172 B=4.83443 C=4.37827 mu=4.6788 alpha=28.56 epsHOMO=-0.3018 epsLUMO=0.0022 epsgap=0.3039 =280.6659 zpve=0.050262 U0=-207.916786 U=-207.912215 H=-207.911271 G=-207.943384 Cv=13.845 pbc="F F F" -O -0.03738221 1.44547841 0.01010769 -C 0.01292906 0.03441661 0.00209165 -C -1.35747452 -0.48874523 0.01239738 -N -2.42135277 -0.93685184 0.02016053 -H 0.86898057 1.76972949 0.00417577 -H 0.51692713 -0.36329790 -0.89215814 -H 0.53387616 -0.37278749 0.88223921 -10 -Properties=species:S:1:pos:R:3 A=26.14564 B=4.2893 C=4.12773 mu=2.6741 alpha=35.83 epsHOMO=-0.25 epsLUMO=-0.0205 epsgap=0.2295 =333.3276 zpve=0.084175 U0=-193.075202 U=-193.070116 H=-193.069171 G=-193.102798 Cv=15.954 pbc="F F F" -C 0.16879340 1.52513042 -0.15747056 -C -0.18737625 0.06194763 0.14679374 -C 0.50917645 -0.43996215 1.39125850 -O 1.18191079 -1.43565585 1.45816384 -H 1.23126511 1.63137254 -0.39632693 -H -0.40744668 1.89431681 -1.00969246 -H -0.04931034 2.17266260 0.69857432 -H -1.26905160 -0.01668067 0.32854433 -H 0.06279062 -0.60250479 -0.68474036 -H 0.35384841 0.20663370 2.28871052 -9 -Properties=species:S:1:pos:R:3 A=19.98749 B=6.11589 C=4.82584 mu=3.7071 alpha=32.78 epsHOMO=-0.2516 epsLUMO=0.0335 epsgap=0.2851 =279.7863 zpve=0.074166 U0=-209.144909 U=-209.139976 H=-209.139032 G=-209.172305 Cv=15.058 pbc="F F F" -C 0.01474916 1.47888256 -0.03509326 -N 0.18708077 0.03831317 -0.02906851 -C 0.45703661 -0.66858895 1.10069001 -O 0.58457996 -0.19876202 2.21119705 -H 0.76025289 1.96736104 -0.67198126 -H -0.98462423 1.75984483 -0.38485103 -H 0.14269849 1.82387718 0.99149397 -H 0.10386349 -0.46937726 -0.89461379 -H 0.55047568 -1.75253452 0.88868242 -8 -Properties=species:S:1:pos:R:3 A=48.62351 B=4.67298 C=4.38187 mu=3.92 alpha=29.47 epsHOMO=-0.2814 epsLUMO=0.0074 epsgap=0.2888 =293.917 zpve=0.061327 U0=-229.013797 U=-229.009003 H=-229.008059 G=-229.041086 Cv=13.885 pbc="F F F" -C 0.10653781 1.43512049 0.03336100 -O 0.42686564 0.04716929 0.09309945 -C 0.50714862 -0.63652665 -1.06318297 -O 0.76927014 -1.79955017 -1.11010475 -H 0.92349251 2.00385139 0.48465728 -H -0.03757815 1.76927183 -1.00057425 -H -0.81269569 1.60801916 0.59873287 -H 0.30458460 -0.00231902 -1.94965553 -8 -Properties=species:S:1:pos:R:3 A=36.49282 B=4.31175 C=4.04493 mu=1.7341 alpha=28.53 epsHOMO=-0.2537 epsLUMO=-0.0341 epsgap=0.2196 =303.8129 zpve=0.060508 U0=-228.992613 U=-228.987769 H=-228.986825 G=-229.019918 Cv=14.78 pbc="F F F" -O 0.19375856 1.37170995 0.31398297 -C 0.04992637 0.01405481 -0.04526239 -C -1.40888025 -0.39635680 -0.20601574 -O -1.77729462 -1.37233860 -0.80361689 -H -0.01723186 1.91269132 -0.45337758 -H 0.60440478 -0.26519766 -0.95205725 -H 0.46740406 -0.57521679 0.78176270 -H -2.11835055 0.30075962 0.29870515 -14 -Properties=species:S:1:pos:R:3 A=23.44662 B=3.60588 C=3.38683 mu=0 alpha=45.71 epsHOMO=-0.317 epsLUMO=0.0937 epsgap=0.4107 =426.2996 zpve=0.131708 U0=-158.340943 U=-158.33517 H=-158.334226 G=-158.36894 Cv=19.668 pbc="F F F" -C -0.03007624 1.55804912 0.02980774 -C 0.00707277 0.02834877 -0.00712529 -C 0.71320792 -0.52989340 -1.24765581 -C 0.75036895 -2.05959366 -1.28458957 -H 0.98195330 1.97845113 0.02800797 -H -0.55582433 1.96212343 -0.84270157 -H -0.54083762 1.92692755 0.92493697 -H 0.50928672 -0.34909049 0.89361319 -H -1.01733481 -0.36542299 0.03207393 -H 0.21098948 -0.15245770 -2.14839302 -H 1.73761331 -0.13611625 -1.28685672 -H 1.27616854 -2.46366410 -0.41210980 -H -0.26165699 -2.48000447 -1.28273552 -H 1.26108491 -2.42846444 -2.17974803 -12 -Properties=species:S:1:pos:R:3 A=26.59789 B=3.79186 C=3.53779 mu=1.3402 alpha=38.61 epsHOMO=-0.2619 epsLUMO=0.081 epsgap=0.3429 =382.8628 zpve=0.108241 U0=-194.261089 U=-194.255495 H=-194.254551 G=-194.28893 Cv=18.431 pbc="F F F" -C -0.00379012 1.52196790 0.01027996 -C -0.03589372 -0.00778254 0.00225671 -C -1.45219016 -0.56653711 0.01284386 -O -1.36799785 -1.98394604 0.00453606 -H 1.02477576 1.89411231 0.00239793 -H -0.51286674 1.93764614 -0.86653888 -H -0.49584080 1.92801344 0.90121721 -H 0.48140784 -0.39530552 -0.88316064 -H 0.49828589 -0.40486256 0.87330348 -H -1.98734220 -0.20399494 0.90666062 -H -2.00431485 -0.19459473 -0.86666460 -H -2.26171819 -2.33824298 0.01034748 -12 -Properties=species:S:1:pos:R:3 A=28.29567 B=4.14901 C=3.88349 mu=1.0363 alpha=39.34 epsHOMO=-0.2503 epsLUMO=0.0925 epsgap=0.3428 =368.9331 zpve=0.107895 U0=-194.254127 U=-194.248585 H=-194.247641 G=-194.281899 Cv=17.888 pbc="F F F" -C -0.00519551 1.50868224 0.03308760 -C 0.01316580 -0.01037819 0.01988643 -O 0.69758944 -0.46344140 1.16868979 -C 0.76688491 -1.86508183 1.24602669 -H 1.01445476 1.90423799 0.02996018 -H -0.52990866 1.89184059 -0.84726911 -H -0.51263765 1.87805843 0.92875424 -H -1.01599507 -0.40998729 0.00714282 -H 0.51093143 -0.38369435 -0.89219395 -H -0.23415841 -2.32451489 1.28977780 -H 1.30129276 -2.29853663 0.38486095 -H 1.30990287 -2.11811998 2.16013206 -10 -Properties=species:S:1:pos:R:3 A=14.79671 B=5.6956 C=4.58846 mu=0.0075 alpha=31.42 epsHOMO=-0.2594 epsLUMO=0.0584 epsgap=0.3179 =297.8398 zpve=0.085172 U0=-230.183076 U=-230.177723 H=-230.176779 G=-230.211195 Cv=16.837 pbc="F F F" -O -0.01418735 1.42642242 -0.05423651 -C -0.00391839 0.00717714 0.03496062 -C -1.42317019 -0.55560600 -0.01256639 -O -2.11229413 -0.10563875 -1.17228313 -H -0.18987882 1.64154705 -0.97555148 -H 0.59659504 -0.43315430 -0.77409095 -H 0.47311116 -0.24903733 0.98747216 -H -1.39494859 -1.64975279 -0.06283188 -H -1.96497417 -0.27216915 0.90131972 -H -2.35925991 0.80944396 -1.00614597 -12 -Properties=species:S:1:pos:R:3 A=15.61472 B=6.31363 C=5.54514 mu=0.1136 alpha=41.96 epsHOMO=-0.2727 epsLUMO=0.1012 epsgap=0.3738 =298.6061 zpve=0.109284 U0=-157.116735 U=-157.11209 H=-157.111146 G=-157.143262 Cv=16.49 pbc="F F F" -C -0.03602245 1.52092949 0.01569161 -C 0.01938487 0.01004403 0.04814953 -C 1.30891671 -0.71251995 -0.24920416 -C 0.75347423 -0.70267714 1.15564676 -H 0.90105691 1.95389291 0.38283338 -H -0.19640365 1.89437369 -1.00208569 -H -0.84685273 1.90588930 0.64451322 -H -0.88416809 -0.48001889 -0.30566335 -H 1.26900901 -1.63903068 -0.81150371 -H 2.19010738 -0.11131209 -0.44927513 -H 1.26248862 -0.09487455 1.89690028 -H 0.33540836 -1.62248918 1.54980695 -10 -Properties=species:S:1:pos:R:3 A=18.21931 B=6.63877 C=5.92459 mu=1.812 alpha=35.01 epsHOMO=-0.2633 epsLUMO=0.1052 epsgap=0.3685 =267.2979 zpve=0.085275 U0=-193.039603 U=-193.035186 H=-193.034242 G=-193.065979 Cv=14.764 pbc="F F F" -C -0.01646343 1.51830705 0.00771445 -C -0.02743227 0.01657694 -0.10889741 -C 0.77296553 -0.70131886 -1.10863115 -O 1.17212293 -0.66702969 0.26278829 -H 0.91865980 1.92914217 -0.38285880 -H -0.85158014 1.95456506 -0.55109460 -H -0.11487702 1.82596470 1.05397777 -H -0.93582598 -0.47350600 0.24682793 -H 0.44443922 -1.67201495 -1.47810622 -H 1.40538284 -0.13600339 -1.79271491 -11 -Properties=species:S:1:pos:R:3 A=16.58914 B=7.18798 C=6.11415 mu=1.1353 alpha=39.02 epsHOMO=-0.2304 epsLUMO=0.0968 epsgap=0.3271 =270.5508 zpve=0.097671 U0=-173.147782 U=-173.143343 H=-173.142399 G=-173.174073 Cv=15.298 pbc="F F F" -C -0.05275541 1.47419574 0.00999108 -N 0.01940516 0.02189947 0.06334866 -C 1.32511048 -0.56446850 -0.21420725 -C 0.78051759 -0.54057993 1.17242760 -H 0.86784202 1.97176076 0.36297285 -H -0.23845155 1.79095005 -1.02142866 -H -0.88683448 1.81938959 0.62950398 -H 1.31992909 -1.48186782 -0.79602038 -H 2.15202820 0.11020204 -0.43285248 -H 1.24563570 0.14996098 1.87499245 -H 0.37013358 -1.44020447 1.62233365 -10 -Properties=species:S:1:pos:R:3 A=16.79477 B=6.71015 C=5.86781 mu=1.3894 alpha=34.64 epsHOMO=-0.239 epsLUMO=0.0775 epsgap=0.3166 =263.966 zpve=0.085106 U0=-193.034988 U=-193.030356 H=-193.029411 G=-193.061689 Cv=15.679 pbc="F F F" -O 0.21577583 1.35386859 -0.10695107 -C 0.01628099 -0.02832718 -0.02915694 -C -0.39081465 -0.71497009 1.24315669 -C -1.35118138 -0.64280380 0.06273589 -H -0.43642338 1.79062849 0.45006761 -H 0.74981167 -0.52404050 -0.65624767 -H 0.05959375 -1.66840483 1.49729237 -H -0.61369353 -0.08848006 2.10170640 -H -2.19821932 0.03058430 0.15411451 -H -1.56395912 -1.54640426 -0.49826515 -12 -Properties=species:S:1:pos:R:3 A=10.66107 B=10.66107 C=6.30452 mu=0 alpha=41.83 epsHOMO=-0.2982 epsLUMO=0.0956 epsgap=0.3938 =268.4432 zpve=0.110511 U0=-157.115484 U=-157.111322 H=-157.110378 G=-157.141657 Cv=14.696 pbc="F F F" -C -0.02542957 1.54032578 -0.04215944 -C 1.52564399 1.50419989 0.04121276 -C 1.42146727 0.06146790 0.60844885 -C -0.02638833 -0.01070956 0.04935088 -H -0.46591372 1.99639894 0.84962894 -H -0.47900239 2.00028207 -0.92365389 -H 1.98248278 1.51375334 -0.95303183 -H 2.02191365 2.25986579 0.65522129 -H 2.14291679 -0.67956358 0.25531480 -H 1.43105853 0.05751479 1.70261879 -H -0.79053440 -0.48530296 0.66996573 -H -0.05233245 -0.47238446 -0.94236394 -10 -Properties=species:S:1:pos:R:3 A=12.06545 B=11.77731 C=6.71997 mu=1.7978 alpha=34.56 epsHOMO=-0.2424 epsLUMO=0.0859 epsgap=0.3283 =236.9759 zpve=0.086675 U0=-193.034094 U=-193.029968 H=-193.029024 G=-193.060777 Cv=12.915 pbc="F F F" -C -0.03489789 1.55259455 -0.08507691 -C 1.49349183 1.45459700 0.10114677 -O 1.42725884 0.02348990 0.27254886 -C -0.00634758 0.02236494 0.10944630 -H -0.56579210 2.11301721 0.68591527 -H -0.36628352 1.89393380 -1.06692327 -H 2.10031724 1.73525720 -0.76898349 -H 1.90023090 1.95506611 0.98896748 -H -0.51987849 -0.35595179 1.00235948 -H -0.31970062 -0.57567421 -0.75559275 -12 -Properties=species:S:1:pos:R:3 A=8.63483 B=4.11977 C=2.88825 mu=0.6875 alpha=45.37 epsHOMO=-0.2392 epsLUMO=0.0192 epsgap=0.2584 =452.5112 zpve=0.100501 U0=-248.375248 U=-248.368823 H=-248.367879 G=-248.405354 Cv=21.616 pbc="F F F" -C -0.10255370 1.49907127 -0.00133642 -C -0.00519774 -0.00343114 -0.02723470 -C -0.00688536 -0.76890740 1.26434744 -N 0.08186799 -0.69202791 -1.10087306 -O 0.07439067 0.13215394 -2.24195258 -H 0.78342093 1.94690862 -0.46354050 -H -0.96150430 1.83614082 -0.59021965 -H -0.19854173 1.87462989 1.01931607 -H -0.92620012 -0.57297946 1.82982479 -H 0.07256154 -1.84064782 1.07516863 -H 0.82937178 -0.45553340 1.90133089 -H 0.13975924 -0.51254299 -2.95389260 -10 -Properties=species:S:1:pos:R:3 A=9.17136 B=9.04195 C=4.5531 mu=1.8689 alpha=43.14 epsHOMO=-0.2029 epsLUMO=0.0499 epsgap=0.2528 =303.9808 zpve=0.082433 U0=-210.101789 U=-210.097816 H=-210.096872 G=-210.12818 Cv=14.821 pbc="F F F" -N -0.00829694 1.35362838 0.00995971 -C 1.28033254 1.82457421 -0.00022459 -C 2.12216639 0.73698898 -0.01445600 -C 1.30849904 -0.43109580 -0.01284154 -C -0.00342782 -0.01836884 0.00235736 -H -0.83243234 1.92772668 0.02122995 -H 1.48471820 2.88312630 0.00354313 -H 3.20066918 0.77343077 -0.02488416 -H 1.64810599 -1.45539683 -0.02180394 -H -0.92551647 -0.57697054 0.00839402 -9 -Properties=species:S:1:pos:R:3 A=9.77329 B=9.41192 C=4.7946 mu=3.6193 alpha=39.13 epsHOMO=-0.2253 epsLUMO=0.0332 epsgap=0.2585 =283.6817 zpve=0.071145 U0=-226.160842 U=-226.157088 H=-226.156144 G=-226.187104 Cv=13.371 pbc="F F F" -N -0.00996090 1.35636172 0.01000855 -C 1.29535422 1.79575578 -0.00047017 -C 2.05550519 0.65637438 -0.01422927 -N 1.25385745 -0.46275132 -0.01260894 -C 0.02301170 -0.00706382 0.00200146 -H -0.83195718 1.93618436 0.02132575 -H 1.54553961 2.84344998 0.00289054 -H 3.13177434 0.57569766 -0.02524155 -H -0.87667864 -0.60414531 0.00750794 -9 -Properties=species:S:1:pos:R:3 A=9.50228 B=9.29497 C=4.69874 mu=0.5571 alpha=39.2 epsHOMO=-0.2246 epsLUMO=0.0199 epsgap=0.2445 =289.005 zpve=0.069883 U0=-229.969129 U=-229.965414 H=-229.96447 G=-229.995393 Cv=13.358 pbc="F F F" -O -0.03255819 1.35065130 0.00967376 -C 1.25319237 1.79167710 -0.00030351 -C 2.11664681 0.74375623 -0.01441494 -C 1.30397796 -0.43852387 -0.01283316 -C 0.01629568 -0.00777185 0.00191294 -H 1.37951089 2.86161135 0.00427433 -H 3.19376406 0.79865501 -0.02458604 -H 1.63858893 -1.46383271 -0.02156949 -H -0.93736295 -0.50899925 0.00843891 -8 -Properties=species:S:1:pos:R:3 A=10.10926 B=9.69372 C=4.94857 mu=1.5081 alpha=35.17 epsHOMO=-0.2509 epsLUMO=0.001 epsgap=0.2519 =269.2444 zpve=0.058593 U0=-246.02915 U=-246.025614 H=-246.024669 G=-246.055309 Cv=12.04 pbc="F F F" -O -0.03808298 1.35643965 0.00975193 -C 1.26496982 1.76855319 -0.00061744 -C 2.04955750 0.66630033 -0.01406047 -N 1.24227075 -0.46732066 -0.01237541 -C 0.04019820 0.00561577 0.00183569 -H 1.43513907 2.83095265 0.00340823 -H 3.12532898 0.59783015 -0.02480757 -H -0.89387707 -0.53342154 0.00796935 -17 -Properties=species:S:1:pos:R:3 A=4.4178 B=4.41753 C=4.41717 mu=0.0003 alpha=56.01 epsHOMO=-0.3145 epsLUMO=0.0737 epsgap=0.3882 =486.2719 zpve=0.158836 U0=-197.632222 U=-197.625241 H=-197.624297 G=-197.661411 Cv=26.084 pbc="F F F" -C -0.01859029 1.53486266 0.01039904 -C 0.00210320 -0.00413672 0.00196980 -C 0.74643954 -0.51390760 1.24887638 -C -1.44190166 -0.53673615 0.01311457 -C 0.72234971 -0.50049606 -1.26445632 -H 0.99776928 1.94420529 0.00435474 -H -0.54181883 1.92841415 -0.86819911 -H -0.52758402 1.91888376 0.90148530 -H 0.25549576 -0.17602728 2.16833067 -H 0.77815389 -1.60892372 1.27088904 -H 1.77964786 -0.14975834 1.26944035 -H -1.99871895 -0.18888794 -0.86408066 -H -1.45956909 -1.63227235 0.00649916 -H -1.98088827 -0.19979639 0.90561580 -H 1.75482473 -0.13554018 -1.30118083 -H 0.75396989 -1.59524036 -1.29822280 -H 0.21358221 -0.15343900 -2.17067181 -15 -Properties=species:S:1:pos:R:3 A=4.69474 B=4.66938 C=4.47909 mu=1.384 alpha=49.04 epsHOMO=-0.2601 epsLUMO=0.0664 epsgap=0.3265 =449.0573 zpve=0.134977 U0=-233.560626 U=-233.553779 H=-233.552834 G=-233.589759 Cv=25.128 pbc="F F F" -C -0.00855999 1.54270147 0.00152717 -C 0.00506780 0.00702635 0.01856160 -C 0.76137731 -0.51839196 1.24766443 -C -1.42012774 -0.54933183 -0.02116008 -O 0.62590477 -0.48363745 -1.17657057 -H 1.01361406 1.94020776 0.00281128 -H -0.51459669 1.90702466 -0.89705781 -H -0.52214815 1.94882514 0.87918344 -H 0.28130613 -0.20165548 2.17943598 -H 0.79965506 -1.61131746 1.23022549 -H 1.79096916 -0.14072935 1.26083003 -H -1.93854829 -0.19910724 -0.91828281 -H -1.39937632 -1.64259062 -0.04563833 -H -1.98850725 -0.23059656 0.85726520 -H 1.53111188 -0.15488046 -1.19210222 -9 -Properties=species:S:1:pos:R:3 A=10.21876 B=4.04324 C=2.94978 mu=2.8579 alpha=42.02 epsHOMO=-0.2654 epsLUMO=-0.0575 epsgap=0.2079 =416.7799 zpve=0.065175 U0=-229.927277 U=-229.921622 H=-229.920677 G=-229.955843 Cv=18.723 pbc="F F F" -C -0.00693851 1.49559567 -0.01063358 -C -0.02986170 -0.01594623 0.06248673 -O -0.08815239 -0.63215062 1.10334812 -C 0.02336981 -0.70256747 -1.22062379 -C 0.06738132 -1.26058341 -2.28623129 -H 0.90429806 1.83281887 -0.51655384 -H -0.85338524 1.85556926 -0.60552819 -H -0.05237764 1.91099807 0.99695949 -H 0.10466868 -1.77036047 -3.21832617 -8 -Properties=species:S:1:pos:R:3 A=10.16063 B=4.15847 C=3.0056 mu=3.3351 alpha=37.08 epsHOMO=-0.3007 epsLUMO=-0.0889 epsgap=0.2118 =397.7757 zpve=0.05451 U0=-246.027383 U=-246.022024 H=-246.02108 G=-246.055769 Cv=16.963 pbc="F F F" -C -0.01171097 1.49973776 -0.01213268 -C -0.02919740 -0.00338745 0.07151218 -O -0.09164028 -0.64592927 1.08732241 -C 0.03831104 -0.69690258 -1.23748557 -N 0.09136286 -1.22982650 -2.26228068 -H 0.90093132 1.83370670 -0.51814928 -H -0.85663933 1.84864400 -0.61618135 -H -0.06416540 1.92131542 0.99203818 -8 -Properties=species:S:1:pos:R:3 A=10.68423 B=4.3198 C=3.07993 mu=5.1815 alpha=38.31 epsHOMO=-0.2739 epsLUMO=-0.0438 epsgap=0.2301 =384.5574 zpve=0.056619 U0=-242.19573 U=-242.190591 H=-242.189646 G=-242.223513 Cv=17.465 pbc="F F F" -N -0.05232108 1.34991769 0.15535538 -C 0.08549378 -0.01476951 0.02668890 -N 1.14555215 -0.71993906 -0.03966037 -C -1.19426556 -0.71152088 0.01802151 -N -2.22747610 -1.22790543 0.00132148 -H 0.75474469 1.91998737 -0.04411334 -H -0.94458527 1.76600473 -0.05932293 -H 1.98312429 -0.13415500 -0.02878551 -9 -Properties=species:S:1:pos:R:3 A=9.08854 B=4.42607 C=3.03169 mu=0.9461 alpha=36.51 epsHOMO=-0.2538 epsLUMO=-0.0964 epsgap=0.1574 =399.222 zpve=0.064992 U0=-267.10335 U=-267.097658 H=-267.096714 G=-267.132534 Cv=17.806 pbc="F F F" -C -0.43126066 1.30586664 -0.12758585 -C 0.25285715 -0.01026988 0.13138867 -O 0.69872099 -0.36890333 1.19604690 -C 0.38978943 -0.96773680 -1.06226208 -O -0.01611268 -0.71564293 -2.16583718 -H 0.10006565 1.85324490 -0.91332597 -H -1.44270433 1.13069361 -0.50923371 -H -0.46757554 1.89548158 0.78836479 -H 0.90329886 -1.91558845 -0.79531972 -9 -Properties=species:S:1:pos:R:3 A=10.08927 B=4.54115 C=3.13487 mu=2.7707 alpha=38.09 epsHOMO=-0.2561 epsLUMO=-0.064 epsgap=0.192 =384.4827 zpve=0.067668 U0=-263.278851 U=-263.273589 H=-263.272645 G=-263.306835 Cv=17.972 pbc="F F F" -N -0.16032929 1.08528962 -0.45398652 -C 0.18649127 -0.08762327 0.14758021 -N 1.28979627 -0.46427423 0.67927030 -C -0.94204384 -1.08440264 0.20423621 -O -2.01802205 -0.89007687 -0.30583149 -H 0.54574437 1.73837930 -0.74689246 -H -1.04684920 1.09570857 -0.93607796 -H 2.01738195 0.24991828 0.59881811 -H -0.67740148 -2.01003995 0.74746441 -13 -Properties=species:S:1:pos:R:3 A=7.92874 B=3.8272 C=2.82581 mu=0.6578 alpha=51.2 epsHOMO=-0.2589 epsLUMO=0.0571 epsgap=0.316 =481.9854 zpve=0.112471 U0=-195.186772 U=-195.180446 H=-195.179502 G=-195.215658 Cv=22.569 pbc="F F F" -C -0.01763434 1.54859468 -0.00253584 -C -0.01719180 0.00735789 0.03269604 -C 0.75818463 -0.52820155 1.25278862 -C -1.38213303 -0.51768438 0.00775516 -C -2.50796392 -0.94005629 0.00478552 -H 1.00944396 1.92639613 -0.02632830 -H -0.54514614 1.92217052 -0.88393459 -H -0.51243000 1.95339892 0.88560642 -H 0.49366179 -0.34904155 -0.87265890 -H 0.28968458 -0.19345372 2.18334679 -H 0.77833392 -1.62102506 1.25783727 -H 1.78952492 -0.16181727 1.23574954 -H -3.49998244 -1.31810165 -0.00723572 -12 -Properties=species:S:1:pos:R:3 A=7.90009 B=3.95466 C=2.88643 mu=3.9512 alpha=46.23 epsHOMO=-0.318 epsLUMO=0.0365 epsgap=0.3545 =460.694 zpve=0.102281 U0=-211.295796 U=-211.289821 H=-211.288877 G=-211.324525 Cv=20.467 pbc="F F F" -C -0.00242350 1.55035837 0.01056187 -C -0.01856600 0.01004352 -0.02261770 -C -1.45045318 -0.55766178 0.01293347 -C 0.70125749 -0.48583168 -1.20172764 -N 1.26190946 -0.87126766 -2.13619740 -H 1.01959496 1.93673215 0.00349668 -H -0.52972844 1.96141639 -0.85498386 -H -0.50134251 1.90346839 0.91755862 -H 0.52481877 -0.36222456 0.85525462 -H -2.02427903 -0.21428716 -0.85251869 -H -1.44438296 -1.65026927 0.00754107 -H -1.95805494 -0.21725882 0.91996760 -11 -Properties=species:S:1:pos:R:3 A=8.30095 B=3.97615 C=2.93764 mu=2.7429 alpha=42.87 epsHOMO=-0.2704 epsLUMO=0.0278 epsgap=0.2983 =440.0738 zpve=0.091554 U0=-227.338075 U=-227.332253 H=-227.331309 G=-227.366638 Cv=19.946 pbc="F F F" -C -0.01064295 1.54128691 0.00722087 -C -0.00203811 0.00756015 0.03863463 -N 0.76268677 -0.47101822 1.19250856 -C -1.38541954 -0.52099256 -0.00194097 -N -2.45680847 -0.95636072 0.00694969 -H 1.02043523 1.90106589 0.00392365 -H -0.52873161 1.91193452 -0.88031480 -H -0.51849536 1.94150431 0.89013197 -H 0.50418828 -0.35805500 -0.86276072 -H 0.30070681 -0.18364934 2.05155305 -H 0.78148992 -1.48670299 1.20428183 -11 -Properties=species:S:1:pos:R:3 A=8.55919 B=3.89952 C=2.91869 mu=1.3582 alpha=44.03 epsHOMO=-0.2665 epsLUMO=0.0336 epsgap=0.3001 =444.6452 zpve=0.088908 U0=-231.108368 U=-231.102292 H=-231.101348 G=-231.137061 Cv=21.306 pbc="F F F" -C -0.00754278 1.55566144 0.08650633 -C 0.01207541 0.02096553 0.04935916 -O -1.29150251 -0.52785128 0.22126386 -C 0.65093362 -0.47718651 -1.17430507 -C 1.13902673 -0.86577343 -2.20205518 -H 1.00824243 1.95969458 0.04929733 -H -0.56272245 1.95192010 -0.76978085 -H -0.49328161 1.88929438 1.00704123 -H 0.57811967 -0.34464907 0.91458864 -H -1.78516532 -0.35903262 -0.58909400 -H 1.58078877 -1.22365060 -3.09919447 -10 -Properties=species:S:1:pos:R:3 A=8.55908 B=4.01671 C=2.97882 mu=3.269 alpha=39.28 epsHOMO=-0.3051 epsLUMO=0.0115 epsgap=0.3166 =424.3395 zpve=0.078602 U0=-247.214861 U=-247.209162 H=-247.208218 G=-247.243338 Cv=19.252 pbc="F F F" -C -0.00992462 1.54951908 0.00732200 -C 0.00740485 0.01722020 0.05031270 -O 0.72085722 -0.49630479 1.15864583 -C -1.36995095 -0.52355929 0.00894432 -N -2.45081572 -0.93233138 0.00716912 -H 1.01892314 1.91630447 0.02724149 -H -0.50261490 1.90969362 -0.89997951 -H -0.54800426 1.95377611 0.87053664 -H 0.53602846 -0.36494195 -0.82970945 -H 0.27702643 -0.20644534 1.96421773 -13 -Properties=species:S:1:pos:R:3 A=7.66289 B=3.70925 C=2.79937 mu=2.6921 alpha=46.58 epsHOMO=-0.2469 epsLUMO=-0.0188 epsgap=0.2281 =482.0475 zpve=0.112328 U0=-232.364952 U=-232.358577 H=-232.357633 G=-232.394589 Cv=21.434 pbc="F F F" -C -0.02629647 1.53877116 -0.00783747 -C 0.01468904 0.00095310 0.00166037 -C 0.74737862 -0.53388859 1.24420471 -C -1.40211264 -0.53859257 -0.01557820 -O -1.87620455 -1.23582351 -0.87399559 -H 0.98823696 1.94625080 0.03705522 -H -0.50725090 1.92153422 -0.91285565 -H -0.57756514 1.92360883 0.85796386 -H 0.50584680 -0.36421091 -0.90619124 -H 0.22958380 -0.23909403 2.16431052 -H 0.81692736 -1.62559499 1.22973438 -H 1.76267153 -0.12846566 1.29050755 -H -2.00861510 -0.22792742 0.87121666 -11 -Properties=species:S:1:pos:R:3 A=8.31096 B=4.07963 C=2.92918 mu=2.8354 alpha=39.16 epsHOMO=-0.255 epsLUMO=-0.0279 epsgap=0.2271 =432.1489 zpve=0.088443 U0=-268.287661 U=-268.281505 H=-268.280561 G=-268.316982 Cv=20.344 pbc="F F F" -C -0.05483118 1.46032843 -0.17068647 -C 0.07105714 -0.03524485 0.11225841 -O 0.58204486 -0.33316656 1.40176256 -C -1.27639279 -0.74383027 0.03051185 -O -2.11855775 -0.49643175 -0.79163412 -H 0.93158072 1.93091398 -0.23353204 -H -0.57713995 1.61290939 -1.11835562 -H -0.62444315 1.94776119 0.62573670 -H 0.69687521 -0.50071620 -0.67159471 -H 1.45603316 0.06066659 1.47858584 -H -1.40723217 -1.53388627 0.80211181 -12 -Properties=species:S:1:pos:R:3 A=8.9331 B=4.19405 C=2.96046 mu=3.7163 alpha=44.42 epsHOMO=-0.2424 epsLUMO=0.0327 epsgap=0.2751 =441.85 zpve=0.10227 U0=-248.430371 U=-248.424309 H=-248.423365 G=-248.459383 Cv=19.918 pbc="F F F" -C -0.09776499 1.44917268 0.04561984 -N 0.01043053 0.00373818 -0.00070600 -C 0.05323028 -0.71432112 1.25264891 -C 0.06728716 -0.64067225 -1.20060778 -O 0.03636432 -0.11006515 -2.29109228 -H 0.75650660 1.88747109 0.57647421 -H -0.11648577 1.81632103 -0.98049172 -H -1.01641106 1.75521548 0.56151415 -H -0.85453556 -0.53657675 1.84390168 -H 0.13297722 -1.78696336 1.05851351 -H 0.91637348 -0.40737846 1.85761345 -H 0.14762240 -1.73801339 -1.06805139 -11 -Properties=species:S:1:pos:R:3 A=10.13982 B=3.81346 C=2.86965 mu=2.9514 alpha=39.33 epsHOMO=-0.2699 epsLUMO=-0.0262 epsgap=0.2437 =440.1727 zpve=0.088924 U0=-268.301176 U=-268.295084 H=-268.29414 G=-268.331307 Cv=19.824 pbc="F F F" -C -0.01708808 1.44281136 0.03225501 -C -0.05544542 -0.06560572 -0.02375999 -O -0.17379913 -0.76196233 0.96223129 -C 0.06068107 -0.73615179 -1.38825252 -O 0.00877771 -2.12574390 -1.27749486 -H 0.92062950 1.81463913 -0.39522215 -H -0.83050523 1.86542281 -0.56780350 -H -0.10911975 1.78049045 1.06493675 -H -0.75015067 -0.35235060 -2.03056074 -H 1.00312898 -0.40389029 -1.85592942 -H -0.09063255 -2.29450960 -0.32608019 -13 -Properties=species:S:1:pos:R:3 A=9.55216 B=3.57844 C=2.7341 mu=2.6168 alpha=46.19 epsHOMO=-0.2423 epsLUMO=-0.0072 epsgap=0.2351 =489.8518 zpve=0.112006 U0=-232.377706 U=-232.371073 H=-232.370129 G=-232.408256 Cv=21.716 pbc="F F F" -C -0.04384102 1.54099793 -0.03913814 -C 0.02765132 0.01885817 0.04778773 -C 0.76814644 -0.48842218 1.28000847 -C 0.87850208 -1.99746312 1.42393323 -O 1.24179142 0.26651340 2.09788702 -H 0.95796454 1.97690198 -0.06816012 -H -0.58447717 1.85356031 -0.93690369 -H -0.55173626 1.95737759 0.83440529 -H -0.97692824 -0.42709877 0.05353178 -H 0.51853486 -0.40806507 -0.83809409 -H 1.39924980 -2.42678188 0.56053177 -H 1.42001187 -2.24449367 2.33776426 -H -0.11750602 -2.45360651 1.45141965 -12 -Properties=species:S:1:pos:R:3 A=9.78802 B=3.78441 C=2.84416 mu=3.499 alpha=42.54 epsHOMO=-0.2438 epsLUMO=0.0355 epsgap=0.2793 =457.447 zpve=0.101847 U0=-248.448467 U=-248.441988 H=-248.441044 G=-248.478935 Cv=21.374 pbc="F F F" -C -0.00220144 1.53344329 -0.05960369 -C -0.03235837 0.01206167 0.06840265 -C 0.69666086 -0.48196462 1.31828322 -N 0.98104007 -1.82034162 1.31984698 -O 1.00392124 0.24606232 2.24136749 -H 1.02111190 1.89525169 -0.19182672 -H -0.59707204 1.86206427 -0.91645604 -H -0.39728839 1.99920712 0.84545201 -H -1.06936083 -0.34552935 0.13040806 -H 0.39947165 -0.46906818 -0.81777020 -H 0.63281951 -2.43917647 0.60874785 -H 1.35708014 -2.22217793 2.16303522 -12 -Properties=species:S:1:pos:R:3 A=10.01185 B=3.84195 C=2.87635 mu=3.5402 alpha=43.61 epsHOMO=-0.2418 epsLUMO=0.0387 epsgap=0.2805 =458.9803 zpve=0.101735 U0=-248.443503 U=-248.436899 H=-248.435955 G=-248.474272 Cv=20.967 pbc="F F F" -C 0.00619941 1.45933202 -0.03893859 -N -0.06411616 0.01422963 -0.12046901 -C -1.20595394 -0.68196796 0.16117120 -C -1.08982704 -2.19284899 0.02095775 -O -2.24482911 -0.14443077 0.49994418 -H 0.75053989 1.78372520 0.69715573 -H 0.25692001 1.90395263 -1.00876215 -H -0.97856026 1.81121438 0.27082194 -H 0.75820982 -0.49543660 -0.39464024 -H -0.09909703 -2.53543104 -0.28970003 -H -1.34097671 -2.65395851 0.97972885 -H -1.82903100 -2.53483935 -0.70813076 -11 -Properties=species:S:1:pos:R:3 A=10.44045 B=4.04692 C=2.98547 mu=3.5648 alpha=39.51 epsHOMO=-0.2436 epsLUMO=0.0599 epsgap=0.3034 =428.404 zpve=0.091713 U0=-264.504487 U=-264.498452 H=-264.497508 G=-264.533633 Cv=20.274 pbc="F F F" -C 0.00922630 1.45021814 -0.03194543 -N -0.01429488 -0.00054646 -0.01782667 -C -1.21576579 -0.67252403 0.04421896 -N -1.09733352 -2.04644898 -0.15997741 -O -2.28599299 -0.12893711 0.24196027 -H 0.89659038 1.81885022 0.49120949 -H 0.00233950 1.86388504 -1.04914155 -H -0.88562684 1.79943346 0.48353298 -H 0.75257305 -0.47506631 -0.46719265 -H -0.24882333 -2.48772194 0.16375654 -H -1.93419610 -2.53898393 0.11482880 -12 -Properties=species:S:1:pos:R:3 A=9.69683 B=4.1533 C=3.01703 mu=1.1876 alpha=43.57 epsHOMO=-0.2595 epsLUMO=0.0352 epsgap=0.2948 =443.1687 zpve=0.102062 U0=-248.416462 U=-248.410358 H=-248.409414 G=-248.445651 Cv=20.256 pbc="F F F" -C -0.09160695 1.37540046 -0.00729426 -O -0.06964534 -0.04831581 -0.11148812 -C 0.68114625 -0.56142673 -1.10732458 -C 0.60405298 -2.06701550 -1.08766892 -N 1.32645533 0.19266343 -1.89967731 -H 0.91463916 1.76843655 0.16257460 -H -0.48562826 1.82624615 -0.92235216 -H -0.73971430 1.60007423 0.84110605 -H -0.43430065 -2.39242162 -1.20325205 -H 1.20627110 -2.50049977 -1.88745640 -H 0.95651337 -2.44976670 -0.12501838 -H 1.83504115 -0.36104361 -2.58486055 -11 -Properties=species:S:1:pos:R:3 A=10.20392 B=4.16834 C=3.07281 mu=1.7569 alpha=39.33 epsHOMO=-0.2685 epsLUMO=0.0174 epsgap=0.2859 =427.6606 zpve=0.089436 U0=-268.32127 U=-268.315051 H=-268.314106 G=-268.351214 Cv=19.501 pbc="F F F" -C 0.07295254 1.38840919 0.06442046 -O 0.21049114 -0.03740028 0.04612736 -C 0.50102805 -0.57224288 -1.15975346 -C 0.62218715 -2.07378323 -1.06030684 -O 0.63922154 0.08224418 -2.16157325 -H 1.00029720 1.87062570 -0.25532064 -H -0.73338957 1.70782089 -0.60080454 -H -0.15751280 1.65154060 1.09692954 -H -0.31330001 -2.50202851 -0.68941906 -H 0.85646588 -2.48430275 -2.04157352 -H 1.40651685 -2.34055496 -0.34634301 -10 -Properties=species:S:1:pos:R:3 A=10.68182 B=4.39532 C=3.17969 mu=2.329 alpha=35.5 epsHOMO=-0.2669 epsLUMO=0.0575 epsgap=0.3244 =398.5908 zpve=0.079271 U0=-284.385189 U=-284.379361 H=-284.378417 G=-284.414085 Cv=19.084 pbc="F F F" -C -0.02645771 1.38305753 0.02085660 -O 0.04139566 -0.04439503 -0.00277452 -C 0.66775338 -0.56001625 -1.09000344 -N 0.72605063 -1.92219983 -0.98822954 -O 1.12952381 0.09648143 -1.99322180 -H 0.97529779 1.82044307 0.02387306 -H -0.57085698 1.76214621 -0.84790425 -H -0.55410722 1.63835731 0.94043594 -H 0.12662696 -2.38401590 -0.32566929 -H 0.97230070 -2.41798421 -1.82732573 -10 -Properties=species:S:1:pos:R:3 A=10.20696 B=3.93135 C=2.9219 mu=4.5676 alpha=35.87 epsHOMO=-0.246 epsLUMO=0.0273 epsgap=0.2733 =418.5967 zpve=0.078222 U0=-284.360325 U=-284.354148 H=-284.353204 G=-284.390143 Cv=20.089 pbc="F F F" -N -0.08115607 1.31807026 0.01276742 -C 0.05442259 -0.04477733 0.08155388 -O 1.12163074 -0.59783950 0.20940553 -C -1.28687786 -0.79011642 0.06178435 -O -1.04605114 -2.13954195 -0.23957158 -H 0.76623366 1.85991128 -0.04483425 -H -0.93021346 1.74567447 -0.31565615 -H -1.74559696 -0.65521334 1.05526700 -H -1.96026061 -0.31149222 -0.67026256 -H -1.85637296 -2.63017646 -0.07625478 -10 -Properties=species:S:1:pos:R:3 A=10.14389 B=4.0714 C=3.02223 mu=5.3004 alpha=35.19 epsHOMO=-0.2527 epsLUMO=0.0208 epsgap=0.2735 =408.0279 zpve=0.080317 U0=-284.372483 U=-284.367172 H=-284.366228 G=-284.40095 Cv=17.931 pbc="F F F" -N 0.01838100 0.93057357 0.04943058 -H -0.70674506 0.56116798 -0.55550134 -H 0.68248307 0.18430002 0.21487099 -H 0.21928084 2.79858558 -0.32670585 -C -0.54586914 1.44688585 1.30274586 -C -0.80466975 2.96030569 1.21052835 -O -0.21468822 3.53648884 0.15531687 -O -1.45295676 3.56338618 2.02143528 -H -1.47662579 0.96512183 1.62072169 -H 0.17781209 1.30324286 2.11157176 -13 -Properties=species:S:1:pos:R:3 A=6.6508 B=4.19317 C=3.39441 mu=2.2854 alpha=42.01 epsHOMO=-0.2597 epsLUMO=0.0631 epsgap=0.3228 =438.3028 zpve=0.11345 U0=-269.479234 U=-269.472993 H=-269.472049 G=-269.508213 Cv=22.072 pbc="F F F" -C -0.02775167 1.53421433 0.00651151 -C 0.08343246 0.01218277 0.05258986 -O 0.95856392 -0.48200710 -0.96694113 -C 0.69767574 -0.49516451 1.35948812 -O 2.02247062 -0.02842530 1.52222186 -H 0.94333763 1.98841434 0.21841589 -H -0.37320516 1.87354041 -0.97716908 -H -0.74773817 1.89256394 0.74926853 -H -0.91671848 -0.43697036 -0.06040528 -H 0.76726710 -0.01412177 -1.78502043 -H 0.11650002 -0.13351914 2.21392431 -H 0.65814685 -1.59563445 1.36274474 -H 2.46378566 -0.22762476 0.68698431 -17 -Properties=species:S:1:pos:R:3 A=7.26511 B=3.32874 C=2.54491 mu=0.0618 alpha=56.26 epsHOMO=-0.3085 epsLUMO=0.085 epsgap=0.3934 =565.8412 zpve=0.159632 U0=-197.629387 U=-197.622325 H=-197.621381 G=-197.659365 Cv=25.169 pbc="F F F" -C 0.05879035 1.56174169 -0.01945240 -C -0.01544024 0.03182306 0.00308332 -C -1.44103237 -0.54862028 0.05028629 -C -1.40777007 -2.07231030 -0.13012010 -C -2.18400529 -0.17659759 1.34114303 -H 1.09435638 1.90233607 -0.11900072 -H -0.50689566 1.97217319 -0.86388098 -H -0.34407951 2.00628082 0.89579522 -H 0.48926758 -0.36011214 -0.88953902 -H 0.55254938 -0.35139446 0.86269887 -H -2.00031575 -0.12153682 -0.79559684 -H -0.85501131 -2.55065810 0.68775438 -H -2.41787513 -2.49543628 -0.13720127 -H -0.91928762 -2.35363802 -1.06914014 -H -3.19100197 -0.60699559 1.35137051 -H -1.65197294 -0.56010584 2.22063481 -H -2.28793759 0.90575132 1.46079572 -15 -Properties=species:S:1:pos:R:3 A=8.11598 B=3.43678 C=2.66869 mu=1.3894 alpha=49.45 epsHOMO=-0.2617 epsLUMO=0.0733 epsgap=0.335 =521.8605 zpve=0.136091 U0=-233.555951 U=-233.549143 H=-233.548199 G=-233.585602 Cv=23.99 pbc="F F F" -C -0.03841227 1.55621396 0.00935837 -C 0.01907135 0.02716079 -0.02074203 -C 0.73141506 -0.52709328 -1.25454422 -C 0.83975868 -2.05206973 -1.22872655 -O -0.01226470 -0.08514394 -2.38973758 -H 0.96845664 1.98863424 0.03526234 -H -0.54377006 1.93824134 -0.88074407 -H -0.57652955 1.91383386 0.89256698 -H 0.53166867 -0.34991486 0.87240999 -H -0.99727186 -0.38638654 -0.00185289 -H 1.74909346 -0.09943025 -1.28826184 -H 1.34143968 -2.42487137 -2.12957624 -H 1.41822012 -2.39229108 -0.36340871 -H -0.15616765 -2.50443859 -1.18308875 -H 0.43090825 -0.40901616 -3.17979642 -15 -Properties=species:S:1:pos:R:3 A=7.55335 B=3.78323 C=2.80566 mu=1.0758 alpha=49.8 epsHOMO=-0.2476 epsLUMO=0.086 epsgap=0.3336 =507.9614 zpve=0.135681 U0=-233.545899 U=-233.539034 H=-233.53809 G=-233.57582 Cv=23.573 pbc="F F F" -C -0.00857605 1.38097833 0.09454670 -O -0.02255106 -0.00739461 -0.13062576 -C -1.30243341 -0.61997805 -0.02088309 -C -1.18687286 -1.97885751 -0.70303918 -C -1.75587824 -0.74662760 1.43714508 -H 1.00321550 1.73258087 -0.12443496 -H -0.71511468 1.90838275 -0.56674280 -H -0.24909065 1.64967237 1.13384681 -H -2.03737978 -0.00432241 -0.56884198 -H -0.44792779 -2.59945605 -0.18596870 -H -2.14751041 -2.50225525 -0.69102581 -H -0.86524212 -1.85860679 -1.74077836 -H -2.73321482 -1.23674908 1.49205117 -H -1.03621350 -1.34483828 2.00544671 -H -1.84898410 0.22935483 1.92198087 -15 -Properties=species:S:1:pos:R:3 A=6.14183 B=5.17679 C=3.79297 mu=0.1068 alpha=52.92 epsHOMO=-0.2633 epsLUMO=0.0893 epsgap=0.3526 =439.1643 zpve=0.137025 U0=-196.409349 U=-196.403398 H=-196.402453 G=-196.437676 Cv=22.258 pbc="F F F" -C -0.03913204 1.50456305 0.04978913 -C 0.00985856 -0.01113880 0.01248619 -C -1.24167981 -0.66663044 -0.53989979 -C 1.33803387 -0.66895748 -0.27400430 -C 0.75126442 -0.72221586 1.11862571 -H 0.88633040 1.92490040 0.45579358 -H -0.18183878 1.92045743 -0.95489291 -H -0.86832076 1.85815718 0.67439648 -H -1.43570890 -0.34344387 -1.56976411 -H -1.15326757 -1.75757800 -0.54433832 -H -2.12221416 -0.40570820 0.05951361 -H 1.34398374 -1.58235208 -0.86005084 -H 2.20196897 -0.03329084 -0.43929326 -H 1.21949576 -0.12246018 1.89246580 -H 0.36153319 -1.67153717 1.47171932 -13 -Properties=species:S:1:pos:R:3 A=6.49534 B=5.52329 C=3.9108 mu=1.8235 alpha=45.7 epsHOMO=-0.2596 epsLUMO=0.091 epsgap=0.3505 =405.521 zpve=0.112851 U0=-232.335768 U=-232.329981 H=-232.329037 G=-232.364091 Cv=20.719 pbc="F F F" -C -0.03970330 1.50954998 -0.04112233 -C -0.03023556 -0.00335329 -0.06498361 -C -1.24613189 -0.68356648 0.52531190 -C 0.78937201 -0.71129193 -1.06027559 -O 1.22546798 -0.60059036 0.29712283 -H 0.88992772 1.90948478 -0.45296558 -H -0.87952723 1.90350108 -0.62376642 -H -0.14448576 1.87590604 0.98604429 -H -2.15218727 -0.41012616 -0.02629495 -H -1.13412584 -1.77005431 0.49747032 -H -1.38586631 -0.38055453 1.56882757 -H 0.50712690 -1.71386874 -1.38085018 -H 1.37088104 -0.14369322 -1.78640853 -13 -Properties=species:S:1:pos:R:3 A=6.57648 B=5.31603 C=3.98453 mu=1.3822 alpha=45.85 epsHOMO=-0.2494 epsLUMO=0.0776 epsgap=0.327 =401.4888 zpve=0.113237 U0=-232.33341 U=-232.327734 H=-232.32679 G=-232.36147 Cv=21.17 pbc="F F F" -C -0.03714052 1.53215920 0.01332310 -C 0.01344335 0.02117019 0.01922005 -O -1.17476104 -0.59735284 -0.40931260 -C 1.30744798 -0.69902106 -0.27389650 -C 0.70844749 -0.71373504 1.12226318 -H 0.89026861 1.96240279 0.40276135 -H -0.17846151 1.91561262 -1.00518522 -H -0.87011232 1.88955201 0.62730953 -H -1.28352265 -0.40225391 -1.34690641 -H 1.23973813 -1.61642115 -0.84783774 -H 2.20543993 -0.11523820 -0.44744474 -H 1.19539159 -0.13889834 1.90240284 -H 0.24735439 -1.63670241 1.45378480 -10 -Properties=species:S:1:pos:R:3 A=12.18399 B=5.1384 C=3.78868 mu=2.5732 alpha=40.19 epsHOMO=-0.263 epsLUMO=0.0277 epsgap=0.2907 =352.0378 zpve=0.080558 U0=-247.201165 U=-247.196699 H=-247.195755 G=-247.22841 Cv=15.51 pbc="F F F" -N -0.04445152 1.25623533 0.31681915 -C -0.03914559 0.04913710 -0.01670558 -C -1.08749619 -1.04164446 -0.11488033 -C 0.09869688 -1.90754844 -0.58225894 -O 0.99647508 -0.76306629 -0.44028010 -H 0.88935980 1.66481350 0.24916893 -H -1.54324114 -1.32235812 0.83572172 -H -1.86910036 -0.85713839 -0.85325910 -H 0.39739753 -2.72227350 0.08158721 -H 0.06945080 -2.25418345 -1.61796084 -11 -Properties=species:S:1:pos:R:3 A=10.79571 B=4.78732 C=3.54292 mu=2.7119 alpha=42.77 epsHOMO=-0.2415 epsLUMO=-0.0194 epsgap=0.2222 =379.6371 zpve=0.090544 U0=-231.15578 U=-231.150804 H=-231.149859 G=-231.184401 Cv=17.076 pbc="F F F" -O -0.00101746 0.01846628 -0.20114742 -C -0.01405811 1.19658059 0.00205306 -C -1.13542911 2.22979148 0.21957980 -C -0.03500884 3.29097637 0.53052223 -C 1.08469664 2.25947795 0.18994331 -H -1.71642814 2.41772662 -0.68901078 -H -1.83392757 1.98415059 1.02444244 -H -0.05534741 4.17585264 -0.10670963 -H -0.02541477 3.61399048 1.57278917 -H 1.81074389 2.03288534 0.97578973 -H 1.63600801 2.46255380 -0.73376244 -10 -Properties=species:S:1:pos:R:3 A=12.23436 B=4.97895 C=3.70561 mu=3.6671 alpha=39.73 epsHOMO=-0.25 epsLUMO=0.038 epsgap=0.288 =355.0934 zpve=0.080442 U0=-247.225618 U=-247.220897 H=-247.219953 G=-247.253218 Cv=16.1 pbc="F F F" -O 0.03184134 -0.03461682 0.00849836 -C -0.02440334 1.16440533 0.02160811 -C -1.14541322 2.23712809 0.03012901 -C 0.00928805 3.27945878 0.04557758 -N 0.93843838 2.14652109 0.03554396 -H -1.77030529 2.23779730 -0.86428437 -H -1.77632809 2.21765386 0.92007934 -H 0.07170564 3.91863516 -0.84072193 -H 0.06622603 3.89826625 0.94657952 -H 1.94616840 2.09535713 0.03855945 -9 -Properties=species:S:1:pos:R:3 A=12.44643 B=5.23328 C=3.86599 mu=3.9339 alpha=35.4 epsHOMO=-0.2788 epsLUMO=0.0089 epsgap=0.2878 =336.0792 zpve=0.068574 U0=-267.106213 U=-267.101929 H=-267.100985 G=-267.13332 Cv=14.483 pbc="F F F" -O 0.04424004 0.03933243 -0.33071104 -C -0.04854479 1.17416230 0.00410076 -C -1.14211656 2.23611850 0.15338361 -C 0.02332538 3.12357010 0.61681625 -O 0.95663808 2.01190122 0.42603746 -H -1.62573755 2.51482212 -0.78401102 -H -1.90009213 1.99553386 0.90028663 -H 0.28368077 3.96172751 -0.03229287 -H 0.00782250 3.43942655 1.66162725 -10 -Properties=species:S:1:pos:R:3 A=11.54185 B=4.90368 C=3.63309 mu=2.5257 alpha=39.34 epsHOMO=-0.2437 epsLUMO=-0.0258 epsgap=0.2179 =359.152 zpve=0.07965 U0=-247.190194 U=-247.185543 H=-247.184599 G=-247.217688 Cv=16.083 pbc="F F F" -O 0.00053891 0.00665475 -0.09743049 -C -0.01371990 1.19213881 0.03139936 -C -1.10668681 2.26612284 0.17247913 -N -0.03543445 3.25123475 0.48533693 -C 1.05328615 2.29521506 0.14383904 -H -1.65047970 2.42796175 -0.76886054 -H -1.83606786 2.11370530 0.97567410 -H -0.05434600 4.07193609 -0.10727051 -H 1.80754206 2.16277968 0.92736250 -H 1.56737937 2.47130277 -0.81152688 -9 -Properties=species:S:1:pos:R:3 A=12.18264 B=4.94228 C=3.68162 mu=0.8477 alpha=35.94 epsHOMO=-0.2647 epsLUMO=-0.0352 epsgap=0.2295 =344.5473 zpve=0.066989 U0=-267.068488 U=-267.063976 H=-267.063032 G=-267.095887 Cv=15.115 pbc="F F F" -O -0.00031692 0.00676321 -0.17139774 -C -0.01417098 1.18672865 -0.00980279 -C -1.08283120 2.27653381 0.15574094 -O -0.03856115 3.26460423 0.27513986 -C 1.02857270 2.30515693 0.12774311 -H -1.72923855 2.43647507 -0.71671274 -H -1.70219898 2.19346725 1.05800754 -H 1.67366781 2.23923293 1.01324179 -H 1.64705068 2.48224548 -0.76148355 -15 -Properties=species:S:1:pos:R:3 A=9.63416 B=4.10254 C=3.28665 mu=0.095 alpha=52.94 epsHOMO=-0.2896 epsLUMO=0.0927 epsgap=0.3823 =455.0215 zpve=0.138424 U0=-196.407957 U=-196.40245 H=-196.401505 G=-196.436159 Cv=20.299 pbc="F F F" -C -0.47005934 1.44206547 -0.06684548 -C 0.14287306 0.05485144 0.03747984 -C 1.37254693 -0.30295273 -0.84679168 -C 1.95947994 -1.09364561 0.35326039 -C 0.99887433 -0.30768787 1.28563099 -H 0.29210839 2.21844829 0.06844100 -H -0.93614604 1.60094605 -1.04612799 -H -1.24148161 1.59707353 0.69627593 -H -0.63517341 -0.70560132 -0.10055095 -H 1.19338376 -0.84118177 -1.78129390 -H 1.96867777 0.58857708 -1.07004184 -H 1.69873597 -2.15500718 0.30521100 -H 3.03253510 -1.01077235 0.54148058 -H 1.48290386 0.58242428 1.70211026 -H 0.51304296 -0.84979686 2.10121062 -13 -Properties=species:S:1:pos:R:3 A=9.93129 B=4.56362 C=3.63704 mu=1.6826 alpha=45.72 epsHOMO=-0.241 epsLUMO=0.0915 epsgap=0.3325 =407.6909 zpve=0.11459 U0=-232.33123 U=-232.325877 H=-232.324933 G=-232.359589 Cv=18.582 pbc="F F F" -C -0.32308228 1.53751451 0.01947624 -C 0.11847214 0.08900172 -0.00523140 -C 0.87489284 -0.43763303 -1.24721738 -C 1.97841048 -0.80657483 -0.23740356 -O 1.27344926 -0.15419108 0.83794040 -H 0.49211985 2.19658795 -0.29527664 -H -1.17615835 1.68923490 -0.65134808 -H -0.62776963 1.82747912 1.03013205 -H -0.71410200 -0.56389187 0.29597305 -H 0.41033314 -1.26269706 -1.78910031 -H 1.15560811 0.34596269 -1.95459519 -H 2.09501012 -1.88349764 -0.05606083 -H 2.96970344 -0.36924147 -0.40408009 -13 -Properties=species:S:1:pos:R:3 A=10.13697 B=4.32512 C=3.50912 mu=1.8995 alpha=45.45 epsHOMO=-0.2419 epsLUMO=0.0814 epsgap=0.3233 =414.1575 zpve=0.114694 U0=-232.325947 U=-232.320548 H=-232.319604 G=-232.354576 Cv=18.443 pbc="F F F" -C -0.18371124 1.54668909 -0.01671129 -C 0.04513382 0.04386724 0.02270494 -C 1.20301104 -0.54034580 -0.81983464 -O 1.80061686 -1.15530615 0.33924991 -C 0.83391983 -0.54313754 1.21648003 -H 0.75102609 2.09256015 0.15344911 -H -0.58319248 1.86155707 -0.98694169 -H -0.89843724 1.85917126 0.75244153 -H -0.89506736 -0.48768372 -0.14843995 -H 0.93560472 -1.27857588 -1.58543917 -H 1.86392938 0.21379924 -1.27081649 -H 1.29414565 0.20948993 1.87273952 -H 0.31517042 -1.28326856 1.83756393 -13 -Properties=species:S:1:pos:R:3 A=10.09703 B=4.26865 C=3.40487 mu=1.4604 alpha=45.55 epsHOMO=-0.256 epsLUMO=0.0801 epsgap=0.3361 =413.9118 zpve=0.115063 U0=-232.333258 U=-232.328097 H=-232.327153 G=-232.361103 Cv=19.023 pbc="F F F" -O 0.54622847 1.03270424 -0.58398083 -C -0.00112269 -0.11036621 0.01898238 -C 0.13945950 -0.34310429 1.54777117 -C -1.26863784 -0.99876517 1.51632654 -C -1.53844651 -0.26418236 0.17423510 -H 0.20332379 1.80433874 -0.12071219 -H 0.41296647 -0.95857955 -0.53560402 -H 0.98890738 -0.92719626 1.90921867 -H 0.11786549 0.61323034 2.08356053 -H -1.20828813 -2.08191064 1.38036699 -H -1.94143614 -0.79119746 2.35014116 -H -2.00495682 0.71307933 0.34581493 -H -2.09160311 -0.78230202 -0.61249171 -11 -Properties=species:S:1:pos:R:3 A=10.85394 B=4.52867 C=3.62328 mu=2.4158 alpha=38.58 epsHOMO=-0.2465 epsLUMO=0.0604 epsgap=0.3069 =374.4087 zpve=0.091005 U0=-268.248371 U=-268.243234 H=-268.24229 G=-268.276572 Cv=17.344 pbc="F F F" -O 0.07721714 1.41738724 -0.14439016 -C 0.01675156 0.02484791 0.03233817 -C 0.11193823 -0.54392541 1.47000744 -O -1.16387804 -1.20093191 1.32441605 -C -1.41930178 -0.52040930 0.07750673 -H 1.00022214 1.67889479 -0.21697466 -H 0.66036495 -0.50380879 -0.68180327 -H 0.91806589 -1.25385265 1.68878628 -H 0.09146208 0.24427487 2.23499232 -H -2.16139683 0.28183960 0.18222436 -H -1.72867456 -1.20941157 -0.71550887 -15 -Properties=species:S:1:pos:R:3 A=7.14221 B=4.37083 C=3.30265 mu=0.1023 alpha=52.92 epsHOMO=-0.2632 epsLUMO=0.094 epsgap=0.3573 =465.3301 zpve=0.137341 U0=-196.406419 U=-196.40034 H=-196.399396 G=-196.435152 Cv=22.005 pbc="F F F" -C -0.05189850 1.55217435 -0.10318438 -C 0.01226648 0.04690675 0.03747520 -C 1.30115692 -0.70661553 -0.19725724 -C 0.70431939 -0.64893239 1.19003562 -C 1.39750324 0.09481280 2.31078011 -H 0.87404209 2.03543171 0.22162228 -H -0.21641357 1.83695278 -1.14852958 -H -0.87157646 1.97673684 0.48797046 -H -0.88508206 -0.45596941 -0.31580786 -H 1.27663658 -1.64149022 -0.74697967 -H 2.19424067 -0.11404121 -0.37569359 -H 0.21672461 -1.56377934 1.51902400 -H 1.91317356 0.99051175 1.95271747 -H 0.68738009 0.40901468 3.08446972 -H 2.14934633 -0.54151807 2.79111543 -13 -Properties=species:S:1:pos:R:3 A=7.36997 B=4.78662 C=3.53123 mu=1.3092 alpha=45.84 epsHOMO=-0.2518 epsLUMO=0.0822 epsgap=0.334 =418.298 zpve=0.113827 U0=-232.32907 U=-232.323327 H=-232.322383 G=-232.357426 Cv=20.702 pbc="F F F" -C -0.07992786 1.55914739 -0.03875994 -C 0.00042206 0.05111569 0.04346551 -C 1.31748624 -0.66230034 -0.20269965 -C 0.71624326 -0.61854924 1.18128126 -O 1.28134126 0.21474798 2.15341739 -H 0.80207677 2.01979988 0.41147972 -H -0.15235410 1.89225261 -1.07968732 -H -0.95836082 1.93564781 0.49643179 -H -0.88615174 -0.46939753 -0.30918496 -H 1.32384464 -1.60086657 -0.74666557 -H 2.18619315 -0.03316813 -0.37068758 -H 0.28509951 -1.54025035 1.57291630 -H 2.12931661 -0.16234387 2.40895930 -14 -Properties=species:S:1:pos:R:3 A=7.53724 B=4.74514 C=3.54049 mu=1.2483 alpha=49.79 epsHOMO=-0.2199 epsLUMO=0.0931 epsgap=0.313 =432.5081 zpve=0.125556 U0=-212.438188 U=-212.432286 H=-212.431342 G=-212.466725 Cv=20.993 pbc="F F F" -C -0.17034561 1.55765735 -0.10065470 -C 0.01075256 0.05574638 -0.05225833 -C 0.71794239 -0.67802425 -1.14204150 -N 1.29811574 -0.59777508 0.19386866 -C 2.50047290 0.20256923 0.36428818 -H 0.60876703 2.05717913 -0.68119301 -H -1.13238698 1.79957181 -0.56489127 -H -0.17350949 1.98946323 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a/docs/src/examples/basic_usage/usage.rst b/docs/src/examples/basic_usage/usage.rst deleted file mode 100644 index a65bd19678..0000000000 --- a/docs/src/examples/basic_usage/usage.rst +++ /dev/null @@ -1,189 +0,0 @@ - -.. DO NOT EDIT. -.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. -.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: -.. "examples/basic_usage/usage.sh" -.. LINE NUMBERS ARE GIVEN BELOW. - -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - :ref:`Go to the end ` - to download the full example code. - -.. rst-class:: sphx-glr-example-title - -.. _sphx_glr_examples_basic_usage_usage.sh: - -.. _label_basic_usage: - -Basic Usage -=========== - -``metatrain`` is designed for a direct usage from the command line (cli). The program -is registered via the abbreviation ``mtt`` to your command line. The general help of -``metatrain`` can be accessed using - -.. GENERATED FROM PYTHON SOURCE LINES 10-11 - -.. code-block:: Bash - - mtt --help - -.. GENERATED FROM PYTHON SOURCE LINES 12-52 - -We now demonstrate how to ``train`` and ``evaluate`` a model from the command line. -For this example we use the :ref:`architecture-soap-bpnn` architecture and a subset of -the `QM9 dataset `_. You can obtain the -dataset for this example here: :download:`qm9_reduced_100.xyz -<../../../../examples/basic_usage/qm9_reduced_100.xyz>`. - - -Training --------- - -To train models, ``metatrain`` uses a dynamic override strategy for your training -options. We allow a dynamical composition and override of the default architecture -with either your custom ``options.yaml`` and even command line override grammar. For -reference and reproducibility purposes ``metatrain`` always writes the fully expanded, -including the overwritten option to ``options_restart.yaml``. The restart options file -is written into a subfolder named with the current *date* and *time* inside the -``output`` directory of your current training run. - -The sub-command to start a model training is - -.. code-block:: bash - - mtt train - -To train a model you have to define your options. This includes the specific -architecture you want to use and the data including the training systems and target -values - -The default model and training hyperparameter for each model are listed in their -corresponding documentation page. We will use these minimal options to run an example -training using the default hyperparameters of an SOAP BPNN model - -.. literalinclude:: ../../../../examples/basic_usage/options.yaml - :language: yaml - -For each training run a new output directory in the format -``outputs/YYYY-MM-DD/HH-MM-SS`` based on the current *date* and *time* is created. We -use this output directory to store checkpoints, the restart ``options_restart.yaml`` -file as well as the log files. To start the training, create an ``options.yaml`` file -in the current directory and type - -.. GENERATED FROM PYTHON SOURCE LINES 55-57 - -.. code-block:: Bash - - - mtt train options.yaml - -.. GENERATED FROM PYTHON SOURCE LINES 58-62 - -The functions saves the final model `model.pt` to the current output folder for later -evaluation. An `extensions/` folder, which contains the compiled extensions for the -model, might also be saved depending on the architecture. All command line flags of -the train sub-command can be listed via - -.. GENERATED FROM PYTHON SOURCE LINES 66-67 - -.. code-block:: Bash - - mtt train --help - -.. GENERATED FROM PYTHON SOURCE LINES 68-96 - -After the training has finished, the ``mtt train`` command generates the -``model.ckpt`` (final checkpoint) and ``model.pt`` (exported model) files in the -current directory, as well as in the ``output/YYYY-MM-DD/HH-MM-SS`` directory. - -Evaluation ----------- - -The sub-command to evaluate an already trained model is - -.. code-block:: bash - - mtt eval - -Besides the trained ``model``, you will also have to provide a file containing the -system and possible target values for evaluation. The system section of this -``eval.yaml`` is exactly the same as for a dataset in the ``options.yaml`` file. - -.. literalinclude:: ../../../../examples/basic_usage/eval.yaml - :language: yaml - -Note that the ``targets`` section is optional. If the ``targets`` section is present, -the function will calculate and report RMSE values of the predictions with respect to -the real values as loaded from the ``targets`` section. You can run an evaluation by -typing - -We now evaluate the model on the training dataset, where the first arguments specifies -trained model and the second an option file containing the path of the dataset for -evaulation. The extensions of the model, if any, can be specified via the ``-e`` flag. - -.. GENERATED FROM PYTHON SOURCE LINES 99-100 - -.. code-block:: Bash - - mtt eval model.pt eval.yaml -e extensions/ - -.. GENERATED FROM PYTHON SOURCE LINES 101-105 - -The evaluation command predicts those properties the model was trained against; here -``"U0"``. The predictions together with the systems have been written in a file named -``output.xyz`` in the current directory. The written file starts with the following -lines - -.. GENERATED FROM PYTHON SOURCE LINES 108-109 - -.. code-block:: Bash - - head -n 20 output.xyz - -.. GENERATED FROM PYTHON SOURCE LINES 110-111 - -All command line flags of the eval sub-command can be listed via - -.. GENERATED FROM PYTHON SOURCE LINES 114-115 - -.. code-block:: Bash - - mtt eval --help - -.. GENERATED FROM PYTHON SOURCE LINES 115-123 - -An important parameter of ``mtt eval`` is the ``-b`` (or ``--batch-size``) option, -which allows you to specify the batch size for the evaluation. - -Molecular simulations ---------------------- - -The trained model can also be used to run molecular simulations. -You can find how in the :ref:`tutorials` section. - - -.. _sphx_glr_download_examples_basic_usage_usage.sh: - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-example - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Bash source code: usage.sh ` - - .. container:: sphx-glr-download sphx-glr-download-zip - - :download:`Download zipped: usage.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/basic_usage/usage.sh b/docs/src/examples/basic_usage/usage.sh deleted file mode 100644 index cc35dfc1e2..0000000000 --- a/docs/src/examples/basic_usage/usage.sh +++ /dev/null @@ -1,125 +0,0 @@ -# .. _label_basic_usage: -# -# Basic Usage -# =========== -# -# ``metatrain`` is designed for a direct usage from the command line (cli). The program -# is registered via the abbreviation ``mtt`` to your command line. The general help of -# ``metatrain`` can be accessed using -# - -mtt --help - -# %% -# -# We now demonstrate how to ``train`` and ``evaluate`` a model from the command line. -# For this example we use the :ref:`architecture-soap-bpnn` architecture and a subset of -# the `QM9 dataset `_. You can obtain the -# dataset for this example here: :download:`qm9_reduced_100.xyz `. -# -# -# Training -# -------- -# -# To train models, ``metatrain`` uses a dynamic override strategy for your training -# options. We allow a dynamical composition and override of the default architecture -# with either your custom ``options.yaml`` and even command line override grammar. For -# reference and reproducibility purposes ``metatrain`` always writes the fully expanded, -# including the overwritten option to ``options_restart.yaml``. The restart options file -# is written into a subfolder named with the current *date* and *time* inside the -# ``output`` directory of your current training run. -# -# The sub-command to start a model training is -# -# .. code-block:: bash -# -# mtt train -# -# To train a model you have to define your options. This includes the specific -# architecture you want to use and the data including the training systems and target -# values -# -# The default model and training hyperparameter for each model are listed in their -# corresponding documentation page. We will use these minimal options to run an example -# training using the default hyperparameters of an SOAP BPNN model -# -# .. literalinclude:: options-basic.yaml -# :language: yaml -# -# For each training run a new output directory in the format -# ``outputs/YYYY-MM-DD/HH-MM-SS`` based on the current *date* and *time* is created. We -# use this output directory to store checkpoints, the restart ``options_restart.yaml`` -# file as well as the log files. To start the training, create an ``options.yaml`` file -# in the current directory and type - - -mtt train options-basic.yaml - -# %% -# -# The functions saves the final model `model.pt` to the current output folder for later -# evaluation. An `extensions/` folder, which contains the compiled extensions for the -# model, might also be saved depending on the architecture. All command line flags of -# the train sub-command can be listed via -# - -mtt train --help - -# %% -# -# After the training has finished, the ``mtt train`` command generates the -# ``model.ckpt`` (final checkpoint) and ``model.pt`` (exported model) files in the -# current directory, as well as in the ``output/YYYY-MM-DD/HH-MM-SS`` directory. -# -# Evaluation -# ---------- -# -# The sub-command to evaluate an already trained model is -# -# .. code-block:: bash -# -# mtt eval -# -# Besides the trained ``model``, you will also have to provide a file containing the -# system and possible target values for evaluation. The system section of this -# ``eval.yaml`` is exactly the same as for a dataset in the ``options.yaml`` file. -# -# .. literalinclude:: eval-basic.yaml -# :language: yaml -# -# Note that the ``targets`` section is optional. If the ``targets`` section is present, -# the function will calculate and report RMSE values of the predictions with respect to -# the real values as loaded from the ``targets`` section. You can run an evaluation by -# typing -# -# We now evaluate the model on the training dataset, where the first arguments specifies -# trained model and the second an option file containing the path of the dataset for -# evaulation. The extensions of the model, if any, can be specified via the ``-e`` flag. - -mtt eval model.pt eval-basic.yaml -e extensions/ - -# %% -# -# The evaluation command predicts those properties the model was trained against; here -# ``"U0"``. The predictions together with the systems have been written in a file named -# ``output.xyz`` in the current directory. 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- -.. only:: html - - .. image:: /examples/llpr/images/thumb/sphx_glr_llpr_thumb.png - :alt: - - :ref:`sphx_glr_examples_llpr_llpr.py` - -.. raw:: html - -
Computing LLPR uncertainties
-
- - -.. thumbnail-parent-div-close - -.. raw:: html - - - - -.. toctree:: - :hidden: - - /examples/llpr/llpr - - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-gallery - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download all examples as source code: llpr.zip ` - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download all examples in Jupyter notebooks: llpr_jupyter.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/llpr/llpr.codeobj.json b/docs/src/examples/llpr/llpr.codeobj.json deleted file mode 100644 index bd7f237401..0000000000 --- a/docs/src/examples/llpr/llpr.codeobj.json +++ /dev/null @@ -1,728 +0,0 @@ -{ - "LogNorm": [ - { - "is_class": true, - "is_explicit": false, - "module": "matplotlib.colors", - "module_short": "matplotlib.colors", - "name": "LogNorm" - }, - { - "is_class": true, - "is_explicit": false, - "module": "matplotlib", - "module_short": "matplotlib", - "name": "LogNorm" - }, - { - "is_class": true, - "is_explicit": false, - "module": "matplotlib.colors", - "module_short": "matplotlib.colors", - "name": "Normalize" - }, - { - "is_class": true, - "is_explicit": false, - "module": "matplotlib", - "module_short": "matplotlib", - "name": "Normalize" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.colors", - "module_short": "matplotlib.colors", - "name": "LogNorm" - } - ], - "MetatomicCalculator": [ - { - "is_class": true, - "is_explicit": false, - "module": "metatomic.torch.ase_calculator", - "module_short": "metatomic.torch.ase_calculator", - "name": "MetatomicCalculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "metatomic.torch", - "module_short": "metatomic.torch", - "name": "MetatomicCalculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "metatomic", - "module_short": "metatomic", - "name": "MetatomicCalculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.calculators.calculator", - "module_short": "ase.calculators.calculator", - "name": "Calculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.calculators", - "module_short": "ase.calculators", - "name": "Calculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Calculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.calculators.calculator", - "module_short": "ase.calculators.calculator", - "name": "BaseCalculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.calculators", - "module_short": "ase.calculators", - "name": "BaseCalculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "BaseCalculator" - }, - { - "is_class": true, - "is_explicit": false, - "module": "ase.calculators.abc", - "module_short": "ase.calculators.abc", - 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}, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.axes", - "module_short": "matplotlib.axes", - "name": "Axes.set_yticks" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib", - "module_short": "matplotlib", - "name": "Axes.set_yticks" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.axes._base", - "module_short": "matplotlib.axes._base", - "name": "_AxesBase.set_yticks" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.axes", - "module_short": "matplotlib.axes", - "name": "_AxesBase.set_yticks" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib", - "module_short": "matplotlib", - "name": "_AxesBase.set_yticks" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.artist", - "module_short": "matplotlib.artist", - "name": "Artist.set_yticks" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib", - "module_short": "matplotlib", - 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"name": "MetatomicCalculator.run_model" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.calculators.calculator", - "module_short": "ase.calculators.calculator", - "name": "Calculator.run_model" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.calculators", - "module_short": "ase.calculators", - "name": "Calculator.run_model" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Calculator.run_model" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.calculators.calculator", - "module_short": "ase.calculators.calculator", - "name": "BaseCalculator.run_model" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.calculators", - "module_short": "ase.calculators", - "name": "BaseCalculator.run_model" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "BaseCalculator.run_model" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.calculators.abc", - "module_short": "ase.calculators.abc", - "name": "GetPropertiesMixin.run_model" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.calculators", - "module_short": "ase.calculators", - "name": "GetPropertiesMixin.run_model" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "GetPropertiesMixin.run_model" - }, - { - "is_class": false, - "is_explicit": false, - "module": "abc", - "module_short": "abc", - "name": "ABC.run_model" - } - ], - "cbar": [ - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.colorbar", - "module_short": "matplotlib.colorbar", - "name": "Colorbar" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib", - "module_short": "matplotlib", - "name": "Colorbar" - } - ], - "cbar.ax.set_yticklabels": [ - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.axes._axes", - "module_short": "matplotlib.axes", - "name": "Axes.set_yticklabels" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.axes", - "module_short": "matplotlib.axes", - "name": "Axes.set_yticklabels" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib", - "module_short": "matplotlib", - "name": "Axes.set_yticklabels" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.axes._base", - "module_short": "matplotlib.axes._base", - "name": "_AxesBase.set_yticklabels" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.axes", - "module_short": "matplotlib.axes", - "name": "_AxesBase.set_yticklabels" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib", - "module_short": "matplotlib", - "name": "_AxesBase.set_yticklabels" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.artist", - "module_short": "matplotlib.artist", - "name": "Artist.set_yticklabels" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib", - "module_short": "matplotlib", - "name": "Artist.set_yticklabels" - } - ], - "cbar.minorticks_off": [ - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.colorbar", - "module_short": "matplotlib.colorbar", - "name": "Colorbar.minorticks_off" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib", - "module_short": "matplotlib", - "name": "Colorbar.minorticks_off" - } - ], - "colormap": [ - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.colors", - "module_short": "matplotlib.colors", - "name": "ListedColormap" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib", - "module_short": "matplotlib", - "name": "ListedColormap" - } - ], - "colors": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray" - } - ], - "custom_ticks": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "list" - } - ], - "energies": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray" - } - ], - "local_uncertainty": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray" - } - ], - "norm": [ - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.colors", - "module_short": "matplotlib.colors", - "name": "LogNorm" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib", - "module_short": "matplotlib", - "name": "LogNorm" - } - ], - "plot_atoms": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.visualize.plot", - "module_short": "ase.visualize.plot", - "name": "plot_atoms" - } - ], - "plt.cm.ScalarMappable": [ - { - "is_class": true, - "is_explicit": false, - "module": "matplotlib.colorizer", - "module_short": "matplotlib.colorizer", - "name": "_ScalarMappable" - }, - { - "is_class": true, - "is_explicit": false, - "module": "matplotlib", - "module_short": "matplotlib", - "name": "_ScalarMappable" - }, - { - "is_class": true, - "is_explicit": false, - "module": "matplotlib.colorizer", - "module_short": "matplotlib.colorizer", - "name": "_ColorizerInterface" - }, - { - "is_class": true, - "is_explicit": false, - "module": "matplotlib", - "module_short": "matplotlib", - "name": "_ColorizerInterface" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot.cm", - "module_short": "matplotlib.pyplot.cm", - "name": "ScalarMappable" - } - ], - "plt.colorbar": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot", - "module_short": "matplotlib.pyplot", - "name": "colorbar" - } - ], - "plt.get_cmap": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot", - "module_short": "matplotlib.pyplot", - "name": "get_cmap" - } - ], - "plt.show": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot", - "module_short": "matplotlib.pyplot", - "name": "show" - } - ], - "predictions": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "dict" - } - ], - "structure": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms" - } - ], - "structures": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "list" - } - ], - "uncertainties": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray" - } - ] -} diff --git a/docs/src/examples/llpr/llpr.ipynb b/docs/src/examples/llpr/llpr.ipynb deleted file mode 100644 index 5c74493b0f..0000000000 --- a/docs/src/examples/llpr/llpr.ipynb +++ /dev/null @@ -1,68 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# Computing LLPR uncertainties\n\nThis tutorial demonstrates how to train a model with uncertainties using metatrain.\nIt involves the computation of the uncertainties on ethanol molecules, using the\nlast-layer prediction rigidity ([LLPR](LLPR_)) approximation. Both total and local\n([LPR](LPR_)) uncertainties are computed\n\n\nThe baseline model was trained using the following training options, where the training\nset consists of 100 structures from the QM9 dataset.\n\n.. literalinclude:: options.yaml\n :language: yaml\n\nOnce a model is trained, you can add LLPR uncertainties to it by launching a training\nrun with the \"llpr\" architecture, on the same data. In this case, the training options\nto add LLPR uncertainties are as follows:\n\n.. literalinclude:: options-llpr.yaml\n :language: yaml\n\nAdding LLPR uncertainties is very cheap compared to training a model, as it only\ninvolves one pass through the training data (equivalent to one epoch of training).\n\nYou can repeat the same training yourself with\n\n.. literalinclude:: train.sh\n :language: bash\n\nA detailed step-by-step introduction on how to train a model is provided in\nthe `label_basic_usage` tutorial.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As an example, we will compute the energies and uncertainties of the LLPR model on a\nfew ethanol structures.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import ase.io\nimport matplotlib.pyplot as plt\nfrom ase.visualize.plot import plot_atoms\nfrom matplotlib.colors import LogNorm\nfrom metatomic.torch import ModelOutput\nfrom metatomic.torch.ase_calculator import MetatomicCalculator\n\n\n# load 5 ethanol structures\nstructures = ase.io.read(\"ethanol_reduced_100.xyz\", \":5\")\n\n# load the model as an ASE calculator\ncalc = MetatomicCalculator(\n \"model-llpr.pt\", extensions_directory=\"extensions/\", device=\"cpu\"\n)\n\n# the uncertainties are available throguh the ``run_model`` method of the calculator\npredictions = calc.run_model(\n structures,\n {\n \"energy\": ModelOutput(per_atom=False),\n \"energy_uncertainty\": ModelOutput(per_atom=False),\n },\n)\n\n# print the energies and uncertainties\n\nenergies = predictions[\"energy\"].block().values.squeeze().numpy()\nuncertainties = predictions[\"energy_uncertainty\"].block().values.squeeze().numpy()\n\nprint(energies)\nprint(uncertainties)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also obtain per-atom uncertainties (local prediction rigidity, LPR). As an\nexample, we will compute the uncertainties on an ethanol structure.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "structure = structures[0]\npredictions = calc.run_model(\n structure,\n {\n # here, we use per_atom=True to request per-atom uncertainties\n \"energy_uncertainty\": ModelOutput(per_atom=True),\n },\n)\nlocal_uncertainty = predictions[\"energy_uncertainty\"].block().values.squeeze().numpy()\nlocal_uncertainty = local_uncertainty * 1000.0 # convert from eV to meV\n\nnorm = LogNorm(vmin=min(local_uncertainty), vmax=max(local_uncertainty))\ncolormap = plt.get_cmap(\"viridis\")\ncolors = colormap(norm(local_uncertainty))\nax = plot_atoms(structure, colors=colors, rotation=\"180x,0y,0z\")\ncustom_ticks = [0.7, 0.8, 0.9, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5]\ncbar = plt.colorbar(\n plt.cm.ScalarMappable(norm=norm, cmap=colormap),\n ax=ax,\n label=\"Local energy uncertainty (meV)\",\n ticks=custom_ticks,\n)\ncbar.ax.set_yticklabels([f\"{tick}\" for tick in custom_ticks])\ncbar.minorticks_off()\nax.set_xticks([])\nax.set_yticks([])\nplt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/src/examples/llpr/llpr.py b/docs/src/examples/llpr/llpr.py deleted file mode 100644 index 8979340c17..0000000000 --- a/docs/src/examples/llpr/llpr.py +++ /dev/null @@ -1,121 +0,0 @@ -""".. _llprexample: - -Computing LLPR uncertainties -============================ - -This tutorial demonstrates how to train a model with uncertainties using metatrain. -It involves the computation of the uncertainties on ethanol molecules, using the -last-layer prediction rigidity (`LLPR `_) approximation. Both total and local -(`LPR `_) uncertainties are computed - -.. _LLPR: https://arxiv.org/html/2403.02251v1 -.. _LPR: https://pubs.acs.org/doi/10.1021/acs.jctc.3c00704 - -The baseline model was trained using the following training options, where the training -set consists of 100 structures from the QM9 dataset. - -.. literalinclude:: options-no-llpr.yaml - :language: yaml - -Once a model is trained, you can add LLPR uncertainties to it by launching a training -run with the "llpr" architecture, on the same data. In this case, the training options -to add LLPR uncertainties are as follows: - -.. literalinclude:: options-llpr.yaml - :language: yaml - -Adding LLPR uncertainties is very cheap compared to training a model, as it only -involves one pass through the training data (equivalent to one epoch of training). - -You can repeat the same training yourself with - -""" -# %% -# - -import subprocess - -import ase.io -import matplotlib.pyplot as plt -from ase.visualize.plot import plot_atoms -from matplotlib.colors import LogNorm -from metatomic.torch import ModelOutput -from metatomic.torch.ase_calculator import MetatomicCalculator - - -# %% -# -# We first train the baseline model without uncertainties and then the LLPR model. - - -subprocess.run(["mtt", "train", "options-no-llpr.yaml", "-o", "model.pt"]) -subprocess.run(["mtt", "train", "options-llpr.yaml", "-o", "model-llpr.pt"]) - -# %% -# -# A detailed step-by-step introduction on how to train a model is provided in -# the :ref:`label_basic_usage` tutorial. -# -# As an example, we will compute the energies and uncertainties of the LLPR model on a -# few ethanol structures. -# -# Next we load 5 ethanol structures -structures = ase.io.read("ethanol_reduced_100.xyz", ":5") - -# %% -# -# and load the model as an ASE calculator -calc = MetatomicCalculator( - "model-llpr.pt", extensions_directory="extensions/", device="cpu" -) - -# the uncertainties are available throguh the ``run_model`` method of the calculator -predictions = calc.run_model( - structures, - { - "energy": ModelOutput(per_atom=False), - "energy_uncertainty": ModelOutput(per_atom=False), - }, -) - -# print the energies and uncertainties - -energies = predictions["energy"].block().values.squeeze().numpy() -uncertainties = predictions["energy_uncertainty"].block().values.squeeze().numpy() - -print(energies) -print(uncertainties) - - -# %% -# -# We can also obtain per-atom uncertainties (local prediction rigidity, LPR). As an -# example, we will compute the uncertainties on an ethanol structure. - -structure = structures[0] -predictions = calc.run_model( - structure, - { - # here, we use per_atom=True to request per-atom uncertainties - "energy_uncertainty": ModelOutput(per_atom=True), - }, -) -local_uncertainty = predictions["energy_uncertainty"].block().values.squeeze().numpy() -local_uncertainty = local_uncertainty * 1000.0 # convert from eV to meV - -norm = LogNorm(vmin=min(local_uncertainty), vmax=max(local_uncertainty)) -colormap = plt.get_cmap("viridis") -colors = colormap(norm(local_uncertainty)) -ax = plot_atoms(structure, colors=colors, rotation="180x,0y,0z") -custom_ticks = [0.7, 0.8, 0.9, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5] -cbar = plt.colorbar( - plt.cm.ScalarMappable(norm=norm, cmap=colormap), - ax=ax, - label="Local energy uncertainty (meV)", - ticks=custom_ticks, -) -cbar.ax.set_yticklabels([f"{tick}" for tick in custom_ticks]) -cbar.minorticks_off() -ax.set_xticks([]) -ax.set_yticks([]) -plt.show() diff --git a/docs/src/examples/llpr/llpr.py.md5 b/docs/src/examples/llpr/llpr.py.md5 deleted file mode 100644 index d9979d99e5..0000000000 --- a/docs/src/examples/llpr/llpr.py.md5 +++ /dev/null @@ -1 +0,0 @@ -76970f9c741099e3b7ea8946bb48d8e0 \ No newline at end of file diff --git a/docs/src/examples/llpr/llpr.rst b/docs/src/examples/llpr/llpr.rst deleted file mode 100644 index aed660fd85..0000000000 --- a/docs/src/examples/llpr/llpr.rst +++ /dev/null @@ -1,183 +0,0 @@ - -.. DO NOT EDIT. -.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. -.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: -.. "examples/llpr/llpr.py" -.. LINE NUMBERS ARE GIVEN BELOW. - -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - :ref:`Go to the end ` - to download the full example code. - -.. rst-class:: sphx-glr-example-title - -.. _sphx_glr_examples_llpr_llpr.py: - -.. _llprexample: - -Computing LLPR uncertainties -============================ - -This tutorial demonstrates how to train a model with uncertainties using metatrain. -It involves the computation of the uncertainties on ethanol molecules, using the -last-layer prediction rigidity (`LLPR `_) approximation. Both total and local -(`LPR `_) uncertainties are computed - -.. _LLPR: https://arxiv.org/html/2403.02251v1 -.. _LPR: https://pubs.acs.org/doi/10.1021/acs.jctc.3c00704 - -The baseline model was trained using the following training options, where the training -set consists of 100 structures from the QM9 dataset. - -.. literalinclude:: options.yaml - :language: yaml - -Once a model is trained, you can add LLPR uncertainties to it by launching a training -run with the "llpr" architecture, on the same data. In this case, the training options -to add LLPR uncertainties are as follows: - -.. literalinclude:: options-llpr.yaml - :language: yaml - -Adding LLPR uncertainties is very cheap compared to training a model, as it only -involves one pass through the training data (equivalent to one epoch of training). - -You can repeat the same training yourself with - -.. literalinclude:: train.sh - :language: bash - -A detailed step-by-step introduction on how to train a model is provided in -the :ref:`label_basic_usage` tutorial. - -.. GENERATED FROM PYTHON SOURCE LINES 40-42 - -As an example, we will compute the energies and uncertainties of the LLPR model on a -few ethanol structures. - -.. GENERATED FROM PYTHON SOURCE LINES 43-78 - -.. code-block:: Python - - - import ase.io - import matplotlib.pyplot as plt - from ase.visualize.plot import plot_atoms - from matplotlib.colors import LogNorm - from metatomic.torch import ModelOutput - from metatomic.torch.ase_calculator import MetatomicCalculator - - - # load 5 ethanol structures - structures = ase.io.read("ethanol_reduced_100.xyz", ":5") - - # load the model as an ASE calculator - calc = MetatomicCalculator( - "model-llpr.pt", extensions_directory="extensions/", device="cpu" - ) - - # the uncertainties are available throguh the ``run_model`` method of the calculator - predictions = calc.run_model( - structures, - { - "energy": ModelOutput(per_atom=False), - "energy_uncertainty": ModelOutput(per_atom=False), - }, - ) - - # print the energies and uncertainties - - energies = predictions["energy"].block().values.squeeze().numpy() - uncertainties = predictions["energy_uncertainty"].block().values.squeeze().numpy() - - print(energies) - print(uncertainties) - - - - - - -.. rst-class:: sphx-glr-script-out - - .. code-block:: none - - [-154.97468067 -154.97642503 -154.97632212 -154.97600337 -154.97451059] - [0.0097369 0.01553008 0.02451612 0.01447105 0.01356071] - - - - -.. GENERATED FROM PYTHON SOURCE LINES 79-81 - -We can also obtain per-atom uncertainties (local prediction rigidity, LPR). As an -example, we will compute the uncertainties on an ethanol structure. - -.. GENERATED FROM PYTHON SOURCE LINES 82-110 - -.. code-block:: Python - - - structure = structures[0] - predictions = calc.run_model( - structure, - { - # here, we use per_atom=True to request per-atom uncertainties - "energy_uncertainty": ModelOutput(per_atom=True), - }, - ) - local_uncertainty = predictions["energy_uncertainty"].block().values.squeeze().numpy() - local_uncertainty = local_uncertainty * 1000.0 # convert from eV to meV - - norm = LogNorm(vmin=min(local_uncertainty), vmax=max(local_uncertainty)) - colormap = plt.get_cmap("viridis") - colors = colormap(norm(local_uncertainty)) - ax = plot_atoms(structure, colors=colors, rotation="180x,0y,0z") - custom_ticks = [0.7, 0.8, 0.9, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5] - cbar = plt.colorbar( - plt.cm.ScalarMappable(norm=norm, cmap=colormap), - ax=ax, - label="Local energy uncertainty (meV)", - ticks=custom_ticks, - ) - cbar.ax.set_yticklabels([f"{tick}" for tick in custom_ticks]) - cbar.minorticks_off() - ax.set_xticks([]) - ax.set_yticks([]) - plt.show() - - - - - - - - -.. _sphx_glr_download_examples_llpr_llpr.py: - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-example - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: llpr.ipynb ` - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: llpr.py ` - - .. container:: sphx-glr-download sphx-glr-download-zip - - :download:`Download zipped: llpr.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/llpr/llpr.zip b/docs/src/examples/llpr/llpr.zip deleted file mode 100644 index 46bdfa75339844db082667a71e1500958b666a9a..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 3789 zcmb_fU5_NS60ZPG;#cG$Z~Vaggq-WD>W|$X!b4_M)vogO_5C>a_}0~vN3Xv5fc_qR^^fb;L(6;hoBTK_iEiY}MjHtuQBB#dG?oh6M 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A=127.83497 B=24.85872 C=23.97872 mu=1.5258 alpha=16.97 epsHOMO=-0.2653 epsLUMO=0.0784 epsgap=0.3437 =83.794 zpve=0.051208 U0=-115.679136 U=-115.675816 H=-115.674872 G=-115.701876 Cv=8.751 pbc="F F F" -C -0.00828817 1.39046978 -0.00560069 -O -0.00797038 -0.02504537 0.02030606 -H 1.00658338 1.81556366 0.00348335 -H -0.54657475 1.79916975 -0.87390126 -H -0.52288871 1.72555240 0.89907326 -H 0.44142019 -0.33354425 -0.77152059 -7 -Properties=species:S:1:pos:R:3 A=160.28041 B=8.59323 C=8.59321 mu=0.7156 alpha=28.78 epsHOMO=-0.2609 epsLUMO=0.0613 epsgap=0.3222 =177.1963 zpve=0.05541 U0=-116.609549 U=-116.60555 H=-116.604606 G=-116.633775 Cv=12.482 pbc="F F F" -C -0.01782102 1.46435788 0.01009397 -C 0.00208816 0.00950777 0.00201200 -C 0.01834085 -1.19180518 -0.00450508 -H 0.99782210 1.87425349 0.00260606 -H -0.54220434 1.85801178 -0.86721192 -H -0.52533306 1.84834356 0.90148138 -H 0.03231658 -2.25314823 -0.01025999 -6 -Properties=species:S:1:pos:R:3 A=159.03567 B=9.22327 C=9.22324 mu=3.8266 alpha=24.45 epsHOMO=-0.3264 epsLUMO=0.0376 epsgap=0.364 =160.7223 zpve=0.045286 U0=-132.71815 U=-132.714563 H=-132.713619 G=-132.742149 Cv=10.287 pbc="F F F" -C -0.01788629 1.46712787 0.01011311 -C 0.00173819 0.01035260 0.00207588 -N 0.01765235 -1.14452926 -0.00420445 -H 1.00202941 1.86089927 0.00245401 -H -0.54398848 1.84479901 -0.87075460 -H -0.52707783 1.83518166 0.90486329 -7 -Properties=species:S:1:pos:R:3 A=57.22434 B=10.11122 C=9.07368 mu=2.5682 alpha=25.11 epsHOMO=-0.254 epsLUMO=-0.0198 epsgap=0.2342 =166.9728 zpve=0.055355 U0=-153.787612 U=-153.783728 H=-153.782784 G=-153.812518 Cv=11.219 pbc="F F F" -C -0.00294482 1.50991366 0.00867278 -C 0.02608284 0.00327563 -0.03745912 -O 0.94228801 -0.65507035 -0.45682576 -H 0.92278802 1.92634242 -0.39146557 -H -0.86201540 1.87852481 -0.56479538 -H -0.15050638 1.84393383 1.04289100 -H -0.89443009 -0.48643408 0.35774865 -6 -Properties=species:S:1:pos:R:3 A=73.8472 B=11.34793 C=9.83639 mu=3.7286 alpha=21.57 epsHOMO=-0.2543 epsLUMO=0.0302 epsgap=0.2845 =145.3078 zpve=0.045279 U0=-169.860788 U=-169.856903 H=-169.855958 G=-169.885594 Cv=10.89 pbc="F F F" -N -0.02589986 1.34614561 0.00889391 -C 0.04646728 -0.01174341 0.00120425 -O 1.07183520 -0.65258782 -0.01113313 -H 0.82535497 1.88504948 0.00373769 -H -0.90837680 1.82679638 0.01891992 -H -0.96144100 -0.47500376 0.00807391 -11 -Properties=species:S:1:pos:R:3 A=29.45018 B=8.37701 C=7.42076 mu=0.0597 alpha=34.75 epsHOMO=-0.323 epsLUMO=0.0949 epsgap=0.4179 =227.1361 zpve=0.103182 U0=-119.052475 U=-119.047927 H=-119.046983 G=-119.078157 Cv=14.84 pbc="F F F" -C -0.03113825 1.54081582 0.03192126 -C 0.01215347 0.01092235 -0.01603259 -C 0.72169129 -0.52583353 -1.26230570 -H 0.97955987 1.96459116 0.03098367 -H -0.55840223 1.94831192 -0.83816075 -H -0.54252252 1.90153531 0.93005671 -H 0.51522791 -0.36840234 0.88231134 -H -1.01070641 -0.38456999 0.02051783 -H 1.75851210 -0.17376585 -1.30871516 -H 0.74087192 -1.62024959 -1.27516511 -H 0.22023351 -0.19051179 -2.17729020 -9 -Properties=species:S:1:pos:R:3 A=35.09545 B=9.3686 C=8.1497 mu=1.4131 alpha=27.87 epsHOMO=-0.2619 epsLUMO=0.0798 epsgap=0.3417 =193.1659 zpve=0.079754 U0=-154.972731 U=-154.968412 H=-154.967467 G=-154.998148 Cv=13.546 pbc="F F F" -C -0.00860504 1.50203829 -0.00681217 -C 0.01099310 -0.01764877 -0.01377035 -O 0.68088841 -0.44041803 -1.19313210 -H 1.01153334 1.89662030 -0.01920155 -H -0.53159862 1.88076109 -0.88974659 -H -0.51674572 1.87611771 0.88710739 -H 0.52377121 -0.38912295 0.88824082 -H -1.02027544 -0.40507261 0.01690670 -H 0.69529573 -1.40179568 -1.20148495 -9 -Properties=species:S:1:pos:R:3 A=39.37691 B=10.04033 C=8.90353 mu=1.1502 alpha=28.13 epsHOMO=-0.2525 epsLUMO=0.091 epsgap=0.3435 =187.1015 zpve=0.079534 U0=-154.960361 U=-154.956045 H=-154.9551 G=-154.985747 Cv=12.934 pbc="F F F" -C -0.01482147 1.39241234 0.00567104 -O -0.00471537 -0.01360660 0.01459658 -C 0.63794893 -0.55329676 -1.11358232 -H 1.00528358 1.81015807 0.00465631 -H -0.54689560 1.79343530 -0.87251051 -H -0.53002895 1.72291969 0.91101696 -H 0.13993792 -0.25599333 -2.05098375 -H 1.69265275 -0.23868382 -1.17477743 -H 0.59959435 -1.64180161 -1.02407616 -9 -Properties=species:S:1:pos:R:3 A=20.15852 B=20.15302 C=12.5891 mu=0.0005 alpha=30.82 epsHOMO=-0.2888 epsLUMO=0.1042 epsgap=0.393 =155.8145 zpve=0.081231 U0=-117.824798 U=-117.821426 H=-117.820482 G=-117.849087 Cv=11.041 pbc="F F F" -C -0.01193280 1.51433198 0.01031700 -C 1.30299118 0.77886562 -0.00617842 -C 0.00867174 0.00767042 0.00201032 -H -0.30541464 2.01702117 0.92533239 -H -0.32275498 2.02680156 -0.89347839 -H 1.88493396 0.79145020 -0.92119393 -H 1.90226631 0.78165391 0.89766373 -H -0.27088043 -0.51289125 0.91137474 -H -0.28821983 -0.50310409 -0.90740824 -7 -Properties=species:S:1:pos:R:3 A=25.77385 B=22.12109 C=14.18066 mu=1.7675 alpha=24.04 epsHOMO=-0.2682 epsLUMO=0.1042 epsgap=0.3724 =129.891 zpve=0.057289 U0=-153.742562 U=-153.73941 H=-153.738466 G=-153.766642 Cv=9.176 pbc="F F F" -C 0.01537720 1.41764380 0.00956287 -C 1.26478161 0.64923984 -0.00655044 -O -0.00024018 -0.00771006 0.00204055 -H -0.31763326 1.88586598 0.93475751 -H -0.33527583 1.89576673 -0.90397578 -H 1.83246682 0.56256848 -0.93193290 -H 1.85008258 0.55267938 0.90680147 -10 -Properties=species:S:1:pos:R:3 A=10.12193 B=8.49011 C=4.89615 mu=2.7362 alpha=35.53 epsHOMO=-0.2431 epsLUMO=-0.0087 epsgap=0.2344 =292.4367 zpve=0.083382 U0=-193.08834 U=-193.082969 H=-193.082024 G=-193.116476 Cv=16.893 pbc="F F F" -C -0.00310189 1.48050328 -0.17259867 -C -0.04527202 -0.02887059 0.00139493 -C 1.29705800 -0.72194415 0.16889042 -O -1.08643172 -0.64273343 0.00630393 -H 0.59403529 1.74668579 -1.05225043 -H -1.01540407 1.87034023 -0.28382265 -H 0.48099600 1.95070605 0.69099376 -H 1.82512144 -0.32470731 1.04328658 -H 1.14892507 -1.79597689 0.28554393 -H 1.93682635 -0.52949692 -0.69995403 -9 -Properties=species:S:1:pos:R:3 A=10.78886 B=9.27509 C=5.14977 mu=3.6367 alpha=31.83 epsHOMO=-0.2436 epsLUMO=0.0347 epsgap=0.2783 =267.6148 zpve=0.07319 U0=-209.159302 U=-209.15402 H=-209.153076 G=-209.187468 Cv=16.561 pbc="F F F" -C 0.00679889 1.49969736 -0.02567030 -C -0.02740385 -0.01752051 -0.14894811 -N 1.17826667 -0.63324298 0.05691721 -O -1.03721965 -0.63980649 -0.40727572 -H 0.91390280 1.92923755 -0.46100067 -H -0.87080896 1.91124186 -0.52345176 -H -0.02826124 1.78517271 1.03149125 -H 1.98405139 -0.13175861 0.38674906 -H 1.19049421 -1.63919563 0.10006209 -8 -Properties=species:S:1:pos:R:3 A=11.1475 B=10.36388 C=5.42091 mu=3.4869 alpha=28.07 epsHOMO=-0.2495 epsLUMO=0.0556 epsgap=0.3051 =244.2308 zpve=0.063824 U0=-225.221461 U=-225.217075 H=-225.216131 G=-225.247724 Cv=15.292 pbc="F F F" -N 0.03605270 1.36077873 -0.12416403 -C -0.02591138 -0.02076560 0.00200645 -N 1.21968479 -0.62334220 0.11963222 -O -1.06822866 -0.64174597 0.00865573 -H 0.80749439 1.83455148 0.32144915 -H -0.85825775 1.80373500 0.02243770 -H 1.99927148 -0.17303054 -0.33602496 -H 1.18204327 -1.62147901 -0.02109234 -14 -Properties=species:S:1:pos:R:3 A=7.75166 B=7.74847 C=4.48668 mu=0.0897 alpha=45.46 epsHOMO=-0.3167 epsLUMO=0.0843 epsgap=0.401 =355.0621 zpve=0.131146 U0=-158.342346 U=-158.336603 H=-158.335658 G=-158.370016 Cv=20.273 pbc="F F F" -C -0.03215888 1.54021598 0.01074456 -C 0.03381741 0.00745852 0.00180716 -C 0.71375572 -0.50856404 -1.27296949 -C 0.73848999 -0.52220886 1.25750907 -H 0.97522736 1.97414341 0.00528362 -H -0.56196769 1.92232326 -0.86835418 -H -0.54817239 1.91268414 0.90208039 -H -0.99752525 -0.37310578 0.00977297 -H 1.75070021 -0.15586784 -1.33269631 -H 0.73446691 -1.60328288 -1.29939696 -H 0.19367011 -0.16085589 -2.17195508 -H 0.23611990 -0.18382017 2.17005260 -H 0.75964083 -1.61713153 1.27226101 -H 1.77635866 -0.16985318 1.30053019 -12 -Properties=species:S:1:pos:R:3 A=8.67553 B=8.00568 C=4.75542 mu=1.4259 alpha=38.58 epsHOMO=-0.2612 epsLUMO=0.074 epsgap=0.3351 =318.3721 zpve=0.107673 U0=-194.267232 U=-194.261748 H=-194.260804 G=-194.294663 Cv=19.052 pbc="F F F" -C -0.03315871 1.54782566 -0.00438815 -C -0.01108534 0.01859095 0.01676117 -C 0.70938384 -0.53596711 1.23978444 -O -1.33219841 -0.51712554 0.05236057 -H 0.98215692 1.95715900 -0.03182225 -H -0.56388100 1.92087623 -0.88845865 -H -0.54082548 1.93340945 0.88575489 -H 0.51042122 -0.33767241 -0.88819176 -H 0.22240492 -0.18785860 2.15650245 -H 0.68242024 -1.62887638 1.23305141 -H 1.75434301 -0.21246950 1.25524188 -H -1.81818681 -0.15577435 -0.69553962 -6 -Properties=species:S:1:pos:R:3 A=0 B=4.4259726 C=4.4259726 mu=0 alpha=38.52 epsHOMO=-0.2599 epsLUMO=-0.0214 epsgap=0.2386 =278.6264 zpve=0.037354 U0=-153.459846 U=-153.455442 H=-153.454498 G=-153.482621 Cv=15.312 pbc="F F F" -C 0.68098021 0.00000000 0.00000000 -C -0.68098021 0.00000000 0.00000000 -C -1.88766603 0.00000000 0.00000000 -C 1.88766603 0.00000000 0.00000000 -H -2.94960000 0.00000000 0.00000000 -H 2.94960000 0.00000000 0.00000000 -5 -Properties=species:S:1:pos:R:3 A=0 B=4.5793222 C=4.5793222 mu=3.792 alpha=32.66 epsHOMO=-0.3102 epsLUMO=-0.0543 epsgap=0.2559 =260.1896 zpve=0.027259 U0=-169.557758 U=-169.553764 H=-169.55282 G=-169.581024 Cv=12.93 pbc="F F F" -C 0.01510390 0.00000000 1.00000000 -C 1.38233149 0.00000000 1.00000000 -C -1.18862525 0.00000000 1.00000000 -H -2.25177201 0.00000000 1.00000000 -N 2.54296187 0.00000000 1.00000000 -4 -Properties=species:S:1:pos:R:3 A=0 B=4.73269 C=4.73269 mu=0.0023 alpha=27.7 epsHOMO=-0.3696 epsLUMO=-0.0926 epsgap=0.277 =242.9308 zpve=0.015951 U0=-185.648533 U=-185.644825 H=-185.64388 G=-185.667652 Cv=10.398 pbc="F F F" -N 0.01745734 -1.16134217 -0.00415342 -C 0.00253212 -0.00344278 0.00179939 -C -0.01611419 1.37220987 0.00934873 -N -0.03264302 2.53008276 0.01609090 -6 -Properties=species:S:1:pos:R:3 A=68.5171 B=4.8345 C=4.51586 mu=2.7824 alpha=31.14 epsHOMO=-0.2777 epsLUMO=-0.0735 epsgap=0.2042 =268.3921 zpve=0.037208 U0=-190.624631 U=-190.620363 H=-190.619419 G=-190.650543 Cv=13.049 pbc="F F F" -O -0.06776120 -0.04233838 0.00282489 -C -0.00562945 1.16382879 0.00743862 -C 1.22985272 1.91339997 0.00028781 -C 2.23625322 2.57430970 -0.00529277 -H -0.91670701 1.79467546 0.01781352 -H 3.13429662 3.14370267 -0.01037656 -5 -Properties=species:S:1:pos:R:3 A=67.88408 B=5.00823 C=4.66413 mu=2.3112 alpha=26.25 epsHOMO=-0.3166 epsLUMO=-0.11 epsgap=0.2066 =251.0007 zpve=0.02654 U0=-206.721858 U=-206.717875 H=-206.716931 G=-206.747625 Cv=11.329 pbc="F F F" -O -0.04055978 -0.03799144 0.00266513 -C -0.01768870 1.16193251 0.00746008 -C 1.23315534 1.93381336 0.00043371 -N 2.19857378 2.57055097 -0.00483320 -H -0.92627534 1.78959196 0.01764859 -6 -Properties=species:S:1:pos:R:3 A=56.25048 B=4.77441 C=4.40088 mu=0.002 alpha=26.12 epsHOMO=-0.2668 epsLUMO=-0.1113 epsgap=0.1555 =266.8164 zpve=0.036943 U0=-227.798785 U=-227.79457 H=-227.793626 G=-227.825074 Cv=12.147 pbc="F F F" -O 0.00337122 -0.03146586 0.00157206 -C -0.01713174 1.17081409 0.00676444 -C 1.26555783 1.99719410 -0.00007703 -O 1.24507069 3.19946589 0.00681275 -H -0.94690483 1.77678142 0.01852668 -H 2.19534410 1.39121374 -0.01006739 -10 -Properties=species:S:1:pos:R:3 A=80.28766 B=3.3688 C=3.3688 mu=0 alpha=42.32 epsHOMO=-0.2412 epsLUMO=0.0684 epsgap=0.3096 =400.2236 zpve=0.083896 U0=-155.908941 U=-155.90318 H=-155.902236 G=-155.937641 Cv=17.447 pbc="F F F" -C -0.01787959 1.46676893 0.01011180 -C 0.00186644 0.01072913 0.00211109 -C 0.01819704 -1.19272861 -0.00450723 -C 0.03795170 -2.64876793 -0.01251193 -H 0.86460312 1.87589979 0.51479719 -H -0.03217624 1.87210581 -1.00791002 -H -0.90233115 1.85185524 0.53003953 -H -0.83477011 -3.06339007 0.50451436 -H 0.03276422 -3.04315564 -1.03491223 -H 0.93213486 -3.03931642 0.48626606 -10 -Properties=species:S:1:pos:R:3 A=27.39459 B=4.53005 C=4.08342 mu=0.7067 alpha=40.09 epsHOMO=-0.2592 epsLUMO=0.0566 epsgap=0.3157 =333.9589 zpve=0.084338 U0=-155.897345 U=-155.892291 H=-155.891347 G=-155.924226 Cv=17.13 pbc="F F F" -C -0.03095840 1.54775025 0.03167945 -C 0.01485375 0.00962471 -0.02082154 -C 0.69099073 -0.49954575 -1.20857624 -C 1.25222275 -0.89514467 -2.19527879 -H 0.97899300 1.96621609 0.03373834 -H -0.56082354 1.95002902 -0.83580745 -H -0.54576708 1.88225519 0.93709062 -H 0.51920135 -0.37425398 0.87496224 -H -1.00644802 -0.39030838 0.01340632 -H 1.74594541 -1.25604187 -3.06299415 -9 -Properties=species:S:1:pos:R:3 A=28.0016 B=4.67752 C=4.21765 mu=3.9233 alpha=35.38 epsHOMO=-0.3213 epsLUMO=0.034 epsgap=0.3553 =314.5335 zpve=0.07419 U0=-172.006141 U=-172.001467 H=-172.000523 G=-172.032826 Cv=14.988 pbc="F F F" -C -0.02522005 1.54773054 0.02133370 -C 0.01183939 0.00999278 -0.01556702 -C 0.68915114 -0.50836846 -1.20331488 -N 1.22565733 -0.90360846 -2.14737482 -H 0.98571620 1.96212990 0.02370153 -H -0.55522192 1.94599662 -0.84711771 -H -0.53962556 1.88294354 0.92536604 -H 0.52481910 -0.38226048 0.86933355 -H -1.00453542 -0.39832109 0.00530071 -8 -Properties=species:S:1:pos:R:3 A=32.19893 B=4.76299 C=4.30696 mu=4.4361 alpha=31.81 epsHOMO=-0.2683 epsLUMO=0.0173 epsgap=0.2855 =295.6635 zpve=0.063305 U0=-188.042067 U=-188.037478 H=-188.036534 G=-188.06863 Cv=14.488 pbc="F F F" -N -0.03608099 1.48762533 0.03678072 -C -0.01039471 0.02517605 -0.01506608 -C -1.37180956 -0.52108920 0.02739272 -N -2.44026695 -0.95956444 0.03370519 -H 0.91729752 1.83547547 0.05202682 -H -0.47162178 1.85677205 -0.80275569 -H 0.48946161 -0.40880401 -0.89715168 -H 0.52827150 -0.34533338 0.86469547 -8 -Properties=species:S:1:pos:R:3 A=35.11615 B=4.66117 C=4.22354 mu=1.7211 alpha=33.1 epsHOMO=-0.2595 epsLUMO=0.0277 epsgap=0.2872 =300.0993 zpve=0.060632 U0=-191.810916 U=-191.806025 H=-191.805081 G=-191.837634 Cv=15.855 pbc="F F F" -O -0.02013197 1.45738137 0.01010195 -C 0.00168478 0.03579980 0.00224736 -C -1.36366691 -0.47481101 0.01255056 -C -2.47902835 -0.91970202 0.02082451 -H 0.89446911 1.75736553 0.00327363 -H 0.51979028 -0.35175413 -0.88980302 -H 0.53670458 -0.36127661 0.88000894 -H -3.46773502 -1.30737009 0.02818120 -7 -Properties=species:S:1:pos:R:3 A=35.93172 B=4.83443 C=4.37827 mu=4.6788 alpha=28.56 epsHOMO=-0.3018 epsLUMO=0.0022 epsgap=0.3039 =280.6659 zpve=0.050262 U0=-207.916786 U=-207.912215 H=-207.911271 G=-207.943384 Cv=13.845 pbc="F F F" -O -0.03738221 1.44547841 0.01010769 -C 0.01292906 0.03441661 0.00209165 -C -1.35747452 -0.48874523 0.01239738 -N -2.42135277 -0.93685184 0.02016053 -H 0.86898057 1.76972949 0.00417577 -H 0.51692713 -0.36329790 -0.89215814 -H 0.53387616 -0.37278749 0.88223921 -10 -Properties=species:S:1:pos:R:3 A=26.14564 B=4.2893 C=4.12773 mu=2.6741 alpha=35.83 epsHOMO=-0.25 epsLUMO=-0.0205 epsgap=0.2295 =333.3276 zpve=0.084175 U0=-193.075202 U=-193.070116 H=-193.069171 G=-193.102798 Cv=15.954 pbc="F F F" -C 0.16879340 1.52513042 -0.15747056 -C -0.18737625 0.06194763 0.14679374 -C 0.50917645 -0.43996215 1.39125850 -O 1.18191079 -1.43565585 1.45816384 -H 1.23126511 1.63137254 -0.39632693 -H -0.40744668 1.89431681 -1.00969246 -H -0.04931034 2.17266260 0.69857432 -H -1.26905160 -0.01668067 0.32854433 -H 0.06279062 -0.60250479 -0.68474036 -H 0.35384841 0.20663370 2.28871052 -9 -Properties=species:S:1:pos:R:3 A=19.98749 B=6.11589 C=4.82584 mu=3.7071 alpha=32.78 epsHOMO=-0.2516 epsLUMO=0.0335 epsgap=0.2851 =279.7863 zpve=0.074166 U0=-209.144909 U=-209.139976 H=-209.139032 G=-209.172305 Cv=15.058 pbc="F F F" -C 0.01474916 1.47888256 -0.03509326 -N 0.18708077 0.03831317 -0.02906851 -C 0.45703661 -0.66858895 1.10069001 -O 0.58457996 -0.19876202 2.21119705 -H 0.76025289 1.96736104 -0.67198126 -H -0.98462423 1.75984483 -0.38485103 -H 0.14269849 1.82387718 0.99149397 -H 0.10386349 -0.46937726 -0.89461379 -H 0.55047568 -1.75253452 0.88868242 -8 -Properties=species:S:1:pos:R:3 A=48.62351 B=4.67298 C=4.38187 mu=3.92 alpha=29.47 epsHOMO=-0.2814 epsLUMO=0.0074 epsgap=0.2888 =293.917 zpve=0.061327 U0=-229.013797 U=-229.009003 H=-229.008059 G=-229.041086 Cv=13.885 pbc="F F F" -C 0.10653781 1.43512049 0.03336100 -O 0.42686564 0.04716929 0.09309945 -C 0.50714862 -0.63652665 -1.06318297 -O 0.76927014 -1.79955017 -1.11010475 -H 0.92349251 2.00385139 0.48465728 -H -0.03757815 1.76927183 -1.00057425 -H -0.81269569 1.60801916 0.59873287 -H 0.30458460 -0.00231902 -1.94965553 -8 -Properties=species:S:1:pos:R:3 A=36.49282 B=4.31175 C=4.04493 mu=1.7341 alpha=28.53 epsHOMO=-0.2537 epsLUMO=-0.0341 epsgap=0.2196 =303.8129 zpve=0.060508 U0=-228.992613 U=-228.987769 H=-228.986825 G=-229.019918 Cv=14.78 pbc="F F F" -O 0.19375856 1.37170995 0.31398297 -C 0.04992637 0.01405481 -0.04526239 -C -1.40888025 -0.39635680 -0.20601574 -O -1.77729462 -1.37233860 -0.80361689 -H -0.01723186 1.91269132 -0.45337758 -H 0.60440478 -0.26519766 -0.95205725 -H 0.46740406 -0.57521679 0.78176270 -H -2.11835055 0.30075962 0.29870515 -14 -Properties=species:S:1:pos:R:3 A=23.44662 B=3.60588 C=3.38683 mu=0 alpha=45.71 epsHOMO=-0.317 epsLUMO=0.0937 epsgap=0.4107 =426.2996 zpve=0.131708 U0=-158.340943 U=-158.33517 H=-158.334226 G=-158.36894 Cv=19.668 pbc="F F F" -C -0.03007624 1.55804912 0.02980774 -C 0.00707277 0.02834877 -0.00712529 -C 0.71320792 -0.52989340 -1.24765581 -C 0.75036895 -2.05959366 -1.28458957 -H 0.98195330 1.97845113 0.02800797 -H -0.55582433 1.96212343 -0.84270157 -H -0.54083762 1.92692755 0.92493697 -H 0.50928672 -0.34909049 0.89361319 -H -1.01733481 -0.36542299 0.03207393 -H 0.21098948 -0.15245770 -2.14839302 -H 1.73761331 -0.13611625 -1.28685672 -H 1.27616854 -2.46366410 -0.41210980 -H -0.26165699 -2.48000447 -1.28273552 -H 1.26108491 -2.42846444 -2.17974803 -12 -Properties=species:S:1:pos:R:3 A=26.59789 B=3.79186 C=3.53779 mu=1.3402 alpha=38.61 epsHOMO=-0.2619 epsLUMO=0.081 epsgap=0.3429 =382.8628 zpve=0.108241 U0=-194.261089 U=-194.255495 H=-194.254551 G=-194.28893 Cv=18.431 pbc="F F F" -C -0.00379012 1.52196790 0.01027996 -C -0.03589372 -0.00778254 0.00225671 -C -1.45219016 -0.56653711 0.01284386 -O -1.36799785 -1.98394604 0.00453606 -H 1.02477576 1.89411231 0.00239793 -H -0.51286674 1.93764614 -0.86653888 -H -0.49584080 1.92801344 0.90121721 -H 0.48140784 -0.39530552 -0.88316064 -H 0.49828589 -0.40486256 0.87330348 -H -1.98734220 -0.20399494 0.90666062 -H -2.00431485 -0.19459473 -0.86666460 -H -2.26171819 -2.33824298 0.01034748 -12 -Properties=species:S:1:pos:R:3 A=28.29567 B=4.14901 C=3.88349 mu=1.0363 alpha=39.34 epsHOMO=-0.2503 epsLUMO=0.0925 epsgap=0.3428 =368.9331 zpve=0.107895 U0=-194.254127 U=-194.248585 H=-194.247641 G=-194.281899 Cv=17.888 pbc="F F F" -C -0.00519551 1.50868224 0.03308760 -C 0.01316580 -0.01037819 0.01988643 -O 0.69758944 -0.46344140 1.16868979 -C 0.76688491 -1.86508183 1.24602669 -H 1.01445476 1.90423799 0.02996018 -H -0.52990866 1.89184059 -0.84726911 -H -0.51263765 1.87805843 0.92875424 -H -1.01599507 -0.40998729 0.00714282 -H 0.51093143 -0.38369435 -0.89219395 -H -0.23415841 -2.32451489 1.28977780 -H 1.30129276 -2.29853663 0.38486095 -H 1.30990287 -2.11811998 2.16013206 -10 -Properties=species:S:1:pos:R:3 A=14.79671 B=5.6956 C=4.58846 mu=0.0075 alpha=31.42 epsHOMO=-0.2594 epsLUMO=0.0584 epsgap=0.3179 =297.8398 zpve=0.085172 U0=-230.183076 U=-230.177723 H=-230.176779 G=-230.211195 Cv=16.837 pbc="F F F" -O -0.01418735 1.42642242 -0.05423651 -C -0.00391839 0.00717714 0.03496062 -C -1.42317019 -0.55560600 -0.01256639 -O -2.11229413 -0.10563875 -1.17228313 -H -0.18987882 1.64154705 -0.97555148 -H 0.59659504 -0.43315430 -0.77409095 -H 0.47311116 -0.24903733 0.98747216 -H -1.39494859 -1.64975279 -0.06283188 -H -1.96497417 -0.27216915 0.90131972 -H -2.35925991 0.80944396 -1.00614597 -12 -Properties=species:S:1:pos:R:3 A=15.61472 B=6.31363 C=5.54514 mu=0.1136 alpha=41.96 epsHOMO=-0.2727 epsLUMO=0.1012 epsgap=0.3738 =298.6061 zpve=0.109284 U0=-157.116735 U=-157.11209 H=-157.111146 G=-157.143262 Cv=16.49 pbc="F F F" -C -0.03602245 1.52092949 0.01569161 -C 0.01938487 0.01004403 0.04814953 -C 1.30891671 -0.71251995 -0.24920416 -C 0.75347423 -0.70267714 1.15564676 -H 0.90105691 1.95389291 0.38283338 -H -0.19640365 1.89437369 -1.00208569 -H -0.84685273 1.90588930 0.64451322 -H -0.88416809 -0.48001889 -0.30566335 -H 1.26900901 -1.63903068 -0.81150371 -H 2.19010738 -0.11131209 -0.44927513 -H 1.26248862 -0.09487455 1.89690028 -H 0.33540836 -1.62248918 1.54980695 -10 -Properties=species:S:1:pos:R:3 A=18.21931 B=6.63877 C=5.92459 mu=1.812 alpha=35.01 epsHOMO=-0.2633 epsLUMO=0.1052 epsgap=0.3685 =267.2979 zpve=0.085275 U0=-193.039603 U=-193.035186 H=-193.034242 G=-193.065979 Cv=14.764 pbc="F F F" -C -0.01646343 1.51830705 0.00771445 -C -0.02743227 0.01657694 -0.10889741 -C 0.77296553 -0.70131886 -1.10863115 -O 1.17212293 -0.66702969 0.26278829 -H 0.91865980 1.92914217 -0.38285880 -H -0.85158014 1.95456506 -0.55109460 -H -0.11487702 1.82596470 1.05397777 -H -0.93582598 -0.47350600 0.24682793 -H 0.44443922 -1.67201495 -1.47810622 -H 1.40538284 -0.13600339 -1.79271491 -11 -Properties=species:S:1:pos:R:3 A=16.58914 B=7.18798 C=6.11415 mu=1.1353 alpha=39.02 epsHOMO=-0.2304 epsLUMO=0.0968 epsgap=0.3271 =270.5508 zpve=0.097671 U0=-173.147782 U=-173.143343 H=-173.142399 G=-173.174073 Cv=15.298 pbc="F F F" -C -0.05275541 1.47419574 0.00999108 -N 0.01940516 0.02189947 0.06334866 -C 1.32511048 -0.56446850 -0.21420725 -C 0.78051759 -0.54057993 1.17242760 -H 0.86784202 1.97176076 0.36297285 -H -0.23845155 1.79095005 -1.02142866 -H -0.88683448 1.81938959 0.62950398 -H 1.31992909 -1.48186782 -0.79602038 -H 2.15202820 0.11020204 -0.43285248 -H 1.24563570 0.14996098 1.87499245 -H 0.37013358 -1.44020447 1.62233365 -10 -Properties=species:S:1:pos:R:3 A=16.79477 B=6.71015 C=5.86781 mu=1.3894 alpha=34.64 epsHOMO=-0.239 epsLUMO=0.0775 epsgap=0.3166 =263.966 zpve=0.085106 U0=-193.034988 U=-193.030356 H=-193.029411 G=-193.061689 Cv=15.679 pbc="F F F" -O 0.21577583 1.35386859 -0.10695107 -C 0.01628099 -0.02832718 -0.02915694 -C -0.39081465 -0.71497009 1.24315669 -C -1.35118138 -0.64280380 0.06273589 -H -0.43642338 1.79062849 0.45006761 -H 0.74981167 -0.52404050 -0.65624767 -H 0.05959375 -1.66840483 1.49729237 -H -0.61369353 -0.08848006 2.10170640 -H -2.19821932 0.03058430 0.15411451 -H -1.56395912 -1.54640426 -0.49826515 -12 -Properties=species:S:1:pos:R:3 A=10.66107 B=10.66107 C=6.30452 mu=0 alpha=41.83 epsHOMO=-0.2982 epsLUMO=0.0956 epsgap=0.3938 =268.4432 zpve=0.110511 U0=-157.115484 U=-157.111322 H=-157.110378 G=-157.141657 Cv=14.696 pbc="F F F" -C -0.02542957 1.54032578 -0.04215944 -C 1.52564399 1.50419989 0.04121276 -C 1.42146727 0.06146790 0.60844885 -C -0.02638833 -0.01070956 0.04935088 -H -0.46591372 1.99639894 0.84962894 -H -0.47900239 2.00028207 -0.92365389 -H 1.98248278 1.51375334 -0.95303183 -H 2.02191365 2.25986579 0.65522129 -H 2.14291679 -0.67956358 0.25531480 -H 1.43105853 0.05751479 1.70261879 -H -0.79053440 -0.48530296 0.66996573 -H -0.05233245 -0.47238446 -0.94236394 -10 -Properties=species:S:1:pos:R:3 A=12.06545 B=11.77731 C=6.71997 mu=1.7978 alpha=34.56 epsHOMO=-0.2424 epsLUMO=0.0859 epsgap=0.3283 =236.9759 zpve=0.086675 U0=-193.034094 U=-193.029968 H=-193.029024 G=-193.060777 Cv=12.915 pbc="F F F" -C -0.03489789 1.55259455 -0.08507691 -C 1.49349183 1.45459700 0.10114677 -O 1.42725884 0.02348990 0.27254886 -C -0.00634758 0.02236494 0.10944630 -H -0.56579210 2.11301721 0.68591527 -H -0.36628352 1.89393380 -1.06692327 -H 2.10031724 1.73525720 -0.76898349 -H 1.90023090 1.95506611 0.98896748 -H -0.51987849 -0.35595179 1.00235948 -H -0.31970062 -0.57567421 -0.75559275 -12 -Properties=species:S:1:pos:R:3 A=8.63483 B=4.11977 C=2.88825 mu=0.6875 alpha=45.37 epsHOMO=-0.2392 epsLUMO=0.0192 epsgap=0.2584 =452.5112 zpve=0.100501 U0=-248.375248 U=-248.368823 H=-248.367879 G=-248.405354 Cv=21.616 pbc="F F F" -C -0.10255370 1.49907127 -0.00133642 -C -0.00519774 -0.00343114 -0.02723470 -C -0.00688536 -0.76890740 1.26434744 -N 0.08186799 -0.69202791 -1.10087306 -O 0.07439067 0.13215394 -2.24195258 -H 0.78342093 1.94690862 -0.46354050 -H -0.96150430 1.83614082 -0.59021965 -H -0.19854173 1.87462989 1.01931607 -H -0.92620012 -0.57297946 1.82982479 -H 0.07256154 -1.84064782 1.07516863 -H 0.82937178 -0.45553340 1.90133089 -H 0.13975924 -0.51254299 -2.95389260 -10 -Properties=species:S:1:pos:R:3 A=9.17136 B=9.04195 C=4.5531 mu=1.8689 alpha=43.14 epsHOMO=-0.2029 epsLUMO=0.0499 epsgap=0.2528 =303.9808 zpve=0.082433 U0=-210.101789 U=-210.097816 H=-210.096872 G=-210.12818 Cv=14.821 pbc="F F F" -N -0.00829694 1.35362838 0.00995971 -C 1.28033254 1.82457421 -0.00022459 -C 2.12216639 0.73698898 -0.01445600 -C 1.30849904 -0.43109580 -0.01284154 -C -0.00342782 -0.01836884 0.00235736 -H -0.83243234 1.92772668 0.02122995 -H 1.48471820 2.88312630 0.00354313 -H 3.20066918 0.77343077 -0.02488416 -H 1.64810599 -1.45539683 -0.02180394 -H -0.92551647 -0.57697054 0.00839402 -9 -Properties=species:S:1:pos:R:3 A=9.77329 B=9.41192 C=4.7946 mu=3.6193 alpha=39.13 epsHOMO=-0.2253 epsLUMO=0.0332 epsgap=0.2585 =283.6817 zpve=0.071145 U0=-226.160842 U=-226.157088 H=-226.156144 G=-226.187104 Cv=13.371 pbc="F F F" -N -0.00996090 1.35636172 0.01000855 -C 1.29535422 1.79575578 -0.00047017 -C 2.05550519 0.65637438 -0.01422927 -N 1.25385745 -0.46275132 -0.01260894 -C 0.02301170 -0.00706382 0.00200146 -H -0.83195718 1.93618436 0.02132575 -H 1.54553961 2.84344998 0.00289054 -H 3.13177434 0.57569766 -0.02524155 -H -0.87667864 -0.60414531 0.00750794 -9 -Properties=species:S:1:pos:R:3 A=9.50228 B=9.29497 C=4.69874 mu=0.5571 alpha=39.2 epsHOMO=-0.2246 epsLUMO=0.0199 epsgap=0.2445 =289.005 zpve=0.069883 U0=-229.969129 U=-229.965414 H=-229.96447 G=-229.995393 Cv=13.358 pbc="F F F" -O -0.03255819 1.35065130 0.00967376 -C 1.25319237 1.79167710 -0.00030351 -C 2.11664681 0.74375623 -0.01441494 -C 1.30397796 -0.43852387 -0.01283316 -C 0.01629568 -0.00777185 0.00191294 -H 1.37951089 2.86161135 0.00427433 -H 3.19376406 0.79865501 -0.02458604 -H 1.63858893 -1.46383271 -0.02156949 -H -0.93736295 -0.50899925 0.00843891 -8 -Properties=species:S:1:pos:R:3 A=10.10926 B=9.69372 C=4.94857 mu=1.5081 alpha=35.17 epsHOMO=-0.2509 epsLUMO=0.001 epsgap=0.2519 =269.2444 zpve=0.058593 U0=-246.02915 U=-246.025614 H=-246.024669 G=-246.055309 Cv=12.04 pbc="F F F" -O -0.03808298 1.35643965 0.00975193 -C 1.26496982 1.76855319 -0.00061744 -C 2.04955750 0.66630033 -0.01406047 -N 1.24227075 -0.46732066 -0.01237541 -C 0.04019820 0.00561577 0.00183569 -H 1.43513907 2.83095265 0.00340823 -H 3.12532898 0.59783015 -0.02480757 -H -0.89387707 -0.53342154 0.00796935 -17 -Properties=species:S:1:pos:R:3 A=4.4178 B=4.41753 C=4.41717 mu=0.0003 alpha=56.01 epsHOMO=-0.3145 epsLUMO=0.0737 epsgap=0.3882 =486.2719 zpve=0.158836 U0=-197.632222 U=-197.625241 H=-197.624297 G=-197.661411 Cv=26.084 pbc="F F F" -C -0.01859029 1.53486266 0.01039904 -C 0.00210320 -0.00413672 0.00196980 -C 0.74643954 -0.51390760 1.24887638 -C -1.44190166 -0.53673615 0.01311457 -C 0.72234971 -0.50049606 -1.26445632 -H 0.99776928 1.94420529 0.00435474 -H -0.54181883 1.92841415 -0.86819911 -H -0.52758402 1.91888376 0.90148530 -H 0.25549576 -0.17602728 2.16833067 -H 0.77815389 -1.60892372 1.27088904 -H 1.77964786 -0.14975834 1.26944035 -H -1.99871895 -0.18888794 -0.86408066 -H -1.45956909 -1.63227235 0.00649916 -H -1.98088827 -0.19979639 0.90561580 -H 1.75482473 -0.13554018 -1.30118083 -H 0.75396989 -1.59524036 -1.29822280 -H 0.21358221 -0.15343900 -2.17067181 -15 -Properties=species:S:1:pos:R:3 A=4.69474 B=4.66938 C=4.47909 mu=1.384 alpha=49.04 epsHOMO=-0.2601 epsLUMO=0.0664 epsgap=0.3265 =449.0573 zpve=0.134977 U0=-233.560626 U=-233.553779 H=-233.552834 G=-233.589759 Cv=25.128 pbc="F F F" -C -0.00855999 1.54270147 0.00152717 -C 0.00506780 0.00702635 0.01856160 -C 0.76137731 -0.51839196 1.24766443 -C -1.42012774 -0.54933183 -0.02116008 -O 0.62590477 -0.48363745 -1.17657057 -H 1.01361406 1.94020776 0.00281128 -H -0.51459669 1.90702466 -0.89705781 -H -0.52214815 1.94882514 0.87918344 -H 0.28130613 -0.20165548 2.17943598 -H 0.79965506 -1.61131746 1.23022549 -H 1.79096916 -0.14072935 1.26083003 -H -1.93854829 -0.19910724 -0.91828281 -H -1.39937632 -1.64259062 -0.04563833 -H -1.98850725 -0.23059656 0.85726520 -H 1.53111188 -0.15488046 -1.19210222 -9 -Properties=species:S:1:pos:R:3 A=10.21876 B=4.04324 C=2.94978 mu=2.8579 alpha=42.02 epsHOMO=-0.2654 epsLUMO=-0.0575 epsgap=0.2079 =416.7799 zpve=0.065175 U0=-229.927277 U=-229.921622 H=-229.920677 G=-229.955843 Cv=18.723 pbc="F F F" -C -0.00693851 1.49559567 -0.01063358 -C -0.02986170 -0.01594623 0.06248673 -O -0.08815239 -0.63215062 1.10334812 -C 0.02336981 -0.70256747 -1.22062379 -C 0.06738132 -1.26058341 -2.28623129 -H 0.90429806 1.83281887 -0.51655384 -H -0.85338524 1.85556926 -0.60552819 -H -0.05237764 1.91099807 0.99695949 -H 0.10466868 -1.77036047 -3.21832617 -8 -Properties=species:S:1:pos:R:3 A=10.16063 B=4.15847 C=3.0056 mu=3.3351 alpha=37.08 epsHOMO=-0.3007 epsLUMO=-0.0889 epsgap=0.2118 =397.7757 zpve=0.05451 U0=-246.027383 U=-246.022024 H=-246.02108 G=-246.055769 Cv=16.963 pbc="F F F" -C -0.01171097 1.49973776 -0.01213268 -C -0.02919740 -0.00338745 0.07151218 -O -0.09164028 -0.64592927 1.08732241 -C 0.03831104 -0.69690258 -1.23748557 -N 0.09136286 -1.22982650 -2.26228068 -H 0.90093132 1.83370670 -0.51814928 -H -0.85663933 1.84864400 -0.61618135 -H -0.06416540 1.92131542 0.99203818 -8 -Properties=species:S:1:pos:R:3 A=10.68423 B=4.3198 C=3.07993 mu=5.1815 alpha=38.31 epsHOMO=-0.2739 epsLUMO=-0.0438 epsgap=0.2301 =384.5574 zpve=0.056619 U0=-242.19573 U=-242.190591 H=-242.189646 G=-242.223513 Cv=17.465 pbc="F F F" -N -0.05232108 1.34991769 0.15535538 -C 0.08549378 -0.01476951 0.02668890 -N 1.14555215 -0.71993906 -0.03966037 -C -1.19426556 -0.71152088 0.01802151 -N -2.22747610 -1.22790543 0.00132148 -H 0.75474469 1.91998737 -0.04411334 -H -0.94458527 1.76600473 -0.05932293 -H 1.98312429 -0.13415500 -0.02878551 -9 -Properties=species:S:1:pos:R:3 A=9.08854 B=4.42607 C=3.03169 mu=0.9461 alpha=36.51 epsHOMO=-0.2538 epsLUMO=-0.0964 epsgap=0.1574 =399.222 zpve=0.064992 U0=-267.10335 U=-267.097658 H=-267.096714 G=-267.132534 Cv=17.806 pbc="F F F" -C -0.43126066 1.30586664 -0.12758585 -C 0.25285715 -0.01026988 0.13138867 -O 0.69872099 -0.36890333 1.19604690 -C 0.38978943 -0.96773680 -1.06226208 -O -0.01611268 -0.71564293 -2.16583718 -H 0.10006565 1.85324490 -0.91332597 -H -1.44270433 1.13069361 -0.50923371 -H -0.46757554 1.89548158 0.78836479 -H 0.90329886 -1.91558845 -0.79531972 -9 -Properties=species:S:1:pos:R:3 A=10.08927 B=4.54115 C=3.13487 mu=2.7707 alpha=38.09 epsHOMO=-0.2561 epsLUMO=-0.064 epsgap=0.192 =384.4827 zpve=0.067668 U0=-263.278851 U=-263.273589 H=-263.272645 G=-263.306835 Cv=17.972 pbc="F F F" -N -0.16032929 1.08528962 -0.45398652 -C 0.18649127 -0.08762327 0.14758021 -N 1.28979627 -0.46427423 0.67927030 -C -0.94204384 -1.08440264 0.20423621 -O -2.01802205 -0.89007687 -0.30583149 -H 0.54574437 1.73837930 -0.74689246 -H -1.04684920 1.09570857 -0.93607796 -H 2.01738195 0.24991828 0.59881811 -H -0.67740148 -2.01003995 0.74746441 -13 -Properties=species:S:1:pos:R:3 A=7.92874 B=3.8272 C=2.82581 mu=0.6578 alpha=51.2 epsHOMO=-0.2589 epsLUMO=0.0571 epsgap=0.316 =481.9854 zpve=0.112471 U0=-195.186772 U=-195.180446 H=-195.179502 G=-195.215658 Cv=22.569 pbc="F F F" -C -0.01763434 1.54859468 -0.00253584 -C -0.01719180 0.00735789 0.03269604 -C 0.75818463 -0.52820155 1.25278862 -C -1.38213303 -0.51768438 0.00775516 -C -2.50796392 -0.94005629 0.00478552 -H 1.00944396 1.92639613 -0.02632830 -H -0.54514614 1.92217052 -0.88393459 -H -0.51243000 1.95339892 0.88560642 -H 0.49366179 -0.34904155 -0.87265890 -H 0.28968458 -0.19345372 2.18334679 -H 0.77833392 -1.62102506 1.25783727 -H 1.78952492 -0.16181727 1.23574954 -H -3.49998244 -1.31810165 -0.00723572 -12 -Properties=species:S:1:pos:R:3 A=7.90009 B=3.95466 C=2.88643 mu=3.9512 alpha=46.23 epsHOMO=-0.318 epsLUMO=0.0365 epsgap=0.3545 =460.694 zpve=0.102281 U0=-211.295796 U=-211.289821 H=-211.288877 G=-211.324525 Cv=20.467 pbc="F F F" -C -0.00242350 1.55035837 0.01056187 -C -0.01856600 0.01004352 -0.02261770 -C -1.45045318 -0.55766178 0.01293347 -C 0.70125749 -0.48583168 -1.20172764 -N 1.26190946 -0.87126766 -2.13619740 -H 1.01959496 1.93673215 0.00349668 -H -0.52972844 1.96141639 -0.85498386 -H -0.50134251 1.90346839 0.91755862 -H 0.52481877 -0.36222456 0.85525462 -H -2.02427903 -0.21428716 -0.85251869 -H -1.44438296 -1.65026927 0.00754107 -H -1.95805494 -0.21725882 0.91996760 -11 -Properties=species:S:1:pos:R:3 A=8.30095 B=3.97615 C=2.93764 mu=2.7429 alpha=42.87 epsHOMO=-0.2704 epsLUMO=0.0278 epsgap=0.2983 =440.0738 zpve=0.091554 U0=-227.338075 U=-227.332253 H=-227.331309 G=-227.366638 Cv=19.946 pbc="F F F" -C -0.01064295 1.54128691 0.00722087 -C -0.00203811 0.00756015 0.03863463 -N 0.76268677 -0.47101822 1.19250856 -C -1.38541954 -0.52099256 -0.00194097 -N -2.45680847 -0.95636072 0.00694969 -H 1.02043523 1.90106589 0.00392365 -H -0.52873161 1.91193452 -0.88031480 -H -0.51849536 1.94150431 0.89013197 -H 0.50418828 -0.35805500 -0.86276072 -H 0.30070681 -0.18364934 2.05155305 -H 0.78148992 -1.48670299 1.20428183 -11 -Properties=species:S:1:pos:R:3 A=8.55919 B=3.89952 C=2.91869 mu=1.3582 alpha=44.03 epsHOMO=-0.2665 epsLUMO=0.0336 epsgap=0.3001 =444.6452 zpve=0.088908 U0=-231.108368 U=-231.102292 H=-231.101348 G=-231.137061 Cv=21.306 pbc="F F F" -C -0.00754278 1.55566144 0.08650633 -C 0.01207541 0.02096553 0.04935916 -O -1.29150251 -0.52785128 0.22126386 -C 0.65093362 -0.47718651 -1.17430507 -C 1.13902673 -0.86577343 -2.20205518 -H 1.00824243 1.95969458 0.04929733 -H -0.56272245 1.95192010 -0.76978085 -H -0.49328161 1.88929438 1.00704123 -H 0.57811967 -0.34464907 0.91458864 -H -1.78516532 -0.35903262 -0.58909400 -H 1.58078877 -1.22365060 -3.09919447 -10 -Properties=species:S:1:pos:R:3 A=8.55908 B=4.01671 C=2.97882 mu=3.269 alpha=39.28 epsHOMO=-0.3051 epsLUMO=0.0115 epsgap=0.3166 =424.3395 zpve=0.078602 U0=-247.214861 U=-247.209162 H=-247.208218 G=-247.243338 Cv=19.252 pbc="F F F" -C -0.00992462 1.54951908 0.00732200 -C 0.00740485 0.01722020 0.05031270 -O 0.72085722 -0.49630479 1.15864583 -C -1.36995095 -0.52355929 0.00894432 -N -2.45081572 -0.93233138 0.00716912 -H 1.01892314 1.91630447 0.02724149 -H -0.50261490 1.90969362 -0.89997951 -H -0.54800426 1.95377611 0.87053664 -H 0.53602846 -0.36494195 -0.82970945 -H 0.27702643 -0.20644534 1.96421773 -13 -Properties=species:S:1:pos:R:3 A=7.66289 B=3.70925 C=2.79937 mu=2.6921 alpha=46.58 epsHOMO=-0.2469 epsLUMO=-0.0188 epsgap=0.2281 =482.0475 zpve=0.112328 U0=-232.364952 U=-232.358577 H=-232.357633 G=-232.394589 Cv=21.434 pbc="F F F" -C -0.02629647 1.53877116 -0.00783747 -C 0.01468904 0.00095310 0.00166037 -C 0.74737862 -0.53388859 1.24420471 -C -1.40211264 -0.53859257 -0.01557820 -O -1.87620455 -1.23582351 -0.87399559 -H 0.98823696 1.94625080 0.03705522 -H -0.50725090 1.92153422 -0.91285565 -H -0.57756514 1.92360883 0.85796386 -H 0.50584680 -0.36421091 -0.90619124 -H 0.22958380 -0.23909403 2.16431052 -H 0.81692736 -1.62559499 1.22973438 -H 1.76267153 -0.12846566 1.29050755 -H -2.00861510 -0.22792742 0.87121666 -11 -Properties=species:S:1:pos:R:3 A=8.31096 B=4.07963 C=2.92918 mu=2.8354 alpha=39.16 epsHOMO=-0.255 epsLUMO=-0.0279 epsgap=0.2271 =432.1489 zpve=0.088443 U0=-268.287661 U=-268.281505 H=-268.280561 G=-268.316982 Cv=20.344 pbc="F F F" -C -0.05483118 1.46032843 -0.17068647 -C 0.07105714 -0.03524485 0.11225841 -O 0.58204486 -0.33316656 1.40176256 -C -1.27639279 -0.74383027 0.03051185 -O -2.11855775 -0.49643175 -0.79163412 -H 0.93158072 1.93091398 -0.23353204 -H -0.57713995 1.61290939 -1.11835562 -H -0.62444315 1.94776119 0.62573670 -H 0.69687521 -0.50071620 -0.67159471 -H 1.45603316 0.06066659 1.47858584 -H -1.40723217 -1.53388627 0.80211181 -12 -Properties=species:S:1:pos:R:3 A=8.9331 B=4.19405 C=2.96046 mu=3.7163 alpha=44.42 epsHOMO=-0.2424 epsLUMO=0.0327 epsgap=0.2751 =441.85 zpve=0.10227 U0=-248.430371 U=-248.424309 H=-248.423365 G=-248.459383 Cv=19.918 pbc="F F F" -C -0.09776499 1.44917268 0.04561984 -N 0.01043053 0.00373818 -0.00070600 -C 0.05323028 -0.71432112 1.25264891 -C 0.06728716 -0.64067225 -1.20060778 -O 0.03636432 -0.11006515 -2.29109228 -H 0.75650660 1.88747109 0.57647421 -H -0.11648577 1.81632103 -0.98049172 -H -1.01641106 1.75521548 0.56151415 -H -0.85453556 -0.53657675 1.84390168 -H 0.13297722 -1.78696336 1.05851351 -H 0.91637348 -0.40737846 1.85761345 -H 0.14762240 -1.73801339 -1.06805139 -11 -Properties=species:S:1:pos:R:3 A=10.13982 B=3.81346 C=2.86965 mu=2.9514 alpha=39.33 epsHOMO=-0.2699 epsLUMO=-0.0262 epsgap=0.2437 =440.1727 zpve=0.088924 U0=-268.301176 U=-268.295084 H=-268.29414 G=-268.331307 Cv=19.824 pbc="F F F" -C -0.01708808 1.44281136 0.03225501 -C -0.05544542 -0.06560572 -0.02375999 -O -0.17379913 -0.76196233 0.96223129 -C 0.06068107 -0.73615179 -1.38825252 -O 0.00877771 -2.12574390 -1.27749486 -H 0.92062950 1.81463913 -0.39522215 -H -0.83050523 1.86542281 -0.56780350 -H -0.10911975 1.78049045 1.06493675 -H -0.75015067 -0.35235060 -2.03056074 -H 1.00312898 -0.40389029 -1.85592942 -H -0.09063255 -2.29450960 -0.32608019 -13 -Properties=species:S:1:pos:R:3 A=9.55216 B=3.57844 C=2.7341 mu=2.6168 alpha=46.19 epsHOMO=-0.2423 epsLUMO=-0.0072 epsgap=0.2351 =489.8518 zpve=0.112006 U0=-232.377706 U=-232.371073 H=-232.370129 G=-232.408256 Cv=21.716 pbc="F F F" -C -0.04384102 1.54099793 -0.03913814 -C 0.02765132 0.01885817 0.04778773 -C 0.76814644 -0.48842218 1.28000847 -C 0.87850208 -1.99746312 1.42393323 -O 1.24179142 0.26651340 2.09788702 -H 0.95796454 1.97690198 -0.06816012 -H -0.58447717 1.85356031 -0.93690369 -H -0.55173626 1.95737759 0.83440529 -H -0.97692824 -0.42709877 0.05353178 -H 0.51853486 -0.40806507 -0.83809409 -H 1.39924980 -2.42678188 0.56053177 -H 1.42001187 -2.24449367 2.33776426 -H -0.11750602 -2.45360651 1.45141965 -12 -Properties=species:S:1:pos:R:3 A=9.78802 B=3.78441 C=2.84416 mu=3.499 alpha=42.54 epsHOMO=-0.2438 epsLUMO=0.0355 epsgap=0.2793 =457.447 zpve=0.101847 U0=-248.448467 U=-248.441988 H=-248.441044 G=-248.478935 Cv=21.374 pbc="F F F" -C -0.00220144 1.53344329 -0.05960369 -C -0.03235837 0.01206167 0.06840265 -C 0.69666086 -0.48196462 1.31828322 -N 0.98104007 -1.82034162 1.31984698 -O 1.00392124 0.24606232 2.24136749 -H 1.02111190 1.89525169 -0.19182672 -H -0.59707204 1.86206427 -0.91645604 -H -0.39728839 1.99920712 0.84545201 -H -1.06936083 -0.34552935 0.13040806 -H 0.39947165 -0.46906818 -0.81777020 -H 0.63281951 -2.43917647 0.60874785 -H 1.35708014 -2.22217793 2.16303522 -12 -Properties=species:S:1:pos:R:3 A=10.01185 B=3.84195 C=2.87635 mu=3.5402 alpha=43.61 epsHOMO=-0.2418 epsLUMO=0.0387 epsgap=0.2805 =458.9803 zpve=0.101735 U0=-248.443503 U=-248.436899 H=-248.435955 G=-248.474272 Cv=20.967 pbc="F F F" -C 0.00619941 1.45933202 -0.03893859 -N -0.06411616 0.01422963 -0.12046901 -C -1.20595394 -0.68196796 0.16117120 -C -1.08982704 -2.19284899 0.02095775 -O -2.24482911 -0.14443077 0.49994418 -H 0.75053989 1.78372520 0.69715573 -H 0.25692001 1.90395263 -1.00876215 -H -0.97856026 1.81121438 0.27082194 -H 0.75820982 -0.49543660 -0.39464024 -H -0.09909703 -2.53543104 -0.28970003 -H -1.34097671 -2.65395851 0.97972885 -H -1.82903100 -2.53483935 -0.70813076 -11 -Properties=species:S:1:pos:R:3 A=10.44045 B=4.04692 C=2.98547 mu=3.5648 alpha=39.51 epsHOMO=-0.2436 epsLUMO=0.0599 epsgap=0.3034 =428.404 zpve=0.091713 U0=-264.504487 U=-264.498452 H=-264.497508 G=-264.533633 Cv=20.274 pbc="F F F" -C 0.00922630 1.45021814 -0.03194543 -N -0.01429488 -0.00054646 -0.01782667 -C -1.21576579 -0.67252403 0.04421896 -N -1.09733352 -2.04644898 -0.15997741 -O -2.28599299 -0.12893711 0.24196027 -H 0.89659038 1.81885022 0.49120949 -H 0.00233950 1.86388504 -1.04914155 -H -0.88562684 1.79943346 0.48353298 -H 0.75257305 -0.47506631 -0.46719265 -H -0.24882333 -2.48772194 0.16375654 -H -1.93419610 -2.53898393 0.11482880 -12 -Properties=species:S:1:pos:R:3 A=9.69683 B=4.1533 C=3.01703 mu=1.1876 alpha=43.57 epsHOMO=-0.2595 epsLUMO=0.0352 epsgap=0.2948 =443.1687 zpve=0.102062 U0=-248.416462 U=-248.410358 H=-248.409414 G=-248.445651 Cv=20.256 pbc="F F F" -C -0.09160695 1.37540046 -0.00729426 -O -0.06964534 -0.04831581 -0.11148812 -C 0.68114625 -0.56142673 -1.10732458 -C 0.60405298 -2.06701550 -1.08766892 -N 1.32645533 0.19266343 -1.89967731 -H 0.91463916 1.76843655 0.16257460 -H -0.48562826 1.82624615 -0.92235216 -H -0.73971430 1.60007423 0.84110605 -H -0.43430065 -2.39242162 -1.20325205 -H 1.20627110 -2.50049977 -1.88745640 -H 0.95651337 -2.44976670 -0.12501838 -H 1.83504115 -0.36104361 -2.58486055 -11 -Properties=species:S:1:pos:R:3 A=10.20392 B=4.16834 C=3.07281 mu=1.7569 alpha=39.33 epsHOMO=-0.2685 epsLUMO=0.0174 epsgap=0.2859 =427.6606 zpve=0.089436 U0=-268.32127 U=-268.315051 H=-268.314106 G=-268.351214 Cv=19.501 pbc="F F F" -C 0.07295254 1.38840919 0.06442046 -O 0.21049114 -0.03740028 0.04612736 -C 0.50102805 -0.57224288 -1.15975346 -C 0.62218715 -2.07378323 -1.06030684 -O 0.63922154 0.08224418 -2.16157325 -H 1.00029720 1.87062570 -0.25532064 -H -0.73338957 1.70782089 -0.60080454 -H -0.15751280 1.65154060 1.09692954 -H -0.31330001 -2.50202851 -0.68941906 -H 0.85646588 -2.48430275 -2.04157352 -H 1.40651685 -2.34055496 -0.34634301 -10 -Properties=species:S:1:pos:R:3 A=10.68182 B=4.39532 C=3.17969 mu=2.329 alpha=35.5 epsHOMO=-0.2669 epsLUMO=0.0575 epsgap=0.3244 =398.5908 zpve=0.079271 U0=-284.385189 U=-284.379361 H=-284.378417 G=-284.414085 Cv=19.084 pbc="F F F" -C -0.02645771 1.38305753 0.02085660 -O 0.04139566 -0.04439503 -0.00277452 -C 0.66775338 -0.56001625 -1.09000344 -N 0.72605063 -1.92219983 -0.98822954 -O 1.12952381 0.09648143 -1.99322180 -H 0.97529779 1.82044307 0.02387306 -H -0.57085698 1.76214621 -0.84790425 -H -0.55410722 1.63835731 0.94043594 -H 0.12662696 -2.38401590 -0.32566929 -H 0.97230070 -2.41798421 -1.82732573 -10 -Properties=species:S:1:pos:R:3 A=10.20696 B=3.93135 C=2.9219 mu=4.5676 alpha=35.87 epsHOMO=-0.246 epsLUMO=0.0273 epsgap=0.2733 =418.5967 zpve=0.078222 U0=-284.360325 U=-284.354148 H=-284.353204 G=-284.390143 Cv=20.089 pbc="F F F" -N -0.08115607 1.31807026 0.01276742 -C 0.05442259 -0.04477733 0.08155388 -O 1.12163074 -0.59783950 0.20940553 -C -1.28687786 -0.79011642 0.06178435 -O -1.04605114 -2.13954195 -0.23957158 -H 0.76623366 1.85991128 -0.04483425 -H -0.93021346 1.74567447 -0.31565615 -H -1.74559696 -0.65521334 1.05526700 -H -1.96026061 -0.31149222 -0.67026256 -H -1.85637296 -2.63017646 -0.07625478 -10 -Properties=species:S:1:pos:R:3 A=10.14389 B=4.0714 C=3.02223 mu=5.3004 alpha=35.19 epsHOMO=-0.2527 epsLUMO=0.0208 epsgap=0.2735 =408.0279 zpve=0.080317 U0=-284.372483 U=-284.367172 H=-284.366228 G=-284.40095 Cv=17.931 pbc="F F F" -N 0.01838100 0.93057357 0.04943058 -H -0.70674506 0.56116798 -0.55550134 -H 0.68248307 0.18430002 0.21487099 -H 0.21928084 2.79858558 -0.32670585 -C -0.54586914 1.44688585 1.30274586 -C -0.80466975 2.96030569 1.21052835 -O -0.21468822 3.53648884 0.15531687 -O -1.45295676 3.56338618 2.02143528 -H -1.47662579 0.96512183 1.62072169 -H 0.17781209 1.30324286 2.11157176 -13 -Properties=species:S:1:pos:R:3 A=6.6508 B=4.19317 C=3.39441 mu=2.2854 alpha=42.01 epsHOMO=-0.2597 epsLUMO=0.0631 epsgap=0.3228 =438.3028 zpve=0.11345 U0=-269.479234 U=-269.472993 H=-269.472049 G=-269.508213 Cv=22.072 pbc="F F F" -C -0.02775167 1.53421433 0.00651151 -C 0.08343246 0.01218277 0.05258986 -O 0.95856392 -0.48200710 -0.96694113 -C 0.69767574 -0.49516451 1.35948812 -O 2.02247062 -0.02842530 1.52222186 -H 0.94333763 1.98841434 0.21841589 -H -0.37320516 1.87354041 -0.97716908 -H -0.74773817 1.89256394 0.74926853 -H -0.91671848 -0.43697036 -0.06040528 -H 0.76726710 -0.01412177 -1.78502043 -H 0.11650002 -0.13351914 2.21392431 -H 0.65814685 -1.59563445 1.36274474 -H 2.46378566 -0.22762476 0.68698431 -17 -Properties=species:S:1:pos:R:3 A=7.26511 B=3.32874 C=2.54491 mu=0.0618 alpha=56.26 epsHOMO=-0.3085 epsLUMO=0.085 epsgap=0.3934 =565.8412 zpve=0.159632 U0=-197.629387 U=-197.622325 H=-197.621381 G=-197.659365 Cv=25.169 pbc="F F F" -C 0.05879035 1.56174169 -0.01945240 -C -0.01544024 0.03182306 0.00308332 -C -1.44103237 -0.54862028 0.05028629 -C -1.40777007 -2.07231030 -0.13012010 -C -2.18400529 -0.17659759 1.34114303 -H 1.09435638 1.90233607 -0.11900072 -H -0.50689566 1.97217319 -0.86388098 -H -0.34407951 2.00628082 0.89579522 -H 0.48926758 -0.36011214 -0.88953902 -H 0.55254938 -0.35139446 0.86269887 -H -2.00031575 -0.12153682 -0.79559684 -H -0.85501131 -2.55065810 0.68775438 -H -2.41787513 -2.49543628 -0.13720127 -H -0.91928762 -2.35363802 -1.06914014 -H -3.19100197 -0.60699559 1.35137051 -H -1.65197294 -0.56010584 2.22063481 -H -2.28793759 0.90575132 1.46079572 -15 -Properties=species:S:1:pos:R:3 A=8.11598 B=3.43678 C=2.66869 mu=1.3894 alpha=49.45 epsHOMO=-0.2617 epsLUMO=0.0733 epsgap=0.335 =521.8605 zpve=0.136091 U0=-233.555951 U=-233.549143 H=-233.548199 G=-233.585602 Cv=23.99 pbc="F F F" -C -0.03841227 1.55621396 0.00935837 -C 0.01907135 0.02716079 -0.02074203 -C 0.73141506 -0.52709328 -1.25454422 -C 0.83975868 -2.05206973 -1.22872655 -O -0.01226470 -0.08514394 -2.38973758 -H 0.96845664 1.98863424 0.03526234 -H -0.54377006 1.93824134 -0.88074407 -H -0.57652955 1.91383386 0.89256698 -H 0.53166867 -0.34991486 0.87240999 -H -0.99727186 -0.38638654 -0.00185289 -H 1.74909346 -0.09943025 -1.28826184 -H 1.34143968 -2.42487137 -2.12957624 -H 1.41822012 -2.39229108 -0.36340871 -H -0.15616765 -2.50443859 -1.18308875 -H 0.43090825 -0.40901616 -3.17979642 -15 -Properties=species:S:1:pos:R:3 A=7.55335 B=3.78323 C=2.80566 mu=1.0758 alpha=49.8 epsHOMO=-0.2476 epsLUMO=0.086 epsgap=0.3336 =507.9614 zpve=0.135681 U0=-233.545899 U=-233.539034 H=-233.53809 G=-233.57582 Cv=23.573 pbc="F F F" -C -0.00857605 1.38097833 0.09454670 -O -0.02255106 -0.00739461 -0.13062576 -C -1.30243341 -0.61997805 -0.02088309 -C -1.18687286 -1.97885751 -0.70303918 -C -1.75587824 -0.74662760 1.43714508 -H 1.00321550 1.73258087 -0.12443496 -H -0.71511468 1.90838275 -0.56674280 -H -0.24909065 1.64967237 1.13384681 -H -2.03737978 -0.00432241 -0.56884198 -H -0.44792779 -2.59945605 -0.18596870 -H -2.14751041 -2.50225525 -0.69102581 -H -0.86524212 -1.85860679 -1.74077836 -H -2.73321482 -1.23674908 1.49205117 -H -1.03621350 -1.34483828 2.00544671 -H -1.84898410 0.22935483 1.92198087 -15 -Properties=species:S:1:pos:R:3 A=6.14183 B=5.17679 C=3.79297 mu=0.1068 alpha=52.92 epsHOMO=-0.2633 epsLUMO=0.0893 epsgap=0.3526 =439.1643 zpve=0.137025 U0=-196.409349 U=-196.403398 H=-196.402453 G=-196.437676 Cv=22.258 pbc="F F F" -C -0.03913204 1.50456305 0.04978913 -C 0.00985856 -0.01113880 0.01248619 -C -1.24167981 -0.66663044 -0.53989979 -C 1.33803387 -0.66895748 -0.27400430 -C 0.75126442 -0.72221586 1.11862571 -H 0.88633040 1.92490040 0.45579358 -H -0.18183878 1.92045743 -0.95489291 -H -0.86832076 1.85815718 0.67439648 -H -1.43570890 -0.34344387 -1.56976411 -H -1.15326757 -1.75757800 -0.54433832 -H -2.12221416 -0.40570820 0.05951361 -H 1.34398374 -1.58235208 -0.86005084 -H 2.20196897 -0.03329084 -0.43929326 -H 1.21949576 -0.12246018 1.89246580 -H 0.36153319 -1.67153717 1.47171932 -13 -Properties=species:S:1:pos:R:3 A=6.49534 B=5.52329 C=3.9108 mu=1.8235 alpha=45.7 epsHOMO=-0.2596 epsLUMO=0.091 epsgap=0.3505 =405.521 zpve=0.112851 U0=-232.335768 U=-232.329981 H=-232.329037 G=-232.364091 Cv=20.719 pbc="F F F" -C -0.03970330 1.50954998 -0.04112233 -C -0.03023556 -0.00335329 -0.06498361 -C -1.24613189 -0.68356648 0.52531190 -C 0.78937201 -0.71129193 -1.06027559 -O 1.22546798 -0.60059036 0.29712283 -H 0.88992772 1.90948478 -0.45296558 -H -0.87952723 1.90350108 -0.62376642 -H -0.14448576 1.87590604 0.98604429 -H -2.15218727 -0.41012616 -0.02629495 -H -1.13412584 -1.77005431 0.49747032 -H -1.38586631 -0.38055453 1.56882757 -H 0.50712690 -1.71386874 -1.38085018 -H 1.37088104 -0.14369322 -1.78640853 -13 -Properties=species:S:1:pos:R:3 A=6.57648 B=5.31603 C=3.98453 mu=1.3822 alpha=45.85 epsHOMO=-0.2494 epsLUMO=0.0776 epsgap=0.327 =401.4888 zpve=0.113237 U0=-232.33341 U=-232.327734 H=-232.32679 G=-232.36147 Cv=21.17 pbc="F F F" -C -0.03714052 1.53215920 0.01332310 -C 0.01344335 0.02117019 0.01922005 -O -1.17476104 -0.59735284 -0.40931260 -C 1.30744798 -0.69902106 -0.27389650 -C 0.70844749 -0.71373504 1.12226318 -H 0.89026861 1.96240279 0.40276135 -H -0.17846151 1.91561262 -1.00518522 -H -0.87011232 1.88955201 0.62730953 -H -1.28352265 -0.40225391 -1.34690641 -H 1.23973813 -1.61642115 -0.84783774 -H 2.20543993 -0.11523820 -0.44744474 -H 1.19539159 -0.13889834 1.90240284 -H 0.24735439 -1.63670241 1.45378480 -10 -Properties=species:S:1:pos:R:3 A=12.18399 B=5.1384 C=3.78868 mu=2.5732 alpha=40.19 epsHOMO=-0.263 epsLUMO=0.0277 epsgap=0.2907 =352.0378 zpve=0.080558 U0=-247.201165 U=-247.196699 H=-247.195755 G=-247.22841 Cv=15.51 pbc="F F F" -N -0.04445152 1.25623533 0.31681915 -C -0.03914559 0.04913710 -0.01670558 -C -1.08749619 -1.04164446 -0.11488033 -C 0.09869688 -1.90754844 -0.58225894 -O 0.99647508 -0.76306629 -0.44028010 -H 0.88935980 1.66481350 0.24916893 -H -1.54324114 -1.32235812 0.83572172 -H -1.86910036 -0.85713839 -0.85325910 -H 0.39739753 -2.72227350 0.08158721 -H 0.06945080 -2.25418345 -1.61796084 -11 -Properties=species:S:1:pos:R:3 A=10.79571 B=4.78732 C=3.54292 mu=2.7119 alpha=42.77 epsHOMO=-0.2415 epsLUMO=-0.0194 epsgap=0.2222 =379.6371 zpve=0.090544 U0=-231.15578 U=-231.150804 H=-231.149859 G=-231.184401 Cv=17.076 pbc="F F F" -O -0.00101746 0.01846628 -0.20114742 -C -0.01405811 1.19658059 0.00205306 -C -1.13542911 2.22979148 0.21957980 -C -0.03500884 3.29097637 0.53052223 -C 1.08469664 2.25947795 0.18994331 -H -1.71642814 2.41772662 -0.68901078 -H -1.83392757 1.98415059 1.02444244 -H -0.05534741 4.17585264 -0.10670963 -H -0.02541477 3.61399048 1.57278917 -H 1.81074389 2.03288534 0.97578973 -H 1.63600801 2.46255380 -0.73376244 -10 -Properties=species:S:1:pos:R:3 A=12.23436 B=4.97895 C=3.70561 mu=3.6671 alpha=39.73 epsHOMO=-0.25 epsLUMO=0.038 epsgap=0.288 =355.0934 zpve=0.080442 U0=-247.225618 U=-247.220897 H=-247.219953 G=-247.253218 Cv=16.1 pbc="F F F" -O 0.03184134 -0.03461682 0.00849836 -C -0.02440334 1.16440533 0.02160811 -C -1.14541322 2.23712809 0.03012901 -C 0.00928805 3.27945878 0.04557758 -N 0.93843838 2.14652109 0.03554396 -H -1.77030529 2.23779730 -0.86428437 -H -1.77632809 2.21765386 0.92007934 -H 0.07170564 3.91863516 -0.84072193 -H 0.06622603 3.89826625 0.94657952 -H 1.94616840 2.09535713 0.03855945 -9 -Properties=species:S:1:pos:R:3 A=12.44643 B=5.23328 C=3.86599 mu=3.9339 alpha=35.4 epsHOMO=-0.2788 epsLUMO=0.0089 epsgap=0.2878 =336.0792 zpve=0.068574 U0=-267.106213 U=-267.101929 H=-267.100985 G=-267.13332 Cv=14.483 pbc="F F F" -O 0.04424004 0.03933243 -0.33071104 -C -0.04854479 1.17416230 0.00410076 -C -1.14211656 2.23611850 0.15338361 -C 0.02332538 3.12357010 0.61681625 -O 0.95663808 2.01190122 0.42603746 -H -1.62573755 2.51482212 -0.78401102 -H -1.90009213 1.99553386 0.90028663 -H 0.28368077 3.96172751 -0.03229287 -H 0.00782250 3.43942655 1.66162725 -10 -Properties=species:S:1:pos:R:3 A=11.54185 B=4.90368 C=3.63309 mu=2.5257 alpha=39.34 epsHOMO=-0.2437 epsLUMO=-0.0258 epsgap=0.2179 =359.152 zpve=0.07965 U0=-247.190194 U=-247.185543 H=-247.184599 G=-247.217688 Cv=16.083 pbc="F F F" -O 0.00053891 0.00665475 -0.09743049 -C -0.01371990 1.19213881 0.03139936 -C -1.10668681 2.26612284 0.17247913 -N -0.03543445 3.25123475 0.48533693 -C 1.05328615 2.29521506 0.14383904 -H -1.65047970 2.42796175 -0.76886054 -H -1.83606786 2.11370530 0.97567410 -H -0.05434600 4.07193609 -0.10727051 -H 1.80754206 2.16277968 0.92736250 -H 1.56737937 2.47130277 -0.81152688 -9 -Properties=species:S:1:pos:R:3 A=12.18264 B=4.94228 C=3.68162 mu=0.8477 alpha=35.94 epsHOMO=-0.2647 epsLUMO=-0.0352 epsgap=0.2295 =344.5473 zpve=0.066989 U0=-267.068488 U=-267.063976 H=-267.063032 G=-267.095887 Cv=15.115 pbc="F F F" -O -0.00031692 0.00676321 -0.17139774 -C -0.01417098 1.18672865 -0.00980279 -C -1.08283120 2.27653381 0.15574094 -O -0.03856115 3.26460423 0.27513986 -C 1.02857270 2.30515693 0.12774311 -H -1.72923855 2.43647507 -0.71671274 -H -1.70219898 2.19346725 1.05800754 -H 1.67366781 2.23923293 1.01324179 -H 1.64705068 2.48224548 -0.76148355 -15 -Properties=species:S:1:pos:R:3 A=9.63416 B=4.10254 C=3.28665 mu=0.095 alpha=52.94 epsHOMO=-0.2896 epsLUMO=0.0927 epsgap=0.3823 =455.0215 zpve=0.138424 U0=-196.407957 U=-196.40245 H=-196.401505 G=-196.436159 Cv=20.299 pbc="F F F" -C -0.47005934 1.44206547 -0.06684548 -C 0.14287306 0.05485144 0.03747984 -C 1.37254693 -0.30295273 -0.84679168 -C 1.95947994 -1.09364561 0.35326039 -C 0.99887433 -0.30768787 1.28563099 -H 0.29210839 2.21844829 0.06844100 -H -0.93614604 1.60094605 -1.04612799 -H -1.24148161 1.59707353 0.69627593 -H -0.63517341 -0.70560132 -0.10055095 -H 1.19338376 -0.84118177 -1.78129390 -H 1.96867777 0.58857708 -1.07004184 -H 1.69873597 -2.15500718 0.30521100 -H 3.03253510 -1.01077235 0.54148058 -H 1.48290386 0.58242428 1.70211026 -H 0.51304296 -0.84979686 2.10121062 -13 -Properties=species:S:1:pos:R:3 A=9.93129 B=4.56362 C=3.63704 mu=1.6826 alpha=45.72 epsHOMO=-0.241 epsLUMO=0.0915 epsgap=0.3325 =407.6909 zpve=0.11459 U0=-232.33123 U=-232.325877 H=-232.324933 G=-232.359589 Cv=18.582 pbc="F F F" -C -0.32308228 1.53751451 0.01947624 -C 0.11847214 0.08900172 -0.00523140 -C 0.87489284 -0.43763303 -1.24721738 -C 1.97841048 -0.80657483 -0.23740356 -O 1.27344926 -0.15419108 0.83794040 -H 0.49211985 2.19658795 -0.29527664 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- -.. only:: html - - .. image:: /examples/multi-gpu/images/thumb/sphx_glr_multi-gpu_thumb.png - :alt: - - :ref:`sphx_glr_examples_multi-gpu_multi-gpu.py` - -.. raw:: html - -
Multi-GPU training
-
- - -.. thumbnail-parent-div-close - -.. raw:: html - - - - -.. toctree:: - :hidden: - - /examples/multi-gpu/multi-gpu - - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-gallery - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download all examples in Python source code: multi-gpu_python.zip ` - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download all examples in Jupyter notebooks: multi-gpu_jupyter.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/multi-gpu/multi-gpu.ipynb b/docs/src/examples/multi-gpu/multi-gpu.ipynb deleted file mode 100644 index 6cc034d0f1..0000000000 --- a/docs/src/examples/multi-gpu/multi-gpu.ipynb +++ /dev/null @@ -1,32 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# Multi-GPU training\n\n``metatrain`` supports training a model with several GPUs, which can accelerate the\ntraining, especially when the training dataset is large / there are many training\nepochs. This feature is enabled by the :py:mod:`torch.distributed` module, and thus can\ndo multiprocess parallelism across several nodes.\n\nIn multi-GPU training, every batch of samples is split into smaller mini-batches and the\ncomputation is run for each of the smaller mini-batches in parallel on different GPUs.\nThe different gradients obtained on each device are then summed. This approach allows\nthe user to reduce the time it takes to train models.\n\nTo know if the model supports multi-GPU training, please check [Available Architectures](../../docs/src/architectures/index.rst) and see if the default hyperparameters have\nthe ``distributed`` option.\n\n## Input file\nTo do this, you only need to switch on the ``distributed`` option in the ``.yaml`` file\nfor the training. Let's take [this tutorial](../beginner_tutorials/train-from-scratch.rst) as an example. Now, the\n``options.yaml`` is\n\n.. literalinclude:: ./options-distributed.yaml\n :language: yaml\n :linenos:\n\n## Slurm script\n\nBelow is an example Slurm script for submitting the job. Please be aware that the actual\nconfigurations vary from clusters to clusters, so you have to modify it. Different\nscheduler will require similar options. ``metatrain`` will automatically use all the\nGPUs that you have asked for. You should make a single GPU visible for each process\n(setting `--gpus-per-node` equal to the number of GPUs, or setting `--gpus-per-task=1`,\ndepending on your cluster configuration).\n\n```bash\n#!/bin/bash\n#SBATCH --nodes 1\n#SBATCH --ntasks 2 # must equal to the number of GPUs\n#SBATCH --ntasks-per-node 2\n#SBATCH --gpus-per-node 2 # use 2 GPUs\n#SBATCH --cpus-per-task 8\n#SBATCH --exclusive\n#SBATCH --partition=h100 # adapt this to your cluster\n#SBATCH --time=1:00:00\n\n# load modules and/or virtual environments and/or containers here\n\nsrun mtt train options-distributed.yaml\n```\n## Performance\n\nIf the multi-GPU training runs successfully, you should see this in the training log:\n\n```bash\n[2025-10-08 11:34:22][INFO] - Distributed environment set up with MASTER_ADDR=kh080,\nMASTER_PORT=39591, WORLD_SIZE=2, RANK=0, LOCAL_RANK=0\n[2025-10-08 11:34:23][INFO] - Training on 2 devices with dtype torch.float32\n```\nThis 100-epoch training takes 23 seconds.\n\n```bash\n[2025-10-08 11:34:22][INFO] - Starting training from scratch\n...\n[2025-10-08 11:34:45][INFO] - Training finished!\n```\nNow let's switch off the multi-GPU training by writing ``distributed: false``, and\nsubmit this job again. The training takes 69 seconds.\n\n```bash\n[2025-10-08 11:37:38][INFO] - Setting up model\n...\n[2025-10-08 11:38:47][INFO] - Training finished!\n```\n## Multi-GPU fine-tuning\nYou can use multi-GPU for fine-tuning too, by writing ``distributed: True`` in the\n``.yaml`` input. For information about fine-tuning, please refer to [this tutorial on\nfine-tuning](../../getting-started/finetuning-example.rst).\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/src/examples/multi-gpu/multi-gpu.py b/docs/src/examples/multi-gpu/multi-gpu.py deleted file mode 100644 index d0293a37f5..0000000000 --- a/docs/src/examples/multi-gpu/multi-gpu.py +++ /dev/null @@ -1,91 +0,0 @@ -""" -Multi-GPU training -================== - -``metatrain`` supports training a model with several GPUs, which can accelerate the -training, especially when the training dataset is large / there are many training -epochs. This feature is enabled by the :py:mod:`torch.distributed` module, and thus can -do multiprocess parallelism across several nodes. - -In multi-GPU training, every batch of samples is split into smaller mini-batches and the -computation is run for each of the smaller mini-batches in parallel on different GPUs. -The different gradients obtained on each device are then summed. This approach allows -the user to reduce the time it takes to train models. - -To know if the model supports multi-GPU training, please check -:ref:`available-architectures` and see if the default hyperparameters have the -``distributed`` option. - -Input file ----------- - -To do this, you only need to switch on the ``distributed`` option in the ``.yaml`` file -for the training. Let's take the -:ref:`sphx_glr_generated_examples_0-beginner_03-train_from_scratch.py` example and -adjust the ``options.yaml`` file. - -.. literalinclude:: options-distributed.yaml - :language: yaml - :linenos: - -Slurm script ------------- - -Below is an example Slurm script for submitting the job. Please be aware that the actual -configurations vary from clusters to clusters, so you have to modify it. Different -scheduler will require similar options. ``metatrain`` will automatically use all the -GPUs that you have asked for. You should make a single GPU visible for each process -(setting ``--gpus-per-node`` equal to the number of GPUs, or setting -``--gpus-per-task=1``, depending on your cluster configuration). - -.. code-block:: bash - - #!/bin/bash - #SBATCH --nodes 1 - #SBATCH --ntasks 2 # must equal to the number of GPUs - #SBATCH --ntasks-per-node 2 - #SBATCH --gpus-per-node 2 # use 2 GPUs - #SBATCH --cpus-per-task 8 - #SBATCH --exclusive - #SBATCH --partition=h100 # adapt this to your cluster - #SBATCH --time=1:00:00 - - # load modules and/or virtual environments and/or containers here - - srun mtt train options-distributed.yaml - -Performance ------------ - -If the multi-GPU training runs successfully, you should see this in the training log: - -.. code-block:: bash - - [2025-10-08 11:34:22][INFO] - Distributed environment set up with MASTER_ADDR=kh080, - MASTER_PORT=39591, WORLD_SIZE=2, RANK=0, LOCAL_RANK=0 - [2025-10-08 11:34:23][INFO] - Training on 2 devices with dtype torch.float32 - -This 100-epoch training takes 23 seconds. - -.. code-block:: bash - - [2025-10-08 11:34:22][INFO] - Starting training from scratch - ... - [2025-10-08 11:34:45][INFO] - Training finished! - -Now let's switch off the multi-GPU training by writing ``distributed: false``, and -submit this job again. The training takes 69 seconds. - -.. code-block:: bash - - [2025-10-08 11:37:38][INFO] - Setting up model - ... - [2025-10-08 11:38:47][INFO] - Training finished! - -Multi-GPU fine-tuning ---------------------- - -You can use multi-GPU for fine-tuning too, by writing ``distributed: True`` in the -``.yaml`` input. For information about fine-tuning, please refer to the -:ref:`sphx_glr_generated_examples_0-beginner_02-fine-tuning.py` example. -""" diff --git a/docs/src/examples/multi-gpu/multi-gpu.py.md5 b/docs/src/examples/multi-gpu/multi-gpu.py.md5 deleted file mode 100644 index 86e00a927d..0000000000 --- a/docs/src/examples/multi-gpu/multi-gpu.py.md5 +++ /dev/null @@ -1 +0,0 @@ -4666621a9eb6317d63ffb285c171dd9a \ No newline at end of file diff --git a/docs/src/examples/multi-gpu/multi-gpu.rst b/docs/src/examples/multi-gpu/multi-gpu.rst deleted file mode 100644 index e1d6b46bf7..0000000000 --- a/docs/src/examples/multi-gpu/multi-gpu.rst +++ /dev/null @@ -1,134 +0,0 @@ - -.. DO NOT EDIT. -.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. -.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: -.. "examples/multi-gpu/multi-gpu.py" -.. LINE NUMBERS ARE GIVEN BELOW. - -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - :ref:`Go to the end ` - to download the full example code. - -.. rst-class:: sphx-glr-example-title - -.. _sphx_glr_examples_multi-gpu_multi-gpu.py: - - -================== -Multi-GPU training -================== - -``metatrain`` supports training a model with several GPUs, which can accelerate the -training, especially when the training dataset is large / there are many training -epochs. This feature is enabled by the :py:mod:`torch.distributed` module, and thus can -do multiprocess parallelism across several nodes. - -In multi-GPU training, every batch of samples is split into smaller mini-batches and the -computation is run for each of the smaller mini-batches in parallel on different GPUs. -The different gradients obtained on each device are then summed. This approach allows -the user to reduce the time it takes to train models. - -To know if the model supports multi-GPU training, please check `Available Architectures -<../../docs/src/architectures/index.rst>`_ and see if the default hyperparameters have -the ``distributed`` option. - -Input file ----------- -To do this, you only need to switch on the ``distributed`` option in the ``.yaml`` file -for the training. Let's take `this tutorial -<../beginner_tutorials/train-from-scratch.rst>`_ as an example. Now, the -``options.yaml`` is - -.. literalinclude:: ./options-distributed.yaml - :language: yaml - :linenos: - -Slurm script ------------- - -Below is an example Slurm script for submitting the job. Please be aware that the actual -configurations vary from clusters to clusters, so you have to modify it. Different -scheduler will require similar options. ``metatrain`` will automatically use all the -GPUs that you have asked for. You should make a single GPU visible for each process -(setting `--gpus-per-node` equal to the number of GPUs, or setting `--gpus-per-task=1`, -depending on your cluster configuration). - -.. code-block:: bash - - #!/bin/bash - #SBATCH --nodes 1 - #SBATCH --ntasks 2 # must equal to the number of GPUs - #SBATCH --ntasks-per-node 2 - #SBATCH --gpus-per-node 2 # use 2 GPUs - #SBATCH --cpus-per-task 8 - #SBATCH --exclusive - #SBATCH --partition=h100 # adapt this to your cluster - #SBATCH --time=1:00:00 - - # load modules and/or virtual environments and/or containers here - - srun mtt train options-distributed.yaml - -Performance ------------ - -If the multi-GPU training runs successfully, you should see this in the training log: - -.. code-block:: bash - - [2025-10-08 11:34:22][INFO] - Distributed environment set up with MASTER_ADDR=kh080, - MASTER_PORT=39591, WORLD_SIZE=2, RANK=0, LOCAL_RANK=0 - [2025-10-08 11:34:23][INFO] - Training on 2 devices with dtype torch.float32 - -This 100-epoch training takes 23 seconds. - -.. code-block:: bash - - [2025-10-08 11:34:22][INFO] - Starting training from scratch - ... - [2025-10-08 11:34:45][INFO] - Training finished! - -Now let's switch off the multi-GPU training by writing ``distributed: false``, and -submit this job again. The training takes 69 seconds. - -.. code-block:: bash - - [2025-10-08 11:37:38][INFO] - Setting up model - ... - [2025-10-08 11:38:47][INFO] - Training finished! - -Multi-GPU fine-tuning ---------------------- -You can use multi-GPU for fine-tuning too, by writing ``distributed: True`` in the -``.yaml`` input. For information about fine-tuning, please refer to `this tutorial on -fine-tuning <../../getting-started/finetuning-example.rst>`_. - - -.. _sphx_glr_download_examples_multi-gpu_multi-gpu.py: - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-example - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: multi-gpu.ipynb ` - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: multi-gpu.py ` - - .. container:: sphx-glr-download sphx-glr-download-zip - - :download:`Download zipped: multi-gpu.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/multi-gpu/multi-gpu.zip b/docs/src/examples/multi-gpu/multi-gpu.zip deleted file mode 100644 index b4c6adaa55d42c51bb1e2789ea2fc089a57de639..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 7233 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zpve=0.045286 U0=-100.0 U=-132.714563 H=-132.713619 G=-132.742149 Cv=10.287 pbc="F F F" -C -0.01788629 1.46712787 0.01011311 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 0.00173819 0.01035260 0.00207588 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -N 0.01765235 -1.14452926 -0.00420445 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 1.00202941 1.86089927 0.00245401 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.54398848 1.84479901 -0.87075460 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.52707783 1.83518166 0.90486329 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -7 -Properties=species:S:1:pos:R:3:forces:R:3:my_tensor:R:9 A=57.22434 B=10.11122 C=9.07368 mu=2.5682 alpha=25.11 epsHOMO=-0.254 epsLUMO=-0.0198 epsgap=0.2342 =166.9728 zpve=0.055355 U0=-100.0 U=-153.783728 H=-153.782784 G=-153.812518 Cv=11.219 pbc="F F F" -C -0.00294482 1.50991366 0.00867278 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 0.02608284 0.00327563 -0.03745912 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -O 0.94228801 -0.65507035 -0.45682576 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 0.92278802 1.92634242 -0.39146557 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 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zpve=0.103182 U0=-100.0 U=-119.047927 H=-119.046983 G=-119.078157 Cv=14.84 pbc="F F F" -C -0.03113825 1.54081582 0.03192126 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 0.01215347 0.01092235 -0.01603259 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 0.72169129 -0.52583353 -1.26230570 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 0.97955987 1.96459116 0.03098367 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.55840223 1.94831192 -0.83816075 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.54252252 1.90153531 0.93005671 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 0.51522791 -0.36840234 0.88231134 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -1.01070641 -0.38456999 0.02051783 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 1.75851210 -0.17376585 -1.30871516 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 0.74087192 -1.62024959 -1.27516511 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 0.22023351 -0.19051179 -2.17729020 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -9 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0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.51674572 1.87611771 0.88710739 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 0.52377121 -0.38912295 0.88824082 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -1.02027544 -0.40507261 0.01690670 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 0.69529573 -1.40179568 -1.20148495 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -9 -Properties=species:S:1:pos:R:3:forces:R:3:my_tensor:R:9 A=39.37691 B=10.04033 C=8.90353 mu=1.1502 alpha=28.13 epsHOMO=-0.2525 epsLUMO=0.091 epsgap=0.3435 =187.1015 zpve=0.079534 U0=-100.0 U=-154.956045 H=-154.9551 G=-154.985747 Cv=12.934 pbc="F F F" -C -0.01482147 1.39241234 0.00567104 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -O -0.00471537 -0.01360660 0.01459658 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 0.63794893 -0.55329676 -1.11358232 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 1.00528358 1.81015807 0.00465631 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.54689560 1.79343530 -0.87251051 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.53002895 1.72291969 0.91101696 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 0.13993792 -0.25599333 -2.05098375 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 1.69265275 -0.23868382 -1.17477743 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 0.59959435 -1.64180161 -1.02407616 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -9 -Properties=species:S:1:pos:R:3:forces:R:3:my_tensor:R:9 A=20.15852 B=20.15302 C=12.5891 mu=0.0005 alpha=30.82 epsHOMO=-0.2888 epsLUMO=0.1042 epsgap=0.393 =155.8145 zpve=0.081231 U0=-100.0 U=-117.821426 H=-117.820482 G=-117.849087 Cv=11.041 pbc="F F F" -C -0.01193280 1.51433198 0.01031700 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 1.30299118 0.77886562 -0.00617842 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 0.00867174 0.00767042 0.00201032 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.30541464 2.01702117 0.92533239 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.32275498 2.02680156 -0.89347839 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 1.88493396 0.79145020 -0.92119393 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 1.90226631 0.78165391 0.89766373 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.27088043 -0.51289125 0.91137474 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.28821983 -0.50310409 -0.90740824 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -7 -Properties=species:S:1:pos:R:3:forces:R:3:my_tensor:R:9 A=25.77385 B=22.12109 C=14.18066 mu=1.7675 alpha=24.04 epsHOMO=-0.2682 epsLUMO=0.1042 epsgap=0.3724 =129.891 zpve=0.057289 U0=-100.0 U=-153.73941 H=-153.738466 G=-153.766642 Cv=9.176 pbc="F F F" -C 0.01537720 1.41764380 0.00956287 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 1.26478161 0.64923984 -0.00655044 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -O -0.00024018 -0.00771006 0.00204055 0.00000000 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1.91339997 0.00028781 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 2.23625322 2.57430970 -0.00529277 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.91670701 1.79467546 0.01781352 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 3.13429662 3.14370267 -0.01037656 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -5 -Properties=species:S:1:pos:R:3:forces:R:3:my_tensor:R:9 A=67.88408 B=5.00823 C=4.66413 mu=2.3112 alpha=26.25 epsHOMO=-0.3166 epsLUMO=-0.11 epsgap=0.2066 =251.0007 zpve=0.02654 U0=-100.0 U=-206.717875 H=-206.716931 G=-206.747625 Cv=11.329 pbc="F F F" -O -0.04055978 -0.03799144 0.00266513 0.00000000 0.00000000 0.00000000 0.00000000 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Cv=12.147 pbc="F F F" -O 0.00337122 -0.03146586 0.00157206 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C -0.01713174 1.17081409 0.00676444 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 1.26555783 1.99719410 -0.00007703 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -O 1.24507069 3.19946589 0.00681275 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.94690483 1.77678142 0.01852668 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 2.19534410 1.39121374 -0.01006739 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 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"is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray.flush" - } - ], - "e_path": [ - { - "is_class": false, - "is_explicit": false, - "module": "pathlib._local", - "module_short": "pathlib", - "name": "PosixPath" - }, - { - "is_class": false, - "is_explicit": false, - "module": "pathlib", - "module_short": "pathlib", - "name": "PosixPath" - } - ], - "energy": [ - { - "is_class": false, - "is_explicit": false, - "module": "torch", - "module_short": "torch", - "name": "ScriptObject" - } - ], - "f_mm": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "memmap" - } - ], - "f_mm.flush": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "memmap.flush" - }, - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray.flush" - } - ], - "f_path": [ - { - "is_class": false, - "is_explicit": false, - "module": "pathlib._local", - "module_short": "pathlib", - "name": "PosixPath" - }, - { - "is_class": false, - "is_explicit": false, - "module": "pathlib", - "module_short": "pathlib", - "name": "PosixPath" - } - ], - "filelist": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "list" - } - ], - "fname": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "str" - } - ], - "frames": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "list" - } - ], - "frames.append": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "builtin_function_or_method" - } - ], - "get_system_with_neighbor_lists": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": 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"is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "int64" - }, - { - "is_class": true, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "signedinteger" - }, - { - "is_class": true, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "integer" - }, - { - "is_class": true, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "number" - }, - { - "is_class": true, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "generic" - }, - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "int64" - } - ], - "np.memmap": [ - { - "is_class": true, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "memmap" - }, - { - "is_class": true, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray" - }, - { - "is_class": false, - 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"module": "pathlib", - "module_short": "pathlib", - "name": "PosixPath" - } - ], - "root": [ - { - "is_class": false, - "is_explicit": false, - "module": "pathlib._local", - "module_short": "pathlib", - "name": "PosixPath" - }, - { - "is_class": false, - "is_explicit": false, - "module": "pathlib", - "module_short": "pathlib", - "name": "PosixPath" - } - ], - "root.mkdir": [ - { - "is_class": false, - "is_explicit": false, - "module": "pathlib._local", - "module_short": "pathlib", - "name": "PosixPath.mkdir" - }, - { - "is_class": false, - "is_explicit": false, - "module": "pathlib", - "module_short": "pathlib", - "name": "PosixPath.mkdir" - }, - { - "is_class": false, - "is_explicit": false, - "module": "pathlib._local", - "module_short": "pathlib", - "name": "Path.mkdir" - }, - { - "is_class": false, - "is_explicit": false, - "module": "pathlib", - "module_short": "pathlib", - "name": "Path.mkdir" - }, - { - "is_class": false, - "is_explicit": false, - "module": "pathlib._local", - "module_short": "pathlib._local", - "name": "PurePosixPath.mkdir" - }, - { - "is_class": false, - "is_explicit": false, - "module": "pathlib", - "module_short": "pathlib", - "name": "PurePosixPath.mkdir" - }, - { - "is_class": false, - "is_explicit": false, - "module": "pathlib._abc", - "module_short": "pathlib._abc", - "name": "PathBase.mkdir" - }, - { - "is_class": false, - "is_explicit": false, - "module": "pathlib", - "module_short": "pathlib", - "name": "PathBase.mkdir" - }, - { - "is_class": false, - "is_explicit": false, - "module": "pathlib._local", - "module_short": "pathlib._local", - "name": "PurePath.mkdir" - }, - { - "is_class": false, - "is_explicit": false, - "module": "pathlib", - "module_short": "pathlib", - "name": "PurePath.mkdir" - }, - { - "is_class": false, - "is_explicit": false, - "module": "pathlib._abc", - "module_short": "pathlib._abc", - "name": "PurePathBase.mkdir" - }, - { - "is_class": false, - "is_explicit": false, - "module": "pathlib", - "module_short": "pathlib", - "name": "PurePathBase.mkdir" - } - ], - "s": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms" - } - ], - "s.arrays": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "dict" - } - ], - "s.get_cell": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.get_cell" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.get_cell" - } - ], - "s.get_positions": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.get_positions" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.get_positions" - } - ], - "s.get_potential_energy": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.get_potential_energy" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.get_potential_energy" - } - ], - "s.get_volume": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.get_volume" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.get_volume" - } - ], - "s.info": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "dict" - } - ], - "s.numbers": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.numbers" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.numbers" - } - ], - "s_mm": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "memmap" - } - ], - "s_mm.flush": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "memmap.flush" - }, - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray.flush" - } - ], - "s_path": [ - { - "is_class": false, - "is_explicit": false, - "module": "pathlib._local", - "module_short": "pathlib", - "name": "PosixPath" - }, - { - "is_class": false, - "is_explicit": false, - "module": "pathlib", - "module_short": "pathlib", - "name": "PosixPath" - } - ], - "structures": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "list" - } - ], - "subprocess.run": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "subprocess", - "module_short": "subprocess", - "name": "run" - } - ], - "system": [ - { - "is_class": false, - "is_explicit": false, - "module": "torch", - "module_short": "torch", - "name": "ScriptObject" - } - ], - "systems": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "list" - } - ], - "systems_to_torch": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "metatomic.torch", - "module_short": "metatomic.torch", - "name": "systems_to_torch" - } - ], - "torch.float64": [ - { - "is_class": false, - "is_explicit": false, - "module": "torch", - "module_short": "torch", - "name": "dtype" - }, - { - "is_class": false, - "is_explicit": false, - "module": "torch", - "module_short": "torch", - "name": "float64" - } - ], - "torch.tensor": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "builtin_function_or_method" - }, - { - "is_class": false, - "is_explicit": false, - "module": "torch", - "module_short": "torch", - "name": "tensor" - } - ], - "x_mm": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "memmap" - } - ], - "x_mm.flush": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "memmap.flush" - }, - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray.flush" - } - ], - "x_path": [ - { - "is_class": false, - "is_explicit": false, - "module": "pathlib._local", - "module_short": "pathlib", - "name": "PosixPath" - }, - { - "is_class": false, - "is_explicit": false, - "module": "pathlib", - "module_short": "pathlib", - "name": "PosixPath" - } - ] -} diff --git a/docs/src/examples/programmatic/data_preparation/data_preparation.ipynb b/docs/src/examples/programmatic/data_preparation/data_preparation.ipynb deleted file mode 100644 index bf87a8c055..0000000000 --- a/docs/src/examples/programmatic/data_preparation/data_preparation.ipynb +++ /dev/null @@ -1,169 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# How to prepare data for training\n\n.. attention::\n\n This tutorial is only relevant for users who need to prepare their data from scratch\n from several files or for big datasets. If you already have your data in a common\n file format (like XYZ or `ASE database`_), you can skip this tutorial and directly\n start training.\n\n\nXYZ, ASE databases, and also from metrain's\n:class:`metatrain.utils.data.dataset.DiskDataset ` file.\n\nFor the small datasets (<10k structures), you can simply provide an XYZ file or an ASE\ndatabase to ``metatrain``, and it will handle the data loading for you. Large datasets\n(>10k structures) may not fit into the GPU memory. In such cases, it is useful to\npre-process the dataset, save it to disk and load it on the fly during training.\n\nIn this tutorial, we will show how to prepare data for training using three different\nformats. You can choose the one that best fits your needs.\n\nWe start by importing the necessary packages.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import subprocess\nfrom pathlib import Path\n\nimport ase.io\nimport numpy as np\nimport torch\nfrom metatensor.torch import Labels, TensorBlock, TensorMap\nfrom metatomic.torch import NeighborListOptions, systems_to_torch\n\nfrom metatrain.utils.data.writers import DiskDatasetWriter\nfrom metatrain.utils.neighbor_lists import get_system_with_neighbor_lists" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create a XYZ training file (small datasets)\n\nFirst, we will show how to create a XYZ file with fields corresponding to the target\nproperties. On modern HPC systems, this format is suitable for datasets up to around\n1M structures. As an example, we will use 100 structures from a file read by ASE_.\nSince files from reference calculations may be located in different directories, we\nfirst create a list of all path that we want to read from. Here, for simplicity, we\nassume that all files are located in the same directory.\n\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "filelist = 100 * [\"qm9_reduced_100.xyz\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now read the structures using the ASE package. Check the ase documentation for\nmore details on how to read different file formats. Instead of creating the ``atoms``\nobject by reading from disk, you can also create an\n:class:`ase.Atoms` object containing the chemical ``symbols``, ``positions``, the\n``cell`` and the periodic boundary conditions (``pbc``) by hand using its constructor.\n\n.. hint::\n\n If a property is not read by the :func:`ase.io.read` function, you can add custom\n scalar properties to the ``info`` dictionary. Vector properties (e.g. forces) can be\n added to the ``arrays`` dictionary. Tensor properties (e.g. stress) must\n be flattened before adding them to the ``arrays`` dictionary.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "frames = []\nfor i, fname in enumerate(filelist):\n atoms = ase.io.read(fname, index=i)\n\n n_atoms = len(atoms)\n # scalar\n atoms.info[\"U0\"] = -100.0\n # vector\n atoms.arrays[\"forces\"] = np.zeros((n_atoms, 3))\n # tensor\n atoms.arrays[\"my_tensor\"] = np.zeros((n_atoms, 3, 3)).reshape(n_atoms, 9)\n\n frames.append(atoms)\n\nase.io.write(\"data.xyz\", frames)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Note

The names of the added properties (like, ``U0``, etc.) must be referenced correctly\n in the ``options.yaml`` file.

\n\n## Create a ``DiskDataset`` (large datasets)\n\nIn addition to the systems and targets (as above), we also save the neighbor\nlists that the model will use during training. We first create the writer object that\nwill write the data to a zip file.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "disk_dataset_writer = DiskDatasetWriter(\"qm9_reduced_100.zip\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then we loop over all structures, convert them to the internal torch format using\n:func:`metatomic.torch.systems_to_torch`, compute the neighbor lists using\n:func:`metatrain.utils.neighbor_lists.get_system_with_neighbor_lists` and write\neverything to disk using the writer's ``write()`` method.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "for i, fname in enumerate(filelist):\n atoms = ase.io.read(fname, index=i)\n\n system = systems_to_torch(atoms, dtype=torch.float64)\n system = get_system_with_neighbor_lists(\n system,\n [NeighborListOptions(cutoff=5.0, full_list=True, strict=True)],\n )\n energy = TensorMap(\n keys=Labels.single(),\n blocks=[\n TensorBlock(\n values=torch.tensor([[atoms.info[\"U0\"]]], dtype=torch.float64),\n samples=Labels(\n names=[\"system\"],\n values=torch.tensor([[i]]),\n ),\n components=[],\n properties=Labels(\"energy\", torch.tensor([[0]])),\n )\n ],\n )\n disk_dataset_writer.write([system], {\"energy\": energy})\n\ndisk_dataset_writer.finish()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively, you can also write the whole dataset at once, which might be more\nefficient (but also potentially run into memory issues). We use the same ``frames``\nthat we created above.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "disk_dataset_writer = DiskDatasetWriter(\"qm9_reduced_100_all_at_once.zip\")\n\nsystems = systems_to_torch(frames, dtype=torch.float64)\nsystems = [\n get_system_with_neighbor_lists(\n system,\n [NeighborListOptions(cutoff=5.0, full_list=True, strict=True)],\n )\n for system in systems\n]\nenergy = TensorMap(\n keys=Labels.single(),\n blocks=[\n TensorBlock(\n values=torch.tensor(\n [frame.info[\"U0\"] for frame in frames], dtype=torch.float64\n ).reshape(-1, 1),\n samples=Labels.range(\"system\", len(frames)),\n components=[],\n properties=Labels(\"energy\", torch.tensor([[0]])),\n )\n ],\n)\n\ndisk_dataset_writer.write(systems, {\"energy\": energy})\ndisk_dataset_writer.finish()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The dataset is saved to disk. You can now provide it to ``metatrain`` as a\ndataset to train from, simply by replacing your ``.xyz`` file with the newly created\nzip file (e.g. ``read_from: qm9_reduced_100.zip``).\n\n## Create a ``MemmapDataset`` (large datasets, parallel filesystems)\n\nIf your dataset is large and you are using a parallel filesystem (e.g. on an HPC\ncluster), it is recommended to use a ``MemmapDataset`` instead of a ``DiskDataset``.\nThe ``MemmapDataset`` stores the data inside memory-mapped numpy arrays instead of a\nzip file. Reading from this format avoids I/O bottlenecks, but it does not support\nspherical targets or storing neighbor lists.\n\nAs an example, we will use 100 structures from a dataset of carbon structures. The\nnumpy arrays must be saved inside a directory, using the following format.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "structures = ase.io.read(\"carbon_reduced_100.xyz\", index=\":\")\n\nroot = Path(\"carbon_reduced_100_memmap/\")\nroot.mkdir()\n\nns_path = root / \"ns.npy\"\nna_path = root / \"na.npy\"\na_path = root / \"a.bin\"\nx_path = root / \"x.bin\"\nc_path = root / \"c.bin\"\ne_path = root / \"e.bin\"\nf_path = root / \"f.bin\"\ns_path = root / \"s.bin\"\n\nns = len(structures)\nna = np.cumsum(np.array([0] + [len(s) for s in structures], dtype=np.int64))\nnp.save(ns_path, ns)\nnp.save(na_path, na)\n\na_mm = np.memmap(a_path, dtype=\"int32\", mode=\"w+\", shape=(na[-1],))\nx_mm = np.memmap(x_path, dtype=\"float32\", mode=\"w+\", shape=(na[-1], 3))\nc_mm = np.memmap(c_path, dtype=\"float32\", mode=\"w+\", shape=(ns, 3, 3))\ne_mm = np.memmap(e_path, dtype=\"float32\", mode=\"w+\", shape=(ns, 1))\nf_mm = np.memmap(f_path, dtype=\"float32\", mode=\"w+\", shape=(na[-1], 3))\ns_mm = np.memmap(s_path, dtype=\"float32\", mode=\"w+\", shape=(ns, 3, 3))\n\nfor i, s in enumerate(structures):\n a_mm[na[i] : na[i + 1]] = s.numbers\n x_mm[na[i] : na[i + 1]] = s.get_positions()\n c_mm[i] = s.get_cell()[:]\n e_mm[i] = s.get_potential_energy()\n f_mm[na[i] : na[i + 1]] = s.arrays[\"force\"]\n s_mm[i] = -s.info[\"virial\"] / s.get_volume()\n\na_mm.flush()\nx_mm.flush()\nc_mm.flush()\ne_mm.flush()\nf_mm.flush()\ns_mm.flush()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The dataset is saved to disk. You can now provide it to ``metatrain`` as a\ndataset to train from, simply by specifying the newly created\ndirectory as the path from which to read the systems\n(e.g. ``read_from: carbon_reduced_100_memmap/``).\n\nFor example, you can use the following options file:\n\n.. literalinclude:: options.yaml\n :language: yaml\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "subprocess.run([\"mtt\", \"train\", \"options.yaml\"])" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/src/examples/programmatic/data_preparation/data_preparation.py b/docs/src/examples/programmatic/data_preparation/data_preparation.py deleted file mode 100644 index a8dfa80f25..0000000000 --- a/docs/src/examples/programmatic/data_preparation/data_preparation.py +++ /dev/null @@ -1,242 +0,0 @@ -""" -How to prepare data for training -================================ - -.. attention:: - - This tutorial is only relevant for users who need to prepare their data from scratch - from several files or for big datasets. If you already have your data in a common - file format (like XYZ or `ASE database`_), you can skip this tutorial and directly - start training. - -.. _ASE database: https://ase-lib.org/ase/db/db.html - -XYZ, ASE databases, and also from metrain's -:class:`metatrain.utils.data.dataset.DiskDataset ` file. - -For the small datasets (<10k structures), you can simply provide an XYZ file or an ASE -database to ``metatrain``, and it will handle the data loading for you. Large datasets -(>10k structures) may not fit into the GPU memory. In such cases, it is useful to -pre-process the dataset, save it to disk and load it on the fly during training. - -In this tutorial, we will show how to prepare data for training using three different -formats. You can choose the one that best fits your needs. - -We start by importing the necessary packages. -""" - -# %% -# -import subprocess -from pathlib import Path - -import ase.io -import numpy as np -import torch -from metatensor.torch import Labels, TensorBlock, TensorMap -from metatomic.torch import NeighborListOptions, systems_to_torch - -from metatrain.utils.data.writers import DiskDatasetWriter -from metatrain.utils.neighbor_lists import get_system_with_neighbor_lists - - -# %% -# Create a XYZ training file (small datasets) -# ------------------------------------------- -# -# First, we will show how to create a XYZ file with fields corresponding to the target -# properties. On modern HPC systems, this format is suitable for datasets up to around -# 1M structures. As an example, we will use 100 structures from a file read by ASE_. -# Since files from reference calculations may be located in different directories, we -# first create a list of all path that we want to read from. Here, for simplicity, we -# assume that all files are located in the same directory. -# -# .. _ASE: https://ase-lib.org/ - -filelist = 100 * ["qm9_reduced_100.xyz"] - -# %% -# -# We will now read the structures using the ASE package. Check the ase documentation for -# more details on how to read different file formats. Instead of creating the ``atoms`` -# object by reading from disk, you can also create an -# :class:`ase.Atoms` object containing the chemical ``symbols``, ``positions``, the -# ``cell`` and the periodic boundary conditions (``pbc``) by hand using its constructor. -# -# .. hint:: -# -# If a property is not read by the :func:`ase.io.read` function, you can add custom -# scalar properties to the ``info`` dictionary. Vector properties (e.g. forces) can be -# added to the ``arrays`` dictionary. Tensor properties (e.g. stress) must -# be flattened before adding them to the ``arrays`` dictionary. - -frames = [] -for i, fname in enumerate(filelist): - atoms = ase.io.read(fname, index=i) - - n_atoms = len(atoms) - # scalar - atoms.info["U0"] = -100.0 - # vector - atoms.arrays["forces"] = np.zeros((n_atoms, 3)) - # tensor - atoms.arrays["my_tensor"] = np.zeros((n_atoms, 3, 3)).reshape(n_atoms, 9) - - frames.append(atoms) - -ase.io.write("data.xyz", frames) - -# %% -# -# .. note:: -# -# The names of the added properties (like, ``U0``, etc.) must be referenced correctly -# in the ``options.yaml`` file. -# -# Create a ``DiskDataset`` (large datasets) -# ----------------------------------------- -# -# In addition to the systems and targets (as above), we also save the neighbor -# lists that the model will use during training. We first create the writer object that -# will write the data to a zip file. - -disk_dataset_writer = DiskDatasetWriter("qm9_reduced_100.zip") - -# %% -# -# Then we loop over all structures, convert them to the internal torch format using -# :func:`metatomic.torch.systems_to_torch`, compute the neighbor lists using -# :func:`metatrain.utils.neighbor_lists.get_system_with_neighbor_lists` and write -# everything to disk using the writer's ``write()`` method. - -for i, fname in enumerate(filelist): - atoms = ase.io.read(fname, index=i) - - system = systems_to_torch(atoms, dtype=torch.float64) - system = get_system_with_neighbor_lists( - system, - [NeighborListOptions(cutoff=5.0, full_list=True, strict=True)], - ) - energy = TensorMap( - keys=Labels.single(), - blocks=[ - TensorBlock( - values=torch.tensor([[atoms.info["U0"]]], dtype=torch.float64), - samples=Labels( - names=["system"], - values=torch.tensor([[i]]), - ), - components=[], - properties=Labels("energy", torch.tensor([[0]])), - ) - ], - ) - disk_dataset_writer.write([system], {"energy": energy}) - -disk_dataset_writer.finish() - -# %% -# -# Alternatively, you can also write the whole dataset at once, which might be more -# efficient (but also potentially run into memory issues). We use the same ``frames`` -# that we created above. - -disk_dataset_writer = DiskDatasetWriter("qm9_reduced_100_all_at_once.zip") - -systems = systems_to_torch(frames, dtype=torch.float64) -systems = [ - get_system_with_neighbor_lists( - system, - [NeighborListOptions(cutoff=5.0, full_list=True, strict=True)], - ) - for system in systems -] -energy = TensorMap( - keys=Labels.single(), - blocks=[ - TensorBlock( - values=torch.tensor( - [frame.info["U0"] for frame in frames], dtype=torch.float64 - ).reshape(-1, 1), - samples=Labels.range("system", len(frames)), - components=[], - properties=Labels("energy", torch.tensor([[0]])), - ) - ], -) - -disk_dataset_writer.write(systems, {"energy": energy}) -disk_dataset_writer.finish() - -# %% -# -# The dataset is saved to disk. You can now provide it to ``metatrain`` as a -# dataset to train from, simply by replacing your ``.xyz`` file with the newly created -# zip file (e.g. ``read_from: qm9_reduced_100.zip``). -# -# Create a ``MemmapDataset`` (large datasets, parallel filesystems) -# ----------------------------------------------------------------- -# -# If your dataset is large and you are using a parallel filesystem (e.g. on an HPC -# cluster), it is recommended to use a ``MemmapDataset`` instead of a ``DiskDataset``. -# The ``MemmapDataset`` stores the data inside memory-mapped numpy arrays instead of a -# zip file. Reading from this format avoids I/O bottlenecks, but it does not support -# spherical targets or storing neighbor lists. -# -# As an example, we will use 100 structures from a dataset of carbon structures. The -# numpy arrays must be saved inside a directory, using the following format. - -structures = ase.io.read("carbon_reduced_100.xyz", index=":") - -root = Path("carbon_reduced_100_memmap/") -root.mkdir(exist_ok=True) - -ns_path = root / "ns.npy" -na_path = root / "na.npy" -a_path = root / "a.bin" -x_path = root / "x.bin" -c_path = root / "c.bin" -e_path = root / "e.bin" -f_path = root / "f.bin" -s_path = root / "s.bin" - -ns = len(structures) -na = np.cumsum(np.array([0] + [len(s) for s in structures], dtype=np.int64)) -np.save(ns_path, ns) -np.save(na_path, na) - -a_mm = np.memmap(a_path, dtype="int32", mode="w+", shape=(na[-1],)) -x_mm = np.memmap(x_path, dtype="float32", mode="w+", shape=(na[-1], 3)) -c_mm = np.memmap(c_path, dtype="float32", mode="w+", shape=(ns, 3, 3)) -e_mm = np.memmap(e_path, dtype="float32", mode="w+", shape=(ns, 1)) -f_mm = np.memmap(f_path, dtype="float32", mode="w+", shape=(na[-1], 3)) -s_mm = np.memmap(s_path, dtype="float32", mode="w+", shape=(ns, 3, 3)) - -for i, s in enumerate(structures): - a_mm[na[i] : na[i + 1]] = s.numbers - x_mm[na[i] : na[i + 1]] = s.get_positions() - c_mm[i] = s.get_cell()[:] - e_mm[i] = s.get_potential_energy() - f_mm[na[i] : na[i + 1]] = s.arrays["force"] - s_mm[i] = -s.info["virial"] / s.get_volume() - -a_mm.flush() -x_mm.flush() -c_mm.flush() -e_mm.flush() -f_mm.flush() -s_mm.flush() - -# %% -# -# The dataset is saved to disk. You can now provide it to ``metatrain`` as a -# dataset to train from, simply by specifying the newly created -# directory as the path from which to read the systems -# (e.g. ``read_from: carbon_reduced_100_memmap/``). -# -# For example, you can use the following options file: -# -# .. literalinclude:: options-memmap.yaml -# :language: yaml - -subprocess.run(["mtt", "train", "options-memmap.yaml"]) diff --git a/docs/src/examples/programmatic/data_preparation/data_preparation.py.md5 b/docs/src/examples/programmatic/data_preparation/data_preparation.py.md5 deleted file mode 100644 index ec11fcf764..0000000000 --- a/docs/src/examples/programmatic/data_preparation/data_preparation.py.md5 +++ /dev/null @@ -1 +0,0 @@ -54ca454338c67496640c3b734e586992 \ No newline at end of file diff --git a/docs/src/examples/programmatic/data_preparation/data_preparation.rst b/docs/src/examples/programmatic/data_preparation/data_preparation.rst deleted file mode 100644 index 6aa58fb836..0000000000 --- a/docs/src/examples/programmatic/data_preparation/data_preparation.rst +++ /dev/null @@ -1,390 +0,0 @@ - -.. DO NOT EDIT. -.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. -.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: -.. "examples/programmatic/data_preparation/data_preparation.py" -.. LINE NUMBERS ARE GIVEN BELOW. - -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - :ref:`Go to the end ` - to download the full example code. - -.. rst-class:: sphx-glr-example-title - -.. _sphx_glr_examples_programmatic_data_preparation_data_preparation.py: - - -How to prepare data for training -================================ - -.. attention:: - - This tutorial is only relevant for users who need to prepare their data from scratch - from several files or for big datasets. If you already have your data in a common - file format (like XYZ or `ASE database`_), you can skip this tutorial and directly - start training. - -.. _ASE database: https://ase-lib.org/ase/db/db.html - -XYZ, ASE databases, and also from metrain's -:class:`metatrain.utils.data.dataset.DiskDataset ` file. - -For the small datasets (<10k structures), you can simply provide an XYZ file or an ASE -database to ``metatrain``, and it will handle the data loading for you. Large datasets -(>10k structures) may not fit into the GPU memory. In such cases, it is useful to -pre-process the dataset, save it to disk and load it on the fly during training. - -In this tutorial, we will show how to prepare data for training using three different -formats. You can choose the one that best fits your needs. - -We start by importing the necessary packages. - -.. GENERATED FROM PYTHON SOURCE LINES 30-43 - -.. code-block:: Python - - import subprocess - from pathlib import Path - - import ase.io - import numpy as np - import torch - from metatensor.torch import Labels, TensorBlock, TensorMap - from metatomic.torch import NeighborListOptions, systems_to_torch - - from metatrain.utils.data.writers import DiskDatasetWriter - from metatrain.utils.neighbor_lists import get_system_with_neighbor_lists - - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 44-55 - -Create a XYZ training file (small datasets) -------------------------------------------- - -First, we will show how to create a XYZ file with fields corresponding to the target -properties. On modern HPC systems, this format is suitable for datasets up to around -1M structures. As an example, we will use 100 structures from a file read by ASE_. -Since files from reference calculations may be located in different directories, we -first create a list of all path that we want to read from. Here, for simplicity, we -assume that all files are located in the same directory. - -.. _ASE: https://ase-lib.org/ - -.. GENERATED FROM PYTHON SOURCE LINES 55-58 - -.. code-block:: Python - - - filelist = 100 * ["qm9_reduced_100.xyz"] - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 59-71 - -We will now read the structures using the ASE package. Check the ase documentation for -more details on how to read different file formats. Instead of creating the ``atoms`` -object by reading from disk, you can also create an -:class:`ase.Atoms` object containing the chemical ``symbols``, ``positions``, the -``cell`` and the periodic boundary conditions (``pbc``) by hand using its constructor. - -.. hint:: - - If a property is not read by the :func:`ase.io.read` function, you can add custom - scalar properties to the ``info`` dictionary. Vector properties (e.g. forces) can be - added to the ``arrays`` dictionary. Tensor properties (e.g. stress) must - be flattened before adding them to the ``arrays`` dictionary. - -.. GENERATED FROM PYTHON SOURCE LINES 72-89 - -.. code-block:: Python - - - frames = [] - for i, fname in enumerate(filelist): - atoms = ase.io.read(fname, index=i) - - n_atoms = len(atoms) - # scalar - atoms.info["U0"] = -100.0 - # vector - atoms.arrays["forces"] = np.zeros((n_atoms, 3)) - # tensor - atoms.arrays["my_tensor"] = np.zeros((n_atoms, 3, 3)).reshape(n_atoms, 9) - - frames.append(atoms) - - ase.io.write("data.xyz", frames) - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 90-101 - -.. note:: - - The names of the added properties (like, ``U0``, etc.) must be referenced correctly - in the ``options.yaml`` file. - -Create a ``DiskDataset`` (large datasets) ------------------------------------------ - -In addition to the systems and targets (as above), we also save the neighbor -lists that the model will use during training. We first create the writer object that -will write the data to a zip file. - -.. GENERATED FROM PYTHON SOURCE LINES 102-105 - -.. code-block:: Python - - - disk_dataset_writer = DiskDatasetWriter("qm9_reduced_100.zip") - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 106-110 - -Then we loop over all structures, convert them to the internal torch format using -:func:`metatomic.torch.systems_to_torch`, compute the neighbor lists using -:func:`metatrain.utils.neighbor_lists.get_system_with_neighbor_lists` and write -everything to disk using the writer's ``write()`` method. - -.. GENERATED FROM PYTHON SOURCE LINES 111-138 - -.. code-block:: Python - - - for i, fname in enumerate(filelist): - atoms = ase.io.read(fname, index=i) - - system = systems_to_torch(atoms, dtype=torch.float64) - system = get_system_with_neighbor_lists( - system, - [NeighborListOptions(cutoff=5.0, full_list=True, strict=True)], - ) - energy = TensorMap( - keys=Labels.single(), - blocks=[ - TensorBlock( - values=torch.tensor([[atoms.info["U0"]]], dtype=torch.float64), - samples=Labels( - names=["system"], - values=torch.tensor([[i]]), - ), - components=[], - properties=Labels("energy", torch.tensor([[0]])), - ) - ], - ) - disk_dataset_writer.write([system], {"energy": energy}) - - disk_dataset_writer.finish() - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 139-142 - -Alternatively, you can also write the whole dataset at once, which might be more -efficient (but also potentially run into memory issues). We use the same ``frames`` -that we created above. - -.. GENERATED FROM PYTHON SOURCE LINES 143-171 - -.. code-block:: Python - - - disk_dataset_writer = DiskDatasetWriter("qm9_reduced_100_all_at_once.zip") - - systems = systems_to_torch(frames, dtype=torch.float64) - systems = [ - get_system_with_neighbor_lists( - system, - [NeighborListOptions(cutoff=5.0, full_list=True, strict=True)], - ) - for system in systems - ] - energy = TensorMap( - keys=Labels.single(), - blocks=[ - TensorBlock( - values=torch.tensor( - [frame.info["U0"] for frame in frames], dtype=torch.float64 - ).reshape(-1, 1), - samples=Labels.range("system", len(frames)), - components=[], - properties=Labels("energy", torch.tensor([[0]])), - ) - ], - ) - - disk_dataset_writer.write(systems, {"energy": energy}) - disk_dataset_writer.finish() - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 172-187 - -The dataset is saved to disk. You can now provide it to ``metatrain`` as a -dataset to train from, simply by replacing your ``.xyz`` file with the newly created -zip file (e.g. ``read_from: qm9_reduced_100.zip``). - -Create a ``MemmapDataset`` (large datasets, parallel filesystems) ------------------------------------------------------------------ - -If your dataset is large and you are using a parallel filesystem (e.g. on an HPC -cluster), it is recommended to use a ``MemmapDataset`` instead of a ``DiskDataset``. -The ``MemmapDataset`` stores the data inside memory-mapped numpy arrays instead of a -zip file. Reading from this format avoids I/O bottlenecks, but it does not support -spherical targets or storing neighbor lists. - -As an example, we will use 100 structures from a dataset of carbon structures. The -numpy arrays must be saved inside a directory, using the following format. - -.. GENERATED FROM PYTHON SOURCE LINES 188-230 - -.. code-block:: Python - - - structures = ase.io.read("carbon_reduced_100.xyz", index=":") - - root = Path("carbon_reduced_100_memmap/") - root.mkdir() - - ns_path = root / "ns.npy" - na_path = root / "na.npy" - a_path = root / "a.bin" - x_path = root / "x.bin" - c_path = root / "c.bin" - e_path = root / "e.bin" - f_path = root / "f.bin" - s_path = root / "s.bin" - - ns = len(structures) - na = np.cumsum(np.array([0] + [len(s) for s in structures], dtype=np.int64)) - np.save(ns_path, ns) - np.save(na_path, na) - - a_mm = np.memmap(a_path, dtype="int32", mode="w+", shape=(na[-1],)) - x_mm = np.memmap(x_path, dtype="float32", mode="w+", shape=(na[-1], 3)) - c_mm = np.memmap(c_path, dtype="float32", mode="w+", shape=(ns, 3, 3)) - e_mm = np.memmap(e_path, dtype="float32", mode="w+", shape=(ns, 1)) - f_mm = np.memmap(f_path, dtype="float32", mode="w+", shape=(na[-1], 3)) - s_mm = np.memmap(s_path, dtype="float32", mode="w+", shape=(ns, 3, 3)) - - for i, s in enumerate(structures): - a_mm[na[i] : na[i + 1]] = s.numbers - x_mm[na[i] : na[i + 1]] = s.get_positions() - c_mm[i] = s.get_cell()[:] - e_mm[i] = s.get_potential_energy() - f_mm[na[i] : na[i + 1]] = s.arrays["force"] - s_mm[i] = -s.info["virial"] / s.get_volume() - - a_mm.flush() - x_mm.flush() - c_mm.flush() - e_mm.flush() - f_mm.flush() - s_mm.flush() - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 231-240 - -The dataset is saved to disk. You can now provide it to ``metatrain`` as a -dataset to train from, simply by specifying the newly created -directory as the path from which to read the systems -(e.g. ``read_from: carbon_reduced_100_memmap/``). - -For example, you can use the following options file: - -.. literalinclude:: options.yaml - :language: yaml - -.. GENERATED FROM PYTHON SOURCE LINES 241-243 - -.. code-block:: Python - - - subprocess.run(["mtt", "train", "options.yaml"]) - - - - -.. rst-class:: sphx-glr-script-out - - .. code-block:: none - - - CompletedProcess(args=['mtt', 'train', 'options.yaml'], returncode=0) - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** (0 minutes 10.476 seconds) - - -.. _sphx_glr_download_examples_programmatic_data_preparation_data_preparation.py: - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-example - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: data_preparation.ipynb ` - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: data_preparation.py ` - - .. container:: sphx-glr-download sphx-glr-download-zip - - :download:`Download zipped: data_preparation.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by 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- -.. only:: html - - .. image:: /examples/programmatic/data_preparation/images/thumb/sphx_glr_data_preparation_thumb.png - :alt: - - :ref:`sphx_glr_examples_programmatic_data_preparation_data_preparation.py` - -.. raw:: html - -
How to prepare data for training
-
- - -.. thumbnail-parent-div-close - -.. raw:: html - - - - -.. toctree:: - :hidden: - - /examples/programmatic/data_preparation/data_preparation - - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-gallery - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download all examples in Python source code: data_preparation_python.zip ` - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download all examples in Jupyter notebooks: data_preparation_jupyter.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/programmatic/data_preparation/options.yaml b/docs/src/examples/programmatic/data_preparation/options.yaml deleted file mode 100644 index f8de24c036..0000000000 --- a/docs/src/examples/programmatic/data_preparation/options.yaml +++ /dev/null @@ -1,38 +0,0 @@ -seed: 42 - -architecture: - name: pet - training: - num_epochs: 1 - batch_size: 8 - -training_set: - systems: - read_from: carbon_reduced_100_memmap/ - length_unit: angstrom - targets: - energy: - key: e - unit: eV - forces: - key: f - stress: - key: s - non_conservative_forces: - key: f - quantity: force - unit: eV/A - per_atom: true - type: - cartesian: - rank: 1 - non_conservative_stress: - key: s - quantity: pressure - unit: eV/A^3 - type: - cartesian: - rank: 2 - -test_set: 0.1 -validation_set: 0.1 diff --git a/docs/src/examples/programmatic/data_preparation/qm9_reduced_100.xyz b/docs/src/examples/programmatic/data_preparation/qm9_reduced_100.xyz deleted file mode 100644 index 14b2a0416e..0000000000 --- a/docs/src/examples/programmatic/data_preparation/qm9_reduced_100.xyz +++ /dev/null @@ -1,1201 +0,0 @@ -5 -Properties=species:S:1:pos:R:3 A=157.7118 B=157.70997 C=157.70699 mu=0 alpha=13.21 epsHOMO=-0.3877 epsLUMO=0.1171 epsgap=0.5048 =35.3641 zpve=0.044749 U0=-40.47893 U=-40.476062 H=-40.475117 G=-40.498597 Cv=6.469 pbc="F F F" -C -0.01269814 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zpve=0.015951 U0=-185.648533 U=-185.644825 H=-185.64388 G=-185.667652 Cv=10.398 pbc="F F F" -N 0.01745734 -1.16134217 -0.00415342 -C 0.00253212 -0.00344278 0.00179939 -C -0.01611419 1.37220987 0.00934873 -N -0.03264302 2.53008276 0.01609090 -6 -Properties=species:S:1:pos:R:3 A=68.5171 B=4.8345 C=4.51586 mu=2.7824 alpha=31.14 epsHOMO=-0.2777 epsLUMO=-0.0735 epsgap=0.2042 =268.3921 zpve=0.037208 U0=-190.624631 U=-190.620363 H=-190.619419 G=-190.650543 Cv=13.049 pbc="F F F" -O -0.06776120 -0.04233838 0.00282489 -C -0.00562945 1.16382879 0.00743862 -C 1.22985272 1.91339997 0.00028781 -C 2.23625322 2.57430970 -0.00529277 -H -0.91670701 1.79467546 0.01781352 -H 3.13429662 3.14370267 -0.01037656 -5 -Properties=species:S:1:pos:R:3 A=67.88408 B=5.00823 C=4.66413 mu=2.3112 alpha=26.25 epsHOMO=-0.3166 epsLUMO=-0.11 epsgap=0.2066 =251.0007 zpve=0.02654 U0=-206.721858 U=-206.717875 H=-206.716931 G=-206.747625 Cv=11.329 pbc="F F F" -O -0.04055978 -0.03799144 0.00266513 -C -0.01768870 1.16193251 0.00746008 -C 1.23315534 1.93381336 0.00043371 -N 2.19857378 2.57055097 -0.00483320 -H -0.92627534 1.78959196 0.01764859 -6 -Properties=species:S:1:pos:R:3 A=56.25048 B=4.77441 C=4.40088 mu=0.002 alpha=26.12 epsHOMO=-0.2668 epsLUMO=-0.1113 epsgap=0.1555 =266.8164 zpve=0.036943 U0=-227.798785 U=-227.79457 H=-227.793626 G=-227.825074 Cv=12.147 pbc="F F F" -O 0.00337122 -0.03146586 0.00157206 -C -0.01713174 1.17081409 0.00676444 -C 1.26555783 1.99719410 -0.00007703 -O 1.24507069 3.19946589 0.00681275 -H -0.94690483 1.77678142 0.01852668 -H 2.19534410 1.39121374 -0.01006739 -10 -Properties=species:S:1:pos:R:3 A=80.28766 B=3.3688 C=3.3688 mu=0 alpha=42.32 epsHOMO=-0.2412 epsLUMO=0.0684 epsgap=0.3096 =400.2236 zpve=0.083896 U0=-155.908941 U=-155.90318 H=-155.902236 G=-155.937641 Cv=17.447 pbc="F F F" -C -0.01787959 1.46676893 0.01011180 -C 0.00186644 0.01072913 0.00211109 -C 0.01819704 -1.19272861 -0.00450723 -C 0.03795170 -2.64876793 -0.01251193 -H 0.86460312 1.87589979 0.51479719 -H -0.03217624 1.87210581 -1.00791002 -H -0.90233115 1.85185524 0.53003953 -H -0.83477011 -3.06339007 0.50451436 -H 0.03276422 -3.04315564 -1.03491223 -H 0.93213486 -3.03931642 0.48626606 -10 -Properties=species:S:1:pos:R:3 A=27.39459 B=4.53005 C=4.08342 mu=0.7067 alpha=40.09 epsHOMO=-0.2592 epsLUMO=0.0566 epsgap=0.3157 =333.9589 zpve=0.084338 U0=-155.897345 U=-155.892291 H=-155.891347 G=-155.924226 Cv=17.13 pbc="F F F" -C -0.03095840 1.54775025 0.03167945 -C 0.01485375 0.00962471 -0.02082154 -C 0.69099073 -0.49954575 -1.20857624 -C 1.25222275 -0.89514467 -2.19527879 -H 0.97899300 1.96621609 0.03373834 -H -0.56082354 1.95002902 -0.83580745 -H -0.54576708 1.88225519 0.93709062 -H 0.51920135 -0.37425398 0.87496224 -H -1.00644802 -0.39030838 0.01340632 -H 1.74594541 -1.25604187 -3.06299415 -9 -Properties=species:S:1:pos:R:3 A=28.0016 B=4.67752 C=4.21765 mu=3.9233 alpha=35.38 epsHOMO=-0.3213 epsLUMO=0.034 epsgap=0.3553 =314.5335 zpve=0.07419 U0=-172.006141 U=-172.001467 H=-172.000523 G=-172.032826 Cv=14.988 pbc="F F F" -C -0.02522005 1.54773054 0.02133370 -C 0.01183939 0.00999278 -0.01556702 -C 0.68915114 -0.50836846 -1.20331488 -N 1.22565733 -0.90360846 -2.14737482 -H 0.98571620 1.96212990 0.02370153 -H -0.55522192 1.94599662 -0.84711771 -H -0.53962556 1.88294354 0.92536604 -H 0.52481910 -0.38226048 0.86933355 -H -1.00453542 -0.39832109 0.00530071 -8 -Properties=species:S:1:pos:R:3 A=32.19893 B=4.76299 C=4.30696 mu=4.4361 alpha=31.81 epsHOMO=-0.2683 epsLUMO=0.0173 epsgap=0.2855 =295.6635 zpve=0.063305 U0=-188.042067 U=-188.037478 H=-188.036534 G=-188.06863 Cv=14.488 pbc="F F F" -N -0.03608099 1.48762533 0.03678072 -C -0.01039471 0.02517605 -0.01506608 -C -1.37180956 -0.52108920 0.02739272 -N -2.44026695 -0.95956444 0.03370519 -H 0.91729752 1.83547547 0.05202682 -H -0.47162178 1.85677205 -0.80275569 -H 0.48946161 -0.40880401 -0.89715168 -H 0.52827150 -0.34533338 0.86469547 -8 -Properties=species:S:1:pos:R:3 A=35.11615 B=4.66117 C=4.22354 mu=1.7211 alpha=33.1 epsHOMO=-0.2595 epsLUMO=0.0277 epsgap=0.2872 =300.0993 zpve=0.060632 U0=-191.810916 U=-191.806025 H=-191.805081 G=-191.837634 Cv=15.855 pbc="F F F" -O -0.02013197 1.45738137 0.01010195 -C 0.00168478 0.03579980 0.00224736 -C -1.36366691 -0.47481101 0.01255056 -C -2.47902835 -0.91970202 0.02082451 -H 0.89446911 1.75736553 0.00327363 -H 0.51979028 -0.35175413 -0.88980302 -H 0.53670458 -0.36127661 0.88000894 -H -3.46773502 -1.30737009 0.02818120 -7 -Properties=species:S:1:pos:R:3 A=35.93172 B=4.83443 C=4.37827 mu=4.6788 alpha=28.56 epsHOMO=-0.3018 epsLUMO=0.0022 epsgap=0.3039 =280.6659 zpve=0.050262 U0=-207.916786 U=-207.912215 H=-207.911271 G=-207.943384 Cv=13.845 pbc="F F F" -O -0.03738221 1.44547841 0.01010769 -C 0.01292906 0.03441661 0.00209165 -C -1.35747452 -0.48874523 0.01239738 -N -2.42135277 -0.93685184 0.02016053 -H 0.86898057 1.76972949 0.00417577 -H 0.51692713 -0.36329790 -0.89215814 -H 0.53387616 -0.37278749 0.88223921 -10 -Properties=species:S:1:pos:R:3 A=26.14564 B=4.2893 C=4.12773 mu=2.6741 alpha=35.83 epsHOMO=-0.25 epsLUMO=-0.0205 epsgap=0.2295 =333.3276 zpve=0.084175 U0=-193.075202 U=-193.070116 H=-193.069171 G=-193.102798 Cv=15.954 pbc="F F F" -C 0.16879340 1.52513042 -0.15747056 -C -0.18737625 0.06194763 0.14679374 -C 0.50917645 -0.43996215 1.39125850 -O 1.18191079 -1.43565585 1.45816384 -H 1.23126511 1.63137254 -0.39632693 -H -0.40744668 1.89431681 -1.00969246 -H -0.04931034 2.17266260 0.69857432 -H -1.26905160 -0.01668067 0.32854433 -H 0.06279062 -0.60250479 -0.68474036 -H 0.35384841 0.20663370 2.28871052 -9 -Properties=species:S:1:pos:R:3 A=19.98749 B=6.11589 C=4.82584 mu=3.7071 alpha=32.78 epsHOMO=-0.2516 epsLUMO=0.0335 epsgap=0.2851 =279.7863 zpve=0.074166 U0=-209.144909 U=-209.139976 H=-209.139032 G=-209.172305 Cv=15.058 pbc="F F F" -C 0.01474916 1.47888256 -0.03509326 -N 0.18708077 0.03831317 -0.02906851 -C 0.45703661 -0.66858895 1.10069001 -O 0.58457996 -0.19876202 2.21119705 -H 0.76025289 1.96736104 -0.67198126 -H -0.98462423 1.75984483 -0.38485103 -H 0.14269849 1.82387718 0.99149397 -H 0.10386349 -0.46937726 -0.89461379 -H 0.55047568 -1.75253452 0.88868242 -8 -Properties=species:S:1:pos:R:3 A=48.62351 B=4.67298 C=4.38187 mu=3.92 alpha=29.47 epsHOMO=-0.2814 epsLUMO=0.0074 epsgap=0.2888 =293.917 zpve=0.061327 U0=-229.013797 U=-229.009003 H=-229.008059 G=-229.041086 Cv=13.885 pbc="F F F" -C 0.10653781 1.43512049 0.03336100 -O 0.42686564 0.04716929 0.09309945 -C 0.50714862 -0.63652665 -1.06318297 -O 0.76927014 -1.79955017 -1.11010475 -H 0.92349251 2.00385139 0.48465728 -H -0.03757815 1.76927183 -1.00057425 -H -0.81269569 1.60801916 0.59873287 -H 0.30458460 -0.00231902 -1.94965553 -8 -Properties=species:S:1:pos:R:3 A=36.49282 B=4.31175 C=4.04493 mu=1.7341 alpha=28.53 epsHOMO=-0.2537 epsLUMO=-0.0341 epsgap=0.2196 =303.8129 zpve=0.060508 U0=-228.992613 U=-228.987769 H=-228.986825 G=-229.019918 Cv=14.78 pbc="F F F" -O 0.19375856 1.37170995 0.31398297 -C 0.04992637 0.01405481 -0.04526239 -C -1.40888025 -0.39635680 -0.20601574 -O -1.77729462 -1.37233860 -0.80361689 -H -0.01723186 1.91269132 -0.45337758 -H 0.60440478 -0.26519766 -0.95205725 -H 0.46740406 -0.57521679 0.78176270 -H -2.11835055 0.30075962 0.29870515 -14 -Properties=species:S:1:pos:R:3 A=23.44662 B=3.60588 C=3.38683 mu=0 alpha=45.71 epsHOMO=-0.317 epsLUMO=0.0937 epsgap=0.4107 =426.2996 zpve=0.131708 U0=-158.340943 U=-158.33517 H=-158.334226 G=-158.36894 Cv=19.668 pbc="F F F" -C -0.03007624 1.55804912 0.02980774 -C 0.00707277 0.02834877 -0.00712529 -C 0.71320792 -0.52989340 -1.24765581 -C 0.75036895 -2.05959366 -1.28458957 -H 0.98195330 1.97845113 0.02800797 -H -0.55582433 1.96212343 -0.84270157 -H -0.54083762 1.92692755 0.92493697 -H 0.50928672 -0.34909049 0.89361319 -H -1.01733481 -0.36542299 0.03207393 -H 0.21098948 -0.15245770 -2.14839302 -H 1.73761331 -0.13611625 -1.28685672 -H 1.27616854 -2.46366410 -0.41210980 -H -0.26165699 -2.48000447 -1.28273552 -H 1.26108491 -2.42846444 -2.17974803 -12 -Properties=species:S:1:pos:R:3 A=26.59789 B=3.79186 C=3.53779 mu=1.3402 alpha=38.61 epsHOMO=-0.2619 epsLUMO=0.081 epsgap=0.3429 =382.8628 zpve=0.108241 U0=-194.261089 U=-194.255495 H=-194.254551 G=-194.28893 Cv=18.431 pbc="F F F" -C -0.00379012 1.52196790 0.01027996 -C -0.03589372 -0.00778254 0.00225671 -C -1.45219016 -0.56653711 0.01284386 -O -1.36799785 -1.98394604 0.00453606 -H 1.02477576 1.89411231 0.00239793 -H -0.51286674 1.93764614 -0.86653888 -H -0.49584080 1.92801344 0.90121721 -H 0.48140784 -0.39530552 -0.88316064 -H 0.49828589 -0.40486256 0.87330348 -H -1.98734220 -0.20399494 0.90666062 -H -2.00431485 -0.19459473 -0.86666460 -H -2.26171819 -2.33824298 0.01034748 -12 -Properties=species:S:1:pos:R:3 A=28.29567 B=4.14901 C=3.88349 mu=1.0363 alpha=39.34 epsHOMO=-0.2503 epsLUMO=0.0925 epsgap=0.3428 =368.9331 zpve=0.107895 U0=-194.254127 U=-194.248585 H=-194.247641 G=-194.281899 Cv=17.888 pbc="F F F" -C -0.00519551 1.50868224 0.03308760 -C 0.01316580 -0.01037819 0.01988643 -O 0.69758944 -0.46344140 1.16868979 -C 0.76688491 -1.86508183 1.24602669 -H 1.01445476 1.90423799 0.02996018 -H -0.52990866 1.89184059 -0.84726911 -H -0.51263765 1.87805843 0.92875424 -H -1.01599507 -0.40998729 0.00714282 -H 0.51093143 -0.38369435 -0.89219395 -H -0.23415841 -2.32451489 1.28977780 -H 1.30129276 -2.29853663 0.38486095 -H 1.30990287 -2.11811998 2.16013206 -10 -Properties=species:S:1:pos:R:3 A=14.79671 B=5.6956 C=4.58846 mu=0.0075 alpha=31.42 epsHOMO=-0.2594 epsLUMO=0.0584 epsgap=0.3179 =297.8398 zpve=0.085172 U0=-230.183076 U=-230.177723 H=-230.176779 G=-230.211195 Cv=16.837 pbc="F F F" -O -0.01418735 1.42642242 -0.05423651 -C -0.00391839 0.00717714 0.03496062 -C -1.42317019 -0.55560600 -0.01256639 -O -2.11229413 -0.10563875 -1.17228313 -H -0.18987882 1.64154705 -0.97555148 -H 0.59659504 -0.43315430 -0.77409095 -H 0.47311116 -0.24903733 0.98747216 -H -1.39494859 -1.64975279 -0.06283188 -H -1.96497417 -0.27216915 0.90131972 -H -2.35925991 0.80944396 -1.00614597 -12 -Properties=species:S:1:pos:R:3 A=15.61472 B=6.31363 C=5.54514 mu=0.1136 alpha=41.96 epsHOMO=-0.2727 epsLUMO=0.1012 epsgap=0.3738 =298.6061 zpve=0.109284 U0=-157.116735 U=-157.11209 H=-157.111146 G=-157.143262 Cv=16.49 pbc="F F F" -C -0.03602245 1.52092949 0.01569161 -C 0.01938487 0.01004403 0.04814953 -C 1.30891671 -0.71251995 -0.24920416 -C 0.75347423 -0.70267714 1.15564676 -H 0.90105691 1.95389291 0.38283338 -H -0.19640365 1.89437369 -1.00208569 -H -0.84685273 1.90588930 0.64451322 -H -0.88416809 -0.48001889 -0.30566335 -H 1.26900901 -1.63903068 -0.81150371 -H 2.19010738 -0.11131209 -0.44927513 -H 1.26248862 -0.09487455 1.89690028 -H 0.33540836 -1.62248918 1.54980695 -10 -Properties=species:S:1:pos:R:3 A=18.21931 B=6.63877 C=5.92459 mu=1.812 alpha=35.01 epsHOMO=-0.2633 epsLUMO=0.1052 epsgap=0.3685 =267.2979 zpve=0.085275 U0=-193.039603 U=-193.035186 H=-193.034242 G=-193.065979 Cv=14.764 pbc="F F F" -C -0.01646343 1.51830705 0.00771445 -C -0.02743227 0.01657694 -0.10889741 -C 0.77296553 -0.70131886 -1.10863115 -O 1.17212293 -0.66702969 0.26278829 -H 0.91865980 1.92914217 -0.38285880 -H -0.85158014 1.95456506 -0.55109460 -H -0.11487702 1.82596470 1.05397777 -H -0.93582598 -0.47350600 0.24682793 -H 0.44443922 -1.67201495 -1.47810622 -H 1.40538284 -0.13600339 -1.79271491 -11 -Properties=species:S:1:pos:R:3 A=16.58914 B=7.18798 C=6.11415 mu=1.1353 alpha=39.02 epsHOMO=-0.2304 epsLUMO=0.0968 epsgap=0.3271 =270.5508 zpve=0.097671 U0=-173.147782 U=-173.143343 H=-173.142399 G=-173.174073 Cv=15.298 pbc="F F F" -C -0.05275541 1.47419574 0.00999108 -N 0.01940516 0.02189947 0.06334866 -C 1.32511048 -0.56446850 -0.21420725 -C 0.78051759 -0.54057993 1.17242760 -H 0.86784202 1.97176076 0.36297285 -H -0.23845155 1.79095005 -1.02142866 -H -0.88683448 1.81938959 0.62950398 -H 1.31992909 -1.48186782 -0.79602038 -H 2.15202820 0.11020204 -0.43285248 -H 1.24563570 0.14996098 1.87499245 -H 0.37013358 -1.44020447 1.62233365 -10 -Properties=species:S:1:pos:R:3 A=16.79477 B=6.71015 C=5.86781 mu=1.3894 alpha=34.64 epsHOMO=-0.239 epsLUMO=0.0775 epsgap=0.3166 =263.966 zpve=0.085106 U0=-193.034988 U=-193.030356 H=-193.029411 G=-193.061689 Cv=15.679 pbc="F F F" -O 0.21577583 1.35386859 -0.10695107 -C 0.01628099 -0.02832718 -0.02915694 -C -0.39081465 -0.71497009 1.24315669 -C -1.35118138 -0.64280380 0.06273589 -H -0.43642338 1.79062849 0.45006761 -H 0.74981167 -0.52404050 -0.65624767 -H 0.05959375 -1.66840483 1.49729237 -H -0.61369353 -0.08848006 2.10170640 -H -2.19821932 0.03058430 0.15411451 -H -1.56395912 -1.54640426 -0.49826515 -12 -Properties=species:S:1:pos:R:3 A=10.66107 B=10.66107 C=6.30452 mu=0 alpha=41.83 epsHOMO=-0.2982 epsLUMO=0.0956 epsgap=0.3938 =268.4432 zpve=0.110511 U0=-157.115484 U=-157.111322 H=-157.110378 G=-157.141657 Cv=14.696 pbc="F F F" -C -0.02542957 1.54032578 -0.04215944 -C 1.52564399 1.50419989 0.04121276 -C 1.42146727 0.06146790 0.60844885 -C -0.02638833 -0.01070956 0.04935088 -H -0.46591372 1.99639894 0.84962894 -H -0.47900239 2.00028207 -0.92365389 -H 1.98248278 1.51375334 -0.95303183 -H 2.02191365 2.25986579 0.65522129 -H 2.14291679 -0.67956358 0.25531480 -H 1.43105853 0.05751479 1.70261879 -H -0.79053440 -0.48530296 0.66996573 -H -0.05233245 -0.47238446 -0.94236394 -10 -Properties=species:S:1:pos:R:3 A=12.06545 B=11.77731 C=6.71997 mu=1.7978 alpha=34.56 epsHOMO=-0.2424 epsLUMO=0.0859 epsgap=0.3283 =236.9759 zpve=0.086675 U0=-193.034094 U=-193.029968 H=-193.029024 G=-193.060777 Cv=12.915 pbc="F F F" -C -0.03489789 1.55259455 -0.08507691 -C 1.49349183 1.45459700 0.10114677 -O 1.42725884 0.02348990 0.27254886 -C -0.00634758 0.02236494 0.10944630 -H -0.56579210 2.11301721 0.68591527 -H -0.36628352 1.89393380 -1.06692327 -H 2.10031724 1.73525720 -0.76898349 -H 1.90023090 1.95506611 0.98896748 -H -0.51987849 -0.35595179 1.00235948 -H -0.31970062 -0.57567421 -0.75559275 -12 -Properties=species:S:1:pos:R:3 A=8.63483 B=4.11977 C=2.88825 mu=0.6875 alpha=45.37 epsHOMO=-0.2392 epsLUMO=0.0192 epsgap=0.2584 =452.5112 zpve=0.100501 U0=-248.375248 U=-248.368823 H=-248.367879 G=-248.405354 Cv=21.616 pbc="F F F" -C -0.10255370 1.49907127 -0.00133642 -C -0.00519774 -0.00343114 -0.02723470 -C -0.00688536 -0.76890740 1.26434744 -N 0.08186799 -0.69202791 -1.10087306 -O 0.07439067 0.13215394 -2.24195258 -H 0.78342093 1.94690862 -0.46354050 -H -0.96150430 1.83614082 -0.59021965 -H -0.19854173 1.87462989 1.01931607 -H -0.92620012 -0.57297946 1.82982479 -H 0.07256154 -1.84064782 1.07516863 -H 0.82937178 -0.45553340 1.90133089 -H 0.13975924 -0.51254299 -2.95389260 -10 -Properties=species:S:1:pos:R:3 A=9.17136 B=9.04195 C=4.5531 mu=1.8689 alpha=43.14 epsHOMO=-0.2029 epsLUMO=0.0499 epsgap=0.2528 =303.9808 zpve=0.082433 U0=-210.101789 U=-210.097816 H=-210.096872 G=-210.12818 Cv=14.821 pbc="F F F" -N -0.00829694 1.35362838 0.00995971 -C 1.28033254 1.82457421 -0.00022459 -C 2.12216639 0.73698898 -0.01445600 -C 1.30849904 -0.43109580 -0.01284154 -C -0.00342782 -0.01836884 0.00235736 -H -0.83243234 1.92772668 0.02122995 -H 1.48471820 2.88312630 0.00354313 -H 3.20066918 0.77343077 -0.02488416 -H 1.64810599 -1.45539683 -0.02180394 -H -0.92551647 -0.57697054 0.00839402 -9 -Properties=species:S:1:pos:R:3 A=9.77329 B=9.41192 C=4.7946 mu=3.6193 alpha=39.13 epsHOMO=-0.2253 epsLUMO=0.0332 epsgap=0.2585 =283.6817 zpve=0.071145 U0=-226.160842 U=-226.157088 H=-226.156144 G=-226.187104 Cv=13.371 pbc="F F F" -N -0.00996090 1.35636172 0.01000855 -C 1.29535422 1.79575578 -0.00047017 -C 2.05550519 0.65637438 -0.01422927 -N 1.25385745 -0.46275132 -0.01260894 -C 0.02301170 -0.00706382 0.00200146 -H -0.83195718 1.93618436 0.02132575 -H 1.54553961 2.84344998 0.00289054 -H 3.13177434 0.57569766 -0.02524155 -H -0.87667864 -0.60414531 0.00750794 -9 -Properties=species:S:1:pos:R:3 A=9.50228 B=9.29497 C=4.69874 mu=0.5571 alpha=39.2 epsHOMO=-0.2246 epsLUMO=0.0199 epsgap=0.2445 =289.005 zpve=0.069883 U0=-229.969129 U=-229.965414 H=-229.96447 G=-229.995393 Cv=13.358 pbc="F F F" -O -0.03255819 1.35065130 0.00967376 -C 1.25319237 1.79167710 -0.00030351 -C 2.11664681 0.74375623 -0.01441494 -C 1.30397796 -0.43852387 -0.01283316 -C 0.01629568 -0.00777185 0.00191294 -H 1.37951089 2.86161135 0.00427433 -H 3.19376406 0.79865501 -0.02458604 -H 1.63858893 -1.46383271 -0.02156949 -H -0.93736295 -0.50899925 0.00843891 -8 -Properties=species:S:1:pos:R:3 A=10.10926 B=9.69372 C=4.94857 mu=1.5081 alpha=35.17 epsHOMO=-0.2509 epsLUMO=0.001 epsgap=0.2519 =269.2444 zpve=0.058593 U0=-246.02915 U=-246.025614 H=-246.024669 G=-246.055309 Cv=12.04 pbc="F F F" -O -0.03808298 1.35643965 0.00975193 -C 1.26496982 1.76855319 -0.00061744 -C 2.04955750 0.66630033 -0.01406047 -N 1.24227075 -0.46732066 -0.01237541 -C 0.04019820 0.00561577 0.00183569 -H 1.43513907 2.83095265 0.00340823 -H 3.12532898 0.59783015 -0.02480757 -H -0.89387707 -0.53342154 0.00796935 -17 -Properties=species:S:1:pos:R:3 A=4.4178 B=4.41753 C=4.41717 mu=0.0003 alpha=56.01 epsHOMO=-0.3145 epsLUMO=0.0737 epsgap=0.3882 =486.2719 zpve=0.158836 U0=-197.632222 U=-197.625241 H=-197.624297 G=-197.661411 Cv=26.084 pbc="F F F" -C -0.01859029 1.53486266 0.01039904 -C 0.00210320 -0.00413672 0.00196980 -C 0.74643954 -0.51390760 1.24887638 -C -1.44190166 -0.53673615 0.01311457 -C 0.72234971 -0.50049606 -1.26445632 -H 0.99776928 1.94420529 0.00435474 -H -0.54181883 1.92841415 -0.86819911 -H -0.52758402 1.91888376 0.90148530 -H 0.25549576 -0.17602728 2.16833067 -H 0.77815389 -1.60892372 1.27088904 -H 1.77964786 -0.14975834 1.26944035 -H -1.99871895 -0.18888794 -0.86408066 -H -1.45956909 -1.63227235 0.00649916 -H -1.98088827 -0.19979639 0.90561580 -H 1.75482473 -0.13554018 -1.30118083 -H 0.75396989 -1.59524036 -1.29822280 -H 0.21358221 -0.15343900 -2.17067181 -15 -Properties=species:S:1:pos:R:3 A=4.69474 B=4.66938 C=4.47909 mu=1.384 alpha=49.04 epsHOMO=-0.2601 epsLUMO=0.0664 epsgap=0.3265 =449.0573 zpve=0.134977 U0=-233.560626 U=-233.553779 H=-233.552834 G=-233.589759 Cv=25.128 pbc="F F F" -C -0.00855999 1.54270147 0.00152717 -C 0.00506780 0.00702635 0.01856160 -C 0.76137731 -0.51839196 1.24766443 -C -1.42012774 -0.54933183 -0.02116008 -O 0.62590477 -0.48363745 -1.17657057 -H 1.01361406 1.94020776 0.00281128 -H -0.51459669 1.90702466 -0.89705781 -H -0.52214815 1.94882514 0.87918344 -H 0.28130613 -0.20165548 2.17943598 -H 0.79965506 -1.61131746 1.23022549 -H 1.79096916 -0.14072935 1.26083003 -H -1.93854829 -0.19910724 -0.91828281 -H -1.39937632 -1.64259062 -0.04563833 -H -1.98850725 -0.23059656 0.85726520 -H 1.53111188 -0.15488046 -1.19210222 -9 -Properties=species:S:1:pos:R:3 A=10.21876 B=4.04324 C=2.94978 mu=2.8579 alpha=42.02 epsHOMO=-0.2654 epsLUMO=-0.0575 epsgap=0.2079 =416.7799 zpve=0.065175 U0=-229.927277 U=-229.921622 H=-229.920677 G=-229.955843 Cv=18.723 pbc="F F F" -C -0.00693851 1.49559567 -0.01063358 -C -0.02986170 -0.01594623 0.06248673 -O -0.08815239 -0.63215062 1.10334812 -C 0.02336981 -0.70256747 -1.22062379 -C 0.06738132 -1.26058341 -2.28623129 -H 0.90429806 1.83281887 -0.51655384 -H -0.85338524 1.85556926 -0.60552819 -H -0.05237764 1.91099807 0.99695949 -H 0.10466868 -1.77036047 -3.21832617 -8 -Properties=species:S:1:pos:R:3 A=10.16063 B=4.15847 C=3.0056 mu=3.3351 alpha=37.08 epsHOMO=-0.3007 epsLUMO=-0.0889 epsgap=0.2118 =397.7757 zpve=0.05451 U0=-246.027383 U=-246.022024 H=-246.02108 G=-246.055769 Cv=16.963 pbc="F F F" -C -0.01171097 1.49973776 -0.01213268 -C -0.02919740 -0.00338745 0.07151218 -O -0.09164028 -0.64592927 1.08732241 -C 0.03831104 -0.69690258 -1.23748557 -N 0.09136286 -1.22982650 -2.26228068 -H 0.90093132 1.83370670 -0.51814928 -H -0.85663933 1.84864400 -0.61618135 -H -0.06416540 1.92131542 0.99203818 -8 -Properties=species:S:1:pos:R:3 A=10.68423 B=4.3198 C=3.07993 mu=5.1815 alpha=38.31 epsHOMO=-0.2739 epsLUMO=-0.0438 epsgap=0.2301 =384.5574 zpve=0.056619 U0=-242.19573 U=-242.190591 H=-242.189646 G=-242.223513 Cv=17.465 pbc="F F F" -N -0.05232108 1.34991769 0.15535538 -C 0.08549378 -0.01476951 0.02668890 -N 1.14555215 -0.71993906 -0.03966037 -C -1.19426556 -0.71152088 0.01802151 -N -2.22747610 -1.22790543 0.00132148 -H 0.75474469 1.91998737 -0.04411334 -H -0.94458527 1.76600473 -0.05932293 -H 1.98312429 -0.13415500 -0.02878551 -9 -Properties=species:S:1:pos:R:3 A=9.08854 B=4.42607 C=3.03169 mu=0.9461 alpha=36.51 epsHOMO=-0.2538 epsLUMO=-0.0964 epsgap=0.1574 =399.222 zpve=0.064992 U0=-267.10335 U=-267.097658 H=-267.096714 G=-267.132534 Cv=17.806 pbc="F F F" -C -0.43126066 1.30586664 -0.12758585 -C 0.25285715 -0.01026988 0.13138867 -O 0.69872099 -0.36890333 1.19604690 -C 0.38978943 -0.96773680 -1.06226208 -O -0.01611268 -0.71564293 -2.16583718 -H 0.10006565 1.85324490 -0.91332597 -H -1.44270433 1.13069361 -0.50923371 -H -0.46757554 1.89548158 0.78836479 -H 0.90329886 -1.91558845 -0.79531972 -9 -Properties=species:S:1:pos:R:3 A=10.08927 B=4.54115 C=3.13487 mu=2.7707 alpha=38.09 epsHOMO=-0.2561 epsLUMO=-0.064 epsgap=0.192 =384.4827 zpve=0.067668 U0=-263.278851 U=-263.273589 H=-263.272645 G=-263.306835 Cv=17.972 pbc="F F F" -N -0.16032929 1.08528962 -0.45398652 -C 0.18649127 -0.08762327 0.14758021 -N 1.28979627 -0.46427423 0.67927030 -C -0.94204384 -1.08440264 0.20423621 -O -2.01802205 -0.89007687 -0.30583149 -H 0.54574437 1.73837930 -0.74689246 -H -1.04684920 1.09570857 -0.93607796 -H 2.01738195 0.24991828 0.59881811 -H -0.67740148 -2.01003995 0.74746441 -13 -Properties=species:S:1:pos:R:3 A=7.92874 B=3.8272 C=2.82581 mu=0.6578 alpha=51.2 epsHOMO=-0.2589 epsLUMO=0.0571 epsgap=0.316 =481.9854 zpve=0.112471 U0=-195.186772 U=-195.180446 H=-195.179502 G=-195.215658 Cv=22.569 pbc="F F F" -C -0.01763434 1.54859468 -0.00253584 -C -0.01719180 0.00735789 0.03269604 -C 0.75818463 -0.52820155 1.25278862 -C -1.38213303 -0.51768438 0.00775516 -C -2.50796392 -0.94005629 0.00478552 -H 1.00944396 1.92639613 -0.02632830 -H -0.54514614 1.92217052 -0.88393459 -H -0.51243000 1.95339892 0.88560642 -H 0.49366179 -0.34904155 -0.87265890 -H 0.28968458 -0.19345372 2.18334679 -H 0.77833392 -1.62102506 1.25783727 -H 1.78952492 -0.16181727 1.23574954 -H -3.49998244 -1.31810165 -0.00723572 -12 -Properties=species:S:1:pos:R:3 A=7.90009 B=3.95466 C=2.88643 mu=3.9512 alpha=46.23 epsHOMO=-0.318 epsLUMO=0.0365 epsgap=0.3545 =460.694 zpve=0.102281 U0=-211.295796 U=-211.289821 H=-211.288877 G=-211.324525 Cv=20.467 pbc="F F F" -C -0.00242350 1.55035837 0.01056187 -C -0.01856600 0.01004352 -0.02261770 -C -1.45045318 -0.55766178 0.01293347 -C 0.70125749 -0.48583168 -1.20172764 -N 1.26190946 -0.87126766 -2.13619740 -H 1.01959496 1.93673215 0.00349668 -H -0.52972844 1.96141639 -0.85498386 -H -0.50134251 1.90346839 0.91755862 -H 0.52481877 -0.36222456 0.85525462 -H -2.02427903 -0.21428716 -0.85251869 -H -1.44438296 -1.65026927 0.00754107 -H -1.95805494 -0.21725882 0.91996760 -11 -Properties=species:S:1:pos:R:3 A=8.30095 B=3.97615 C=2.93764 mu=2.7429 alpha=42.87 epsHOMO=-0.2704 epsLUMO=0.0278 epsgap=0.2983 =440.0738 zpve=0.091554 U0=-227.338075 U=-227.332253 H=-227.331309 G=-227.366638 Cv=19.946 pbc="F F F" -C -0.01064295 1.54128691 0.00722087 -C -0.00203811 0.00756015 0.03863463 -N 0.76268677 -0.47101822 1.19250856 -C -1.38541954 -0.52099256 -0.00194097 -N -2.45680847 -0.95636072 0.00694969 -H 1.02043523 1.90106589 0.00392365 -H -0.52873161 1.91193452 -0.88031480 -H -0.51849536 1.94150431 0.89013197 -H 0.50418828 -0.35805500 -0.86276072 -H 0.30070681 -0.18364934 2.05155305 -H 0.78148992 -1.48670299 1.20428183 -11 -Properties=species:S:1:pos:R:3 A=8.55919 B=3.89952 C=2.91869 mu=1.3582 alpha=44.03 epsHOMO=-0.2665 epsLUMO=0.0336 epsgap=0.3001 =444.6452 zpve=0.088908 U0=-231.108368 U=-231.102292 H=-231.101348 G=-231.137061 Cv=21.306 pbc="F F F" -C -0.00754278 1.55566144 0.08650633 -C 0.01207541 0.02096553 0.04935916 -O -1.29150251 -0.52785128 0.22126386 -C 0.65093362 -0.47718651 -1.17430507 -C 1.13902673 -0.86577343 -2.20205518 -H 1.00824243 1.95969458 0.04929733 -H -0.56272245 1.95192010 -0.76978085 -H -0.49328161 1.88929438 1.00704123 -H 0.57811967 -0.34464907 0.91458864 -H -1.78516532 -0.35903262 -0.58909400 -H 1.58078877 -1.22365060 -3.09919447 -10 -Properties=species:S:1:pos:R:3 A=8.55908 B=4.01671 C=2.97882 mu=3.269 alpha=39.28 epsHOMO=-0.3051 epsLUMO=0.0115 epsgap=0.3166 =424.3395 zpve=0.078602 U0=-247.214861 U=-247.209162 H=-247.208218 G=-247.243338 Cv=19.252 pbc="F F F" -C -0.00992462 1.54951908 0.00732200 -C 0.00740485 0.01722020 0.05031270 -O 0.72085722 -0.49630479 1.15864583 -C -1.36995095 -0.52355929 0.00894432 -N -2.45081572 -0.93233138 0.00716912 -H 1.01892314 1.91630447 0.02724149 -H -0.50261490 1.90969362 -0.89997951 -H -0.54800426 1.95377611 0.87053664 -H 0.53602846 -0.36494195 -0.82970945 -H 0.27702643 -0.20644534 1.96421773 -13 -Properties=species:S:1:pos:R:3 A=7.66289 B=3.70925 C=2.79937 mu=2.6921 alpha=46.58 epsHOMO=-0.2469 epsLUMO=-0.0188 epsgap=0.2281 =482.0475 zpve=0.112328 U0=-232.364952 U=-232.358577 H=-232.357633 G=-232.394589 Cv=21.434 pbc="F F F" -C -0.02629647 1.53877116 -0.00783747 -C 0.01468904 0.00095310 0.00166037 -C 0.74737862 -0.53388859 1.24420471 -C -1.40211264 -0.53859257 -0.01557820 -O -1.87620455 -1.23582351 -0.87399559 -H 0.98823696 1.94625080 0.03705522 -H -0.50725090 1.92153422 -0.91285565 -H -0.57756514 1.92360883 0.85796386 -H 0.50584680 -0.36421091 -0.90619124 -H 0.22958380 -0.23909403 2.16431052 -H 0.81692736 -1.62559499 1.22973438 -H 1.76267153 -0.12846566 1.29050755 -H -2.00861510 -0.22792742 0.87121666 -11 -Properties=species:S:1:pos:R:3 A=8.31096 B=4.07963 C=2.92918 mu=2.8354 alpha=39.16 epsHOMO=-0.255 epsLUMO=-0.0279 epsgap=0.2271 =432.1489 zpve=0.088443 U0=-268.287661 U=-268.281505 H=-268.280561 G=-268.316982 Cv=20.344 pbc="F F F" -C -0.05483118 1.46032843 -0.17068647 -C 0.07105714 -0.03524485 0.11225841 -O 0.58204486 -0.33316656 1.40176256 -C -1.27639279 -0.74383027 0.03051185 -O -2.11855775 -0.49643175 -0.79163412 -H 0.93158072 1.93091398 -0.23353204 -H -0.57713995 1.61290939 -1.11835562 -H -0.62444315 1.94776119 0.62573670 -H 0.69687521 -0.50071620 -0.67159471 -H 1.45603316 0.06066659 1.47858584 -H -1.40723217 -1.53388627 0.80211181 -12 -Properties=species:S:1:pos:R:3 A=8.9331 B=4.19405 C=2.96046 mu=3.7163 alpha=44.42 epsHOMO=-0.2424 epsLUMO=0.0327 epsgap=0.2751 =441.85 zpve=0.10227 U0=-248.430371 U=-248.424309 H=-248.423365 G=-248.459383 Cv=19.918 pbc="F F F" -C -0.09776499 1.44917268 0.04561984 -N 0.01043053 0.00373818 -0.00070600 -C 0.05323028 -0.71432112 1.25264891 -C 0.06728716 -0.64067225 -1.20060778 -O 0.03636432 -0.11006515 -2.29109228 -H 0.75650660 1.88747109 0.57647421 -H -0.11648577 1.81632103 -0.98049172 -H -1.01641106 1.75521548 0.56151415 -H -0.85453556 -0.53657675 1.84390168 -H 0.13297722 -1.78696336 1.05851351 -H 0.91637348 -0.40737846 1.85761345 -H 0.14762240 -1.73801339 -1.06805139 -11 -Properties=species:S:1:pos:R:3 A=10.13982 B=3.81346 C=2.86965 mu=2.9514 alpha=39.33 epsHOMO=-0.2699 epsLUMO=-0.0262 epsgap=0.2437 =440.1727 zpve=0.088924 U0=-268.301176 U=-268.295084 H=-268.29414 G=-268.331307 Cv=19.824 pbc="F F F" -C -0.01708808 1.44281136 0.03225501 -C -0.05544542 -0.06560572 -0.02375999 -O -0.17379913 -0.76196233 0.96223129 -C 0.06068107 -0.73615179 -1.38825252 -O 0.00877771 -2.12574390 -1.27749486 -H 0.92062950 1.81463913 -0.39522215 -H -0.83050523 1.86542281 -0.56780350 -H -0.10911975 1.78049045 1.06493675 -H -0.75015067 -0.35235060 -2.03056074 -H 1.00312898 -0.40389029 -1.85592942 -H -0.09063255 -2.29450960 -0.32608019 -13 -Properties=species:S:1:pos:R:3 A=9.55216 B=3.57844 C=2.7341 mu=2.6168 alpha=46.19 epsHOMO=-0.2423 epsLUMO=-0.0072 epsgap=0.2351 =489.8518 zpve=0.112006 U0=-232.377706 U=-232.371073 H=-232.370129 G=-232.408256 Cv=21.716 pbc="F F F" -C -0.04384102 1.54099793 -0.03913814 -C 0.02765132 0.01885817 0.04778773 -C 0.76814644 -0.48842218 1.28000847 -C 0.87850208 -1.99746312 1.42393323 -O 1.24179142 0.26651340 2.09788702 -H 0.95796454 1.97690198 -0.06816012 -H -0.58447717 1.85356031 -0.93690369 -H -0.55173626 1.95737759 0.83440529 -H -0.97692824 -0.42709877 0.05353178 -H 0.51853486 -0.40806507 -0.83809409 -H 1.39924980 -2.42678188 0.56053177 -H 1.42001187 -2.24449367 2.33776426 -H -0.11750602 -2.45360651 1.45141965 -12 -Properties=species:S:1:pos:R:3 A=9.78802 B=3.78441 C=2.84416 mu=3.499 alpha=42.54 epsHOMO=-0.2438 epsLUMO=0.0355 epsgap=0.2793 =457.447 zpve=0.101847 U0=-248.448467 U=-248.441988 H=-248.441044 G=-248.478935 Cv=21.374 pbc="F F F" -C -0.00220144 1.53344329 -0.05960369 -C -0.03235837 0.01206167 0.06840265 -C 0.69666086 -0.48196462 1.31828322 -N 0.98104007 -1.82034162 1.31984698 -O 1.00392124 0.24606232 2.24136749 -H 1.02111190 1.89525169 -0.19182672 -H -0.59707204 1.86206427 -0.91645604 -H -0.39728839 1.99920712 0.84545201 -H -1.06936083 -0.34552935 0.13040806 -H 0.39947165 -0.46906818 -0.81777020 -H 0.63281951 -2.43917647 0.60874785 -H 1.35708014 -2.22217793 2.16303522 -12 -Properties=species:S:1:pos:R:3 A=10.01185 B=3.84195 C=2.87635 mu=3.5402 alpha=43.61 epsHOMO=-0.2418 epsLUMO=0.0387 epsgap=0.2805 =458.9803 zpve=0.101735 U0=-248.443503 U=-248.436899 H=-248.435955 G=-248.474272 Cv=20.967 pbc="F F F" -C 0.00619941 1.45933202 -0.03893859 -N -0.06411616 0.01422963 -0.12046901 -C -1.20595394 -0.68196796 0.16117120 -C -1.08982704 -2.19284899 0.02095775 -O -2.24482911 -0.14443077 0.49994418 -H 0.75053989 1.78372520 0.69715573 -H 0.25692001 1.90395263 -1.00876215 -H -0.97856026 1.81121438 0.27082194 -H 0.75820982 -0.49543660 -0.39464024 -H -0.09909703 -2.53543104 -0.28970003 -H -1.34097671 -2.65395851 0.97972885 -H -1.82903100 -2.53483935 -0.70813076 -11 -Properties=species:S:1:pos:R:3 A=10.44045 B=4.04692 C=2.98547 mu=3.5648 alpha=39.51 epsHOMO=-0.2436 epsLUMO=0.0599 epsgap=0.3034 =428.404 zpve=0.091713 U0=-264.504487 U=-264.498452 H=-264.497508 G=-264.533633 Cv=20.274 pbc="F F F" -C 0.00922630 1.45021814 -0.03194543 -N -0.01429488 -0.00054646 -0.01782667 -C -1.21576579 -0.67252403 0.04421896 -N -1.09733352 -2.04644898 -0.15997741 -O -2.28599299 -0.12893711 0.24196027 -H 0.89659038 1.81885022 0.49120949 -H 0.00233950 1.86388504 -1.04914155 -H -0.88562684 1.79943346 0.48353298 -H 0.75257305 -0.47506631 -0.46719265 -H -0.24882333 -2.48772194 0.16375654 -H -1.93419610 -2.53898393 0.11482880 -12 -Properties=species:S:1:pos:R:3 A=9.69683 B=4.1533 C=3.01703 mu=1.1876 alpha=43.57 epsHOMO=-0.2595 epsLUMO=0.0352 epsgap=0.2948 =443.1687 zpve=0.102062 U0=-248.416462 U=-248.410358 H=-248.409414 G=-248.445651 Cv=20.256 pbc="F F F" -C -0.09160695 1.37540046 -0.00729426 -O -0.06964534 -0.04831581 -0.11148812 -C 0.68114625 -0.56142673 -1.10732458 -C 0.60405298 -2.06701550 -1.08766892 -N 1.32645533 0.19266343 -1.89967731 -H 0.91463916 1.76843655 0.16257460 -H -0.48562826 1.82624615 -0.92235216 -H -0.73971430 1.60007423 0.84110605 -H -0.43430065 -2.39242162 -1.20325205 -H 1.20627110 -2.50049977 -1.88745640 -H 0.95651337 -2.44976670 -0.12501838 -H 1.83504115 -0.36104361 -2.58486055 -11 -Properties=species:S:1:pos:R:3 A=10.20392 B=4.16834 C=3.07281 mu=1.7569 alpha=39.33 epsHOMO=-0.2685 epsLUMO=0.0174 epsgap=0.2859 =427.6606 zpve=0.089436 U0=-268.32127 U=-268.315051 H=-268.314106 G=-268.351214 Cv=19.501 pbc="F F F" -C 0.07295254 1.38840919 0.06442046 -O 0.21049114 -0.03740028 0.04612736 -C 0.50102805 -0.57224288 -1.15975346 -C 0.62218715 -2.07378323 -1.06030684 -O 0.63922154 0.08224418 -2.16157325 -H 1.00029720 1.87062570 -0.25532064 -H -0.73338957 1.70782089 -0.60080454 -H -0.15751280 1.65154060 1.09692954 -H -0.31330001 -2.50202851 -0.68941906 -H 0.85646588 -2.48430275 -2.04157352 -H 1.40651685 -2.34055496 -0.34634301 -10 -Properties=species:S:1:pos:R:3 A=10.68182 B=4.39532 C=3.17969 mu=2.329 alpha=35.5 epsHOMO=-0.2669 epsLUMO=0.0575 epsgap=0.3244 =398.5908 zpve=0.079271 U0=-284.385189 U=-284.379361 H=-284.378417 G=-284.414085 Cv=19.084 pbc="F F F" -C -0.02645771 1.38305753 0.02085660 -O 0.04139566 -0.04439503 -0.00277452 -C 0.66775338 -0.56001625 -1.09000344 -N 0.72605063 -1.92219983 -0.98822954 -O 1.12952381 0.09648143 -1.99322180 -H 0.97529779 1.82044307 0.02387306 -H -0.57085698 1.76214621 -0.84790425 -H -0.55410722 1.63835731 0.94043594 -H 0.12662696 -2.38401590 -0.32566929 -H 0.97230070 -2.41798421 -1.82732573 -10 -Properties=species:S:1:pos:R:3 A=10.20696 B=3.93135 C=2.9219 mu=4.5676 alpha=35.87 epsHOMO=-0.246 epsLUMO=0.0273 epsgap=0.2733 =418.5967 zpve=0.078222 U0=-284.360325 U=-284.354148 H=-284.353204 G=-284.390143 Cv=20.089 pbc="F F F" -N -0.08115607 1.31807026 0.01276742 -C 0.05442259 -0.04477733 0.08155388 -O 1.12163074 -0.59783950 0.20940553 -C -1.28687786 -0.79011642 0.06178435 -O -1.04605114 -2.13954195 -0.23957158 -H 0.76623366 1.85991128 -0.04483425 -H -0.93021346 1.74567447 -0.31565615 -H -1.74559696 -0.65521334 1.05526700 -H -1.96026061 -0.31149222 -0.67026256 -H -1.85637296 -2.63017646 -0.07625478 -10 -Properties=species:S:1:pos:R:3 A=10.14389 B=4.0714 C=3.02223 mu=5.3004 alpha=35.19 epsHOMO=-0.2527 epsLUMO=0.0208 epsgap=0.2735 =408.0279 zpve=0.080317 U0=-284.372483 U=-284.367172 H=-284.366228 G=-284.40095 Cv=17.931 pbc="F F F" -N 0.01838100 0.93057357 0.04943058 -H -0.70674506 0.56116798 -0.55550134 -H 0.68248307 0.18430002 0.21487099 -H 0.21928084 2.79858558 -0.32670585 -C -0.54586914 1.44688585 1.30274586 -C -0.80466975 2.96030569 1.21052835 -O -0.21468822 3.53648884 0.15531687 -O -1.45295676 3.56338618 2.02143528 -H -1.47662579 0.96512183 1.62072169 -H 0.17781209 1.30324286 2.11157176 -13 -Properties=species:S:1:pos:R:3 A=6.6508 B=4.19317 C=3.39441 mu=2.2854 alpha=42.01 epsHOMO=-0.2597 epsLUMO=0.0631 epsgap=0.3228 =438.3028 zpve=0.11345 U0=-269.479234 U=-269.472993 H=-269.472049 G=-269.508213 Cv=22.072 pbc="F F F" -C -0.02775167 1.53421433 0.00651151 -C 0.08343246 0.01218277 0.05258986 -O 0.95856392 -0.48200710 -0.96694113 -C 0.69767574 -0.49516451 1.35948812 -O 2.02247062 -0.02842530 1.52222186 -H 0.94333763 1.98841434 0.21841589 -H -0.37320516 1.87354041 -0.97716908 -H -0.74773817 1.89256394 0.74926853 -H -0.91671848 -0.43697036 -0.06040528 -H 0.76726710 -0.01412177 -1.78502043 -H 0.11650002 -0.13351914 2.21392431 -H 0.65814685 -1.59563445 1.36274474 -H 2.46378566 -0.22762476 0.68698431 -17 -Properties=species:S:1:pos:R:3 A=7.26511 B=3.32874 C=2.54491 mu=0.0618 alpha=56.26 epsHOMO=-0.3085 epsLUMO=0.085 epsgap=0.3934 =565.8412 zpve=0.159632 U0=-197.629387 U=-197.622325 H=-197.621381 G=-197.659365 Cv=25.169 pbc="F F F" -C 0.05879035 1.56174169 -0.01945240 -C -0.01544024 0.03182306 0.00308332 -C -1.44103237 -0.54862028 0.05028629 -C -1.40777007 -2.07231030 -0.13012010 -C -2.18400529 -0.17659759 1.34114303 -H 1.09435638 1.90233607 -0.11900072 -H -0.50689566 1.97217319 -0.86388098 -H -0.34407951 2.00628082 0.89579522 -H 0.48926758 -0.36011214 -0.88953902 -H 0.55254938 -0.35139446 0.86269887 -H -2.00031575 -0.12153682 -0.79559684 -H -0.85501131 -2.55065810 0.68775438 -H -2.41787513 -2.49543628 -0.13720127 -H -0.91928762 -2.35363802 -1.06914014 -H -3.19100197 -0.60699559 1.35137051 -H -1.65197294 -0.56010584 2.22063481 -H -2.28793759 0.90575132 1.46079572 -15 -Properties=species:S:1:pos:R:3 A=8.11598 B=3.43678 C=2.66869 mu=1.3894 alpha=49.45 epsHOMO=-0.2617 epsLUMO=0.0733 epsgap=0.335 =521.8605 zpve=0.136091 U0=-233.555951 U=-233.549143 H=-233.548199 G=-233.585602 Cv=23.99 pbc="F F F" -C -0.03841227 1.55621396 0.00935837 -C 0.01907135 0.02716079 -0.02074203 -C 0.73141506 -0.52709328 -1.25454422 -C 0.83975868 -2.05206973 -1.22872655 -O -0.01226470 -0.08514394 -2.38973758 -H 0.96845664 1.98863424 0.03526234 -H -0.54377006 1.93824134 -0.88074407 -H -0.57652955 1.91383386 0.89256698 -H 0.53166867 -0.34991486 0.87240999 -H -0.99727186 -0.38638654 -0.00185289 -H 1.74909346 -0.09943025 -1.28826184 -H 1.34143968 -2.42487137 -2.12957624 -H 1.41822012 -2.39229108 -0.36340871 -H -0.15616765 -2.50443859 -1.18308875 -H 0.43090825 -0.40901616 -3.17979642 -15 -Properties=species:S:1:pos:R:3 A=7.55335 B=3.78323 C=2.80566 mu=1.0758 alpha=49.8 epsHOMO=-0.2476 epsLUMO=0.086 epsgap=0.3336 =507.9614 zpve=0.135681 U0=-233.545899 U=-233.539034 H=-233.53809 G=-233.57582 Cv=23.573 pbc="F F F" -C -0.00857605 1.38097833 0.09454670 -O -0.02255106 -0.00739461 -0.13062576 -C -1.30243341 -0.61997805 -0.02088309 -C -1.18687286 -1.97885751 -0.70303918 -C -1.75587824 -0.74662760 1.43714508 -H 1.00321550 1.73258087 -0.12443496 -H -0.71511468 1.90838275 -0.56674280 -H -0.24909065 1.64967237 1.13384681 -H -2.03737978 -0.00432241 -0.56884198 -H -0.44792779 -2.59945605 -0.18596870 -H -2.14751041 -2.50225525 -0.69102581 -H -0.86524212 -1.85860679 -1.74077836 -H -2.73321482 -1.23674908 1.49205117 -H -1.03621350 -1.34483828 2.00544671 -H -1.84898410 0.22935483 1.92198087 -15 -Properties=species:S:1:pos:R:3 A=6.14183 B=5.17679 C=3.79297 mu=0.1068 alpha=52.92 epsHOMO=-0.2633 epsLUMO=0.0893 epsgap=0.3526 =439.1643 zpve=0.137025 U0=-196.409349 U=-196.403398 H=-196.402453 G=-196.437676 Cv=22.258 pbc="F F F" -C -0.03913204 1.50456305 0.04978913 -C 0.00985856 -0.01113880 0.01248619 -C -1.24167981 -0.66663044 -0.53989979 -C 1.33803387 -0.66895748 -0.27400430 -C 0.75126442 -0.72221586 1.11862571 -H 0.88633040 1.92490040 0.45579358 -H -0.18183878 1.92045743 -0.95489291 -H -0.86832076 1.85815718 0.67439648 -H -1.43570890 -0.34344387 -1.56976411 -H -1.15326757 -1.75757800 -0.54433832 -H -2.12221416 -0.40570820 0.05951361 -H 1.34398374 -1.58235208 -0.86005084 -H 2.20196897 -0.03329084 -0.43929326 -H 1.21949576 -0.12246018 1.89246580 -H 0.36153319 -1.67153717 1.47171932 -13 -Properties=species:S:1:pos:R:3 A=6.49534 B=5.52329 C=3.9108 mu=1.8235 alpha=45.7 epsHOMO=-0.2596 epsLUMO=0.091 epsgap=0.3505 =405.521 zpve=0.112851 U0=-232.335768 U=-232.329981 H=-232.329037 G=-232.364091 Cv=20.719 pbc="F F F" -C -0.03970330 1.50954998 -0.04112233 -C -0.03023556 -0.00335329 -0.06498361 -C -1.24613189 -0.68356648 0.52531190 -C 0.78937201 -0.71129193 -1.06027559 -O 1.22546798 -0.60059036 0.29712283 -H 0.88992772 1.90948478 -0.45296558 -H -0.87952723 1.90350108 -0.62376642 -H -0.14448576 1.87590604 0.98604429 -H -2.15218727 -0.41012616 -0.02629495 -H -1.13412584 -1.77005431 0.49747032 -H -1.38586631 -0.38055453 1.56882757 -H 0.50712690 -1.71386874 -1.38085018 -H 1.37088104 -0.14369322 -1.78640853 -13 -Properties=species:S:1:pos:R:3 A=6.57648 B=5.31603 C=3.98453 mu=1.3822 alpha=45.85 epsHOMO=-0.2494 epsLUMO=0.0776 epsgap=0.327 =401.4888 zpve=0.113237 U0=-232.33341 U=-232.327734 H=-232.32679 G=-232.36147 Cv=21.17 pbc="F F F" -C -0.03714052 1.53215920 0.01332310 -C 0.01344335 0.02117019 0.01922005 -O -1.17476104 -0.59735284 -0.40931260 -C 1.30744798 -0.69902106 -0.27389650 -C 0.70844749 -0.71373504 1.12226318 -H 0.89026861 1.96240279 0.40276135 -H -0.17846151 1.91561262 -1.00518522 -H -0.87011232 1.88955201 0.62730953 -H -1.28352265 -0.40225391 -1.34690641 -H 1.23973813 -1.61642115 -0.84783774 -H 2.20543993 -0.11523820 -0.44744474 -H 1.19539159 -0.13889834 1.90240284 -H 0.24735439 -1.63670241 1.45378480 -10 -Properties=species:S:1:pos:R:3 A=12.18399 B=5.1384 C=3.78868 mu=2.5732 alpha=40.19 epsHOMO=-0.263 epsLUMO=0.0277 epsgap=0.2907 =352.0378 zpve=0.080558 U0=-247.201165 U=-247.196699 H=-247.195755 G=-247.22841 Cv=15.51 pbc="F F F" -N -0.04445152 1.25623533 0.31681915 -C -0.03914559 0.04913710 -0.01670558 -C -1.08749619 -1.04164446 -0.11488033 -C 0.09869688 -1.90754844 -0.58225894 -O 0.99647508 -0.76306629 -0.44028010 -H 0.88935980 1.66481350 0.24916893 -H -1.54324114 -1.32235812 0.83572172 -H -1.86910036 -0.85713839 -0.85325910 -H 0.39739753 -2.72227350 0.08158721 -H 0.06945080 -2.25418345 -1.61796084 -11 -Properties=species:S:1:pos:R:3 A=10.79571 B=4.78732 C=3.54292 mu=2.7119 alpha=42.77 epsHOMO=-0.2415 epsLUMO=-0.0194 epsgap=0.2222 =379.6371 zpve=0.090544 U0=-231.15578 U=-231.150804 H=-231.149859 G=-231.184401 Cv=17.076 pbc="F F F" -O -0.00101746 0.01846628 -0.20114742 -C -0.01405811 1.19658059 0.00205306 -C -1.13542911 2.22979148 0.21957980 -C -0.03500884 3.29097637 0.53052223 -C 1.08469664 2.25947795 0.18994331 -H -1.71642814 2.41772662 -0.68901078 -H -1.83392757 1.98415059 1.02444244 -H -0.05534741 4.17585264 -0.10670963 -H -0.02541477 3.61399048 1.57278917 -H 1.81074389 2.03288534 0.97578973 -H 1.63600801 2.46255380 -0.73376244 -10 -Properties=species:S:1:pos:R:3 A=12.23436 B=4.97895 C=3.70561 mu=3.6671 alpha=39.73 epsHOMO=-0.25 epsLUMO=0.038 epsgap=0.288 =355.0934 zpve=0.080442 U0=-247.225618 U=-247.220897 H=-247.219953 G=-247.253218 Cv=16.1 pbc="F F F" -O 0.03184134 -0.03461682 0.00849836 -C -0.02440334 1.16440533 0.02160811 -C -1.14541322 2.23712809 0.03012901 -C 0.00928805 3.27945878 0.04557758 -N 0.93843838 2.14652109 0.03554396 -H -1.77030529 2.23779730 -0.86428437 -H -1.77632809 2.21765386 0.92007934 -H 0.07170564 3.91863516 -0.84072193 -H 0.06622603 3.89826625 0.94657952 -H 1.94616840 2.09535713 0.03855945 -9 -Properties=species:S:1:pos:R:3 A=12.44643 B=5.23328 C=3.86599 mu=3.9339 alpha=35.4 epsHOMO=-0.2788 epsLUMO=0.0089 epsgap=0.2878 =336.0792 zpve=0.068574 U0=-267.106213 U=-267.101929 H=-267.100985 G=-267.13332 Cv=14.483 pbc="F F F" -O 0.04424004 0.03933243 -0.33071104 -C -0.04854479 1.17416230 0.00410076 -C -1.14211656 2.23611850 0.15338361 -C 0.02332538 3.12357010 0.61681625 -O 0.95663808 2.01190122 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"ase.units", - "name": "fs" - } - ], - "atoms": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms" - } - ], - "atoms.calc": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.calc" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.calc" - } - ], - "copy.deepcopy": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "copy", - "module_short": "copy", - "name": "deepcopy" - } - ], - "dyn": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.md.verlet", - "module_short": "ase.md", - "name": "VelocityVerlet" - }, - { - 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"is_explicit": false, - "module": "ase.md", - "module_short": "ase.md", - "name": "VelocityVerlet.run" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "VelocityVerlet.run" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.md.md", - "module_short": "ase.md.md", - "name": "MolecularDynamics.run" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.md", - "module_short": "ase.md", - "name": "MolecularDynamics.run" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "MolecularDynamics.run" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.optimize.optimize", - "module_short": "ase.optimize.optimize", - "name": "Dynamics.run" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.optimize", - "module_short": "ase.optimize", - "name": "Dynamics.run" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Dynamics.run" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.utils", - "module_short": "ase.utils", - "name": "IOContext.run" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "IOContext.run" - } - ], - "frame_ahead": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms" - } - ], - "frame_ahead.get_momenta": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.get_momenta" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.get_momenta" - } - ], - "frame_ahead.get_positions": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.get_positions" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.get_positions" - } - ], - "frame_ahead_trev": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms" - } - ], - "frame_ahead_trev.get_momenta": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.get_momenta" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.get_momenta" - } - ], - "frame_ahead_trev.set_momenta": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.set_momenta" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.set_momenta" - } - ], - "frame_now": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms" - } - ], - "frame_now_trev": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms" - } - ], - "frame_now_trev.get_momenta": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.get_momenta" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.get_momenta" - } - ], - "frame_now_trev.set_momenta": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.set_momenta" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.set_momenta" - } - ], - "get_structure_for_dataset": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - } - ], - "i": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "int" - } - ], - "s": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms" - } - ], - "s.arrays": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "dict" - } - ], - "spacing": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "int" - } - ], - "store_trajectory": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - } - ], - "structures_for_dataset": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "list" - } - ], - "structures_for_dataset.append": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "builtin_function_or_method" - } - ], - "subprocess.run": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "subprocess", - "module_short": "subprocess", - "name": "run" - } - ], - "time_lag": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "int" - } - ], - "trajectory": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "list" - } - ], - "trajectory.append": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "builtin_function_or_method" - } - ] -} diff --git a/docs/src/examples/programmatic/flashmd/flashmd.ipynb b/docs/src/examples/programmatic/flashmd/flashmd.ipynb deleted file mode 100644 index 6333cb2726..0000000000 --- a/docs/src/examples/programmatic/flashmd/flashmd.ipynb +++ /dev/null @@ -1,97 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# Training a FlashMD model\n\nThis tutorial demonstrates how to train a FlashMD model for the direct prediction\nof molecular dynamics. This type of model affords faster MD simulations compared to\nMLIPs by a factor between 10 and 30 (https://arxiv.org/abs/2505.19350).\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import copy\nimport subprocess\n\nimport ase\nimport ase.build\nimport ase.io\nimport ase.units\nfrom ase.calculators.emt import EMT\nfrom ase.md import VelocityVerlet\nfrom ase.md.langevin import Langevin\nfrom ase.md.velocitydistribution import MaxwellBoltzmannDistribution" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data generation\n\nFlashMD models train on molecular dynamics trajectories in the NVE ensemble (i.e.,\nmost often with the velocity Verlet integrator). These trajectories can be generated\nwith almost any MD code (i-PI, LAMMPS, etc.). Here, for simplicity, we will use ASE\nand its built-in EMT potential. In reality, you might want to use a more accurate\nbaseline such as ab initio MD or a machine-learned interatomic potential (MLIP).\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# We start by creating a simple system (a small box of aluminum).\natoms = ase.build.bulk(\"Al\", \"fcc\", cubic=True) * (2, 2, 2)\n\n# We first equilibrate the system at 300K using a Langevin thermostat.\nMaxwellBoltzmannDistribution(atoms, temperature_K=300)\natoms.calc = EMT()\ndyn = Langevin(\n atoms, 2 * ase.units.fs, temperature_K=300, friction=1 / (100 * ase.units.fs)\n)\ndyn.run(1000) # 2 ps equilibration (around 10 ps is better in practice)\n\n# Then, we run a production simulation in the NVE ensemble.\ntrajectory = []\n\n\ndef store_trajectory():\n trajectory.append(copy.deepcopy(atoms))\n\n\ndyn = VelocityVerlet(atoms, 1 * ase.units.fs)\ndyn.attach(store_trajectory, interval=1)\ndyn.run(2000) # 2 ps NVE run" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data preparation\n\nNow, we need to generate the training data from the trajectory. FlashMD models\nrequire future positions and momenta as targets. We will save them in an `.xyz`\nfile under the `future_positions` and `future_momenta` keys.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# The FlashMD model will be trained to predict 30 steps into the future, i.e., 30 fs\n# since we ran the reference simulation with a time step of 1 fs. For this type\n# of system, FlashMD is expected to perform well up to around 60-80 fs.\ntime_lag = 30\n\n# We pick starting structures that are 200 steps apart. To avoid wasting training\n# structures, this should be set to be around the expected velocity-velocity\n# autocorrelation time for the system. This is the time scale that quantifies how long\n# it takes for the system to forget its original velocities.\nspacing = 200\n\n\ndef get_structure_for_dataset(frame_now, frame_ahead):\n s = copy.deepcopy(frame_now)\n s.arrays[\"future_positions\"] = frame_ahead.get_positions()\n s.arrays[\"future_momenta\"] = frame_ahead.get_momenta()\n return s\n\n\nstructures_for_dataset = []\nfor i in range(0, len(trajectory) - time_lag, spacing):\n frame_now = trajectory[i]\n frame_ahead = trajectory[i + time_lag]\n s = get_structure_for_dataset(frame_now, frame_ahead)\n structures_for_dataset.append(s)\n\n # Here, we also add the time-reversed pair (optional)\n frame_now_trev = copy.deepcopy(frame_now)\n frame_ahead_trev = copy.deepcopy(frame_ahead)\n frame_now_trev.set_momenta(-frame_now_trev.get_momenta())\n frame_ahead_trev.set_momenta(-frame_ahead_trev.get_momenta())\n s = get_structure_for_dataset(frame_ahead_trev, frame_now_trev)\n structures_for_dataset.append(s)\n\n# Write the structures to an xyz file\nase.io.write(\"flashmd.xyz\", structures_for_dataset)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Training the model\n\nThe dataset is now ready for training. You can now provide it to ``metatrain`` and\ntrain your FlashMD model!\n\nFor example, you can use the following options file:\n\n.. literalinclude:: options.yaml\n :language: yaml\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "subprocess.run([\"mtt\", \"train\", \"options.yaml\"])" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/src/examples/programmatic/flashmd/flashmd.py b/docs/src/examples/programmatic/flashmd/flashmd.py deleted file mode 100644 index 970b523723..0000000000 --- a/docs/src/examples/programmatic/flashmd/flashmd.py +++ /dev/null @@ -1,120 +0,0 @@ -""" -Training a FlashMD model -======================== - -This tutorial demonstrates how to train a FlashMD model for the direct prediction -of molecular dynamics. This type of model affords faster MD simulations compared to -MLIPs by a factor between 10 and 30 (https://arxiv.org/abs/2505.19350). -""" - -# %% -# - -import copy -import subprocess - -import ase -import ase.build -import ase.io -import ase.units -from ase.calculators.emt import EMT -from ase.md import VelocityVerlet -from ase.md.langevin import Langevin -from ase.md.velocitydistribution import MaxwellBoltzmannDistribution - - -# %% -# -# Data generation -# --------------- -# -# FlashMD models train on molecular dynamics trajectories in the NVE ensemble (i.e., -# most often with the velocity Verlet integrator). These trajectories can be generated -# with almost any MD code (i-PI, LAMMPS, etc.). Here, for simplicity, we will use ASE -# and its built-in EMT potential. In reality, you might want to use a more accurate -# baseline such as ab initio MD or a machine-learned interatomic potential (MLIP). - -# We start by creating a simple system (a small box of aluminum). -atoms = ase.build.bulk("Al", "fcc", cubic=True) * (2, 2, 2) - -# We first equilibrate the system at 300K using a Langevin thermostat. -MaxwellBoltzmannDistribution(atoms, temperature_K=300) -atoms.calc = EMT() -dyn = Langevin( - atoms, 2 * ase.units.fs, temperature_K=300, friction=1 / (100 * ase.units.fs) -) -dyn.run(1000) # 2 ps equilibration (around 10 ps is better in practice) - -# Then, we run a production simulation in the NVE ensemble. -trajectory = [] - - -def store_trajectory(): - trajectory.append(copy.deepcopy(atoms)) - - -dyn = VelocityVerlet(atoms, 1 * ase.units.fs) -dyn.attach(store_trajectory, interval=1) -dyn.run(2000) # 2 ps NVE run - - -# %% -# -# Data preparation -# ---------------- -# -# Now, we need to generate the training data from the trajectory. FlashMD models -# require future positions and momenta as targets. We will save them in an `.xyz` -# file under the `future_positions` and `future_momenta` keys. - -# The FlashMD model will be trained to predict 30 steps into the future, i.e., 30 fs -# since we ran the reference simulation with a time step of 1 fs. For this type -# of system, FlashMD is expected to perform well up to around 60-80 fs. -time_lag = 30 - -# We pick starting structures that are 200 steps apart. To avoid wasting training -# structures, this should be set to be around the expected velocity-velocity -# autocorrelation time for the system. This is the time scale that quantifies how long -# it takes for the system to forget its original velocities. -spacing = 200 - - -def get_structure_for_dataset(frame_now, frame_ahead): - s = copy.deepcopy(frame_now) - s.arrays["future_positions"] = frame_ahead.get_positions() - s.arrays["future_momenta"] = frame_ahead.get_momenta() - return s - - -structures_for_dataset = [] -for i in range(0, len(trajectory) - time_lag, spacing): - frame_now = trajectory[i] - frame_ahead = trajectory[i + time_lag] - s = get_structure_for_dataset(frame_now, frame_ahead) - structures_for_dataset.append(s) - - # Here, we also add the time-reversed pair (optional) - frame_now_trev = copy.deepcopy(frame_now) - frame_ahead_trev = copy.deepcopy(frame_ahead) - frame_now_trev.set_momenta(-frame_now_trev.get_momenta()) - frame_ahead_trev.set_momenta(-frame_ahead_trev.get_momenta()) - s = get_structure_for_dataset(frame_ahead_trev, frame_now_trev) - structures_for_dataset.append(s) - -# Write the structures to an xyz file -ase.io.write("flashmd.xyz", structures_for_dataset) - -# %% -# -# Training the model -# ------------------ -# -# The dataset is now ready for training. You can now provide it to ``metatrain`` and -# train your FlashMD model! -# -# For example, you can use the following options file: -# -# .. literalinclude:: options-flashmd.yaml -# :language: yaml - -subprocess.run(["mtt", "train", "options-flashmd.yaml"]) diff --git a/docs/src/examples/programmatic/flashmd/flashmd.py.md5 b/docs/src/examples/programmatic/flashmd/flashmd.py.md5 deleted file mode 100644 index 09379633c1..0000000000 --- a/docs/src/examples/programmatic/flashmd/flashmd.py.md5 +++ /dev/null @@ -1 +0,0 @@ -dc5c04be3dfe900edcf26c73a0ad6880 \ No newline at end of file diff --git a/docs/src/examples/programmatic/flashmd/flashmd.rst b/docs/src/examples/programmatic/flashmd/flashmd.rst deleted file mode 100644 index 1e80ddd319..0000000000 --- a/docs/src/examples/programmatic/flashmd/flashmd.rst +++ /dev/null @@ -1,225 +0,0 @@ - -.. DO NOT EDIT. -.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. -.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: -.. "examples/programmatic/flashmd/flashmd.py" -.. LINE NUMBERS ARE GIVEN BELOW. - -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - :ref:`Go to the end ` - to download the full example code. - -.. rst-class:: sphx-glr-example-title - -.. _sphx_glr_examples_programmatic_flashmd_flashmd.py: - - -Training a FlashMD model -======================== - -This tutorial demonstrates how to train a FlashMD model for the direct prediction -of molecular dynamics. This type of model affords faster MD simulations compared to -MLIPs by a factor between 10 and 30 (https://arxiv.org/abs/2505.19350). - -.. GENERATED FROM PYTHON SOURCE LINES 12-26 - -.. code-block:: Python - - - import copy - import subprocess - - import ase - import ase.build - import ase.io - import ase.units - from ase.calculators.emt import EMT - from ase.md import VelocityVerlet - from ase.md.langevin import Langevin - from ase.md.velocitydistribution import MaxwellBoltzmannDistribution - - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 27-35 - -Data generation ---------------- - -FlashMD models train on molecular dynamics trajectories in the NVE ensemble (i.e., -most often with the velocity Verlet integrator). These trajectories can be generated -with almost any MD code (i-PI, LAMMPS, etc.). Here, for simplicity, we will use ASE -and its built-in EMT potential. In reality, you might want to use a more accurate -baseline such as ab initio MD or a machine-learned interatomic potential (MLIP). - -.. GENERATED FROM PYTHON SOURCE LINES 36-61 - -.. code-block:: Python - - - # We start by creating a simple system (a small box of aluminum). - atoms = ase.build.bulk("Al", "fcc", cubic=True) * (2, 2, 2) - - # We first equilibrate the system at 300K using a Langevin thermostat. - MaxwellBoltzmannDistribution(atoms, temperature_K=300) - atoms.calc = EMT() - dyn = Langevin( - atoms, 2 * ase.units.fs, temperature_K=300, friction=1 / (100 * ase.units.fs) - ) - dyn.run(1000) # 2 ps equilibration (around 10 ps is better in practice) - - # Then, we run a production simulation in the NVE ensemble. - trajectory = [] - - - def store_trajectory(): - trajectory.append(copy.deepcopy(atoms)) - - - dyn = VelocityVerlet(atoms, 1 * ase.units.fs) - dyn.attach(store_trajectory, interval=1) - dyn.run(2000) # 2 ps NVE run - - - - - - -.. rst-class:: sphx-glr-script-out - - .. code-block:: none - - - True - - - -.. GENERATED FROM PYTHON SOURCE LINES 62-68 - -Data preparation ----------------- - -Now, we need to generate the training data from the trajectory. FlashMD models -require future positions and momenta as targets. We will save them in an `.xyz` -file under the `future_positions` and `future_momenta` keys. - -.. GENERATED FROM PYTHON SOURCE LINES 69-107 - -.. code-block:: Python - - - # The FlashMD model will be trained to predict 30 steps into the future, i.e., 30 fs - # since we ran the reference simulation with a time step of 1 fs. For this type - # of system, FlashMD is expected to perform well up to around 60-80 fs. - time_lag = 30 - - # We pick starting structures that are 200 steps apart. To avoid wasting training - # structures, this should be set to be around the expected velocity-velocity - # autocorrelation time for the system. This is the time scale that quantifies how long - # it takes for the system to forget its original velocities. - spacing = 200 - - - def get_structure_for_dataset(frame_now, frame_ahead): - s = copy.deepcopy(frame_now) - s.arrays["future_positions"] = frame_ahead.get_positions() - s.arrays["future_momenta"] = frame_ahead.get_momenta() - return s - - - structures_for_dataset = [] - for i in range(0, len(trajectory) - time_lag, spacing): - frame_now = trajectory[i] - frame_ahead = trajectory[i + time_lag] - s = get_structure_for_dataset(frame_now, frame_ahead) - structures_for_dataset.append(s) - - # Here, we also add the time-reversed pair (optional) - frame_now_trev = copy.deepcopy(frame_now) - frame_ahead_trev = copy.deepcopy(frame_ahead) - frame_now_trev.set_momenta(-frame_now_trev.get_momenta()) - frame_ahead_trev.set_momenta(-frame_ahead_trev.get_momenta()) - s = get_structure_for_dataset(frame_ahead_trev, frame_now_trev) - structures_for_dataset.append(s) - - # Write the structures to an xyz file - ase.io.write("flashmd.xyz", structures_for_dataset) - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 108-118 - -Training the model ------------------- - -The dataset is now ready for training. You can now provide it to ``metatrain`` and -train your FlashMD model! - -For example, you can use the following options file: - -.. literalinclude:: options.yaml - :language: yaml - -.. GENERATED FROM PYTHON SOURCE LINES 119-121 - -.. code-block:: Python - - - subprocess.run(["mtt", "train", "options.yaml"]) - - - - -.. rst-class:: sphx-glr-script-out - - .. code-block:: none - - - CompletedProcess(args=['mtt', 'train', 'options.yaml'], returncode=0) - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** (0 minutes 16.440 seconds) - - -.. _sphx_glr_download_examples_programmatic_flashmd_flashmd.py: - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-example - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: flashmd.ipynb ` - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: flashmd.py ` - - .. container:: sphx-glr-download sphx-glr-download-zip - - :download:`Download zipped: flashmd.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/programmatic/flashmd/flashmd.xyz b/docs/src/examples/programmatic/flashmd/flashmd.xyz deleted file mode 100644 index 7a9bb6e0ee..0000000000 --- a/docs/src/examples/programmatic/flashmd/flashmd.xyz +++ /dev/null @@ -1,680 +0,0 @@ -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=0.8948308788199846 free_energy=0.8948308788199846 stress="0.00582921114852916 0.000442369304973728 0.0011759981285591327 0.000442369304973728 0.004847050505392154 1.8183467986477127e-06 0.0011759981285591327 1.8183467986477127e-06 0.007675243103858247" pbc="T T T" -Al 0.16464029 0.00846807 0.08508833 -0.51435652 -1.29123021 -0.40577065 0.06123463 -0.13595049 0.07731856 -1.12368820 -1.14173022 0.32159375 0.02624888 -0.43303143 -0.16939770 0.17687361 -Al 0.04333576 2.12405991 2.14636843 0.32093878 -0.17299006 1.71382084 0.06562479 2.10857776 2.28688450 -0.03325533 -0.03348663 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-0.02406983 4.10656556 4.26687676 -1.00051381 0.85934586 -0.07518529 0.02755296 -0.10461418 0.07870897 -0.31515772 -Al 0.12009291 5.93594422 6.01046922 -1.18594847 0.30481321 -0.29021400 0.01409687 6.01670929 6.01936247 -0.62232620 1.02709759 0.47965827 0.02709685 0.02595310 0.35991925 0.20568057 -Al 2.17590269 3.96699525 6.18607872 1.82666693 0.88078720 0.01094781 2.31621025 4.10654665 6.18066021 0.51549710 1.49197521 -0.08331562 0.03066521 -0.16477845 0.36717608 -0.07313241 -Al 2.11661542 6.14055183 4.02959445 -1.21684111 0.18480678 0.01285619 1.99885366 6.16084043 4.05270339 -0.78737459 0.07326348 0.35179917 0.01482222 -0.05129393 0.08802409 0.16252990 -Al 4.08197493 0.10490606 0.17085796 1.18346222 0.39760916 -0.36612355 4.19092003 0.13248906 0.08093809 0.61699589 0.09336852 -1.26574518 0.01022865 0.01600799 -0.07344046 -0.29331826 -Al 3.93645088 2.18854557 2.09631276 1.82853584 -1.42156010 0.59584715 4.14907873 2.00771831 2.17584454 1.73625133 -1.65709058 0.77212338 0.05895968 0.28880840 -0.31079094 0.11048325 -Al 6.22340708 0.06013780 2.05279540 -1.24895018 0.55835249 -0.97816458 6.08747412 0.10501728 1.95480608 -0.94397278 0.14753550 -0.52447199 0.02477423 -0.20011305 -0.03687653 -0.14529374 -Al 6.02132034 2.07641943 0.14100884 0.65846298 0.04825727 0.02423917 6.14091227 2.06706350 0.11684310 1.45056452 -0.18007649 -0.52704098 0.02479758 0.29892593 -0.11730661 -0.11169449 -Al 4.18660823 -0.00242056 4.12764259 -0.01325988 -0.40875091 -0.61885377 4.14992585 -0.03770428 4.06126252 -0.56901563 -0.18660433 -0.46420485 0.02748321 -0.28348925 0.01573260 -0.08948531 -Al 4.09333604 1.99813612 6.08092681 0.40479253 1.09617456 -0.45695243 4.16603783 2.07017504 6.04884325 0.74864192 0.03050378 -0.02498149 0.00968249 0.27257342 -0.11621141 0.05646164 -Al 6.23948472 -0.22255063 6.05928214 1.59323678 -0.41493906 1.09744752 6.33990711 -0.19936221 6.21256097 -0.07209436 0.78147382 1.34706809 0.04886645 -0.21504796 0.45884785 0.45930897 -Al 6.09879426 1.89696639 4.07909200 -0.70948079 1.25639048 0.10789133 6.02790682 2.05765944 4.11081737 -0.39377453 1.39654577 0.50055116 0.02507895 -0.05684998 0.34628181 0.09833994 -Al 4.15353887 3.98323517 -0.00693228 0.07234339 -0.91889267 0.42736080 4.13333436 3.92650157 0.06146214 -0.32182670 -0.09386815 0.87097930 0.03166976 -0.23582388 0.21337205 0.08628749 -Al 4.13836479 6.07840665 1.93039247 -0.65108121 0.29673392 0.30647594 4.03241715 6.12659693 1.99383823 -1.21564110 0.47432752 0.76998567 0.02564584 -0.23677724 0.16515317 0.23204498 -Al 5.91356098 4.05332772 2.30407210 -0.11207184 -0.57732050 -0.43473224 6.00009513 3.99166004 2.18735879 1.52404557 -0.43452393 -1.67790947 0.04651277 0.65974572 -0.07548183 -0.39155185 -Al 6.04952230 6.03040117 0.00447251 -0.69510041 0.14983012 0.70220847 6.03184281 6.04602950 0.10751122 0.23359967 0.03041771 1.11755336 0.02607780 0.41601265 0.07073372 0.19255014 -Al 3.90492926 4.15165660 4.00534018 1.40576279 -0.78672061 0.03995367 4.05583797 4.06899126 4.06876345 1.18275263 -0.50712983 0.81502571 0.05153768 0.15003627 -0.14429056 0.51414408 -Al 4.07173410 6.18254887 6.19658769 -0.49226230 1.23361909 0.07352391 4.00532367 6.28118217 6.18870997 -0.47037602 0.49815600 -0.18710539 0.00176712 -0.22482859 -0.16464236 -0.11230117 -Al 6.02614131 4.07344579 5.98266586 -0.63584207 0.56407956 0.72323350 5.95129296 4.14512639 6.05337693 -0.64565669 0.77230350 0.62910140 -0.01167201 -0.08023362 0.05346028 -0.05969545 -Al 6.11856558 6.19884837 3.97257857 0.28150990 -0.47468254 -1.39776206 6.11209593 6.11582640 3.89617986 -0.39056651 -0.79178237 0.04705835 0.06848149 -0.20059260 -0.37868432 0.36157672 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=0.9775593220180188 free_energy=0.9775593220180188 stress="0.00561896524110683 0.00010955929300027058 0.0005917793502733804 0.00010955929300027058 0.004925564335948191 0.0001104654741423019 0.0005917793502733804 0.0001104654741423019 0.00593663920646172" pbc="T T T" -Al 0.06123463 -0.13595049 0.07731856 1.12368820 1.14173022 -0.32159375 0.16464029 0.00846807 0.08508833 0.51435652 1.29123021 0.40577065 0.02598423 0.06720061 0.25551433 0.28941604 -Al 0.06562479 2.10857776 2.28688450 0.03325533 0.03348663 -0.72445134 0.04333576 2.12405991 2.14636843 -0.32093878 0.17299006 -1.71382084 0.02256491 -0.22565110 0.11432577 -0.45364058 -Al 2.10411598 0.19875347 2.10572830 1.45828161 0.77836419 1.04256479 2.21922479 0.20052938 2.21911130 0.44642369 -0.68326702 0.86115870 0.03250304 -0.12232050 -0.53465264 0.10968291 -Al 2.07343613 2.03409454 0.21257465 -0.87860336 -0.18665112 -1.78096908 1.96155048 2.02748026 -0.04177685 -0.96270926 0.14683668 -2.36933351 0.03670059 -0.20199496 0.03991207 -0.65029019 -Al 0.06765827 0.10043852 4.11442519 0.00327411 0.12717917 1.13769447 0.05568839 0.05574736 4.18671791 -0.06245496 -0.81230955 0.20747444 0.03628151 -0.17114302 -0.41222499 -0.27086310 -Al -0.09891252 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-0.14281365 0.04460780 -Al 4.19092003 0.13248906 0.08093809 -0.61699589 -0.09336852 1.26574518 4.08197493 0.10490606 0.17085796 -1.18346222 -0.39760916 0.36612355 0.02757163 -0.36567294 -0.10165634 -0.24845931 -Al 4.14907873 2.00771831 2.17584454 -1.73625133 1.65709058 -0.77212338 3.93645088 2.18854557 2.09631276 -1.82853584 1.42156010 -0.59584715 0.05028353 -0.37036750 0.16837018 -0.06963989 -Al 6.08747412 0.10501728 1.95480608 0.94397278 -0.14753550 0.52447199 6.22340708 0.06013780 2.05279540 1.24895018 -0.55835249 0.97816458 0.03575680 0.38855109 -0.26863586 0.41829455 -Al 6.14091227 2.06706350 0.11684310 -1.45056452 0.18007649 0.52704098 6.02132034 2.07641943 0.14100884 -0.65846298 -0.04825727 -0.02423917 0.03669586 0.13526855 -0.05052286 -0.22414578 -Al 4.14992585 -0.03770428 4.06126252 0.56901563 0.18660433 0.46420485 4.18660823 -0.00242056 4.12764259 0.01325988 0.40875091 0.61885377 0.01061884 -0.10696225 0.11600464 0.18260704 -Al 4.16603783 2.07017504 6.04884325 -0.74864192 -0.03050378 0.02498149 4.09333604 1.99813612 6.08092681 -0.40479253 -1.09617456 0.45695243 0.02857475 -0.07814720 -0.49425034 0.26260027 -Al 6.33990711 -0.19936221 6.21256097 0.07209436 -0.78147382 -1.34706809 6.23948472 -0.22255063 6.05928214 -1.59323678 0.41493906 -1.09744752 0.07280774 -0.82921050 0.33606149 -0.26767690 -Al 6.02790682 2.05765944 4.11081737 0.39377453 -1.39654577 -0.50055116 6.09879426 1.89696639 4.07909200 0.70948079 -1.25639048 -0.10789133 0.04101935 0.32693833 -0.22536169 0.15011873 -Al 4.13333436 3.92650157 0.06146214 0.32182670 0.09386815 -0.87097930 4.15353887 3.98323517 -0.00693228 -0.07234339 0.91889267 -0.42736080 0.02816260 -0.00405970 0.27124079 0.16760924 -Al 4.03241715 6.12659693 1.99383823 1.21564110 -0.47432752 -0.76998567 4.13836479 6.07840665 1.93039247 0.65108121 -0.29673392 -0.30647594 0.00215586 -0.09098616 -0.05117162 0.05839465 -Al 6.00009513 3.99166004 2.18735879 -1.52404557 0.43452393 1.67790947 5.91356098 4.05332772 2.30407210 0.11207184 0.57732050 0.43473224 0.03399237 0.33102920 0.15723171 -0.33119674 -Al 6.03184281 6.04602950 0.10751122 -0.23359967 -0.03041771 -1.11755336 6.04952230 6.03040117 0.00447251 0.69510041 -0.14983012 -0.70220847 0.00997002 0.14757047 -0.13376238 0.05685800 -Al 4.05583797 4.06899126 4.06876345 -1.18275263 0.50712983 -0.81502571 3.90492926 4.15165660 4.00534018 -1.40576279 0.78672061 -0.03995367 0.02750048 -0.21361793 0.30143009 -0.08016059 -Al 4.00532367 6.28118217 6.18870997 0.47037602 -0.49815600 0.18710539 4.07173410 6.18254887 6.19658769 0.49226230 -1.23361909 -0.07352391 0.02729147 0.27109491 -0.31810102 -0.05656443 -Al 5.95129296 4.14512639 6.05337693 0.64565669 -0.77230350 -0.62910140 6.02614131 4.07344579 5.98266586 0.63584207 -0.56407956 -0.72323350 0.01327066 0.09816123 0.09290604 0.05317744 -Al 6.11209593 6.11582640 3.89617986 0.39056651 0.79178237 -0.04705835 6.11856558 6.19884837 3.97257857 -0.28150990 0.47468254 1.39776206 0.02946727 -0.18687223 0.12201556 0.46460257 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=0.8431874507022643 free_energy=0.8431874507022643 stress="0.006244031443369388 0.0007165788141183207 -0.0001627289267088679 0.0007165788141183207 0.005467715397826384 0.00047222160456977303 -0.0001627289267088679 0.00047222160456977303 0.0065578157468693605" pbc="T T T" -Al 0.00264000 0.07844353 -0.12467518 -0.24833940 -0.96183864 0.49485711 -0.00613836 -0.01402459 -0.02917609 0.11464281 -0.61415357 1.04937503 0.02354378 0.07510104 -0.00840324 0.37378042 -Al 0.01973625 1.94453284 2.07781540 -1.14858181 0.43868461 0.26323310 -0.06034934 1.99671851 2.07559403 -0.06503116 0.39052762 -0.24704382 0.01148113 0.05369455 0.09880419 -0.20381966 -Al 2.12606659 -0.00311886 2.19972208 0.41302172 -0.56223045 -0.78333704 2.16448074 -0.04161635 2.08799427 0.27800815 0.11297422 -1.05153068 0.01817628 -0.04086683 -0.06843728 -0.30666921 -Al 2.08365382 1.99498282 0.09681029 0.06882021 0.63561182 0.23612025 2.06579896 2.08443873 0.08890775 -0.32866700 0.89063479 -0.32704702 0.00011097 -0.18451322 0.18586768 -0.22949960 -Al 0.12624551 0.03061316 4.28957870 -0.65159606 0.30279168 -0.14250778 0.01688464 0.02731380 4.25777021 -1.11169736 -0.18773383 -0.34981465 0.02738317 -0.41340684 -0.31632109 -0.16491883 -Al -0.12317083 1.98438986 6.08615311 -0.68033828 -1.02831320 0.81904355 -0.13183977 1.92300711 6.19473968 0.38176425 0.04409683 1.08051663 0.07402757 0.51180957 0.15316222 0.16612647 -Al 2.21175225 -0.05970562 6.17702698 1.71592151 -0.01437753 0.71618332 2.33255833 -0.03585943 6.20959721 0.16274206 0.29935664 -0.22485442 0.01697825 -0.15226446 0.25002542 -0.16014115 -Al 1.89525038 2.00666748 4.05656774 -0.19076047 1.70309258 0.77182852 1.93014534 2.15094689 4.16742316 0.67201897 0.56694128 1.13024239 0.03364249 0.44639722 0.06190913 0.26114939 -Al 0.06324146 4.13002584 0.21584986 -2.29720152 -0.73838022 0.28813726 -0.15219333 4.02591028 0.20765365 -1.23671285 -1.01266081 -0.62514988 0.01526475 -0.04390983 -0.24796852 -0.14860606 -Al -0.03568176 6.33257459 2.11146462 -0.42398127 1.76229272 0.33760784 -0.07002781 6.45271076 2.14810086 -0.14533080 0.17006376 0.39452288 -0.00237100 0.05110291 -0.25026777 -0.04388833 -Al 2.10471123 4.07299382 2.11509901 0.47122512 0.43019454 0.05486540 2.11829867 4.14627541 2.10388427 -0.29998206 0.77682088 -0.38295811 0.01438037 -0.14629987 0.20813554 0.00577124 -Al 1.97633693 6.14574144 0.04358988 -1.05480691 0.28916459 1.55234288 1.90811728 6.14999876 0.22260918 -0.17152022 -0.35306556 1.41560132 0.01932936 0.25472308 -0.06090382 0.23382698 -Al 0.05576764 4.07998668 4.01400335 0.57940655 -0.69956036 -0.77452503 0.08668458 4.01869212 3.92835072 -0.17503553 -0.25442022 -0.69464186 0.01526375 -0.04964464 -0.03080400 -0.05355566 -Al 0.02355291 5.97583815 6.21389775 1.29498290 -1.18774745 -1.11923094 0.15227294 5.91293447 6.07765261 0.83971435 0.19996515 -1.17915732 0.03685764 0.07105344 0.27335028 -0.22054127 -Al 2.03002194 4.09589495 5.96428476 -1.40614172 0.07190351 0.68383253 1.89204490 4.05702037 6.10583172 -0.78217195 -0.76552063 1.83558429 0.02500748 -0.18326424 -0.28465803 0.43550242 -Al 2.10294141 6.13517588 4.16052368 0.37528798 0.06546951 -1.32171616 2.12907650 6.12170770 4.01699769 0.07558778 -0.25067742 -1.03910889 0.02080381 -0.08017251 -0.15558951 -0.18798561 -Al 4.05154633 0.04180795 0.09587695 0.65312298 -0.02520858 -1.44851649 4.11018101 -0.00892605 -0.00370261 0.47925477 -0.82387382 -0.06336589 0.03791563 -0.11142811 -0.33346680 0.10579317 -Al 3.99463169 2.11178591 2.08298854 -0.76972578 0.43303785 1.45166045 3.92031111 2.12118979 2.21012431 -0.46042768 -0.30497609 0.66557543 0.04547036 -0.02623818 -0.15264150 0.00397004 -Al 6.06752037 0.16677762 2.13683865 0.78676613 0.52003149 -0.55703345 6.12683194 0.12611308 2.05526672 0.04974244 -1.11123483 -0.86262203 0.05797723 0.02567701 -0.60535123 -0.15552347 -Al 5.86666543 1.98359002 0.13390663 0.14656181 0.34355113 1.00219990 5.94749354 2.06926988 0.20344323 1.06654883 1.03497871 0.17235924 0.03670554 0.54268980 0.34223721 -0.17161289 -Al 4.16577409 0.00121745 4.10194437 1.27315080 0.00074655 -0.12617867 4.26403075 -0.00106685 4.08963455 0.44919701 -0.14322398 -0.23043618 0.01149277 -0.17588287 0.06046008 0.09255009 -Al 4.07582143 2.12841289 6.08245812 1.38235068 -2.27850567 -0.84858267 4.19026486 1.88563097 6.03156973 0.57677380 -1.58734033 -0.07971653 0.02925887 -0.11131245 -0.33460233 0.22692870 -Al 5.98796219 0.06005076 6.14967933 -0.30947349 0.38719007 1.15895734 5.98875373 0.07194356 6.22854943 0.59269769 -0.40128101 0.30465130 0.02411334 0.04497396 -0.01868090 -0.27046677 -Al 6.06100384 2.05139366 3.94813839 -0.49745219 -0.04888184 -0.80333862 6.06592325 2.06154953 3.94251448 0.55104434 0.29164259 0.63815221 0.05209771 0.33987961 0.02698787 0.48412612 -Al 3.98653190 4.16133100 -0.02674004 0.73984761 0.38361114 -0.54089747 4.07643916 4.17447757 -0.05548456 0.74131189 -0.16140478 -0.03102748 0.01030857 0.16089545 -0.14489250 0.22158812 -Al 4.20417461 5.98448795 2.23233022 -0.20346446 -0.31251847 0.04086883 4.17099734 5.99067130 2.19698932 -0.23762055 0.42943188 -0.68889399 0.00680334 -0.18787453 0.23188721 -0.23712622 -Al 6.10462315 4.08037294 2.06163275 0.38894279 0.29950384 -1.10535613 6.12924789 4.11675117 1.96135721 -0.17956907 0.39845727 -0.28799727 0.01783917 0.05689690 -0.00826779 -0.15578421 -Al 6.09358588 6.26490740 -0.03882978 0.91781049 -1.07153163 0.44265129 6.17717319 6.10261802 0.04548933 0.55468604 -1.63616333 0.85314067 0.02017304 -0.05987567 -0.42047706 0.36560357 -Al 4.10680770 3.82744588 4.14546261 -0.81027804 -0.44715914 0.36296012 4.02050430 3.89795647 4.17643776 -0.67074170 1.63304057 0.32819991 0.06887453 -0.05253364 0.72242265 -0.15967413 -Al 4.11682929 5.93532890 6.08322235 0.81699115 0.44743862 -0.34601489 4.19368248 6.01081999 6.04143619 0.35039176 0.86499219 -0.32412669 0.02209869 0.09430284 0.22566179 -0.06844949 -Al 6.30009733 3.91190767 6.18755949 -1.39862320 0.96217439 -1.09305327 6.09853461 4.03300185 6.08106214 -1.90717822 0.93747738 -0.80260399 0.04093503 -0.55378817 0.34079773 0.04585371 -Al 6.19758956 5.96463840 4.22287004 0.06655417 -0.10023746 0.33293893 6.18804712 5.99231814 4.22843248 -0.16444078 0.56632849 -0.37582460 0.01124382 -0.15592133 0.26002436 -0.08430789 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.199462930854991 free_energy=1.199462930854991 stress="0.004767208885128757 0.0018557943876572078 0.0006883010676196985 0.0018557943876572078 0.0037451877241799847 0.00010112084390507357 0.0006883010676196985 0.00010112084390507357 0.005853968447125383" pbc="T T T" -Al -0.00613836 -0.01402459 -0.02917609 -0.11464281 0.61415357 -1.04937503 0.00264000 0.07844353 -0.12467518 0.24833940 0.96183864 -0.49485711 0.03349547 0.13631418 0.22371231 -0.02665473 -Al -0.06034934 1.99671851 2.07559403 0.06503116 -0.39052762 0.24704382 0.01973625 1.94453284 2.07781540 1.14858181 -0.43868461 -0.26323310 0.03069566 0.54953215 -0.15485292 -0.09515438 -Al 2.16448074 -0.04161635 2.08799427 -0.27800815 -0.11297422 1.05153068 2.12606659 -0.00311886 2.19972208 -0.41302172 0.56223045 0.78333704 0.02903386 -0.07183026 0.43753749 0.11822710 -Al 2.06579896 2.08443873 0.08890775 0.32866700 -0.89063479 0.32704702 2.08365382 1.99498282 0.09681029 -0.06882021 -0.63561182 -0.23612025 -0.01261383 -0.05041681 -0.04377495 -0.12021375 -Al 0.01688464 0.02731380 4.25777021 1.11169736 0.18773383 0.34981465 0.12624551 0.03061316 4.28957870 0.65159606 -0.30279168 0.14250778 0.00959253 0.06685513 0.02610194 0.01406195 -Al -0.13183977 1.92300711 6.19473968 -0.38176425 -0.04409683 -1.08051663 -0.12317083 1.98438986 6.08615311 0.68033828 1.02831320 -0.81904355 0.02594094 0.22847308 0.43171417 -0.01594792 -Al 2.33255833 -0.03585943 6.20959721 -0.16274206 -0.29935664 0.22485442 2.21175225 -0.05970562 6.17702698 -1.71592151 0.01437753 -0.71618332 0.04715044 -0.80048541 -0.04283806 -0.36059252 -Al 1.93014534 2.15094689 4.16742316 -0.67201897 -0.56694128 -1.13024239 1.89525038 2.00666748 4.05656774 0.19076047 -1.70309258 -0.77182852 0.04547979 0.13494897 -0.66677570 -0.00006346 -Al -0.15219333 4.02591028 0.20765365 1.23671285 1.01266081 0.62514988 0.06324146 4.13002584 0.21584986 2.29720152 0.73838022 -0.28813726 0.05785902 0.75702091 0.06974274 -0.48877325 -Al -0.07002781 6.45271076 2.14810086 0.14533080 -0.17006376 -0.39452288 -0.03568176 6.33257459 2.11146462 0.42398127 -1.76229272 -0.33760784 0.06474253 0.17077224 -0.77802528 0.07261592 -Al 2.11829867 4.14627541 2.10388427 0.29998206 -0.77682088 0.38295811 2.10471123 4.07299382 2.11509901 -0.47122512 -0.43019454 -0.05486540 0.04325769 -0.29954835 -0.06494235 -0.23070870 -Al 1.90811728 6.14999876 0.22260918 0.17152022 0.35306556 -1.41560132 1.97633693 6.14574144 0.04358988 1.05480691 -0.28916459 -1.55234288 0.03400526 0.30380031 -0.33563679 -0.38137659 -Al 0.08668458 4.01869212 3.92835072 0.17503553 0.25442022 0.69464186 0.05576764 4.07998668 4.01400335 -0.57940655 0.69956036 0.77452503 0.03395830 -0.35545700 0.29317011 0.14656180 -Al 0.15227294 5.91293447 6.07765261 -0.83971435 -0.19996515 1.17915732 0.02355291 5.97583815 6.21389775 -1.29498290 1.18774745 1.11923094 0.05380499 -0.35515339 0.58618643 0.16363494 -Al 1.89204490 4.05702037 6.10583172 0.78217195 0.76552063 -1.83558429 2.03002194 4.09589495 5.96428476 1.40614172 -0.07190351 -0.68383253 0.05655014 0.48087682 -0.23330014 0.28346975 -Al 2.12907650 6.12170770 4.01699769 -0.07558778 0.25067742 1.03910889 2.10294141 6.13517588 4.16052368 -0.37528798 -0.06546951 1.32171616 0.01383164 -0.13423586 -0.02847313 0.33897934 -Al 4.11018101 -0.00892605 -0.00370261 -0.47925477 0.82387382 0.06336589 4.05154633 0.04180795 0.09587695 -0.65312298 0.02520858 1.44851649 0.05512610 -0.01100678 -0.17680588 0.69708319 -Al 3.92031111 2.12118979 2.21012431 0.46042768 0.30497609 -0.66557543 3.99463169 2.11178591 2.08298854 0.76972578 -0.43303785 -1.45166045 0.03966986 0.23246647 -0.23272805 -0.41295966 -Al 6.12683194 0.12611308 2.05526672 -0.04974244 1.11123483 0.86262203 6.06752037 0.16677762 2.13683865 -0.78676613 -0.52003149 0.55703345 0.06854007 -0.45842272 -0.30669064 -0.01343308 -Al 5.94749354 2.06926988 0.20344323 -1.06654883 -1.03497871 -0.17235924 5.86666543 1.98359002 0.13390663 -0.14656181 -0.34355113 -1.00219990 0.02575345 0.02044889 -0.02948428 -0.36560040 -Al 4.26403075 -0.00106685 4.08963455 -0.44919701 0.14322398 0.23043618 4.16577409 0.00121745 4.10194437 -1.27315080 -0.00074655 0.12617867 0.02971316 -0.33221050 -0.12872405 -0.15561704 -Al 4.19026486 1.88563097 6.03156973 -0.57677380 1.58734033 0.07971653 4.07582143 2.12841289 6.08245812 -1.38235068 2.27850567 0.84858267 0.07937888 -0.38169238 0.78551687 0.25347125 -Al 5.98875373 0.07194356 6.22854943 -0.59269769 0.40128101 -0.30465130 5.98796219 0.06005076 6.14967933 0.30947349 -0.38719007 -1.15895734 0.08191988 0.55016627 -0.46022891 -0.23925496 -Al 6.06592325 2.06154953 3.94251448 -0.55104434 -0.29164259 -0.63815221 6.06100384 2.05139366 3.94813839 0.49745219 0.04888184 0.80333862 0.03121993 0.27105175 0.13738104 0.36978353 -Al 4.07643916 4.17447757 -0.05548456 -0.74131189 0.16140478 0.03102748 3.98653190 4.16133100 -0.02674004 -0.73984761 -0.38361114 0.54089747 0.00022805 -0.16847597 -0.18531003 0.13206767 -Al 4.17099734 5.99067130 2.19698932 0.23762055 -0.42943188 0.68889399 4.20417461 5.98448795 2.23233022 0.20346446 0.31251847 -0.04086883 0.01073897 0.13320509 0.24022310 -0.23658737 -Al 6.12924789 4.11675117 1.96135721 0.17956907 -0.39845727 0.28799727 6.10462315 4.08037294 2.06163275 -0.38894279 -0.29950384 1.10535613 0.07379302 -0.39731176 0.04396371 0.70195297 -Al 6.17717319 6.10261802 0.04548933 -0.55468604 1.63616333 -0.85314067 6.09358588 6.26490740 -0.03882978 -0.91781049 1.07153163 -0.44265129 0.01294625 -0.17323240 0.10112039 -0.12388794 -Al 4.02050430 3.89795647 4.17643776 0.67074170 -1.63304057 -0.32819991 4.10680770 3.82744588 4.14546261 0.81027804 0.44715914 -0.36296012 0.04311384 0.13335415 0.51719125 0.05994098 -Al 4.19368248 6.01081999 6.04143619 -0.35039176 -0.86499219 0.32412669 4.11682929 5.93532890 6.08322235 -0.81699115 -0.44743862 0.34601489 0.04040675 -0.38230370 0.08651462 0.12154124 -Al 6.09853461 4.03300185 6.08106214 1.90717822 -0.93747738 0.80260399 6.30009733 3.91190767 6.18755949 1.39862320 -0.96217439 1.09305327 0.01528697 0.21304517 -0.28664949 0.15362757 -Al 6.18804712 5.99231814 4.22843248 0.16444078 -0.56632849 0.37582460 6.19758956 5.96463840 4.22287004 -0.06655417 0.10023746 -0.33293893 0.02484329 -0.01054830 0.17516447 -0.36019346 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=0.9065094691624092 free_energy=0.9065094691624092 stress="0.006507009423339605 0.0024200002914994603 -0.00022075970102456615 0.0024200002914994603 0.003678820038033757 0.0003131131401096385 -0.00022075970102456615 0.0003131131401096385 0.007312699442367703" pbc="T T T" -Al -0.05687315 -0.03388876 0.16287718 -0.62415401 0.48575283 -0.55836291 -0.09751901 0.03599860 0.09321066 -0.12394847 0.73523479 -0.62019847 0.01112616 0.16295799 0.13968044 -0.12882471 -Al -0.12234110 2.19231320 2.15387766 1.00861367 -1.57941151 0.66276345 0.01139616 1.96556019 2.19882218 1.19138721 -2.21416778 -0.02033057 0.03917191 0.35088781 -0.57624461 -0.01860402 -Al 2.09633081 -0.05920609 2.03506729 -0.21253948 0.87178322 -1.02489539 2.03574680 0.05555292 1.96041217 -0.75263694 0.89100467 -0.28676913 0.02203356 -0.28844433 0.33445779 0.17295760 -Al 1.92195757 2.08566514 0.09981783 -0.92722952 1.07135520 0.46979408 1.92061339 2.10189245 0.14542741 0.70217813 -0.55943463 0.26313512 0.05592186 0.63441651 -0.62812231 0.01798893 -Al -0.00741120 -0.03077141 4.04497130 -0.44031493 -0.32289174 1.47291583 -0.03049963 -0.05056105 4.19982037 0.11424348 -0.12324487 1.23577439 -0.00389129 0.08429529 0.14616256 0.05357573 -Al 0.06992523 2.04324862 6.11008585 -0.26161740 -0.57231641 -0.29198561 0.03932700 1.98467390 6.03866854 -0.21793746 -0.35217998 -0.75598771 0.03158290 -0.06447048 -0.05904760 -0.35260513 -Al 1.96694875 0.08177180 6.08827943 1.53776900 -0.23958866 -0.09184192 2.11906023 0.03117454 6.13345218 1.10025815 -0.70347641 0.86064749 0.01614244 0.01636473 -0.13935295 0.32015827 -Al 2.08259697 2.05876291 3.99972874 -0.71842131 -0.82714834 0.00275744 2.00973480 1.97622512 4.02010251 -0.48526775 -0.46694493 0.35788291 0.01335781 -0.05040502 -0.13893131 0.16019511 -Al 0.20340214 3.89848819 -0.04075709 -0.25118538 0.02070701 -0.38765410 0.09294391 4.03739570 -0.04416142 -1.63262323 2.16328731 0.41349061 0.09654046 -0.50035612 0.94298176 0.16323541 -Al 0.07689771 5.95830737 2.05970779 0.11743480 1.13969252 1.04743038 0.10625812 6.11052427 2.14867226 0.36136407 1.64129812 0.42440703 0.01154623 0.12392107 0.15510911 -0.02683190 -Al 2.19147068 3.95328790 2.10796172 -0.00514515 1.84588262 -0.70971788 2.16131747 4.15904964 2.03922631 -0.50961952 1.63928809 -0.41946845 0.02905612 -0.22188394 0.22307029 -0.06689831 -Al 2.03339315 6.16149670 0.04281063 0.52403967 -0.58476155 0.40192600 2.11644421 6.07746229 0.05635386 0.90003300 -0.72050319 -0.08069261 0.00581559 0.21754649 -0.25349589 -0.22580373 -Al 0.10294054 4.02601996 4.12311921 -1.36287959 0.33238141 -1.97616984 -0.06872219 4.07655930 3.97710345 -1.49994028 0.48060013 -0.63148491 0.02093027 -0.31100270 0.15198661 0.28935017 -Al -0.10075637 6.07907040 6.25758904 -0.71819774 1.03251371 0.50502251 -0.16586567 6.17207637 6.29087578 -0.33258390 0.53377023 0.07704100 -0.01041834 0.00855072 -0.01225639 -0.11767073 -Al 2.18238812 4.10976764 6.06159169 -0.71634565 -0.34096018 1.71826176 2.08821832 4.07619989 6.22921551 -1.02299598 -0.27678490 1.08482611 0.01440522 -0.08110527 0.00289577 0.09799612 -Al 1.95471271 6.07099717 4.20016064 0.75539272 -1.48482873 0.13063489 2.05265912 5.92594709 4.20224038 0.91757975 -1.07261277 0.00018278 0.02004944 0.17276229 0.02533458 -0.12964673 -Al 4.14520964 0.09029539 0.03096883 0.29927440 -0.41128145 0.24919694 4.12053850 0.05927389 0.02285378 -0.65678862 -0.16807727 -0.34884643 0.02387441 -0.36354715 0.05905165 -0.19197670 -Al 4.19095976 1.82325787 2.09487272 -0.44549341 -0.59107529 -0.26642180 4.11967879 1.85218666 2.05326459 -0.79192471 1.11950872 -0.41421409 0.03170229 -0.16143128 0.52091574 -0.12674294 -Al 6.10398953 -0.00041724 2.11202646 -1.21671586 -0.70773067 0.45664155 6.00074920 -0.06527471 2.19013045 -0.49063111 -0.32781240 0.85265293 0.04838621 0.02988905 -0.02665872 0.19671820 -Al 6.28235358 2.07527407 -0.03202213 -0.36282641 1.18302989 0.09069705 6.17846980 2.17374566 0.02863224 -1.34417167 0.41372027 1.02766412 0.03038438 -0.49310962 -0.03787292 0.26705480 -Al 3.95149447 0.02423752 4.27168672 0.02698226 -0.88889052 -0.35920435 3.99165084 -0.06423077 4.17176729 0.70901358 -0.55830115 -1.24128662 0.03865897 0.17595498 -0.06817318 -0.46785640 -Al 4.02852893 2.12907013 6.09892033 -0.58939947 0.32605456 -0.05051467 3.96240436 2.12229634 6.11590360 -0.46660715 -0.46612718 0.21238287 0.02675293 -0.11126647 -0.19710196 0.24401900 -Al 6.04747231 0.11921411 6.12828967 0.11896527 -0.01256561 0.14624619 6.07684964 0.09564256 6.14706748 0.15923475 -0.26946916 0.11189882 0.02629113 0.26802832 -0.25192606 0.08901300 -Al 5.98145593 1.97409250 4.18293057 1.06899451 1.18135986 -1.09935286 6.15374823 2.07830632 4.06222184 1.79928918 0.49655379 -0.99841919 0.04114691 0.49166094 0.06508637 -0.11671418 -Al 4.12069553 4.01088427 0.09583877 -0.10679640 -0.08411900 -0.57593156 4.08884167 4.01005571 0.06664839 -0.45825209 0.11176405 0.05765539 0.00141192 -0.12361624 0.01410260 0.14955568 -Al 3.91385716 6.07753167 2.15237661 1.47169633 0.34783071 1.20608420 4.08951615 6.12905060 2.27741939 1.40640378 0.54343716 0.89411612 0.02683878 0.31858513 0.07829785 0.05659930 -Al 6.23662182 4.14496687 2.09678581 -0.61185756 1.69440233 -1.32144143 6.13771079 4.27103656 1.92537324 -0.91341194 0.34631586 -1.61980826 0.05937909 -0.43106324 -0.11741197 -0.21663190 -Al 5.90523809 6.19847867 0.25338484 -0.45080405 0.48458824 0.63436761 5.92213432 6.19010676 0.23478642 0.72199407 -0.46862759 -1.06309747 0.05178327 0.38838352 -0.40836912 -0.42988073 -Al 4.29741270 4.00473024 3.96935420 1.84337850 -0.79392536 -0.34554586 4.38331856 3.95080738 3.95984944 -0.37118994 -0.18458946 0.17716336 0.04375586 -0.55020238 0.17184611 0.17495914 -Al 4.20614660 6.12307351 6.04186950 0.67167299 -0.92593602 -0.32961301 4.21571980 6.01739599 6.05861261 -0.37489047 -0.93957132 0.52387035 0.03886869 -0.44187052 -0.07436257 0.36902865 -Al 5.97036097 4.09193150 6.18669339 -0.33576846 -0.97460702 0.34933105 6.02210565 3.99831477 6.20417538 1.21472514 -0.62595645 -0.09808774 0.02905907 0.51335127 -0.02178934 -0.07992779 -Al 6.06685097 6.13854115 4.10618547 0.91347767 -0.67529603 -0.15541771 6.18968122 6.06604804 4.08890238 1.14771696 -0.61790175 0.02390026 0.01484524 0.23621866 -0.01986234 -0.12578925 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=0.8936910426021347 free_energy=0.8936910426021347 stress="0.006456601417350244 0.001245362542856234 0.000314848374501027 0.001245362542856234 0.004203273121418416 0.0004897180596170072 0.000314848374501027 0.0004897180596170072 0.006303993845666421" pbc="T T T" -Al -0.09751901 0.03599860 0.09321066 0.12394847 -0.73523479 0.62019847 -0.05687315 -0.03388876 0.16287718 0.62415401 -0.48575283 0.55836291 0.00654396 0.15740654 0.01938202 0.05850025 -Al 0.01139616 1.96556019 2.19882218 -1.19138721 2.21416778 0.02033057 -0.12234110 2.19231320 2.15387766 -1.00861367 1.57941151 -0.66276345 0.03104177 -0.16760058 0.15216819 -0.37220376 -Al 2.03574680 0.05555292 1.96041217 0.75263694 -0.89100467 0.28676913 2.09633081 -0.05920609 2.03506729 0.21253948 -0.87178322 1.02489539 0.02657263 0.00907289 -0.33967957 0.29360387 -Al 1.92061339 2.10189245 0.14542741 -0.70217813 0.55943463 -0.26313512 1.92195757 2.08566514 0.09981783 0.92722952 -1.07135520 -0.46979408 0.02608954 0.27583605 -0.24057319 -0.18386389 -Al -0.03049963 -0.05056105 4.19982037 -0.11424348 0.12324487 -1.23577439 -0.00741120 -0.03077141 4.04497130 0.44031493 0.32289174 -1.47291583 0.02126788 0.27900379 -0.02032248 -0.20400825 -Al 0.03932700 1.98467390 6.03866854 0.21793746 0.35217998 0.75598771 0.06992523 2.04324862 6.11008585 0.26161740 0.57231641 0.29198561 0.00833124 0.09559553 0.22599691 0.14420947 -Al 2.11906023 0.03117454 6.13345218 -1.10025815 0.70347641 -0.86064749 1.96694875 0.08177180 6.08827943 -1.53776900 0.23958866 0.09184192 0.01002900 -0.28461997 -0.16785895 0.20314519 -Al 2.00973480 1.97622512 4.02010251 0.48526775 0.46694493 -0.35788291 2.08259697 2.05876291 3.99972874 0.71842131 0.82714834 -0.00275744 0.01197306 0.20906580 0.29939441 0.12684890 -Al 0.09294391 4.03739570 -0.04416142 1.63262323 -2.16328731 -0.41349061 0.20340214 3.89848819 -0.04075709 0.25118538 -0.02070701 0.38765410 0.04140552 -0.24556934 0.27879586 0.34258475 -Al 0.10625812 6.11052427 2.14867226 -0.36136407 -1.64129812 -0.42440703 0.07689771 5.95830737 2.05970779 -0.11743480 -1.13969252 -1.04743038 0.01396109 0.00777312 0.13408010 -0.33941082 -Al 2.16131747 4.15904964 2.03922631 0.50961952 -1.63928809 0.41946845 2.19147068 3.95328790 2.10796172 0.00514515 -1.84588262 0.70971788 0.01707042 -0.14382861 -0.34434364 0.20024871 -Al 2.11644421 6.07746229 0.05635386 -0.90003300 0.72050319 0.08069261 2.03339315 6.16149670 0.04281063 -0.52403967 0.58476155 -0.40192600 0.00206081 0.02033281 0.20432512 -0.08595024 -Al -0.06872219 4.07655930 3.97710345 1.49994028 -0.48060013 0.63148491 0.10294054 4.02601996 4.12311921 1.36287959 -0.33238141 1.97616984 0.02819301 0.26157916 -0.06778076 0.43655768 -Al -0.16586567 6.17207637 6.29087578 0.33258390 -0.53377023 -0.07704100 -0.10075637 6.07907040 6.25758904 0.71819774 -1.03251371 -0.50502251 0.01598050 0.28840170 -0.28415746 -0.17346984 -Al 2.08821832 4.07619989 6.22921551 1.02299598 0.27678490 -1.08482611 2.18238812 4.10976764 6.06159169 0.71634565 0.34096018 -1.71826176 0.04921525 -0.11593535 -0.00429203 -0.43390088 -Al 2.05265912 5.92594709 4.20224038 -0.91757975 1.07261277 -0.00018278 1.95471271 6.07099717 4.20016064 -0.75539272 1.48482873 -0.13063489 0.01529379 -0.06989421 0.23106615 0.05048688 -Al 4.12053850 0.05927389 0.02285378 0.65678862 0.16807727 0.34884643 4.14520964 0.09029539 0.03096883 -0.29927440 0.41128145 -0.24919694 0.02351633 -0.17773931 0.04432774 -0.09262356 -Al 4.11967879 1.85218666 2.05326459 0.79192471 -1.11950872 0.41421409 4.19095976 1.82325787 2.09487272 0.44549341 0.59107529 0.26642180 0.02433009 -0.03320538 0.51507295 0.03712893 -Al 6.00074920 -0.06527471 2.19013045 0.49063111 0.32781240 -0.85265293 6.10398953 -0.00041724 2.11202646 1.21671586 0.70773067 -0.45664155 0.04017997 0.40328961 0.27513973 -0.04573773 -Al 6.17846980 2.17374566 0.02863224 1.34417167 -0.41372027 -1.02766412 6.28235358 2.07527407 -0.03202213 0.36282641 -1.18302989 -0.09069705 0.02467001 -0.11429253 -0.44737685 0.27739501 -Al 3.99165084 -0.06423077 4.17176729 -0.70901358 0.55830115 1.24128662 3.95149447 0.02423752 4.27168672 -0.02698226 0.88889052 0.35920435 0.02679854 0.17328928 0.28407592 -0.00929847 -Al 3.96240436 2.12229634 6.11590360 0.46660715 0.46612718 -0.21238287 4.02852893 2.12907013 6.09892033 0.58939947 -0.32605456 0.05051467 0.01460498 0.19503240 -0.22878397 -0.04784979 -Al 6.07684964 0.09564256 6.14706748 -0.15923475 0.26946916 -0.11189882 6.04747231 0.11921411 6.12828967 -0.11896527 0.01256561 -0.14624619 0.01093029 -0.24023966 0.04731775 -0.08321038 -Al 6.15374823 2.07830632 4.06222184 -1.79928918 -0.49655379 0.99841919 5.98145593 1.97409250 4.18293057 -1.06899451 -1.18135986 1.09935286 0.05435442 -0.08874101 -0.39336106 0.11115771 -Al 4.08884167 4.01005571 0.06664839 0.45825209 -0.11176405 -0.05765539 4.12069553 4.01088427 0.09583877 0.10679640 0.08411900 0.57593156 0.02565535 -0.08767682 0.09731989 0.18006274 -Al 4.08951615 6.12905060 2.27741939 -1.40640378 -0.54343716 -0.89411612 3.91385716 6.07753167 2.15237661 -1.47169633 -0.34783071 -1.20608420 0.04858553 -0.34151032 -0.03621993 -0.32699512 -Al 6.13771079 4.27103656 1.92537324 0.91341194 -0.34631586 1.61980826 6.23662182 4.14496687 2.09678581 0.61185756 -1.69440233 1.32144143 0.06559281 0.14105015 -0.65322859 0.17886841 -Al 5.92213432 6.19010676 0.23478642 -0.72199407 0.46862759 1.06309747 5.90523809 6.19847867 0.25338484 0.45080405 -0.48458824 -0.63436761 0.06961904 0.31176115 -0.08418942 -0.59340663 -Al 4.38331856 3.95080738 3.95984944 0.37118994 0.18458946 -0.17716336 4.29741270 4.00473024 3.96935420 -1.84337850 0.79392536 0.34554586 0.05705253 -0.75167478 0.18459187 0.18259713 -Al 4.21571980 6.01739599 6.05861261 0.37489047 0.93957132 -0.52387035 4.20614660 6.12307351 6.04186950 -0.67167299 0.92593602 0.32961301 0.00309027 -0.20696170 0.06265517 0.15023070 -Al 6.02210565 3.99831477 6.20417538 -1.21472514 0.62595645 0.09808774 5.97036097 4.09193150 6.18669339 0.33576846 0.97460702 -0.34933105 0.03698178 0.38061707 0.19963221 -0.21003717 -Al 6.18968122 6.06604804 4.08890238 -1.14771696 0.61790175 -0.02390026 6.06685097 6.13854115 4.10618547 -0.91347767 0.67529603 0.15541771 0.04269961 -0.13961749 0.05682592 0.22834020 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.0496213996973287 free_energy=1.0496213996973287 stress="0.0050412540679878135 0.0006477853945826963 -0.0001668343085574835 0.0006477853945826963 0.004431297660261729 -8.201800653474678e-05 -0.0001668343085574835 -8.201800653474678e-05 0.006065264190204926" pbc="T T T" -Al 0.10122258 -0.10966878 0.11583238 -0.65183463 0.62379972 1.47616753 -0.02789025 -0.03552281 0.22769330 -1.61899170 0.61514262 0.44175987 0.01627154 -0.33604521 0.15207386 -0.15394549 -Al 0.04298388 1.98852636 2.08224377 -0.65436195 -0.13546458 0.57742725 -0.04970584 2.06319289 2.12731842 -0.90325891 1.27448603 0.38532747 0.04248188 -0.16366281 0.59843150 -0.19576280 -Al 1.99206376 0.14503676 1.98033833 -1.79781972 0.55562620 -1.56759129 1.88811699 0.11538020 1.91465887 -0.00780193 -1.00060361 0.43777414 0.06728315 0.40458477 -0.52792677 0.49227841 -Al 2.10797560 1.99011134 0.11372571 -0.44475192 0.44312265 0.92397889 2.05613089 2.09040771 0.15826877 -0.41214089 1.17508447 -0.20829008 0.03413359 -0.10775604 0.37858706 -0.21800418 -Al -0.15936233 0.17057456 3.91544150 -1.29978743 -0.97098952 0.00587215 -0.21763631 0.00103324 3.96971247 0.20235474 -1.72504926 0.78135607 0.06272531 0.49123211 -0.60877838 0.43075244 -Al -0.01152506 2.00306105 6.12939722 0.94784953 0.11075907 -1.37244704 0.08819301 1.99481137 6.01982464 0.68219599 -0.23021869 -0.46865056 0.03775713 0.12358988 -0.11514859 0.09023538 -Al 2.14323951 0.20075913 6.10010622 -1.73812154 -0.17182853 0.29106492 1.90025233 0.11955926 6.13488825 -2.24051188 -1.22601727 0.33663713 0.06683578 -0.58901543 -0.43573075 0.04714049 -Al 1.97865080 2.04835628 4.18844391 0.45187924 0.63826775 -0.80148921 2.07250112 2.11931772 4.06044556 1.20307312 0.43090982 -1.37451993 0.01568506 0.28505701 0.16048250 -0.32379161 -Al -0.04370556 3.90728441 -0.10008633 0.43830490 0.25321690 -1.23832876 0.03923277 3.99019439 -0.18228154 0.83114663 1.06259081 -0.33475925 0.04327799 0.35962944 0.46916804 0.36505477 -Al -0.09389839 5.86400230 2.19930966 0.51305389 -1.33457864 0.55029550 -0.00117448 5.78115198 2.20513526 1.07547071 -0.23326776 -0.41136707 0.03528062 0.27999378 0.39776872 -0.32603095 -Al 2.22516603 4.07412600 2.04313126 0.21175610 -0.54153479 -0.14682908 2.18346539 3.97293698 2.04838010 -0.79429155 -1.02113711 0.29409029 0.03528168 -0.52255050 -0.40714140 0.08135634 -Al 1.96041189 5.99331499 0.12839923 0.58861329 -0.89940228 -0.19320009 2.01824176 5.94296639 0.11195992 0.43202325 0.09997826 -0.04108933 0.02267279 0.02547308 0.17486406 -0.02555815 -Al 0.01622504 4.03305362 4.24629539 -0.20897970 -0.43290867 0.94771227 0.02499006 3.99292013 4.31635930 0.28724972 -0.11688449 0.25301718 0.03019158 0.24590168 -0.08028449 -0.13735206 -Al -0.04717474 6.10258323 5.98345334 -0.19924822 1.18923225 -0.23774787 0.02741756 6.21451803 5.99028232 1.35933240 0.67652992 0.45769964 0.03764803 0.63139411 0.02387530 0.14450647 -Al 2.19535093 4.07950163 6.27092103 0.49669069 0.42004002 0.21328905 2.20978472 4.11524578 6.23947720 -0.05515350 0.15201378 -0.84314754 0.02159794 -0.36514127 0.00680721 -0.28237673 -Al 2.05921180 6.16495709 3.90030620 -0.69666048 -0.11172841 0.05873806 1.98920727 6.16778992 3.96357739 -0.43021369 0.08401848 1.03229593 0.00713044 -0.06691243 0.11796941 0.38115234 -Al 4.05202003 0.01070115 0.03065185 0.31142016 0.04184735 -0.76860160 4.10296519 0.03301753 0.00685599 0.37715428 0.37813873 0.31260723 0.03359944 0.23804609 0.06016911 0.32284858 -Al 4.16048556 2.01738399 2.11378911 1.71041171 -0.56244486 -0.40338931 4.28023528 1.98793431 2.08875939 0.24151183 0.00146091 -0.01257181 0.00854906 -0.20256318 0.18174341 0.08334349 -Al 5.97946595 0.04290261 2.07413626 -0.63565391 0.16744851 -0.20527034 5.96914732 0.03056657 2.00681229 0.59948520 -0.41408151 -0.81513480 0.03257702 0.23362178 -0.13418913 -0.35317077 -Al 6.14869392 2.13655303 0.04954117 0.02697863 0.07880540 0.72690466 6.14798601 2.07663662 0.09817659 0.10826231 -0.98203882 -0.12943480 0.02941124 -0.14807618 -0.51257652 0.02897729 -Al 4.13968993 0.01500317 4.13107062 0.36686831 -0.52119845 -0.50714151 4.12849918 -0.01625099 4.07786007 -0.54926543 -0.02627973 -0.33540446 0.02332059 -0.28133810 0.13256547 -0.08767421 -Al 4.12198454 2.06695547 6.18224982 0.23483739 -0.77006126 -0.42204804 4.14691602 1.97643788 6.13939562 0.04527294 -0.78914839 -0.26333537 0.02368015 0.11217059 -0.10695550 -0.06611671 -Al 6.25566481 -0.10128551 6.15264624 0.07767739 -0.66391012 1.34869573 6.19542255 -0.10985806 6.31740764 -1.02804911 0.47648802 1.45681029 0.02828435 -0.47408145 0.33607811 0.20598649 -Al 6.06172674 2.04814788 4.26981803 0.10951947 0.65028507 0.52808798 6.06452125 2.11571020 4.29040165 0.02683886 0.42280976 -0.19900004 0.03186952 -0.09483808 0.08096578 -0.18784736 -Al 4.21267789 4.01513856 0.03586771 0.53490136 0.11884854 1.02157862 4.23023799 4.03581066 0.14419383 -0.29683180 0.22521400 0.78710524 0.04025142 -0.18199172 0.08392670 0.10520734 -Al 4.17385429 5.90062313 2.02517111 0.17128695 1.71280142 0.48869317 4.15723009 6.14546482 2.06602523 -0.61260436 2.36001851 0.26488937 0.04729958 -0.11317749 0.60326078 -0.07991139 -Al 6.13144139 3.99610944 2.30979861 0.23302038 -0.55368936 -1.89995381 6.14445941 3.92170496 2.08885168 0.04995955 -0.54317511 -1.76785371 0.03477328 -0.09796674 -0.23400137 -0.33491051 -Al 6.16283748 5.95400700 0.06897947 0.23148447 -1.03266710 1.40056725 6.19340006 5.90029562 0.21703943 0.34282833 0.12252987 1.11075021 0.01300785 -0.00206709 0.29220819 0.09141552 -Al 4.01950371 4.14201927 4.18919487 0.92682378 -0.14722422 -0.61737093 4.10340797 4.11578306 4.11517003 0.47645258 -0.23647008 -0.85203587 0.01185028 -0.00287716 -0.12312126 0.02622525 -Al 3.92146091 6.31423008 6.24958566 0.24791905 0.66040367 0.55511546 3.98540207 6.29439016 6.25071365 0.76382995 -0.87797052 -0.53694638 0.04517418 0.31196126 -0.60089521 -0.30642207 -Al 6.03248216 4.21619905 6.06675612 -1.07580833 1.32665565 -0.63066453 5.92275141 4.32792490 6.02127062 -0.73867048 0.61925818 0.06170182 0.02368320 -0.06660190 -0.14018440 -0.04115811 -Al 6.00340549 6.19022467 4.05053520 0.57173114 -0.14152942 -0.10211507 6.07052178 6.13902154 4.06241672 0.58334284 -0.75432982 0.17971912 0.04603572 0.07400719 -0.22401144 0.22355249 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.0598670279781714 free_energy=1.0598670279781714 stress="0.005052295521701254 0.0003623609712633322 -0.00036006216669003474 0.0003623609712633322 0.005464424852972196 2.0063803643432364e-05 -0.00036006216669003474 2.0063803643432364e-05 0.0069520428514399865" pbc="T T T" -Al -0.02789025 -0.03552281 0.22769330 1.61899170 -0.61514262 -0.44175987 0.10122258 -0.10966878 0.11583238 0.65183463 -0.62379972 -1.47616753 0.04677184 -0.11585444 -0.08582853 -0.40591217 -Al -0.04970584 2.06319289 2.12731842 0.90325891 -1.27448603 -0.38532747 0.04298388 1.98852636 2.08224377 0.65436195 0.13546458 -0.57742725 0.01754470 0.11219607 0.16306876 0.05525110 -Al 1.88811699 0.11538020 1.91465887 0.00780193 1.00060361 -0.43777414 1.99206376 0.14503676 1.98033833 1.79781972 -0.55562620 1.56759129 0.06239616 0.59048502 -0.34073568 0.63061900 -Al 2.05613089 2.09040771 0.15826877 0.41214089 -1.17508447 0.20829008 2.10797560 1.99011134 0.11372571 0.44475192 -0.44312265 -0.92397889 0.03123103 0.07552269 -0.04896383 -0.42003529 -Al -0.21763631 0.00103324 3.96971247 -0.20235474 1.72504926 -0.78135607 -0.15936233 0.17057456 3.91544150 1.29978743 0.97098952 -0.00587215 0.02442416 0.43939881 0.18014228 0.03613343 -Al 0.08819301 1.99481137 6.01982464 -0.68219599 0.23021869 0.46865056 -0.01152506 2.00306105 6.12939722 -0.94784953 -0.11075907 1.37244704 0.07245426 -0.26654639 -0.05319396 0.41796345 -Al 1.90025233 0.11955926 6.13488825 2.24051188 1.22601727 -0.33663713 2.14323951 0.20075913 6.10010622 1.73812154 0.17182853 -0.29106492 0.03011933 0.34609724 -0.24095767 0.02867786 -Al 2.07250112 2.11931772 4.06044556 -1.20307312 -0.43090982 1.37451993 1.97865080 2.04835628 4.18844391 -0.45187924 -0.63826775 0.80148921 0.00901304 0.14949738 -0.29855848 0.01534920 -Al 0.03923277 3.99019439 -0.18228154 -0.83114663 -1.06259081 0.33475925 -0.04370556 3.90728441 -0.10008633 -0.43830490 -0.25321690 1.23832876 0.01462821 -0.12137574 0.07376550 0.23510663 -Al -0.00117448 5.78115198 2.20513526 -1.07547071 0.23326776 0.41136707 -0.09389839 5.86400230 2.19930966 -0.51305389 1.33457864 -0.55029550 0.03359128 0.07004072 0.31193338 -0.26943981 -Al 2.18346539 3.97293698 2.04838010 0.79429155 1.02113711 -0.29409029 2.22516603 4.07412600 2.04313126 -0.21175610 0.54153479 0.14682908 0.03338994 -0.16830166 0.16270713 0.17446616 -Al 2.01824176 5.94296639 0.11195992 -0.43202325 -0.09997826 0.04108933 1.96041189 5.99331499 0.12839923 -0.58861329 0.89940228 0.19320009 0.01583102 -0.05773059 0.41657068 0.09985010 -Al 0.02499006 3.99292013 4.31635930 -0.28724972 0.11688449 -0.25301718 0.01622504 4.03305362 4.24629539 0.20897970 0.43290867 -0.94771227 0.03385578 0.08442802 0.27496233 -0.29517719 -Al 0.02741756 6.21451803 5.99028232 -1.35933240 -0.67652992 -0.45769964 -0.04717474 6.10258323 5.98345334 0.19924822 -1.18923225 0.23774787 0.03586727 0.21394511 -0.35792691 0.33497121 -Al 2.20978472 4.11524578 6.23947720 0.05515350 -0.15201378 0.84314754 2.19535093 4.07950163 6.27092103 -0.49669069 -0.42004002 -0.21328905 0.01842292 -0.01942167 -0.15205282 -0.38517509 -Al 1.98920727 6.16778992 3.96357739 0.43021369 -0.08401848 -1.03229593 2.05921180 6.16495709 3.90030620 0.69666048 0.11172841 -0.05873806 0.00685140 0.24241490 -0.03414395 0.24087567 -Al 4.10296519 0.03301753 0.00685599 -0.37715428 -0.37813873 -0.31260723 4.05202003 0.01070115 0.03065185 -0.31142016 -0.04184735 0.76860160 0.04250339 -0.24205869 0.08233928 0.29312272 -Al 4.28023528 1.98793431 2.08875939 -0.24151183 -0.00146091 0.01257181 4.16048556 2.01738399 2.11378911 -1.71041171 0.56244486 0.40338931 0.04993652 -0.66461868 0.13158732 0.15889151 -Al 5.96914732 0.03056657 2.00681229 -0.59948520 0.41408151 0.81513480 5.97946595 0.04290261 2.07413626 0.63565391 -0.16744851 0.20527034 0.07777627 0.51622871 -0.16776067 0.06714457 -Al 6.14798601 2.07663662 0.09817659 -0.10826231 0.98203882 0.12943480 6.14869392 2.13655303 0.04954117 -0.02697863 -0.07880540 -0.72690466 0.02947094 0.13317446 -0.12164259 -0.53733238 -Al 4.12849918 -0.01625099 4.07786007 0.54926543 0.02627973 0.33540446 4.13968993 0.01500317 4.13107062 -0.36686831 0.52119845 0.50714151 0.01151456 -0.24044548 0.18451663 0.17698936 -Al 4.14691602 1.97643788 6.13939562 -0.04527294 0.78914839 0.26333537 4.12198454 2.06695547 6.18224982 -0.23483739 0.77006126 0.42204804 0.00773501 -0.23033708 0.09615680 0.12908107 -Al 6.19542255 -0.10985806 6.31740764 1.02804911 -0.47648802 -1.45681029 6.25566481 -0.10128551 6.15264624 -0.07767739 0.66391012 -1.34869573 0.01888789 -0.16980109 0.27931138 -0.22127630 -Al 6.06452125 2.11571020 4.29040165 -0.02683886 -0.42280976 0.19900004 6.06172674 2.04814788 4.26981803 -0.10951947 -0.65028507 -0.52808798 0.03039213 0.06230484 -0.23591972 -0.27487674 -Al 4.23023799 4.03581066 0.14419383 0.29683180 -0.22521400 -0.78710524 4.21267789 4.01513856 0.03586771 -0.53490136 -0.11884854 -1.02157862 0.02983579 -0.34345818 0.01392646 -0.23961528 -Al 4.15723009 6.14546482 2.06602523 0.61260436 -2.36001851 -0.26488937 4.17385429 5.90062313 2.02517111 -0.17128695 -1.71280142 -0.48869317 0.02107550 -0.37514881 -0.20604012 -0.07170910 -Al 6.14445941 3.92170496 2.08885168 -0.04995955 0.54317511 1.76785371 6.13144139 3.99610944 2.30979861 -0.23302038 0.55368936 1.89995381 0.06509629 -0.02235608 0.28176745 0.40562405 -Al 6.19340006 5.90029562 0.21703943 -0.34282833 -0.12252987 -1.11075021 6.16283748 5.95400700 0.06897947 -0.23148447 1.03266710 -1.40056725 0.03240190 0.01905578 0.43541944 -0.30060952 -Al 4.10340797 4.11578306 4.11517003 -0.47645258 0.23647008 0.85203587 4.01950371 4.14201927 4.18919487 -0.92682378 0.14722422 0.61737093 0.02111889 -0.25861542 0.06941220 -0.18311221 -Al 3.98540207 6.29439016 6.25071365 -0.76382995 0.87797052 0.53694638 3.92146091 6.31423008 6.24958566 -0.24791905 -0.66040367 -0.55511546 0.03988236 -0.00157099 -0.31690941 -0.31468515 -Al 5.92275141 4.32792490 6.02127062 0.73867048 -0.61925818 -0.06170182 6.03248216 4.21619905 6.06675612 1.07580833 -1.32665565 0.63066453 0.04791296 0.31466609 -0.35458918 0.46986949 -Al 6.07052178 6.13902154 4.06241672 -0.58334284 0.75432982 -0.17971912 6.00340549 6.19022467 4.05053520 -0.57173114 0.14152942 0.10211507 0.04793427 -0.07181483 -0.14236349 -0.05103034 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.1701265082332784 free_energy=1.1701265082332784 stress="0.004240275766441019 0.0004635646804701818 -0.00019295021039451258 0.0004635646804701818 0.004553247416723432 -7.657078983153338e-05 -0.00019295021039451258 -7.657078983153338e-05 0.007360803371220558" pbc="T T T" -Al 0.02344403 0.01551248 -0.19761095 -1.16323505 -0.26122577 -0.29488629 -0.02535890 -0.01666860 -0.15289106 0.28826462 -0.30466071 1.07928487 0.03848448 0.38635724 -0.01650272 0.45105419 -Al 0.03869236 1.83830745 2.13618222 -0.19190768 -0.80007077 0.53464521 0.08203055 1.76798180 2.14508712 0.89406061 -0.34609683 -0.51131534 0.01523279 0.38861915 0.03657230 -0.17717062 -Al 2.21696033 -0.00369555 2.08103589 0.49172392 -0.98175087 -1.06769837 2.19078758 -0.10086233 1.96301195 -0.84339155 -0.62867711 -0.85437542 0.03245089 -0.51758763 -0.04627487 -0.13487795 -Al 2.16865957 2.03296493 0.08130823 0.73865548 -0.13990468 -0.19515489 2.19124544 2.02189874 0.05478480 -0.28379326 -0.00199163 -0.20331497 0.00608690 -0.34940230 -0.03453984 -0.09684311 -Al 0.11565556 -0.08703035 4.32584055 -0.60495818 0.49127841 -1.39549901 0.04339555 0.01238779 4.16767894 -0.66737671 1.12465011 -1.24941081 0.01355355 -0.06584591 0.38837878 -0.23459667 -Al -0.09739854 2.11083217 6.25209748 -0.29516044 0.35653077 1.04581886 -0.08667627 2.14215956 6.30564995 0.50258723 0.07103308 -0.19853601 0.04747541 0.23084929 0.03537826 -0.24619136 -Al 2.35267551 0.00857491 6.05157987 -0.46205813 0.04785286 0.21264578 2.23590422 0.01790017 6.07751606 -1.72194435 0.13108337 0.20121051 0.02223262 -0.36276836 0.02793866 0.09346508 -Al 1.97634963 2.18784736 4.17164038 0.77580769 1.02425092 -0.82884860 2.02584481 2.21163753 4.13023792 0.08198376 -0.57431653 0.12525941 0.02439692 -0.11994031 -0.47622501 0.20545082 -Al 0.01137334 4.01510313 0.24021383 -0.93415439 0.83732410 -0.98372568 -0.08986066 4.09259345 0.16573580 -0.67317829 0.43937864 -0.21180730 0.01860832 -0.09398153 0.00980540 0.02322964 -Al -0.10751780 6.36589664 2.03998191 0.11822679 -0.54233461 0.26748073 -0.03820982 6.26443372 2.07001341 1.02046144 -1.22516014 0.30771802 0.00482065 0.40180929 -0.31689531 -0.00398226 -Al 2.09552761 4.14451928 2.18418504 0.28280458 -0.14393301 0.00424007 2.09234396 4.13172013 2.10658804 -0.31946865 -0.16413754 -1.41682083 0.03780661 -0.19702601 0.03480980 -0.40777609 -Al 1.99087608 6.03041168 -0.04696864 -0.14353749 0.00033405 0.83188883 2.01064771 6.07441477 0.09136432 0.41685005 0.58729020 1.39480062 0.02136328 0.24767567 0.35521372 0.42009104 -Al -0.06811528 4.04554951 3.88373122 0.19556418 -0.00422045 -0.49531416 -0.02250295 4.08105928 3.87934750 0.51574885 0.51481387 0.42592389 0.03209531 0.21748865 0.27722318 0.27892949 -Al -0.06436346 5.98382832 6.16365794 0.54787878 -0.04163019 -0.40358502 -0.01197730 6.03704335 6.09441764 0.50293963 1.09095144 -0.90914127 0.07055460 -0.07208947 0.22177421 -0.08190182 -Al 1.75291701 4.11946475 6.27009913 -0.75441855 0.62759591 0.88376122 1.78535119 4.11234955 6.29094723 1.31931167 -0.62635373 -0.37150565 0.07954511 0.64657652 -0.45730187 -0.48359978 -Al 2.00672533 6.04974713 4.10309527 -0.18108971 -0.70710692 -0.49781637 1.99809219 5.98659917 4.06941863 -0.01399102 -0.24475885 -0.13255745 0.00993975 0.09944542 -0.06148956 0.11360764 -Al 4.11539389 0.03520825 0.06083098 -0.37689914 -0.24152312 -0.46323548 4.07469952 -0.03498634 0.02684264 -0.19923830 -0.99129311 -0.08715584 0.04132444 -0.15383560 -0.24851788 0.03160766 -Al 3.96405453 2.17752042 2.18998955 0.19189540 0.58832274 1.30730034 4.01802925 2.19751151 2.27659620 0.70331049 -0.25955173 0.13583508 0.04210457 0.22735563 -0.22016853 -0.19501995 -Al 6.02419456 0.06907121 2.15899948 -0.86179089 -0.52327712 -0.55594996 5.95351650 -0.02605980 2.10710666 -0.35401754 -1.01180705 -0.56216711 0.10819101 0.09030648 -0.34026152 0.16686401 -Al 6.06682015 1.83842144 0.35078828 0.90247749 0.13493551 -0.43509367 6.15717909 1.94699849 0.23962681 0.61351328 1.60199540 -1.27106336 0.06957469 0.07509888 0.68263391 -0.61665581 -Al 4.09559994 -0.01213247 3.92601624 -0.26958396 0.72276936 -0.29079217 4.06700132 0.08203661 4.00185596 -0.31957091 0.83035284 1.49004461 0.06181064 0.05031530 0.19264484 0.70677658 -Al 4.29055238 2.04739453 6.18569088 0.58759882 -1.28668758 0.36965449 4.27448707 1.96363150 6.20005144 -0.96264502 0.09207187 -0.21098962 0.05947481 -0.40519337 0.03382496 -0.04825640 -Al 6.12561752 0.17978212 6.10323681 -1.04249764 0.97887128 1.72843701 6.00728087 0.17225198 6.22503583 -0.77944975 -1.27841362 0.36626718 0.04775849 -0.28276633 -0.50715965 -0.23480464 -Al 6.15462339 2.15209799 3.95509619 0.62962767 -0.16602841 -0.68791306 6.19023259 2.09200001 3.94830015 0.00623352 -0.80143549 0.68591550 0.02957361 -0.13902931 -0.34116219 0.27727649 -Al 4.07354781 4.00321974 0.02550575 1.77424394 -0.37405529 0.38363590 4.25264567 3.99322319 0.05213031 1.25414992 0.19130653 -0.01109703 0.00777593 0.07227276 0.18198188 0.00223878 -Al 4.14674599 6.05104520 2.10483322 0.03280139 0.08676754 -0.19676071 4.12920284 6.03270448 2.07159733 -0.32141806 -0.39517706 -0.36038710 0.01713185 -0.15750992 -0.16402530 -0.08583804 -Al 6.08991039 4.12647927 1.95742699 -0.62354249 -0.68569643 0.37027988 6.01740939 4.05582249 2.03816183 -0.50607760 -0.53380887 1.15359371 0.03803245 -0.15271479 -0.02539625 0.22190570 -Al 6.06431252 6.22291666 0.03871599 2.18534921 -1.54573041 0.31498948 6.25920008 6.04374176 0.05037981 1.05099556 -1.43098646 -0.05659016 0.00767646 -0.00159240 -0.23646063 -0.13268791 -Al 4.01942104 3.87517101 4.25704919 -0.88586858 0.93575525 0.82201085 3.96334456 4.04024212 4.31720590 0.00146938 1.77670670 0.27764780 0.07506267 0.09139631 0.56879153 -0.22611728 -Al 4.13837525 5.87134393 6.04631195 -1.03231625 0.54871167 -0.61389713 4.01524921 6.00221715 6.02907801 -1.03591986 1.75098043 0.43472914 0.02877591 -0.19312635 0.47347531 0.17717706 -Al 6.15013780 4.04861391 6.05201872 0.69980634 1.03198283 0.66840442 6.21496319 4.13313340 6.15742268 0.27083775 0.42638431 0.94623041 0.05145999 0.10110606 -0.08241841 0.38824867 -Al 6.11246211 6.07650590 4.14447109 -0.33144313 0.03189243 -0.33902251 6.06873209 6.08937632 4.09675090 -0.44123689 0.18962766 -0.40622548 0.00975180 -0.06226305 0.05435279 -0.15160318 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.070238329593166 free_energy=1.070238329593166 stress="0.005001714831958823 0.0014787882206239893 -8.696280675829449e-05 0.0014787882206239893 0.003844643493447092 -0.0001922692050899891 -8.696280675829449e-05 -0.0001922692050899891 0.0070443138583341485" pbc="T T T" -Al -0.02535890 -0.01666860 -0.15289106 -0.28826462 0.30466071 -1.07928487 0.02344403 0.01551248 -0.19761095 1.16323505 0.26122577 0.29488629 0.03722304 0.42582734 0.02702365 0.38695244 -Al 0.08203055 1.76798180 2.14508712 -0.89406061 0.34609683 0.51131534 0.03869236 1.83830745 2.13618222 0.19190768 0.80007077 -0.53464521 0.02672090 0.21143978 0.32090730 -0.44958622 -Al 2.19078758 -0.10086233 1.96301195 0.84339155 0.62867711 0.85437542 2.21696033 -0.00369555 2.08103589 -0.49172392 0.98175087 1.06769837 0.03911533 -0.27001058 0.29666503 0.33427476 -Al 2.19124544 2.02189874 0.05478480 0.28379326 0.00199163 0.20331497 2.16865957 2.03296493 0.08130823 -0.73865548 0.13990468 0.19515489 -0.00314954 -0.27327489 0.08254668 0.07618516 -Al 0.04339555 0.01238779 4.16767894 0.66737671 -1.12465011 1.24941081 0.11565556 -0.08703035 4.32584055 0.60495818 -0.49127841 1.39549901 0.00963083 0.03281695 -0.01581709 0.26729276 -Al -0.08667627 2.14215956 6.30564995 -0.50258723 -0.07103308 0.19853601 -0.09739854 2.11083217 6.25209748 0.29516044 -0.35653077 -1.04581886 0.04710413 0.25331477 -0.23971365 -0.50879010 -Al 2.23590422 0.01790017 6.07751606 1.72194435 -0.13108337 -0.20121051 2.35267551 0.00857491 6.05157987 0.46205813 -0.04785286 -0.21264578 0.01508188 -0.44633611 0.04535953 -0.02872732 -Al 2.02584481 2.21163753 4.13023792 -0.08198376 0.57431653 -0.12525941 1.97634963 2.18784736 4.17164038 -0.77580769 -1.02425092 0.82884860 0.03426526 -0.22181921 -0.44990337 0.31539808 -Al -0.08986066 4.09259345 0.16573580 0.67317829 -0.43937864 0.21180730 0.01137334 4.01510313 0.24021383 0.93415439 -0.83732410 0.98372568 0.04397739 0.39526212 -0.28067905 0.36081251 -Al -0.03820982 6.26443372 2.07001341 -1.02046144 1.22516014 -0.30771802 -0.10751780 6.36589664 2.03998191 -0.11822679 0.54233461 -0.26748073 0.00806430 0.13589729 -0.13224951 0.04267469 -Al 2.09234396 4.13172013 2.10658804 0.31946865 0.16413754 1.41682083 2.09552761 4.14451928 2.18418504 -0.28280458 0.14393301 -0.00424007 0.03078722 -0.15619484 -0.10972569 -0.38485851 -Al 2.01064771 6.07441477 0.09136432 -0.41685005 -0.58729020 -1.39480062 1.99087608 6.03041168 -0.04696864 0.14353749 -0.00033405 -0.83188883 0.01603801 0.07817414 -0.06739502 -0.18467174 -Al -0.02250295 4.08105928 3.87934750 -0.51574885 -0.51481387 -0.42592389 -0.06811528 4.04554951 3.88373122 -0.19556418 0.00422045 0.49531416 0.02086440 -0.01636202 0.00576295 0.30074486 -Al -0.01197730 6.03704335 6.09441764 -0.50293963 -1.09095144 0.90914127 -0.06436346 5.98382832 6.16365794 -0.54787878 0.04163019 0.40358502 0.05282273 0.08804989 0.36704810 -0.16508080 -Al 1.78535119 4.11234955 6.29094723 -1.31931167 0.62635373 0.37150565 1.75291701 4.11946475 6.27009913 0.75441855 -0.62759591 -0.88376122 0.05243553 0.59585008 -0.22017073 -0.24897694 -Al 1.99809219 5.98659917 4.06941863 0.01399102 0.24475885 0.13255745 2.00672533 6.04974713 4.10309527 0.18108971 0.70710692 0.49781637 0.01532469 0.03231636 0.33995106 0.08668348 -Al 4.07469952 -0.03498634 0.02684264 0.19923830 0.99129311 0.08715584 4.11539389 0.03520825 0.06083098 0.37689914 0.24152312 0.46323548 0.04074881 0.18382248 -0.14097405 0.16679055 -Al 4.01802925 2.19751151 2.27659620 -0.70331049 0.25955173 -0.13583508 3.96405453 2.17752042 2.18998955 -0.19189540 -0.58832274 -1.30730034 0.04682243 0.04302862 -0.29901108 -0.48121469 -Al 5.95351650 -0.02605980 2.10710666 0.35401754 1.01180705 0.56216711 6.02419456 0.06907121 2.15899948 0.86179089 0.52327712 0.55594996 0.03783924 0.24606853 0.05579295 -0.14373582 -Al 6.15717909 1.94699849 0.23962681 -0.61351328 -1.60199540 1.27106336 6.06682015 1.83842144 0.35078828 -0.90247749 -0.13493551 0.43509367 0.03135380 -0.20568383 0.18195181 0.01840027 -Al 4.06700132 0.08203661 4.00185596 0.31957091 -0.83035284 -1.49004461 4.09559994 -0.01213247 3.92601624 0.26958396 -0.72276936 0.29079217 0.02636238 -0.07034558 -0.14278764 0.34231774 -Al 4.27448707 1.96363150 6.20005144 0.96264502 -0.09207187 0.21098962 4.29055238 2.04739453 6.18569088 -0.58759882 1.28668758 -0.36965449 0.07827745 -0.53037369 0.67598726 -0.24720494 -Al 6.00728087 0.17225198 6.22503583 0.77944975 1.27841362 -0.36626718 6.12561752 0.17978212 6.10323681 1.04249764 -0.97887128 -1.72843701 0.08523737 0.37537492 -0.79728642 -0.50933463 -Al 6.19023259 2.09200001 3.94830015 -0.00623352 0.80143549 -0.68591550 6.15462339 2.15209799 3.95509619 -0.62962767 0.16602841 0.68791306 0.02275020 -0.18558137 -0.07633978 0.52265087 -Al 4.25264567 3.99322319 0.05213031 -1.25414992 -0.19130653 0.01109703 4.07354781 4.00321974 0.02550575 -1.77424394 0.37405529 -0.38363590 0.01350932 -0.42846414 0.18846228 -0.22125765 -Al 4.12920284 6.03270448 2.07159733 0.32141806 0.39517706 0.36038710 4.14674599 6.05104520 2.10483322 -0.03280139 -0.08676754 0.19676071 0.01800742 -0.09019933 -0.11484602 0.01389448 -Al 6.01740939 4.05582249 2.03816183 0.50607760 0.53380887 -1.15359371 6.08991039 4.12647927 1.95742699 0.62354249 0.68569643 -0.37027988 0.06125176 0.23028801 0.12380966 0.30482279 -Al 6.25920008 6.04374176 0.05037981 -1.05099556 1.43098646 0.05659016 6.06431252 6.22291666 0.03871599 -2.18534921 1.54573041 -0.31498948 0.04723209 -0.66841658 0.37808134 -0.04875717 -Al 3.96334456 4.04024212 4.31720590 -0.00146938 -1.77670670 -0.27764780 4.01942104 3.87517101 4.25704919 0.88586858 -0.93575525 -0.82201085 0.05314517 0.38205832 -0.04498728 -0.22280964 -Al 4.01524921 6.00221715 6.02907801 1.03591986 -1.75098043 -0.43472914 4.13837525 5.87134393 6.04631195 1.03231625 -0.54871167 0.61389713 0.02835011 0.17479331 0.27056336 0.44538917 -Al 6.21496319 4.13313340 6.15742268 -0.27083775 -0.42638431 -0.94623041 6.15013780 4.04861391 6.05201872 -0.69980634 -1.03198283 -0.66840442 0.03431130 -0.31711057 -0.26362013 -0.24207206 -Al 6.06873209 6.08937632 4.09675090 0.44123689 -0.18962766 0.40622548 6.11246211 6.07650590 4.14447109 0.33144313 -0.03189243 0.33902251 -0.00126660 -0.00421014 0.03559352 0.10179363 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=0.9510780384805617 free_energy=0.9510780384805617 stress="0.006018694982681886 0.000707532545302181 -0.00027031016091648506 0.000707532545302181 0.004672717481672366 -0.00033102340476843346 -0.00027031016091648506 -0.00033102340476843346 0.005535259343197416" pbc="T T T" -Al -0.01275222 0.05726802 0.18005727 -0.19927745 0.57237382 -0.71890947 -0.02605177 0.10230075 0.07012401 0.05724316 0.26450821 -1.29509040 0.01715142 -0.00947614 -0.09650966 -0.17147379 -Al -0.04999668 2.09243871 2.18464611 1.16756593 -1.12969892 -0.21790407 0.07843493 1.98449425 2.15335321 1.05132111 -0.77155548 -0.19687641 0.01395655 0.09345415 0.04660318 -0.17104505 -Al 2.03841892 0.08400047 1.95797645 -0.21404749 0.13441634 -0.50957714 2.03483420 0.10028453 1.98389770 0.16796648 -0.10216174 0.79790502 0.03647746 0.09405088 0.15662974 0.57829811 -Al 1.88149100 2.16983810 0.17118949 -0.37348322 0.82040314 0.79865440 1.90338778 2.19789253 0.20698188 0.73824318 -0.36892834 -0.24218248 0.03894211 0.37671562 -0.31266842 -0.22837753 -Al 0.14528137 0.00609861 4.16828852 0.51651896 0.04198044 1.30642404 0.13990167 -0.00336235 4.22699801 -0.60545575 -0.26572591 -0.30847152 0.01662716 -0.34466581 -0.04509918 -0.38810039 -Al 0.04182469 1.96808443 6.11673011 0.95765160 -0.31122890 -0.76605778 0.13966297 1.96766674 6.06165521 0.68463553 0.33608300 -0.02917049 0.01239413 0.07942294 0.15698910 -0.01756646 -Al 2.14379409 -0.12620265 6.05723504 0.29418097 -0.70748394 -0.21503271 2.15377844 -0.18480510 6.10440512 -0.05892927 -0.28482844 1.00274337 0.03794733 -0.19517314 0.08730603 0.42342000 -Al 2.15660798 1.93854607 4.19392680 -0.18126174 -0.96469831 0.84297807 2.11891387 1.88693950 4.21250084 -0.39286002 0.15025753 -0.44609361 0.01900670 -0.19472662 0.19664517 -0.42360793 -Al -0.06455924 3.99585417 -0.07151346 -0.69408874 -0.10696503 2.18650854 -0.10422580 4.04038397 0.15494813 0.02422449 0.85208347 1.72868698 0.02889958 0.16905584 0.34485614 0.12291162 -Al 0.10204672 5.90301118 2.08022324 -0.36262648 0.97833494 -0.55983239 0.01170307 6.08867921 2.02963497 -1.02291003 2.29986604 -0.33505130 0.05030673 -0.42298381 0.50281089 0.03691161 -Al 2.05870312 4.15893716 2.17240187 -1.16319169 1.06499491 -1.14366143 1.97633583 4.19644025 2.07173789 -0.38942544 -0.34491464 -0.51929877 0.03741481 0.25165522 -0.43786106 -0.00881342 -Al 2.03093237 6.16616343 0.22636209 1.10853852 -0.60495543 0.06030238 2.13028979 6.05720479 0.12437434 0.56192064 -1.10937502 -1.72467316 0.05134746 -0.02221241 -0.42220860 -0.72697251 -Al -0.03248695 4.00614451 4.07624522 -2.09309665 0.34674387 -0.80620611 -0.19927375 4.05760085 4.03833007 -0.53714714 0.63756118 0.00335450 0.01991890 0.07730724 0.05282723 0.36410710 -Al -0.09799495 6.12242326 6.12879692 -0.47834541 1.61534925 -0.08451546 -0.09511775 6.22080798 6.14893328 0.50769178 0.03447663 0.43884577 0.01447713 0.32358306 -0.30293348 0.14506716 -Al 2.18056963 4.02380800 6.07306091 0.40203843 -0.98284014 1.18021244 2.16192487 3.96734595 6.20660576 -0.81339916 0.00838017 1.00797297 0.00791789 -0.31024128 0.23116548 0.18355736 -Al 2.01765645 6.04958972 4.15201148 -0.01861823 -0.58504763 -0.46188455 2.04342734 5.99328302 4.09422797 0.34837466 -0.40386889 -0.49054059 0.02178846 0.24261821 0.03990691 -0.11911530 -Al 4.05950027 -0.06948805 -0.04315484 0.58650611 -0.92751197 -0.32352926 4.12873504 -0.09385544 -0.04070397 0.53032298 0.48212852 0.47057884 0.03338857 0.12557091 0.41969896 0.17389399 -Al 4.19843764 2.12498899 2.10447206 -1.23468406 0.15096150 0.05290245 4.00730035 2.08501061 2.10790045 -1.94036692 -0.69849121 0.07967466 0.04405164 -0.53520078 -0.43452428 -0.10314871 -Al 6.05319828 0.12654426 2.12190414 -0.08523618 -0.98627825 -0.69445973 6.09741148 0.00653072 2.03616757 0.75210300 -1.00224225 -0.63806698 0.03369725 0.38187535 -0.23784589 -0.21805822 -Al 6.08315617 2.13848904 0.03278364 0.34836385 0.78684029 1.80041807 6.11034052 2.14825483 0.21182502 0.08442768 -0.68640145 1.37842183 0.01696111 -0.01858395 -0.34007695 0.00341296 -Al 4.20171060 -0.00534617 4.08410867 0.05322740 0.18446773 -0.22596081 4.18230453 0.01957983 4.08632220 -0.32423008 0.24543437 0.13977754 0.02462109 -0.23015003 0.03204022 0.22542410 -Al 4.02856458 1.93877892 6.15737613 0.25549493 0.15341887 -0.05673810 4.07999080 1.95201866 6.17330241 0.55505176 0.05601350 0.30160711 0.01943686 0.18372586 0.04424184 0.13678912 -Al 6.21393996 0.00944117 6.24427663 -1.40626442 -0.40271162 -0.73926252 6.04080561 0.00917481 6.13038896 -1.45023888 0.50548269 -1.28920177 0.02606800 -0.29677378 0.11749940 -0.24596496 -Al 6.09409643 2.06035727 4.10435555 2.00132806 1.00508627 -0.45776236 6.27316329 2.12537318 4.07608664 0.87434549 -0.05772887 -0.25061144 0.02706454 0.06834237 -0.08794993 0.27737462 -Al 3.97908084 3.96675084 0.12495806 -0.25445326 -0.00480584 -0.31471881 3.95079123 4.02823658 0.05441541 -0.04630141 1.00228514 -0.79129964 0.03631462 -0.09855059 0.42483074 -0.30497350 -Al 4.09072487 6.31549215 2.04941714 0.27041855 -0.22481211 0.29090850 4.08969105 6.21746492 2.11108375 -0.31599317 -1.37128517 0.71912391 0.04687197 -0.15187606 -0.54152674 0.22824196 -Al 5.91610022 4.17335776 1.94905240 -1.06862616 0.85962761 -0.97767055 5.87327491 4.22164516 1.92540329 0.18556879 -0.01720883 0.49616957 0.06920451 0.49634203 -0.23608660 0.48410634 -Al 6.11551220 6.04983000 0.17353957 0.57098905 0.83736373 -0.40771347 6.16862108 6.16298195 0.07892599 0.26759031 0.99142965 -1.25033343 0.03249291 0.01854674 0.26723564 -0.32819190 -Al 4.13322622 3.97642393 4.10132135 0.45184882 1.00528488 0.89563720 4.14516662 4.11240863 4.16214297 -0.21441665 1.18576487 0.17789512 0.03625823 -0.19862484 0.35325575 -0.13373786 -Al 4.26643379 6.07914675 6.09461896 0.28178320 0.06450424 -0.18289899 4.23721508 6.08072290 6.08307472 -0.68653876 -0.21080882 -0.04560572 0.03582803 -0.44375770 0.10709073 0.07250139 -Al 5.91104231 4.07169010 6.11094541 -0.13442338 -1.78041860 0.70994746 5.97789206 3.87813881 6.20956249 1.29526782 -1.44949375 1.04090804 0.04048538 0.50676704 -0.22659101 0.13008584 -Al 6.15996987 6.04803280 4.01943776 0.69527018 -0.90269514 -0.26059786 6.21360121 5.99364994 4.00244441 0.11191382 0.09326383 0.06890248 0.00375953 -0.01603652 0.14024865 0.00304423 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.0918324809063336 free_energy=1.0918324809063336 stress="0.004911883159152096 0.00013220167420827304 0.0002369478789135862 0.00013220167420827304 0.003970602953314662 -0.0003438719444869235 0.0002369478789135862 -0.0003438719444869235 0.0055649138761324265" pbc="T T T" -Al -0.02605177 0.10230075 0.07012401 -0.05724316 -0.26450821 1.29509040 -0.01275222 0.05726802 0.18005727 0.19927745 -0.57237382 0.71890947 0.01089759 0.18593413 -0.07582203 -0.16820964 -Al 0.07843493 1.98449425 2.15335321 -1.05132111 0.77155548 0.19687641 -0.04999668 2.09243871 2.18464611 -1.16756593 1.12969892 0.21790407 0.04567581 -0.17172117 0.19717195 0.13080363 -Al 2.03483420 0.10028453 1.98389770 -0.16796648 0.10216174 -0.79790502 2.03841892 0.08400047 1.95797645 0.21404749 -0.13441634 0.50957714 0.01691608 0.13022594 -0.35664845 0.22282597 -Al 1.90338778 2.19789253 0.20698188 -0.73824318 0.36892834 0.24218248 1.88149100 2.16983810 0.17118949 0.37348322 -0.82040314 -0.79865440 0.06815795 0.31039774 -0.43757469 -0.42454519 -Al 0.13990167 -0.00336235 4.22699801 0.60545575 0.26572591 0.30847152 0.14528137 0.00609861 4.16828852 -0.51651896 -0.04198044 -1.30642404 0.04009873 -0.32360977 -0.13723595 -0.53365538 -Al 0.13966297 1.96766674 6.06165521 -0.68463553 -0.33608300 0.02917049 0.04182469 1.96808443 6.11673011 -0.95765160 0.31122890 0.76605778 0.02510051 -0.22070553 0.21686273 0.39568814 -Al 2.15377844 -0.18480510 6.10440512 0.05892927 0.28482844 -1.00274337 2.14379409 -0.12620265 6.05723504 -0.29418097 0.70748394 0.21503271 0.03250977 -0.08766551 0.24981086 0.27030230 -Al 2.11891387 1.88693950 4.21250084 0.39286002 -0.15025753 0.44609361 2.15660798 1.93854607 4.19392680 0.18126174 0.96469831 -0.84297807 0.04096731 0.02458988 0.46219317 -0.31579068 -Al -0.10422580 4.04038397 0.15494813 -0.02422449 -0.85208347 -1.72868698 -0.06455924 3.99585417 -0.07151346 0.69408874 0.10696503 -2.18650854 0.03328949 0.27590974 0.20745221 -0.34762655 -Al 0.01170307 6.08867921 2.02963497 1.02291003 -2.29986604 0.33505130 0.10204672 5.90301118 2.08022324 0.36262648 -0.97833494 0.55983239 0.02244153 0.09457648 0.25475448 0.09846705 -Al 1.97633583 4.19644025 2.07173789 0.38942544 0.34491464 0.51929877 2.05870312 4.15893716 2.17240187 1.16319169 -1.06499491 1.14366143 0.04186118 0.18711983 -0.38195184 0.35018185 -Al 2.13028979 6.05720479 0.12437434 -0.56192064 1.10937502 1.72467316 2.03093237 6.16616343 0.22636209 -1.10853852 0.60495543 -0.06030238 0.02477497 -0.32445294 0.12500193 -0.32464125 -Al -0.19927375 4.05760085 4.03833007 0.53714714 -0.63756118 -0.00335450 -0.03248695 4.00614451 4.07624522 2.09309665 -0.34674387 0.80620611 0.09022963 0.89309837 0.11954330 0.16250083 -Al -0.09511775 6.22080798 6.14893328 -0.50769178 -0.03447663 -0.43884577 -0.09799495 6.12242326 6.12879692 0.47834541 -1.61534925 0.08451546 0.03257992 0.28207578 -0.61847458 0.11533067 -Al 2.16192487 3.96734595 6.20660576 0.81339916 -0.00838017 -1.00797297 2.18056963 4.02380800 6.07306091 -0.40203843 0.98284014 -1.18021244 0.03489733 -0.44904038 0.35302437 -0.32493968 -Al 2.04342734 5.99328302 4.09422797 -0.34837466 0.40386889 0.49054059 2.01765645 6.04958972 4.15201148 0.01861823 0.58504763 0.46188455 0.00966126 -0.03890277 0.12400499 0.09699627 -Al 4.12873504 -0.09385544 -0.04070397 -0.53032298 -0.48212852 -0.47057884 4.05950027 -0.06948805 -0.04315484 -0.58650611 0.92751197 0.32352926 0.03556891 -0.16783018 0.42389581 0.36210099 -Al 4.00730035 2.08501061 2.10790045 1.94036692 0.69849121 -0.07967466 4.19843764 2.12498899 2.10447206 1.23468406 -0.15096150 -0.05290245 -0.00293970 0.11086241 -0.07470684 0.04269246 -Al 6.09741148 0.00653072 2.03616757 -0.75210300 1.00224225 0.63806698 6.05319828 0.12654426 2.12190414 0.08523618 0.98627825 0.69445973 0.01104747 0.09768037 0.18464649 0.24915921 -Al 6.11034052 2.14825483 0.21182502 -0.08442768 0.68640145 -1.37842183 6.08315617 2.13848904 0.03278364 -0.34836385 -0.78684029 -1.80041807 0.03573598 -0.14190429 -0.49806651 -0.21100660 -Al 4.18230453 0.01957983 4.08632220 0.32423008 -0.24543437 -0.13977754 4.20171060 -0.00534617 4.08410867 -0.05322740 -0.18446773 0.22596081 0.01371445 -0.06840396 -0.02096407 -0.02025451 -Al 4.07999080 1.95201866 6.17330241 -0.55505176 -0.05601350 -0.30160711 4.02856458 1.93877892 6.15737613 -0.25549493 -0.15341887 0.05673810 0.02841402 -0.07095965 -0.01594452 0.05239176 -Al 6.04080561 0.00917481 6.13038896 1.45023888 -0.50548269 1.28920177 6.21393996 0.00944117 6.24427663 1.40626442 0.40271162 0.73926252 0.03629277 0.32068290 0.33369400 -0.11607776 -Al 6.27316329 2.12537318 4.07608664 -0.87434549 0.05772887 0.25061144 6.09409643 2.06035727 4.10435555 -2.00132806 -1.00508627 0.45776236 0.05742928 -0.71649904 -0.54630815 -0.09325273 -Al 3.95079123 4.02823658 0.05441541 0.04630141 -1.00228514 0.79129964 3.97908084 3.96675084 0.12495806 0.25445326 0.00480584 0.31471881 0.05548307 0.31981913 0.16868008 0.05237449 -Al 4.08969105 6.21746492 2.11108375 0.31599317 1.37128517 -0.71912391 4.09072487 6.31549215 2.04941714 -0.27041855 0.22481211 -0.29090850 0.02844169 -0.20167795 -0.15027434 -0.00927075 -Al 5.87327491 4.22164516 1.92540329 -0.18556879 0.01720883 -0.49616957 5.91610022 4.17335776 1.94905240 1.06862616 -0.85962761 0.97767055 0.06559015 0.31393846 -0.32464927 0.39609546 -Al 6.16862108 6.16298195 0.07892599 -0.26759031 -0.99142965 1.25033343 6.11551220 6.04983000 0.17353957 -0.57098905 -0.83736373 0.40771347 0.04572929 -0.24487065 -0.20481584 -0.18512544 -Al 4.14516662 4.11240863 4.16214297 0.21441665 -1.18576487 -0.17789512 4.13322622 3.97642393 4.10132135 -0.45184882 -1.00528488 -0.89563720 0.00884758 -0.17041663 -0.24813674 -0.22303960 -Al 4.23721508 6.08072290 6.08307472 0.68653876 0.21080882 0.04560572 4.26643379 6.07914675 6.09461896 -0.28178320 -0.06450424 0.18289899 0.02204928 -0.18264197 -0.22128409 0.03875775 -Al 5.97789206 3.87813881 6.20956249 -1.29526782 1.44949375 -1.04090804 5.91104231 4.07169010 6.11094541 0.13442338 1.78041860 -0.70994746 0.02835153 0.38848951 0.39279792 0.02366620 -Al 6.21360121 5.99364994 4.00244441 -0.11191382 -0.09326383 -0.06890248 6.15996987 6.04803280 4.01943776 -0.69527018 0.90269514 0.26059786 0.05201764 -0.35409825 0.49932362 0.23710075 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.0130284021902485 free_energy=1.0130284021902485 stress="0.005128678845193628 8.697537281318916e-07 -0.00029998580829961133 8.697537281318916e-07 0.0044229703237791355 6.279173330563964e-05 -0.00029998580829961133 6.279173330563964e-05 0.005507960838444401" pbc="T T T" -Al 0.02213502 -0.08451190 0.25018385 -0.26435718 0.55167561 0.67885138 -0.00601513 -0.01338803 0.26538651 -0.12007549 0.66756936 -0.40063079 0.02408400 -0.06484044 0.12179066 -0.34869014 -Al -0.11538440 1.96031939 2.04291776 -1.43214907 1.27051127 0.75093292 -0.20701340 2.14730757 2.12562758 0.04706424 1.92342147 0.65133519 0.03202621 0.15205486 0.42208068 0.08403587 -Al 2.03849331 0.18810670 2.08207549 0.59865585 -0.11796993 0.07979404 2.11775516 0.08872301 2.09423589 0.61551726 -1.41529797 0.18435427 0.04260248 0.23273160 -0.66690936 -0.00670461 -Al 1.99624644 2.10310818 0.14973596 -0.11425410 -0.48290690 0.22861129 1.99121706 2.05443881 0.16027995 -0.06084396 -0.25244343 0.01634178 0.02138092 0.12577098 -0.11886664 -0.12707024 -Al -0.05837425 0.06203386 4.04433271 -0.79962697 -0.75040507 -0.85240723 -0.07338641 -0.02033140 3.99608904 0.62142255 -0.46085540 0.02651968 0.02673161 0.31441920 -0.18786780 0.19621972 -Al -0.00770457 2.06909293 6.10104693 0.70817611 -0.29592739 -0.03445779 0.09317355 1.97944054 6.12719561 0.95505116 -1.21265131 0.50688098 0.06705886 0.23824199 -0.38420829 0.13357847 -Al 1.97896566 0.12917286 6.23590763 -0.80309374 -0.52241192 1.76837785 1.92181187 0.05904736 6.36191356 -0.07616280 -0.80955649 0.46944109 0.01683225 0.07847420 -0.05961487 -0.31959155 -Al 2.05369233 2.06438910 3.96006411 1.16961714 0.61354318 -0.65646365 2.17885121 2.12222930 3.95703070 0.92327454 0.22511332 0.53560529 0.01971213 0.11191042 0.09528210 0.42521272 -Al 0.00698362 3.98154618 -0.17370970 -0.69730309 1.47085102 -0.97100251 -0.05014748 4.17053588 -0.21631364 -0.16757894 1.42047130 0.17073287 0.03988046 0.00149831 0.52149023 0.40411433 -Al -0.05288955 5.95606644 2.22626273 0.52308388 0.54286053 0.91214082 0.02455706 6.00411830 2.26357548 0.76346422 0.18179056 -0.39144355 0.02830709 0.22229435 0.07992085 -0.25793799 -Al 2.11289958 4.12089312 2.01835908 -1.00165741 -0.25236311 -0.09011107 1.98148873 4.04688871 2.00932370 -1.26842272 -0.98754574 -0.02296711 0.03429157 -0.21815781 -0.31953105 -0.00845476 -Al 2.07385933 6.02691843 0.11851837 -0.51781348 -0.76700998 0.31669745 2.00474070 5.99913554 0.16090479 -0.65142880 0.30661728 0.36585202 0.01347043 -0.14407845 0.26818065 0.10918844 -Al 0.07379181 3.95074811 4.16066022 0.28274843 -0.63740086 1.66182124 0.09728314 3.95784625 4.32313585 0.09514929 0.79912115 1.20054960 0.01885460 -0.01987759 0.37385512 -0.01594232 -Al 0.01970170 6.31508873 6.13616062 0.66174879 -0.13277108 0.33549058 0.09583308 6.22386576 6.15136584 0.67824314 -1.40931939 -0.13831786 0.01521367 0.06967147 -0.52011188 -0.07661197 -Al 2.20594642 4.04358694 6.26889780 0.08089119 -0.05692082 -0.31614389 2.17778868 4.03407371 6.16573066 -0.53269470 -0.06858254 -1.36773904 0.02939705 -0.26239430 -0.04485116 -0.51278746 -Al 2.04939216 6.02783801 4.00101857 -0.07919856 -0.15244839 -0.85845379 2.03830579 6.07919793 3.98217142 -0.00570732 0.94869788 0.57250429 0.02402397 -0.07222048 0.45815395 0.38046490 -Al 4.04865600 0.03497252 -0.07876810 0.10580979 1.34718334 0.49769567 4.06059397 0.14738452 0.07982591 0.20250051 0.45891526 1.92089753 0.05650232 -0.06961689 0.00056470 0.86917019 -Al 4.22026774 1.99102600 2.05061721 0.60510986 -0.84381460 0.06119475 4.25729372 1.92260931 2.06541232 -0.05954731 -0.32524965 0.24966477 -0.00166435 -0.09625805 0.08753587 0.04403594 -Al 6.07549009 -0.08385214 2.14347932 1.30988168 0.84804195 -1.06791337 6.18056567 0.02814837 1.99756354 0.46774409 0.99668802 -1.38919434 0.03859889 -0.07418075 0.24944452 -0.26409109 -Al 6.19186074 2.09740161 -0.02857643 -0.06702953 -0.24934370 0.00668792 6.15579182 2.02780824 -0.04953139 -0.50020516 -0.84999854 -0.51819493 0.02756169 -0.22344633 -0.33945515 -0.00589771 -Al 4.14299385 0.08645619 4.14261476 1.17104305 -0.15778604 0.15932594 4.21210948 0.04321468 4.15160924 0.09843822 -0.56420467 -0.03427003 0.00692053 -0.32967189 -0.20223724 -0.00630615 -Al 4.16366296 1.99290003 6.16679844 -0.66254727 -1.02372937 0.09626487 4.05934754 1.93621067 6.14446934 -1.09792379 -0.05101719 -0.44714627 0.03185297 -0.29924423 0.30695412 -0.20001791 -Al 5.94480474 -0.10034035 6.23393033 -0.04907645 -1.75398260 -0.61710366 6.04243363 -0.23671976 6.16832261 1.67840732 -0.52946212 -0.36547481 0.05717584 0.67699772 0.16576331 -0.15697046 -Al 6.21648228 2.01870097 4.25738983 0.90066960 0.19860041 0.07877886 6.21560078 2.05080104 4.22636022 -0.88568653 0.33096460 -0.42276020 0.04531873 -0.54938602 0.09238392 -0.38424506 -Al 4.14912259 4.01367788 0.05212177 -0.92324580 1.05232549 -0.45720373 4.04592202 4.11349092 0.03147643 -0.97211388 0.62728330 0.13266298 0.02480920 -0.01688396 0.04148863 0.11571892 -Al 4.21417820 6.02712474 2.15191252 -0.70303143 1.65229380 -0.07107904 4.09333020 6.24255705 2.08300373 -1.25278141 1.96456036 -1.02883463 0.05230762 -0.42375731 0.38880200 -0.44569508 -Al 6.08982307 4.00502052 2.00375293 -1.44048406 -1.11928088 -1.31327079 5.96676282 3.90728881 1.94460099 -0.82399861 -0.33750090 0.25827774 0.03384097 0.18680633 -0.09610198 0.42573264 -Al 6.17182647 5.99789598 0.17494200 0.73652825 -1.35023526 0.41123487 6.22025715 5.90599463 0.17281582 -0.00098980 -0.05324400 -0.29314965 0.01796102 -0.11751999 0.13384365 -0.36771480 -Al 4.01232337 4.19970871 4.04024162 -0.39263944 0.14090196 -0.38674701 4.00711671 4.13838430 4.02226635 0.38052503 -0.96741127 -0.06566993 0.05085593 0.13910101 -0.62654015 0.21938361 -Al 4.01156082 6.12397827 6.26155146 0.96586937 0.07250967 -0.16429524 4.07155007 6.13058473 6.19820112 -0.03834417 0.12849413 -0.84500948 0.04665229 -0.10336532 -0.06773895 -0.38038120 -Al 5.93181598 4.19017322 5.97824763 -0.93957766 0.01456575 0.82293726 5.89401831 4.16565552 6.09467449 0.23795735 -0.48981186 1.05748443 0.02535221 0.37888875 -0.14028154 0.34871659 -Al 6.06160705 6.11125177 4.12436328 1.06725222 0.89084394 -1.01018489 6.17529306 6.16395072 4.03832704 0.74974647 -0.19555552 -0.58830190 0.04511526 0.15603860 -0.03321890 0.12953815 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.1047037076242896 free_energy=1.1047037076242896 stress="0.004138521127263458 0.0009391501441625187 0.00011793231796938067 0.0009391501441625187 0.004119625289886451 -0.00018275874172973586 0.00011793231796938067 -0.00018275874172973586 0.007073958518883559" pbc="T T T" -Al -0.00601513 -0.01338803 0.26538651 0.12007549 -0.66756936 0.40063079 0.02213502 -0.08451190 0.25018385 0.26435718 -0.55167561 -0.67885138 0.03076914 0.19096507 -0.04593207 -0.34803027 -Al -0.20701340 2.14730757 2.12562758 -0.04706424 -1.92342147 -0.65133519 -0.11538440 1.96031939 2.04291776 1.43214907 -1.27051127 -0.75093292 0.04393056 0.71520172 -0.03680347 -0.13705089 -Al 2.11775516 0.08872301 2.09423589 -0.61551726 1.41529797 -0.18435427 2.03849331 0.18810670 2.08207549 -0.59865585 0.11796993 -0.07979404 0.02049922 -0.25179988 -0.09134542 0.06993599 -Al 1.99121706 2.05443881 0.16027995 0.06084396 0.25244343 -0.01634178 1.99624644 2.10310818 0.14973596 0.11425410 0.48290690 -0.22861129 0.01246022 -0.03595561 0.18666288 -0.02643292 -Al -0.07338641 -0.02033140 3.99608904 -0.62142255 0.46085540 -0.02651968 -0.05837425 0.06203386 4.04433271 0.79962697 0.75040507 0.85240723 0.03892371 0.50396552 0.38058985 0.30839175 -Al 0.09317355 1.97944054 6.12719561 -0.95505116 1.21265131 -0.50688098 -0.00770457 2.06909293 6.10104693 -0.70817611 0.29592739 0.03445779 0.04634591 -0.12009872 -0.12086814 0.11597975 -Al 1.92181187 0.05904736 6.36191356 0.07616280 0.80955649 -0.46944109 1.97896566 0.12917286 6.23590763 0.80309374 0.52241192 -1.76837785 0.05695456 0.40878577 -0.14799086 -0.47405682 -Al 2.17885121 2.12222930 3.95703070 -0.92327454 -0.22511332 -0.53560529 2.05369233 2.06438910 3.96006411 -1.16961714 -0.61354318 0.65646365 0.02718331 -0.28795978 -0.34411634 0.30539513 -Al -0.05014748 4.17053588 -0.21631364 0.16757894 -1.42047130 -0.17073287 0.00698362 3.98154618 -0.17370970 0.69730309 -1.47085102 0.97100251 0.05741352 0.35071467 -0.58712393 0.37469906 -Al 0.02455706 6.00411830 2.26357548 -0.76346422 -0.18179056 0.39144355 -0.05288955 5.95606644 2.22626273 -0.52308388 -0.54286053 -0.91214082 0.05477183 -0.04443676 -0.23061949 -0.55987018 -Al 1.98148873 4.04688871 2.00932370 1.26842272 0.98754574 0.02296711 2.11289958 4.12089312 2.01835908 1.00165741 0.25236311 0.09011107 0.03509882 0.06153255 -0.08999473 0.09253752 -Al 2.00474070 5.99913554 0.16090479 0.65142880 -0.30661728 -0.36585202 2.07385933 6.02691843 0.11851837 0.51781348 0.76700998 -0.31669745 0.01364835 0.05071369 0.36745917 -0.07874840 -Al 0.09728314 3.95784625 4.32313585 -0.09514929 -0.79912115 -1.20054960 0.07379181 3.95074811 4.16066022 -0.28274843 0.63740086 -1.66182124 0.02306465 -0.13254854 0.43200105 -0.25592987 -Al 0.09583308 6.22386576 6.15136584 -0.67824314 1.40931939 0.13831786 0.01970170 6.31508873 6.13616062 -0.66174879 0.13277108 -0.33549058 0.03722723 -0.04317109 -0.25700677 -0.23399412 -Al 2.17778868 4.03407371 6.16573066 0.53269470 0.06858254 1.36773904 2.20594642 4.04358694 6.26889780 -0.08089119 0.05692082 0.31614389 0.01701042 -0.12802143 0.04953544 -0.10675663 -Al 2.03830579 6.07919793 3.98217142 0.00570732 -0.94869788 -0.57250429 2.04939216 6.02783801 4.00101857 0.07919856 0.15244839 0.85845379 0.02706303 0.15199849 0.17959259 0.49733150 -Al 4.06059397 0.14738452 0.07982591 -0.20250051 -0.45891526 -1.92089753 4.04865600 0.03497252 -0.07876810 -0.10580979 -1.34718334 -0.49769567 0.03940503 0.09237554 -0.49490403 -0.08433439 -Al 4.25729372 1.92260931 2.06541232 0.05954731 0.32524965 -0.24966477 4.22026774 1.99102600 2.05061721 -0.60510986 0.84381460 -0.06119475 0.05047209 -0.35149253 0.26052588 0.11741839 -Al 6.18056567 0.02814837 1.99756354 -0.46774409 -0.99668802 1.38919434 6.07549009 -0.08385214 2.14347932 -1.30988168 -0.84804195 1.06791337 0.06588470 -0.35899921 -0.15016671 0.16723139 -Al 6.15579182 2.02780824 -0.04953139 0.50020516 0.84999854 0.51819493 6.19186074 2.09740161 -0.02857643 0.06702953 0.24934370 -0.00668792 0.00663271 -0.04669239 0.00974961 -0.25286891 -Al 4.21210948 0.04321468 4.15160924 -0.09843822 0.56420467 0.03427003 4.14299385 0.08645619 4.14261476 -1.17104305 0.15778604 -0.15932594 0.02100275 -0.32405759 -0.06506621 -0.08030997 -Al 4.05934754 1.93621067 6.14446934 1.09792379 0.05101719 0.44714627 4.16366296 1.99290003 6.16679844 0.66254727 1.02372937 -0.09626487 0.01455896 0.00164852 0.24458914 -0.08242188 -Al 6.04243363 -0.23671976 6.16832261 -1.67840732 0.52946212 0.36547481 5.94480474 -0.10034035 6.23393033 0.04907645 1.75398260 0.61710366 0.04211444 0.37058568 0.57817759 0.26001291 -Al 6.21560078 2.05080104 4.22636022 0.88568653 -0.33096460 0.42276020 6.21648228 2.01870097 4.25738983 -0.90066960 -0.19860041 -0.07877886 0.02772826 -0.49207051 -0.02178290 0.04977811 -Al 4.04592202 4.11349092 0.03147643 0.97211388 -0.62728330 -0.13266298 4.14912259 4.01367788 0.05212177 0.92324580 -1.05232549 0.45720373 0.02983322 -0.02166875 -0.25777803 0.21986734 -Al 4.09333020 6.24255705 2.08300373 1.25278141 -1.96456036 1.02883463 4.21417820 6.02712474 2.15191252 0.70303143 -1.65229380 0.07107904 0.00366273 0.09250872 -0.27189183 -0.12916503 -Al 5.96676282 3.90728881 1.94460099 0.82399861 0.33750090 -0.25827774 6.08982307 4.00502052 2.00375293 1.44048406 1.11928088 1.31327079 0.08337120 0.18539275 0.55661727 0.48285839 -Al 6.22025715 5.90599463 0.17281582 0.00098980 0.05324400 0.29314965 6.17182647 5.99789598 0.17494200 -0.73652825 1.35023526 -0.41123487 0.05662362 -0.41202555 0.67932921 -0.07057314 -Al 4.00711671 4.13838430 4.02226635 -0.38052503 0.96741127 0.06566993 4.01232337 4.19970871 4.04024162 0.39263944 -0.14090196 0.38674701 0.01472974 0.30459472 -0.05071633 -0.01069060 -Al 4.07155007 6.13058473 6.19820112 0.03834417 -0.12849413 0.84500948 4.01156082 6.12397827 6.26155146 -0.96586937 -0.07250967 0.16429524 0.03660563 -0.43194861 0.08406947 -0.08097677 -Al 5.89401831 4.16565552 6.09467449 -0.23795735 0.48981186 -1.05748443 5.93181598 4.19017322 5.97824763 0.93957766 -0.01456575 -0.82293726 0.02227255 0.35322095 -0.18358794 -0.14640537 -Al 6.17529306 6.16395072 4.03832704 -0.74974647 0.19555552 0.58830190 6.06160705 6.11125177 4.12436328 -1.06725222 -0.89084394 1.01018489 0.04744160 -0.35125741 -0.56120394 0.09717892 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.1705957239373603 free_energy=1.1705957239373603 stress="0.003845133113172732 0.00044194282378009364 -0.0003317322932143324 0.00044194282378009364 0.004823164759565864 -0.00029437895860849356 -0.0003317322932143324 -0.00029437895860849356 0.00632812513627653" pbc="T T T" -Al -0.07821786 0.12108144 -0.16766018 -0.31105836 0.08388703 -0.63141108 -0.05270062 0.06097786 -0.17412374 0.70497751 -1.21496841 0.70935559 0.05109044 0.36299030 -0.33364679 0.23888644 -Al 0.09085967 1.71555159 2.07646922 0.95086557 -0.57698993 -0.08295244 0.19853509 1.69386181 2.06649659 0.90014887 0.23915207 -0.25077749 0.00038390 0.09802415 0.22986456 0.09326055 -Al 2.19426707 -0.15809549 2.02373705 1.03647976 -1.03793313 -0.04976920 2.23658087 -0.19380258 2.03850145 -0.21319336 0.49623588 0.43911276 0.05712845 -0.40657940 0.36796046 0.04493915 -Al 2.09224055 1.98196121 -0.05771524 -0.13416708 0.33750053 -0.29578459 2.07887852 2.01921285 -0.06688840 -0.14288731 0.29808352 0.12765216 -0.00460580 0.01594345 0.04017677 0.13878125 -Al -0.08478475 -0.12847821 4.22978432 0.04816760 1.42413180 0.02858846 -0.08829754 0.07314862 4.22138691 -0.00453537 1.72391754 -0.16935684 0.04496107 -0.06362365 0.61444069 -0.08989657 -Al -0.11142129 2.19243089 6.10770098 -0.29508494 -1.74031306 0.04224444 -0.09678244 1.99392413 6.09074772 0.45594717 -1.43132773 -0.32804129 0.03419664 0.35177307 -0.37446681 -0.10103132 -Al 1.96624328 0.03494261 6.02228034 -0.64379813 0.26059213 1.01041857 1.94546271 0.07343064 6.15728247 0.28186001 0.37447223 1.24972324 0.02227456 0.27337636 0.10202422 0.27831770 -Al 2.06870533 2.22704702 4.07403457 -0.06714251 0.61567618 -0.46811984 2.03518431 2.23346279 4.05781615 -0.47171450 -0.43154372 0.14868740 0.02538211 -0.19260893 -0.40686164 0.21489186 -Al 0.18967762 4.08336422 0.21743947 -0.56962473 1.38747015 -0.09640643 0.03782485 4.17525923 0.16782586 -1.89001058 0.15668653 -0.68728980 0.05252502 -0.64896938 -0.24446278 -0.26072600 -Al 0.01222183 6.25978028 2.03920652 0.80500617 0.40527830 0.35732957 0.09526192 6.28916164 2.07937186 0.56667804 0.12459322 0.38512026 0.01442015 0.07565116 -0.11384181 0.00532914 -Al 2.00164369 3.99107963 1.99727438 -0.12767300 -1.11536353 -2.17565712 2.06495718 3.91150630 1.83616264 1.05294354 -0.25973113 -0.66956097 0.06160533 0.55238040 0.21839811 0.33217773 -Al 2.15614552 6.00964837 0.04723443 -0.36000976 0.40697371 0.78991870 2.10781449 6.08554736 0.11366897 -0.51514776 0.77564353 0.28362216 0.02468155 -0.08517049 0.31151973 -0.00470665 -Al -0.02563888 4.10402757 3.89647265 0.47653601 1.32818343 0.76603495 0.03241275 4.21604793 4.00576873 0.58030786 0.46142675 1.11029174 0.02265039 0.03234221 0.00109147 0.23913714 -Al -0.01618421 6.05639081 6.24826744 0.72018435 -0.07301222 -0.08632860 0.05203349 6.06872737 6.22529035 0.43790944 0.33572790 -0.40137470 0.04648780 0.03031433 0.07432401 -0.05158156 -Al 1.94771696 4.13587640 6.16413404 -0.03963949 0.07061201 -0.48028331 1.97983812 4.10452836 6.11160637 0.57463206 -0.61689164 -0.28871923 0.02109376 0.24079029 -0.22729036 -0.13619823 -Al 1.86084557 6.09813012 4.16931790 0.03314649 -0.25956277 0.59576932 1.94573769 6.05014443 4.19139282 1.41026781 -0.37083548 -0.24236961 0.03404173 0.53960505 -0.28615455 -0.21038931 -Al 4.07882182 0.00677076 0.10893728 0.18806050 -0.25676481 0.97901444 4.07076995 -0.02019098 0.15472006 -0.27146475 -0.15993975 -0.19035691 0.04430474 -0.16355851 -0.05056738 -0.27788435 -Al 4.09698915 2.04241337 2.13967434 0.67562370 -0.07071069 0.19576034 4.11409461 2.04203469 2.13012541 -0.31081755 0.02257674 -0.39023380 0.02657714 -0.33227946 0.06828139 -0.14414582 -Al 5.93247690 -0.07858327 2.09892254 -0.89349890 -0.46046371 -0.84058752 5.93331369 -0.08370514 2.07968937 0.83191229 0.22731745 0.45711485 0.08222741 0.54708726 0.31663741 0.38228639 -Al 6.10748533 1.87416468 0.29330146 0.59206701 -0.84707729 -0.79441788 6.16150442 1.89159061 0.16804059 0.33829256 1.17602453 -1.23748826 0.06435485 -0.00997542 0.54848301 -0.43998100 -Al 3.92045497 0.06163327 4.07252328 -0.91223561 0.47767476 0.79626074 3.87722111 0.10313340 4.18873592 0.15217493 0.27151528 1.14257570 0.02482290 0.29862544 -0.04882112 0.27752877 -Al 4.21848770 2.13036532 6.14141824 -0.41114079 0.10240664 0.23353929 4.11356789 2.15132489 6.16104690 -1.24434655 0.21610760 -0.00815013 0.07155155 -0.51189100 0.06379645 0.07858018 -Al 5.83816546 0.19180441 6.18106669 -0.41228672 -0.38450265 1.11629972 5.88112726 0.07078982 6.23781975 1.00302767 -1.67088185 -0.14582247 0.06566150 0.64779487 -0.54878397 -0.29055007 -Al 6.13965224 2.17354209 3.95807566 0.59199442 -0.20549863 -1.31688692 6.20761334 2.07960042 3.88674503 0.51467342 -1.28628043 0.13875943 0.03296307 0.06960998 -0.52944362 0.28892537 -Al 4.21364971 4.02582056 0.04027400 0.63015129 0.22185216 0.01522223 4.23271569 4.05833408 0.04915904 -0.29191187 0.35947221 0.13961150 0.00859065 -0.28835755 0.05580523 0.04202125 -Al 4.22484888 6.13119549 2.12010265 0.09500864 -0.04043648 -0.08435994 4.15892190 6.01897958 2.13387411 -1.21149386 -1.69843909 0.27506941 0.05828532 -0.47979900 -0.73308901 0.13153562 -Al 6.09355870 4.00713122 1.99412437 -0.42926307 0.18877520 1.56403723 6.08490501 4.06392335 2.14781591 0.36585391 0.69472622 0.98088513 0.04450934 0.16143462 0.29058982 0.11129000 -Al 6.22427957 6.11304095 0.15661615 0.13062756 -1.01140828 0.52108241 6.18973320 6.01762233 0.15892707 -0.73797405 -0.52137499 -0.37754255 0.02995663 -0.33164617 -0.02589610 -0.36225408 -Al 4.17757328 4.00448317 4.26864029 0.21320757 0.73021792 -1.51694048 4.15864148 4.12972052 4.07699427 -0.64123928 1.20563015 -1.63272963 0.04009722 -0.21789368 0.47546617 -0.39690543 -Al 4.12732324 6.06330384 6.15537448 -1.55587482 1.46599085 -0.20539925 3.95865176 6.21074962 6.13660947 -1.37174879 0.99341933 -0.10773843 0.00159240 -0.09847691 0.08233686 0.01224113 -Al 6.25680380 4.06583361 6.25279967 0.05038495 0.04702882 0.57699152 6.22342951 4.07849676 6.29671196 -0.58779115 0.04362041 0.00625911 0.04900440 -0.29861961 0.14390298 0.01326880 -Al 6.12933970 6.08283506 4.22722171 -0.07501366 -1.47421443 -0.46320732 6.10527835 5.95295029 4.16772913 -0.26533035 -0.53413487 -0.46628833 0.01777950 -0.16829377 -0.08177340 -0.15714810 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.1319334923518758 free_energy=1.1319334923518758 stress="0.004724185153377603 7.628423560682366e-05 -0.0002545923994040662 7.628423560682366e-05 0.005218669922821519 -0.0007487346003771602 -0.0002545923994040662 -0.0007487346003771602 0.005293740127255423" pbc="T T T" -Al -0.05270062 0.06097786 -0.17412374 -0.70497751 1.21496841 -0.70935559 -0.07821786 0.12108144 -0.16766018 0.31105836 -0.08388703 0.63141108 0.07696546 0.21245686 -0.37979575 0.61031702 -Al 0.19853509 1.69386181 2.06649659 -0.90014887 -0.23915207 0.25077749 0.09085967 1.71555159 2.07646922 -0.95086557 0.57698993 0.08295244 0.02353866 -0.15155912 0.34707640 -0.19713105 -Al 2.23658087 -0.19380258 2.03850145 0.21319336 -0.49623588 -0.43911276 2.19426707 -0.15809549 2.02373705 -1.03647976 1.03793313 0.04976920 0.09072832 -0.31324147 0.58453916 0.26290110 -Al 2.07887852 2.01921285 -0.06688840 0.14288731 -0.29808352 -0.12765216 2.09224055 1.98196121 -0.05771524 0.13416708 -0.33750053 0.29578459 0.02375263 -0.04589477 -0.05241661 0.14080337 -Al -0.08829754 0.07314862 4.22138691 0.00453537 -1.72391754 0.16935684 -0.08478475 -0.12847821 4.22978432 -0.04816760 -1.42413180 -0.02858846 0.03241941 0.14300888 -0.46633462 -0.07246492 -Al -0.09678244 1.99392413 6.09074772 -0.45594717 1.43132773 0.32804129 -0.11142129 2.19243089 6.10770098 0.29508494 1.74031306 -0.04224444 0.04259439 0.14716773 0.52984534 -0.03711024 -Al 1.94546271 0.07343064 6.15728247 -0.28186001 -0.37447223 -1.24972324 1.96624328 0.03494261 6.02228034 0.64379813 -0.26059213 -1.01041857 0.03894151 0.30674188 -0.02846450 -0.13871674 -Al 2.03518431 2.23346279 4.05781615 0.47171450 0.43154372 -0.14868740 2.06870533 2.22704702 4.07403457 0.06714251 -0.61567618 0.46811984 0.01766624 -0.04441021 -0.27918251 0.17114627 -Al 0.03782485 4.17525923 0.16782586 1.89001058 -0.15668653 0.68728980 0.18967762 4.08336422 0.21743947 0.56962473 -1.38747015 0.09640643 0.04639039 -0.03141727 -0.52073599 -0.03208457 -Al 0.09526192 6.28916164 2.07937186 -0.56667804 -0.12459322 -0.38512026 0.01222183 6.25978028 2.03920652 -0.80500617 -0.40527830 -0.35732957 0.00748749 -0.21630445 -0.13170005 0.02047973 -Al 2.06495718 3.91150630 1.83616264 -1.05294354 0.25973113 0.66956097 2.00164369 3.99107963 1.99727438 0.12767300 1.11536353 2.17565712 0.05023971 0.09226620 0.38100607 0.56053859 -Al 2.10781449 6.08554736 0.11366897 0.51514776 -0.77564353 -0.28362216 2.15614552 6.00964837 0.04723443 0.36000976 -0.40697371 -0.78991870 0.02690310 -0.05944763 -0.09206261 -0.28167760 -Al 0.03241275 4.21604793 4.00576873 -0.58030786 -0.46142675 -1.11029174 -0.02563888 4.10402757 3.89647265 -0.47653601 -1.32818343 -0.76603495 0.04407131 0.01865042 -0.50807453 -0.00717840 -Al 0.05203349 6.06872737 6.22529035 -0.43790944 -0.33572790 0.40137470 -0.01618421 6.05639081 6.24826744 -0.72018435 0.07301222 0.08632860 0.03391349 -0.15246141 0.14326701 -0.17910228 -Al 1.97983812 4.10452836 6.11160637 -0.57463206 0.61689164 0.28871923 1.94771696 4.13587640 6.16413404 0.03963949 -0.07061201 0.48028331 0.01173459 0.13290160 -0.16687558 0.24062884 -Al 1.94573769 6.05014443 4.19139282 -1.41026781 0.37083548 0.24236961 1.86084557 6.09813012 4.16931790 -0.03314649 0.25956277 -0.59576932 0.03391715 0.31065990 0.18796839 -0.31160774 -Al 4.07076995 -0.02019098 0.15472006 0.27146475 0.15993975 0.19035691 4.07882182 0.00677076 0.10893728 -0.18806050 0.25676481 -0.97901444 0.05183028 -0.04269523 0.10126469 -0.37168808 -Al 4.11409461 2.04203469 2.13012541 0.31081755 -0.02257674 0.39023380 4.09698915 2.04241337 2.13967434 -0.67562370 0.07071069 -0.19576034 0.01849459 -0.23017099 -0.01318411 -0.18292420 -Al 5.93331369 -0.08370514 2.07968937 -0.83191229 -0.22731745 -0.45711485 5.93247690 -0.07858327 2.09892254 0.89349890 0.46046371 0.84058752 0.03547313 0.41018630 0.06012462 0.32619196 -Al 6.16150442 1.89159061 0.16804059 -0.33829256 -1.17602453 1.23748826 6.10748533 1.87416468 0.29330146 -0.59206701 0.84707729 0.79441788 0.02401281 -0.12497142 0.56777704 0.07678397 -Al 3.87722111 0.10313340 4.18873592 -0.15217493 -0.27151528 -1.14257570 3.92045497 0.06163327 4.07252328 0.91223561 -0.47767476 -0.79626074 0.02068391 0.36984144 -0.07058954 -0.09454685 -Al 4.11356789 2.15132489 6.16104690 1.24434655 -0.21610760 0.00815013 4.21848770 2.13036532 6.14141824 0.41114079 -0.10240664 -0.23353929 0.01320282 0.00686839 -0.04902407 -0.18646135 -Al 5.88112726 0.07078982 6.23781975 -1.00302767 1.67088185 0.14582247 5.83816546 0.19180441 6.18106669 0.41228672 0.38450265 -1.11629972 0.03781755 0.27659027 -0.23868751 -0.42541641 -Al 6.20761334 2.07960042 3.88674503 -0.51467342 1.28628043 -0.13875943 6.13965224 2.17354209 3.95807566 -0.59199442 0.20549863 1.31688692 0.04506028 -0.20682894 -0.07030927 0.54440327 -Al 4.23271569 4.05833408 0.04915904 0.29191187 -0.35947221 -0.13961150 4.21364971 4.02582056 0.04027400 -0.63015129 -0.22185216 -0.01522223 0.01553884 -0.30541546 0.02468783 0.01859276 -Al 4.15892190 6.01897958 2.13387411 1.21149386 1.69843909 -0.27506941 4.22484888 6.13119549 2.12010265 -0.09500864 0.04043648 0.08435994 0.04838400 -0.30999667 -0.11359591 0.00080967 -Al 6.08490501 4.06392335 2.14781591 -0.36585391 -0.69472622 -0.98088513 6.09355870 4.00713122 1.99412437 0.42926307 -0.18877520 -1.56403723 0.06658670 0.35172883 -0.02878338 -0.42193113 -Al 6.18973320 6.01762233 0.15892707 0.73797405 0.52137499 0.37754255 6.22427957 6.11304095 0.15661615 -0.13062756 1.01140828 -0.52108241 0.03404906 -0.27780295 0.39168594 -0.17430482 -Al 4.15864148 4.12972052 4.07699427 0.64123928 -1.20563015 1.63272963 4.17757328 4.00448317 4.26864029 -0.21320757 -0.73021792 1.51694048 0.03561012 -0.36476290 -0.23650006 0.32937773 -Al 3.95865176 6.21074962 6.13660947 1.37174879 -0.99341933 0.10773843 4.12732324 6.06330384 6.15537448 1.55587482 -1.46599085 0.20539925 0.01435656 0.22762121 -0.39890259 0.07316909 -Al 6.22342951 4.07849676 6.29671196 0.58779115 -0.04362041 -0.00625911 6.25680380 4.06583361 6.25279967 -0.05038495 -0.04702882 -0.57699152 0.03400607 -0.15967021 -0.12221934 -0.42208732 -Al 6.10527835 5.95295029 4.16772913 0.26533035 0.53413487 0.46628833 6.12933970 6.08283506 4.22722171 0.07501366 1.47421443 0.46320732 0.03556289 0.03036121 0.64819608 0.16029033 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=0.8951962562931346 free_energy=0.8951962562931346 stress="0.006762199627886309 0.0004169919220461864 -0.0008031643384668323 0.0004169919220461864 0.005747662582846801 -0.000428147773379447 -0.0008031643384668323 -0.000428147773379447 0.004994554919625807" pbc="T T T" -Al 0.04030615 0.09171277 0.07398912 0.38636985 -0.26785991 -1.18800449 0.06581519 0.04879040 -0.01555996 0.01961857 -0.34441577 -0.41055198 0.02629904 -0.04086984 -0.20727226 0.19639264 -Al -0.12171355 2.17383175 2.12351050 1.74966288 -0.20615385 -0.60071966 0.08038219 2.12948842 2.07436373 1.67722418 -0.80460017 -0.11102183 0.02141505 0.26516093 0.02322641 -0.07555855 -Al 2.03765560 -0.01002666 1.97078838 -0.62320780 -0.94937909 0.88574523 1.95879126 -0.08023619 2.10366340 -0.68772421 -0.33659151 1.23440452 0.02093106 -0.13340948 0.20230105 0.42010620 -Al 2.00171901 2.14455269 0.15523686 0.00321539 0.51057209 -0.48895757 2.04587205 2.12931995 0.08137624 0.87260899 -0.88441054 -0.86187432 0.02624709 0.19260231 -0.31137041 -0.12425624 -Al -0.09804956 0.06403908 3.98361968 -0.68929423 0.25759542 0.04306192 -0.12152908 0.05559522 4.00783576 0.10720760 -0.27312751 0.40484185 0.02128379 0.40046616 -0.30232589 0.12058408 -Al 0.15539836 1.97759483 6.24745943 0.87733745 0.17902510 -0.97164565 0.19943780 2.01780899 6.10701190 -0.20382960 0.54310498 -1.41613739 0.02505161 -0.18748584 0.14341215 -0.30518330 -Al 1.98624033 -0.15814989 6.19826227 -0.60773897 0.07461010 0.32927300 1.96694088 -0.10344309 6.19559125 0.40128717 0.84846884 -0.41404884 0.02540262 0.14631467 0.32192273 -0.18883617 -Al 2.04642751 1.89297675 4.15788683 -0.62869663 -0.13074701 -0.76423065 1.96736042 1.91065810 4.06338249 -0.63139454 0.52782311 -0.79878893 0.01199395 -0.15239640 0.13096791 -0.16119262 -Al -0.01747146 4.10007167 0.08539584 0.90325506 -1.61264617 1.64213071 0.12587224 3.97546132 0.23412357 1.39754803 -0.36322689 0.82907806 0.04763147 0.47137546 0.01176526 -0.01225831 -Al -0.10141215 5.99272522 2.07955022 0.61617945 1.31755784 -0.56483227 0.00340532 6.12824527 2.05016579 1.05375268 0.99523202 -0.05685841 0.01357684 0.37229650 0.05506781 0.22206913 -Al 2.12914650 3.94779452 2.14985644 0.86537330 -0.37586189 -0.99826326 2.20524110 3.96277242 2.08137541 0.63507760 0.61405245 -0.14791065 0.00930486 -0.18502388 0.32459501 0.12354929 -Al 2.10070277 6.13114012 0.16554730 0.10690922 -0.45122988 -0.00453352 2.06587039 6.02516082 0.11880134 -0.65364368 -1.17912109 -0.79129445 0.03197627 -0.32541784 -0.50132716 -0.30043325 -Al -0.00868798 3.98356535 4.22366706 -0.05772660 1.19852707 -0.83847878 -0.00235878 4.11774520 4.10616391 0.07879199 1.08551819 -1.21673047 0.02067019 0.14274806 0.13289078 -0.23181123 -Al -0.00632027 6.12440168 6.00449636 -0.28596208 1.22293318 0.12824325 -0.00767530 6.20129015 6.04712119 0.22000838 0.02427421 0.69871558 0.02763927 0.20998319 -0.17951572 0.13515265 -Al 2.16945475 3.98020268 6.04987816 0.57012647 0.10567414 -0.00825937 2.17804046 4.05003176 6.08039845 -0.50270234 1.13340743 0.36505972 0.02098777 -0.26108484 0.34398379 0.31495971 -Al 2.16814667 6.02303491 4.08164440 -1.14321789 -0.40270175 -0.79903788 2.01239751 6.01421621 3.99371947 -1.47880632 0.19508605 -0.58124010 0.00548880 -0.33213446 0.23488200 -0.14431356 -Al 4.08252758 -0.04479429 0.01262739 0.31174153 -0.51720945 0.16647834 4.11405658 -0.02918545 0.03730931 0.03406929 0.72011542 0.32169577 0.02708811 0.13870641 0.44236477 -0.00466326 -Al 4.11235350 1.97171785 2.18742279 -1.32958999 -1.52447359 0.18497931 3.96856818 1.83732052 2.16108047 -1.08780885 -0.76436510 -0.62167924 0.02836949 -0.14933564 0.02861238 -0.28262655 -Al 6.08381770 0.12544549 1.99268556 -0.44145584 -1.12110738 -0.60654371 6.05551529 -0.01294167 1.94669499 -0.02640194 -1.16919255 -0.05117513 0.02299619 0.06752537 -0.27914362 0.01791927 -Al 6.16610854 2.17394690 0.06744149 0.51495135 1.00235602 0.82543738 6.16218986 2.19148373 0.16019636 -0.53621336 -0.89440095 0.86565878 0.02532385 -0.35542614 -0.33718829 -0.00116998 -Al 4.07349123 0.06111168 4.08523602 -0.52503638 0.25400097 0.79263136 4.03559140 0.03489630 4.18004288 -0.24119216 -0.71501090 0.79505778 0.01948209 0.15015112 -0.28683814 0.13270393 -Al 4.04105891 1.96200610 6.13931792 -0.23991478 0.83969869 -0.56219122 4.03459946 2.03956322 6.10684676 0.01169608 0.46076989 0.00278711 -0.00287159 0.16588633 0.01577100 0.12521895 -Al 6.19833383 0.00456750 6.17281595 -1.73641053 -1.22728472 -0.44627987 6.02227943 -0.05614444 6.11849488 -1.14910064 0.42125459 -0.47054757 0.01187952 -0.15250100 0.17639050 -0.07632223 -Al 6.17546528 2.00658843 4.16939598 -0.89303301 0.41757900 0.13320522 6.04807583 2.08194629 4.18477719 -1.15524930 0.79987472 -0.03870507 0.02492388 -0.37832236 0.26108141 0.13908683 -Al 4.02300101 4.05460594 0.10407229 -0.08466468 0.88580504 -1.05547603 4.01649545 4.15694469 -0.05446419 0.09875659 0.87810922 -1.46360084 0.03982559 -0.07081709 0.10336648 -0.47897970 -Al 4.13349863 6.23926263 2.00253602 -0.32231191 -1.31722110 0.64706809 4.06992697 6.11066867 2.09916276 -0.71747734 -0.99336096 1.03186059 0.06579938 -0.22099494 0.01163989 0.19237468 -Al 5.67385897 4.18246137 2.07058895 0.51978493 -0.13990880 -0.51579553 5.87648240 4.10437225 2.02638211 2.79126132 -1.08325026 -0.21472952 0.12375417 1.11265999 -0.44058548 0.01588787 -Al 6.27963843 6.07074260 0.02249205 -0.14107743 0.95290867 0.00736737 6.20537212 6.16858980 0.02929378 -1.00616679 0.50341561 0.12020389 0.03831953 -0.51261400 0.21842855 0.03203579 -Al 4.07926200 4.08278615 4.13191192 1.64531526 1.38040234 1.10297444 4.24425510 4.18878201 4.27058686 1.15037549 0.54752722 1.18204432 0.05376507 0.06859666 -0.23184758 0.27856857 -Al 4.18633985 6.20421370 6.11489840 0.55329629 1.07419882 0.39805994 4.20374882 6.23617210 6.15914449 -0.48551820 -0.60066098 0.37044394 0.00953607 -0.07119094 -0.39193268 0.05899364 -Al 6.17803387 4.05672476 6.25146659 -0.38203238 -1.59931754 1.76792776 6.11338408 3.91725158 6.36368277 -0.45984604 -0.68749219 0.23439178 0.02796470 -0.36416804 -0.01186680 -0.40726781 -Al 6.07589854 6.00963866 4.02135256 0.50785270 0.16965766 1.35866614 6.12982596 6.06786846 4.17828535 0.47379136 0.79519342 1.21065105 0.02314055 -0.01128042 0.29854411 0.26926950 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=0.9510229816315583 free_energy=0.9510229816315583 stress="0.005355766511518642 -0.00015898575467537403 0.0002944926131876724 -0.00015898575467537403 0.005196658301976934 0.0003584193039226458 0.0002944926131876724 0.0003584193039226458 0.005599291602127005" pbc="T T T" -Al 0.06581519 0.04879040 -0.01555996 -0.01961857 0.34441577 0.41055198 0.04030615 0.09171277 0.07398912 -0.38636985 0.26785991 1.18800449 0.01576916 -0.15968619 0.13283827 0.27661730 -Al 0.08038219 2.12948842 2.07436373 -1.67722418 0.80460017 0.11102183 -0.12171355 2.17383175 2.12351050 -1.74966288 0.20615385 0.60071966 0.04460707 -0.28255525 -0.35259785 0.28011178 -Al 1.95879126 -0.08023619 2.10366340 0.68772421 0.33659151 -1.23440452 2.03765560 -0.01002666 1.97078838 0.62320780 0.94937909 -0.88574523 0.02359368 0.13992990 0.21201345 -0.19557014 -Al 2.04587205 2.12931995 0.08137624 -0.87260899 0.88441054 0.86187432 2.00171901 2.14455269 0.15523686 -0.00321539 -0.51057209 0.48895757 0.03279676 0.30898664 -0.48741908 -0.11215886 -Al -0.12152908 0.05559522 4.00783576 -0.10720760 0.27312751 -0.40484185 -0.09804956 0.06403908 3.98361968 0.68929423 -0.25759542 -0.04306192 0.01347739 0.10973139 -0.03677510 0.13421175 -Al 0.19943780 2.01780899 6.10701190 0.20382960 -0.54310498 1.41613739 0.15539836 1.97759483 6.24745943 -0.87733745 -0.17902510 0.97164565 0.01909485 -0.45547710 0.11054971 0.05347960 -Al 1.96694088 -0.10344309 6.19559125 -0.40128717 -0.84846884 0.41404884 1.98624033 -0.15814989 6.19826227 0.60773897 -0.07461010 -0.32927300 0.02848132 0.42244280 0.16448257 -0.25993558 -Al 1.96736042 1.91065810 4.06338249 0.63139454 -0.52782311 0.79878893 2.04642751 1.89297675 4.15788683 0.62869663 0.13074701 0.76423065 0.03727213 0.21379297 0.27802780 0.17032791 -Al 0.12587224 3.97546132 0.23412357 -1.39754803 0.36322689 -0.82907806 -0.01747146 4.10007167 0.08539584 -0.90325506 1.61264617 -1.64213071 0.06199020 -0.16357235 0.60377604 -0.50190637 -Al 0.00340532 6.12824527 2.05016579 -1.05375268 -0.99523202 0.05685841 -0.10141215 5.99272522 2.07955022 -0.61617945 -1.31755784 0.56483227 0.02054842 -0.12371944 -0.27899698 0.05954010 -Al 2.20524110 3.96277242 2.08137541 -0.63507760 -0.61405245 0.14791065 2.12914650 3.94779452 2.14985644 -0.86537330 0.37586189 0.99826326 0.02316608 0.01740835 0.24796754 0.33990223 -Al 2.06587039 6.02516082 0.11880134 0.65364368 1.17912109 0.79129445 2.10070277 6.13114012 0.16554730 -0.10690922 0.45122988 0.00453352 0.00840819 -0.14770860 0.10625320 -0.18062693 -Al -0.00235878 4.11774520 4.10616391 -0.07879199 -1.08551819 1.21673047 -0.00868798 3.98356535 4.22366706 0.05772660 -1.19852707 0.83847878 0.00081534 -0.04712535 -0.21330453 -0.03399417 -Al -0.00767530 6.20129015 6.04712119 -0.22000838 -0.02427421 -0.69871558 -0.00632027 6.12440168 6.00449636 0.28596208 -1.22293318 -0.12824325 0.03753049 0.12671702 -0.50446786 0.22636876 -Al 2.17804046 4.05003176 6.08039845 0.50270234 -1.13340743 -0.36505972 2.16945475 3.98020268 6.04987816 -0.57012647 -0.10567414 0.00825937 0.02336355 -0.43951671 0.25837907 -0.08081847 -Al 2.01239751 6.01421621 3.99371947 1.47880632 -0.19508605 0.58124010 2.16814667 6.02303491 4.08164440 1.14321789 0.40270175 0.79903788 0.01082547 0.12696809 0.14945724 0.31764973 -Al 4.11405658 -0.02918545 0.03730931 -0.03406929 -0.72011542 -0.32169577 4.08252758 -0.04479429 0.01262739 -0.31174153 0.51720945 -0.16647834 0.01906301 -0.30466960 0.27693186 0.05877250 -Al 3.96856818 1.83732052 2.16108047 1.08780885 0.76436510 0.62167924 4.11235350 1.97171785 2.18742279 1.32958999 1.52447359 -0.18497931 0.03263914 0.27954480 0.38528529 -0.18614564 -Al 6.05551529 -0.01294167 1.94669499 0.02640194 1.16919255 0.05117513 6.08381770 0.12544549 1.99268556 0.44145584 1.12110738 0.60654371 0.02332039 0.16683356 0.20183902 0.37917529 -Al 6.16218986 2.19148373 0.16019636 0.53621336 0.89440095 -0.86565878 6.16610854 2.17394690 0.06744149 -0.51495135 -1.00235602 -0.82543738 0.06315257 -0.25916765 -0.74877786 -0.03497495 -Al 4.03559140 0.03489630 4.18004288 0.24119216 0.71501090 -0.79505778 4.07349123 0.06111168 4.08523602 0.52503638 -0.25400097 -0.79263136 0.02223663 0.01150428 -0.25588228 -0.16690466 -Al 4.03459946 2.03956322 6.10684676 -0.01169608 -0.46076989 -0.00278711 4.04105891 1.96200610 6.13931792 0.23991478 -0.83969869 0.56219122 0.01165805 -0.04428177 -0.21964557 0.19817130 -Al 6.02227943 -0.05614444 6.11849488 1.14910064 -0.42125459 0.47054757 6.19833383 0.00456750 6.17281595 1.73641053 1.22728472 0.44627987 0.06512900 0.49711281 0.76615985 0.09028909 -Al 6.04807583 2.08194629 4.18477719 1.15524930 -0.79987472 0.03870507 6.17546528 2.00658843 4.16939598 0.89303301 -0.41757900 -0.13320522 0.00438215 0.18170041 -0.05711852 -0.23361038 -Al 4.01649545 4.15694469 -0.05446419 -0.09875659 -0.87810922 1.46360084 4.02300101 4.05460594 0.10407229 0.08466468 -0.88580504 1.05547603 0.04556192 0.18355018 -0.12843955 0.27279422 -Al 4.06992697 6.11066867 2.09916276 0.71747734 0.99336096 -1.03186059 4.13349863 6.23926263 2.00253602 0.32231191 1.31722110 -0.64706809 0.01208245 0.02307459 0.12557888 0.01444798 -Al 5.87648240 4.10437225 2.02638211 -2.79126132 1.08325026 0.21472952 5.67385897 4.18246137 2.07058895 -0.51978493 0.13990880 0.51579553 0.01720789 0.34241871 -0.05139472 0.17649261 -Al 6.20537212 6.16858980 0.02929378 1.00616679 -0.50341561 -0.12020389 6.27963843 6.07074260 0.02249205 0.14107743 -0.95290867 -0.00736737 0.02524995 -0.09127068 -0.43879120 0.03037808 -Al 4.24425510 4.18878201 4.27058686 -1.15037549 -0.54752722 -1.18204432 4.07926200 4.08278615 4.13191192 -1.64531526 -1.38040234 -1.10297444 0.04393131 -0.38433958 -0.26739960 -0.22946120 -Al 4.20374882 6.23617210 6.15914449 0.48551820 0.60066098 -0.37044394 4.18633985 6.20421370 6.11489840 -0.55329629 -1.07419882 -0.39805994 0.07028986 -0.53670625 -0.58233609 -0.02857253 -Al 6.11338408 3.91725158 6.36368277 0.45984604 0.68749219 -0.23439178 6.17803387 4.05672476 6.25146659 0.38203238 1.59931754 -1.76792776 0.05020167 0.31411432 0.50908613 -0.50588602 -Al 6.12982596 6.06786846 4.17828535 -0.47379136 -0.79519342 -1.21065105 6.07589854 6.00963866 4.02135256 -0.50785270 -0.16965766 -1.35866614 0.04317689 -0.02603431 0.09472086 -0.32816435 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.1675676068749237 free_energy=1.1675676068749237 stress="0.004908787662223326 -0.00033893401826187074 -0.00032429419235869704 -0.00033893401826187074 0.003913777206277226 -0.0007783761478527335 -0.00032429419235869704 -0.0007783761478527335 0.005075587681380582" pbc="T T T" -Al -0.00185302 0.02426664 0.16434278 0.12720369 -0.18378306 0.97776962 0.04317956 -0.00732802 0.21327814 0.53704185 -0.35326694 -0.12768044 0.02478353 0.23577860 -0.08203303 -0.27762373 -Al 0.01515044 2.07047049 2.12761419 -0.54305909 1.08927945 -0.02476894 -0.07414190 2.16810837 2.11370144 -0.92590854 0.60312353 -0.16969529 0.02646097 -0.24940757 -0.06215032 -0.10220150 -Al 2.12911798 0.06413381 2.11670377 -0.63193583 -0.88833950 -0.73418424 2.05568077 -0.06543782 2.01649404 -0.71430213 -1.06142925 -0.85744623 0.03271342 -0.03776546 -0.44659353 -0.28475965 -Al 2.04692238 2.04879653 0.23296122 0.82191423 -0.39734617 0.89358241 2.10467880 2.02652171 0.26734181 0.14311315 0.07710744 -0.32285886 0.03616315 -0.10735262 0.04534988 -0.31003431 -Al 0.07292698 0.00331094 4.01254402 -0.59531092 -0.61308493 -1.01977183 -0.00335281 -0.04100425 3.96620129 -0.66947288 -0.06458096 0.13750223 0.03282747 -0.14161936 0.01479325 0.37205277 -Al -0.05651734 1.90310706 6.22378422 0.46654326 -0.63291996 0.22779528 0.04412518 1.85090503 6.24204019 1.27846986 -0.29405123 0.12375454 0.01548789 0.32372064 0.09764366 -0.05639423 -Al 2.18769487 0.12011645 6.37437933 1.15993144 -0.78009484 0.96318082 2.25691278 0.00348286 6.41013064 -0.11834806 -1.25356898 -0.34525565 0.01688474 -0.17966655 -0.22714995 -0.40023312 -Al 2.00415971 1.90910936 4.04193885 -1.17122376 0.07582396 0.31190970 1.91023702 1.97021005 4.12805772 -0.49516514 0.73356827 1.00317123 0.03434012 0.14255335 0.51860156 0.46947175 -Al -0.25239450 4.10335980 0.02242564 -0.69683066 1.62570703 -0.32339798 -0.18710041 4.23784600 -0.01968754 1.85059825 0.64667348 -0.33199462 0.05784604 0.77381162 -0.08468665 -0.13149140 -Al -0.01215844 6.16217913 2.15758673 0.11013689 0.10018702 0.73447951 0.02853716 6.11675336 2.17225547 0.55742211 -0.97767944 -0.48468735 0.04879425 0.17788003 -0.27367699 -0.31312362 -Al 1.77529919 4.10550461 1.97620131 0.41382126 -0.67263535 0.28007888 1.88149467 4.03509934 2.04652599 1.43232492 -0.49658820 0.90008700 0.04690850 0.43369480 -0.07990822 0.31127974 -Al 2.02788973 5.98343987 0.07453357 -0.26863157 -0.55294163 -0.51026941 1.96741618 6.01044886 0.03400322 -0.73176476 0.76760756 -0.19601635 0.04568437 -0.18122048 0.63224725 0.04079831 -Al 0.03581419 4.00324193 4.22211570 0.27420213 0.71951715 1.26050839 0.08433936 4.08900385 4.35250833 0.42766594 0.60688305 0.84216730 0.00970041 0.19683984 0.18607845 0.09661851 -Al 0.10862960 6.18459276 6.09922979 1.58546667 -0.98017218 -1.38608442 0.26764130 6.08208888 5.96728535 1.12358099 -0.59757810 -0.83462249 -0.00100301 0.04895198 -0.15932505 -0.00060835 -Al 2.17990164 4.02680262 6.31606666 0.44723311 0.08849840 -0.26741732 2.13417363 3.98587652 6.18938034 -0.98552785 -0.77306952 -1.60734689 0.06492956 -0.68840023 -0.28622209 -0.78400965 -Al 2.07995963 6.02822313 4.03364142 0.57025551 -0.22930274 -0.65458184 2.13760259 6.04368598 4.00481849 0.47447187 0.48658019 0.09740398 0.01668477 -0.01513304 0.24503464 0.25468046 -Al 4.27054899 0.22143854 0.01660427 0.06118528 1.70818804 0.13362958 4.24464168 0.28650627 0.05614728 -0.40179527 -0.61986983 0.47823966 0.05659592 -0.30523991 -0.59143380 0.19369338 -Al 4.07664153 1.98663560 2.14441130 0.06417520 -0.76922333 -0.53474677 4.11325620 1.96361281 2.10187921 0.46184229 0.36275948 -0.04651572 0.02175900 0.25101132 0.33421525 -0.07072516 -Al 6.17171796 -0.01228142 1.97264767 -0.38881147 0.59572477 -0.81452230 6.08468826 0.06627537 1.96578331 -0.97797324 0.72849580 0.64438343 0.03532097 -0.35863836 0.14988671 0.50180839 -Al 6.15807277 2.09675177 0.05468414 0.40063793 0.73764000 -0.44812065 6.16710095 2.16376533 0.02441337 -0.18280135 0.46448263 -0.16731328 0.05807746 -0.23274447 -0.07558895 0.13259220 -Al 4.16097172 0.13497395 4.19020531 -0.02362303 -0.48826234 0.37275462 4.12378863 0.02963705 4.16655131 -0.61938066 -1.25530625 -0.76329818 0.04012127 -0.20597200 -0.41513788 -0.36259576 -Al 4.21794216 2.04681036 6.13715320 -1.16793728 -1.46010669 0.07390457 4.05627445 1.94029903 6.12659085 -1.50241383 -0.26633184 -0.27493638 0.06002539 -0.41023425 0.07874366 -0.09253041 -Al 5.99547231 -0.24515960 6.00903860 0.56026203 -0.68877577 -0.27697202 6.13481543 -0.24111003 6.02398672 1.78596149 0.69732803 0.53149961 0.07150722 0.55520675 0.52240333 0.25974533 -Al 6.23570967 2.11009799 4.24484450 -0.19907115 0.19874853 0.94092219 6.16936911 2.08792428 4.27794876 -0.88830542 -0.54928839 -0.21758544 0.03992958 -0.34185769 -0.29049410 -0.46468112 -Al 3.93007466 4.10046340 0.03181401 0.03098557 1.49654007 -0.50491144 4.04497018 4.19949967 0.07129031 1.72663821 0.25895382 0.99190441 0.07235608 0.79722560 -0.29198894 0.64980201 -Al 4.06922429 6.09154755 2.01019928 -1.54673877 1.27925131 -0.40728082 3.94069600 6.24228568 1.96361284 -0.55756108 1.18992474 -0.39526222 0.00296661 0.05598775 0.25053795 -0.04749189 -Al 6.06034779 4.09099328 2.11504827 0.20450501 -0.15112673 1.10530144 6.08149030 4.05429204 2.20193983 0.09331740 -0.40100700 0.42272420 0.01420419 0.07474584 -0.18139013 -0.13994017 -Al 6.17281603 6.07611340 -0.01198680 0.45516323 -0.25690963 -0.20399511 6.14803655 6.09298262 -0.00285753 -0.87385972 0.65027837 0.40420258 0.03562887 -0.40138833 0.13992465 0.16507872 -Al 4.02008785 4.12273212 3.94214357 -0.67106962 -0.77308791 0.43758277 3.94465049 3.99777711 4.02421544 -0.55242949 -1.33540912 0.96216734 0.02024145 -0.11776071 -0.34871473 0.28078714 -Al 4.08970161 6.00429343 6.11167176 -0.43365585 0.45554041 0.02111044 4.01401383 6.08616142 6.10372337 -0.73265454 0.85275676 -0.13980305 0.04281139 -0.31083308 0.28390454 -0.04738626 -Al 5.90646120 4.04704461 6.11929009 0.40417930 -0.09248174 0.71659416 5.99282082 4.07623325 6.16536055 0.94135989 0.57743653 -0.02512404 0.05472461 0.45436064 0.27419845 -0.06612105 -Al 6.16789697 6.00738288 4.01321235 0.18009723 0.43994835 -1.34007928 6.13219381 6.06809034 3.92213019 -0.90414425 0.59506537 -0.23176503 0.03209141 -0.23653464 0.12293115 0.22354268 -32 -Lattice="8.1 0.0 0.0 0.0 8.1 0.0 0.0 0.0 8.1" Properties=species:S:1:pos:R:3:momenta:R:3:future_positions:R:3:future_momenta:R:3:energies:R:1:forces:R:3 energy=1.0779681848667755 free_energy=1.0779681848667755 stress="0.005570476181737316 0.0008721194749470843 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0.00331094 4.01254402 0.59531092 0.61308493 1.01977183 0.03551378 0.11675540 0.26821401 0.30347234 -Al 0.04412518 1.85090503 6.24204019 -1.27846986 0.29405123 -0.12375454 -0.05651734 1.90310706 6.22378422 -0.46654326 0.63291996 -0.22779528 0.00589069 0.13162838 0.15419406 -0.01786246 -Al 2.25691278 0.00348286 6.41013064 0.11834806 1.25356898 0.34525565 2.18769487 0.12011645 6.37437933 -1.15993144 0.78009484 -0.96318082 0.06410727 -0.61097444 -0.00536474 -0.46713461 -Al 1.91023702 1.97021005 4.12805772 0.49516514 -0.73356827 -1.00317123 2.00415971 1.90910936 4.04193885 1.17122376 -0.07582396 -0.31190970 -0.00334055 0.25471921 -0.09553707 -0.05018883 -Al -0.18710041 4.23784600 -0.01968754 -1.85059825 -0.64667348 0.33199462 -0.25239450 4.10335980 0.02242564 0.69683066 -1.62570703 0.32339798 0.05740507 0.68343079 -0.45841369 0.08957556 -Al 0.02853716 6.11675336 2.17225547 -0.55742211 0.97767944 0.48468735 -0.01215844 6.16217913 2.15758673 -0.11013689 -0.10018702 -0.73447951 0.04668176 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- -.. only:: html - - .. image:: /examples/programmatic/flashmd/images/thumb/sphx_glr_flashmd_thumb.png - :alt: - - :ref:`sphx_glr_examples_programmatic_flashmd_flashmd.py` - -.. raw:: html - -
Training a FlashMD model
-
- - -.. thumbnail-parent-div-close - -.. raw:: html - - - - -.. toctree:: - :hidden: - - /examples/programmatic/flashmd/flashmd - - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-gallery - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download all examples in Python source code: flashmd_python.zip ` - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download all examples in Jupyter notebooks: flashmd_jupyter.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/programmatic/flashmd/options.yaml b/docs/src/examples/programmatic/flashmd/options.yaml deleted file mode 100644 index cc73011ea5..0000000000 --- a/docs/src/examples/programmatic/flashmd/options.yaml +++ /dev/null @@ -1,43 +0,0 @@ -seed: 42 - -architecture: - name: experimental.flashmd - training: - timestep: 30 # in this case 30 (time lag) * 1 fs (timestep of reference MD) - batch_size: 2 # to be increased in a production scenario - num_epochs: 5 # to be increased (at least 1000-10000) in a production scenario - log_interval: 1 - loss: - positions: - type: mse - weight: 1.0 - reduction: mean - momenta: - type: mse - weight: 1.0 - reduction: mean - -training_set: - systems: - read_from: flashmd.xyz - length_unit: A - targets: - positions: - key: future_positions - quantity: length - unit: A - type: - cartesian: - rank: 1 - per_atom: true - momenta: - key: future_momenta - quantity: momentum - unit: (eV*u)^1/2 - type: - cartesian: - rank: 1 - per_atom: true - -validation_set: 0.1 -test_set: 0.1 diff --git a/docs/src/examples/programmatic/use_architectures_outside/images/thumb/sphx_glr_use_outside_thumb.png b/docs/src/examples/programmatic/use_architectures_outside/images/thumb/sphx_glr_use_outside_thumb.png deleted file mode 100644 index 5f3d70d019fb5eec5b6eede318f689c9f410323b..0000000000000000000000000000000000000000 GIT binary patch literal 0 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- -.. only:: html - - .. image:: /examples/programmatic/use_architectures_outside/images/thumb/sphx_glr_use_outside_thumb.png - :alt: - - :ref:`sphx_glr_examples_programmatic_use_architectures_outside_use_outside.py` - -.. raw:: html - -
Using metatrain architectures outside of metatrain
-
- - -.. thumbnail-parent-div-close - -.. raw:: html - - - - -.. toctree:: - :hidden: - - /examples/programmatic/use_architectures_outside/use_outside - - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-gallery - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download all examples in Python source code: use_architectures_outside_python.zip ` - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download all examples in Jupyter notebooks: use_architectures_outside_jupyter.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/programmatic/use_architectures_outside/qm9_reduced_100.xyz b/docs/src/examples/programmatic/use_architectures_outside/qm9_reduced_100.xyz deleted file mode 100644 index 14b2a0416e..0000000000 --- a/docs/src/examples/programmatic/use_architectures_outside/qm9_reduced_100.xyz +++ /dev/null @@ -1,1201 +0,0 @@ -5 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F" -C -0.00828817 1.39046978 -0.00560069 -O -0.00797038 -0.02504537 0.02030606 -H 1.00658338 1.81556366 0.00348335 -H -0.54657475 1.79916975 -0.87390126 -H -0.52288871 1.72555240 0.89907326 -H 0.44142019 -0.33354425 -0.77152059 -7 -Properties=species:S:1:pos:R:3 A=160.28041 B=8.59323 C=8.59321 mu=0.7156 alpha=28.78 epsHOMO=-0.2609 epsLUMO=0.0613 epsgap=0.3222 =177.1963 zpve=0.05541 U0=-116.609549 U=-116.60555 H=-116.604606 G=-116.633775 Cv=12.482 pbc="F F F" -C -0.01782102 1.46435788 0.01009397 -C 0.00208816 0.00950777 0.00201200 -C 0.01834085 -1.19180518 -0.00450508 -H 0.99782210 1.87425349 0.00260606 -H -0.54220434 1.85801178 -0.86721192 -H -0.52533306 1.84834356 0.90148138 -H 0.03231658 -2.25314823 -0.01025999 -6 -Properties=species:S:1:pos:R:3 A=159.03567 B=9.22327 C=9.22324 mu=3.8266 alpha=24.45 epsHOMO=-0.3264 epsLUMO=0.0376 epsgap=0.364 =160.7223 zpve=0.045286 U0=-132.71815 U=-132.714563 H=-132.713619 G=-132.742149 Cv=10.287 pbc="F F F" -C -0.01788629 1.46712787 0.01011311 -C 0.00173819 0.01035260 0.00207588 -N 0.01765235 -1.14452926 -0.00420445 -H 1.00202941 1.86089927 0.00245401 -H -0.54398848 1.84479901 -0.87075460 -H -0.52707783 1.83518166 0.90486329 -7 -Properties=species:S:1:pos:R:3 A=57.22434 B=10.11122 C=9.07368 mu=2.5682 alpha=25.11 epsHOMO=-0.254 epsLUMO=-0.0198 epsgap=0.2342 =166.9728 zpve=0.055355 U0=-153.787612 U=-153.783728 H=-153.782784 G=-153.812518 Cv=11.219 pbc="F F F" -C -0.00294482 1.50991366 0.00867278 -C 0.02608284 0.00327563 -0.03745912 -O 0.94228801 -0.65507035 -0.45682576 -H 0.92278802 1.92634242 -0.39146557 -H -0.86201540 1.87852481 -0.56479538 -H -0.15050638 1.84393383 1.04289100 -H -0.89443009 -0.48643408 0.35774865 -6 -Properties=species:S:1:pos:R:3 A=73.8472 B=11.34793 C=9.83639 mu=3.7286 alpha=21.57 epsHOMO=-0.2543 epsLUMO=0.0302 epsgap=0.2845 =145.3078 zpve=0.045279 U0=-169.860788 U=-169.856903 H=-169.855958 G=-169.885594 Cv=10.89 pbc="F F F" -N -0.02589986 1.34614561 0.00889391 -C 0.04646728 -0.01174341 0.00120425 -O 1.07183520 -0.65258782 -0.01113313 -H 0.82535497 1.88504948 0.00373769 -H -0.90837680 1.82679638 0.01891992 -H -0.96144100 -0.47500376 0.00807391 -11 -Properties=species:S:1:pos:R:3 A=29.45018 B=8.37701 C=7.42076 mu=0.0597 alpha=34.75 epsHOMO=-0.323 epsLUMO=0.0949 epsgap=0.4179 =227.1361 zpve=0.103182 U0=-119.052475 U=-119.047927 H=-119.046983 G=-119.078157 Cv=14.84 pbc="F F F" -C -0.03113825 1.54081582 0.03192126 -C 0.01215347 0.01092235 -0.01603259 -C 0.72169129 -0.52583353 -1.26230570 -H 0.97955987 1.96459116 0.03098367 -H -0.55840223 1.94831192 -0.83816075 -H -0.54252252 1.90153531 0.93005671 -H 0.51522791 -0.36840234 0.88231134 -H -1.01070641 -0.38456999 0.02051783 -H 1.75851210 -0.17376585 -1.30871516 -H 0.74087192 -1.62024959 -1.27516511 -H 0.22023351 -0.19051179 -2.17729020 -9 -Properties=species:S:1:pos:R:3 A=35.09545 B=9.3686 C=8.1497 mu=1.4131 alpha=27.87 epsHOMO=-0.2619 epsLUMO=0.0798 epsgap=0.3417 =193.1659 zpve=0.079754 U0=-154.972731 U=-154.968412 H=-154.967467 G=-154.998148 Cv=13.546 pbc="F F F" -C -0.00860504 1.50203829 -0.00681217 -C 0.01099310 -0.01764877 -0.01377035 -O 0.68088841 -0.44041803 -1.19313210 -H 1.01153334 1.89662030 -0.01920155 -H -0.53159862 1.88076109 -0.88974659 -H -0.51674572 1.87611771 0.88710739 -H 0.52377121 -0.38912295 0.88824082 -H -1.02027544 -0.40507261 0.01690670 -H 0.69529573 -1.40179568 -1.20148495 -9 -Properties=species:S:1:pos:R:3 A=39.37691 B=10.04033 C=8.90353 mu=1.1502 alpha=28.13 epsHOMO=-0.2525 epsLUMO=0.091 epsgap=0.3435 =187.1015 zpve=0.079534 U0=-154.960361 U=-154.956045 H=-154.9551 G=-154.985747 Cv=12.934 pbc="F F F" -C -0.01482147 1.39241234 0.00567104 -O -0.00471537 -0.01360660 0.01459658 -C 0.63794893 -0.55329676 -1.11358232 -H 1.00528358 1.81015807 0.00465631 -H -0.54689560 1.79343530 -0.87251051 -H -0.53002895 1.72291969 0.91101696 -H 0.13993792 -0.25599333 -2.05098375 -H 1.69265275 -0.23868382 -1.17477743 -H 0.59959435 -1.64180161 -1.02407616 -9 -Properties=species:S:1:pos:R:3 A=20.15852 B=20.15302 C=12.5891 mu=0.0005 alpha=30.82 epsHOMO=-0.2888 epsLUMO=0.1042 epsgap=0.393 =155.8145 zpve=0.081231 U0=-117.824798 U=-117.821426 H=-117.820482 G=-117.849087 Cv=11.041 pbc="F F F" -C -0.01193280 1.51433198 0.01031700 -C 1.30299118 0.77886562 -0.00617842 -C 0.00867174 0.00767042 0.00201032 -H -0.30541464 2.01702117 0.92533239 -H -0.32275498 2.02680156 -0.89347839 -H 1.88493396 0.79145020 -0.92119393 -H 1.90226631 0.78165391 0.89766373 -H -0.27088043 -0.51289125 0.91137474 -H -0.28821983 -0.50310409 -0.90740824 -7 -Properties=species:S:1:pos:R:3 A=25.77385 B=22.12109 C=14.18066 mu=1.7675 alpha=24.04 epsHOMO=-0.2682 epsLUMO=0.1042 epsgap=0.3724 =129.891 zpve=0.057289 U0=-153.742562 U=-153.73941 H=-153.738466 G=-153.766642 Cv=9.176 pbc="F F F" -C 0.01537720 1.41764380 0.00956287 -C 1.26478161 0.64923984 -0.00655044 -O -0.00024018 -0.00771006 0.00204055 -H -0.31763326 1.88586598 0.93475751 -H -0.33527583 1.89576673 -0.90397578 -H 1.83246682 0.56256848 -0.93193290 -H 1.85008258 0.55267938 0.90680147 -10 -Properties=species:S:1:pos:R:3 A=10.12193 B=8.49011 C=4.89615 mu=2.7362 alpha=35.53 epsHOMO=-0.2431 epsLUMO=-0.0087 epsgap=0.2344 =292.4367 zpve=0.083382 U0=-193.08834 U=-193.082969 H=-193.082024 G=-193.116476 Cv=16.893 pbc="F F F" -C -0.00310189 1.48050328 -0.17259867 -C -0.04527202 -0.02887059 0.00139493 -C 1.29705800 -0.72194415 0.16889042 -O -1.08643172 -0.64273343 0.00630393 -H 0.59403529 1.74668579 -1.05225043 -H -1.01540407 1.87034023 -0.28382265 -H 0.48099600 1.95070605 0.69099376 -H 1.82512144 -0.32470731 1.04328658 -H 1.14892507 -1.79597689 0.28554393 -H 1.93682635 -0.52949692 -0.69995403 -9 -Properties=species:S:1:pos:R:3 A=10.78886 B=9.27509 C=5.14977 mu=3.6367 alpha=31.83 epsHOMO=-0.2436 epsLUMO=0.0347 epsgap=0.2783 =267.6148 zpve=0.07319 U0=-209.159302 U=-209.15402 H=-209.153076 G=-209.187468 Cv=16.561 pbc="F F F" -C 0.00679889 1.49969736 -0.02567030 -C -0.02740385 -0.01752051 -0.14894811 -N 1.17826667 -0.63324298 0.05691721 -O -1.03721965 -0.63980649 -0.40727572 -H 0.91390280 1.92923755 -0.46100067 -H -0.87080896 1.91124186 -0.52345176 -H -0.02826124 1.78517271 1.03149125 -H 1.98405139 -0.13175861 0.38674906 -H 1.19049421 -1.63919563 0.10006209 -8 -Properties=species:S:1:pos:R:3 A=11.1475 B=10.36388 C=5.42091 mu=3.4869 alpha=28.07 epsHOMO=-0.2495 epsLUMO=0.0556 epsgap=0.3051 =244.2308 zpve=0.063824 U0=-225.221461 U=-225.217075 H=-225.216131 G=-225.247724 Cv=15.292 pbc="F F F" -N 0.03605270 1.36077873 -0.12416403 -C -0.02591138 -0.02076560 0.00200645 -N 1.21968479 -0.62334220 0.11963222 -O -1.06822866 -0.64174597 0.00865573 -H 0.80749439 1.83455148 0.32144915 -H -0.85825775 1.80373500 0.02243770 -H 1.99927148 -0.17303054 -0.33602496 -H 1.18204327 -1.62147901 -0.02109234 -14 -Properties=species:S:1:pos:R:3 A=7.75166 B=7.74847 C=4.48668 mu=0.0897 alpha=45.46 epsHOMO=-0.3167 epsLUMO=0.0843 epsgap=0.401 =355.0621 zpve=0.131146 U0=-158.342346 U=-158.336603 H=-158.335658 G=-158.370016 Cv=20.273 pbc="F F F" -C -0.03215888 1.54021598 0.01074456 -C 0.03381741 0.00745852 0.00180716 -C 0.71375572 -0.50856404 -1.27296949 -C 0.73848999 -0.52220886 1.25750907 -H 0.97522736 1.97414341 0.00528362 -H -0.56196769 1.92232326 -0.86835418 -H -0.54817239 1.91268414 0.90208039 -H -0.99752525 -0.37310578 0.00977297 -H 1.75070021 -0.15586784 -1.33269631 -H 0.73446691 -1.60328288 -1.29939696 -H 0.19367011 -0.16085589 -2.17195508 -H 0.23611990 -0.18382017 2.17005260 -H 0.75964083 -1.61713153 1.27226101 -H 1.77635866 -0.16985318 1.30053019 -12 -Properties=species:S:1:pos:R:3 A=8.67553 B=8.00568 C=4.75542 mu=1.4259 alpha=38.58 epsHOMO=-0.2612 epsLUMO=0.074 epsgap=0.3351 =318.3721 zpve=0.107673 U0=-194.267232 U=-194.261748 H=-194.260804 G=-194.294663 Cv=19.052 pbc="F F F" -C -0.03315871 1.54782566 -0.00438815 -C -0.01108534 0.01859095 0.01676117 -C 0.70938384 -0.53596711 1.23978444 -O -1.33219841 -0.51712554 0.05236057 -H 0.98215692 1.95715900 -0.03182225 -H -0.56388100 1.92087623 -0.88845865 -H -0.54082548 1.93340945 0.88575489 -H 0.51042122 -0.33767241 -0.88819176 -H 0.22240492 -0.18785860 2.15650245 -H 0.68242024 -1.62887638 1.23305141 -H 1.75434301 -0.21246950 1.25524188 -H -1.81818681 -0.15577435 -0.69553962 -6 -Properties=species:S:1:pos:R:3 A=0 B=4.4259726 C=4.4259726 mu=0 alpha=38.52 epsHOMO=-0.2599 epsLUMO=-0.0214 epsgap=0.2386 =278.6264 zpve=0.037354 U0=-153.459846 U=-153.455442 H=-153.454498 G=-153.482621 Cv=15.312 pbc="F F F" -C 0.68098021 0.00000000 0.00000000 -C -0.68098021 0.00000000 0.00000000 -C -1.88766603 0.00000000 0.00000000 -C 1.88766603 0.00000000 0.00000000 -H -2.94960000 0.00000000 0.00000000 -H 2.94960000 0.00000000 0.00000000 -5 -Properties=species:S:1:pos:R:3 A=0 B=4.5793222 C=4.5793222 mu=3.792 alpha=32.66 epsHOMO=-0.3102 epsLUMO=-0.0543 epsgap=0.2559 =260.1896 zpve=0.027259 U0=-169.557758 U=-169.553764 H=-169.55282 G=-169.581024 Cv=12.93 pbc="F F F" -C 0.01510390 0.00000000 1.00000000 -C 1.38233149 0.00000000 1.00000000 -C -1.18862525 0.00000000 1.00000000 -H -2.25177201 0.00000000 1.00000000 -N 2.54296187 0.00000000 1.00000000 -4 -Properties=species:S:1:pos:R:3 A=0 B=4.73269 C=4.73269 mu=0.0023 alpha=27.7 epsHOMO=-0.3696 epsLUMO=-0.0926 epsgap=0.277 =242.9308 zpve=0.015951 U0=-185.648533 U=-185.644825 H=-185.64388 G=-185.667652 Cv=10.398 pbc="F F F" -N 0.01745734 -1.16134217 -0.00415342 -C 0.00253212 -0.00344278 0.00179939 -C -0.01611419 1.37220987 0.00934873 -N -0.03264302 2.53008276 0.01609090 -6 -Properties=species:S:1:pos:R:3 A=68.5171 B=4.8345 C=4.51586 mu=2.7824 alpha=31.14 epsHOMO=-0.2777 epsLUMO=-0.0735 epsgap=0.2042 =268.3921 zpve=0.037208 U0=-190.624631 U=-190.620363 H=-190.619419 G=-190.650543 Cv=13.049 pbc="F F F" -O -0.06776120 -0.04233838 0.00282489 -C -0.00562945 1.16382879 0.00743862 -C 1.22985272 1.91339997 0.00028781 -C 2.23625322 2.57430970 -0.00529277 -H -0.91670701 1.79467546 0.01781352 -H 3.13429662 3.14370267 -0.01037656 -5 -Properties=species:S:1:pos:R:3 A=67.88408 B=5.00823 C=4.66413 mu=2.3112 alpha=26.25 epsHOMO=-0.3166 epsLUMO=-0.11 epsgap=0.2066 =251.0007 zpve=0.02654 U0=-206.721858 U=-206.717875 H=-206.716931 G=-206.747625 Cv=11.329 pbc="F F F" -O -0.04055978 -0.03799144 0.00266513 -C -0.01768870 1.16193251 0.00746008 -C 1.23315534 1.93381336 0.00043371 -N 2.19857378 2.57055097 -0.00483320 -H -0.92627534 1.78959196 0.01764859 -6 -Properties=species:S:1:pos:R:3 A=56.25048 B=4.77441 C=4.40088 mu=0.002 alpha=26.12 epsHOMO=-0.2668 epsLUMO=-0.1113 epsgap=0.1555 =266.8164 zpve=0.036943 U0=-227.798785 U=-227.79457 H=-227.793626 G=-227.825074 Cv=12.147 pbc="F F F" -O 0.00337122 -0.03146586 0.00157206 -C -0.01713174 1.17081409 0.00676444 -C 1.26555783 1.99719410 -0.00007703 -O 1.24507069 3.19946589 0.00681275 -H -0.94690483 1.77678142 0.01852668 -H 2.19534410 1.39121374 -0.01006739 -10 -Properties=species:S:1:pos:R:3 A=80.28766 B=3.3688 C=3.3688 mu=0 alpha=42.32 epsHOMO=-0.2412 epsLUMO=0.0684 epsgap=0.3096 =400.2236 zpve=0.083896 U0=-155.908941 U=-155.90318 H=-155.902236 G=-155.937641 Cv=17.447 pbc="F F F" -C -0.01787959 1.46676893 0.01011180 -C 0.00186644 0.01072913 0.00211109 -C 0.01819704 -1.19272861 -0.00450723 -C 0.03795170 -2.64876793 -0.01251193 -H 0.86460312 1.87589979 0.51479719 -H -0.03217624 1.87210581 -1.00791002 -H -0.90233115 1.85185524 0.53003953 -H -0.83477011 -3.06339007 0.50451436 -H 0.03276422 -3.04315564 -1.03491223 -H 0.93213486 -3.03931642 0.48626606 -10 -Properties=species:S:1:pos:R:3 A=27.39459 B=4.53005 C=4.08342 mu=0.7067 alpha=40.09 epsHOMO=-0.2592 epsLUMO=0.0566 epsgap=0.3157 =333.9589 zpve=0.084338 U0=-155.897345 U=-155.892291 H=-155.891347 G=-155.924226 Cv=17.13 pbc="F F F" -C -0.03095840 1.54775025 0.03167945 -C 0.01485375 0.00962471 -0.02082154 -C 0.69099073 -0.49954575 -1.20857624 -C 1.25222275 -0.89514467 -2.19527879 -H 0.97899300 1.96621609 0.03373834 -H -0.56082354 1.95002902 -0.83580745 -H -0.54576708 1.88225519 0.93709062 -H 0.51920135 -0.37425398 0.87496224 -H -1.00644802 -0.39030838 0.01340632 -H 1.74594541 -1.25604187 -3.06299415 -9 -Properties=species:S:1:pos:R:3 A=28.0016 B=4.67752 C=4.21765 mu=3.9233 alpha=35.38 epsHOMO=-0.3213 epsLUMO=0.034 epsgap=0.3553 =314.5335 zpve=0.07419 U0=-172.006141 U=-172.001467 H=-172.000523 G=-172.032826 Cv=14.988 pbc="F F F" -C -0.02522005 1.54773054 0.02133370 -C 0.01183939 0.00999278 -0.01556702 -C 0.68915114 -0.50836846 -1.20331488 -N 1.22565733 -0.90360846 -2.14737482 -H 0.98571620 1.96212990 0.02370153 -H -0.55522192 1.94599662 -0.84711771 -H -0.53962556 1.88294354 0.92536604 -H 0.52481910 -0.38226048 0.86933355 -H -1.00453542 -0.39832109 0.00530071 -8 -Properties=species:S:1:pos:R:3 A=32.19893 B=4.76299 C=4.30696 mu=4.4361 alpha=31.81 epsHOMO=-0.2683 epsLUMO=0.0173 epsgap=0.2855 =295.6635 zpve=0.063305 U0=-188.042067 U=-188.037478 H=-188.036534 G=-188.06863 Cv=14.488 pbc="F F F" -N -0.03608099 1.48762533 0.03678072 -C -0.01039471 0.02517605 -0.01506608 -C -1.37180956 -0.52108920 0.02739272 -N -2.44026695 -0.95956444 0.03370519 -H 0.91729752 1.83547547 0.05202682 -H -0.47162178 1.85677205 -0.80275569 -H 0.48946161 -0.40880401 -0.89715168 -H 0.52827150 -0.34533338 0.86469547 -8 -Properties=species:S:1:pos:R:3 A=35.11615 B=4.66117 C=4.22354 mu=1.7211 alpha=33.1 epsHOMO=-0.2595 epsLUMO=0.0277 epsgap=0.2872 =300.0993 zpve=0.060632 U0=-191.810916 U=-191.806025 H=-191.805081 G=-191.837634 Cv=15.855 pbc="F F F" -O -0.02013197 1.45738137 0.01010195 -C 0.00168478 0.03579980 0.00224736 -C -1.36366691 -0.47481101 0.01255056 -C -2.47902835 -0.91970202 0.02082451 -H 0.89446911 1.75736553 0.00327363 -H 0.51979028 -0.35175413 -0.88980302 -H 0.53670458 -0.36127661 0.88000894 -H -3.46773502 -1.30737009 0.02818120 -7 -Properties=species:S:1:pos:R:3 A=35.93172 B=4.83443 C=4.37827 mu=4.6788 alpha=28.56 epsHOMO=-0.3018 epsLUMO=0.0022 epsgap=0.3039 =280.6659 zpve=0.050262 U0=-207.916786 U=-207.912215 H=-207.911271 G=-207.943384 Cv=13.845 pbc="F F F" -O -0.03738221 1.44547841 0.01010769 -C 0.01292906 0.03441661 0.00209165 -C -1.35747452 -0.48874523 0.01239738 -N -2.42135277 -0.93685184 0.02016053 -H 0.86898057 1.76972949 0.00417577 -H 0.51692713 -0.36329790 -0.89215814 -H 0.53387616 -0.37278749 0.88223921 -10 -Properties=species:S:1:pos:R:3 A=26.14564 B=4.2893 C=4.12773 mu=2.6741 alpha=35.83 epsHOMO=-0.25 epsLUMO=-0.0205 epsgap=0.2295 =333.3276 zpve=0.084175 U0=-193.075202 U=-193.070116 H=-193.069171 G=-193.102798 Cv=15.954 pbc="F F F" -C 0.16879340 1.52513042 -0.15747056 -C -0.18737625 0.06194763 0.14679374 -C 0.50917645 -0.43996215 1.39125850 -O 1.18191079 -1.43565585 1.45816384 -H 1.23126511 1.63137254 -0.39632693 -H -0.40744668 1.89431681 -1.00969246 -H -0.04931034 2.17266260 0.69857432 -H -1.26905160 -0.01668067 0.32854433 -H 0.06279062 -0.60250479 -0.68474036 -H 0.35384841 0.20663370 2.28871052 -9 -Properties=species:S:1:pos:R:3 A=19.98749 B=6.11589 C=4.82584 mu=3.7071 alpha=32.78 epsHOMO=-0.2516 epsLUMO=0.0335 epsgap=0.2851 =279.7863 zpve=0.074166 U0=-209.144909 U=-209.139976 H=-209.139032 G=-209.172305 Cv=15.058 pbc="F F F" -C 0.01474916 1.47888256 -0.03509326 -N 0.18708077 0.03831317 -0.02906851 -C 0.45703661 -0.66858895 1.10069001 -O 0.58457996 -0.19876202 2.21119705 -H 0.76025289 1.96736104 -0.67198126 -H -0.98462423 1.75984483 -0.38485103 -H 0.14269849 1.82387718 0.99149397 -H 0.10386349 -0.46937726 -0.89461379 -H 0.55047568 -1.75253452 0.88868242 -8 -Properties=species:S:1:pos:R:3 A=48.62351 B=4.67298 C=4.38187 mu=3.92 alpha=29.47 epsHOMO=-0.2814 epsLUMO=0.0074 epsgap=0.2888 =293.917 zpve=0.061327 U0=-229.013797 U=-229.009003 H=-229.008059 G=-229.041086 Cv=13.885 pbc="F F F" -C 0.10653781 1.43512049 0.03336100 -O 0.42686564 0.04716929 0.09309945 -C 0.50714862 -0.63652665 -1.06318297 -O 0.76927014 -1.79955017 -1.11010475 -H 0.92349251 2.00385139 0.48465728 -H -0.03757815 1.76927183 -1.00057425 -H -0.81269569 1.60801916 0.59873287 -H 0.30458460 -0.00231902 -1.94965553 -8 -Properties=species:S:1:pos:R:3 A=36.49282 B=4.31175 C=4.04493 mu=1.7341 alpha=28.53 epsHOMO=-0.2537 epsLUMO=-0.0341 epsgap=0.2196 =303.8129 zpve=0.060508 U0=-228.992613 U=-228.987769 H=-228.986825 G=-229.019918 Cv=14.78 pbc="F F F" -O 0.19375856 1.37170995 0.31398297 -C 0.04992637 0.01405481 -0.04526239 -C -1.40888025 -0.39635680 -0.20601574 -O -1.77729462 -1.37233860 -0.80361689 -H -0.01723186 1.91269132 -0.45337758 -H 0.60440478 -0.26519766 -0.95205725 -H 0.46740406 -0.57521679 0.78176270 -H -2.11835055 0.30075962 0.29870515 -14 -Properties=species:S:1:pos:R:3 A=23.44662 B=3.60588 C=3.38683 mu=0 alpha=45.71 epsHOMO=-0.317 epsLUMO=0.0937 epsgap=0.4107 =426.2996 zpve=0.131708 U0=-158.340943 U=-158.33517 H=-158.334226 G=-158.36894 Cv=19.668 pbc="F F F" -C -0.03007624 1.55804912 0.02980774 -C 0.00707277 0.02834877 -0.00712529 -C 0.71320792 -0.52989340 -1.24765581 -C 0.75036895 -2.05959366 -1.28458957 -H 0.98195330 1.97845113 0.02800797 -H -0.55582433 1.96212343 -0.84270157 -H -0.54083762 1.92692755 0.92493697 -H 0.50928672 -0.34909049 0.89361319 -H -1.01733481 -0.36542299 0.03207393 -H 0.21098948 -0.15245770 -2.14839302 -H 1.73761331 -0.13611625 -1.28685672 -H 1.27616854 -2.46366410 -0.41210980 -H -0.26165699 -2.48000447 -1.28273552 -H 1.26108491 -2.42846444 -2.17974803 -12 -Properties=species:S:1:pos:R:3 A=26.59789 B=3.79186 C=3.53779 mu=1.3402 alpha=38.61 epsHOMO=-0.2619 epsLUMO=0.081 epsgap=0.3429 =382.8628 zpve=0.108241 U0=-194.261089 U=-194.255495 H=-194.254551 G=-194.28893 Cv=18.431 pbc="F F F" -C -0.00379012 1.52196790 0.01027996 -C -0.03589372 -0.00778254 0.00225671 -C -1.45219016 -0.56653711 0.01284386 -O -1.36799785 -1.98394604 0.00453606 -H 1.02477576 1.89411231 0.00239793 -H -0.51286674 1.93764614 -0.86653888 -H -0.49584080 1.92801344 0.90121721 -H 0.48140784 -0.39530552 -0.88316064 -H 0.49828589 -0.40486256 0.87330348 -H -1.98734220 -0.20399494 0.90666062 -H -2.00431485 -0.19459473 -0.86666460 -H -2.26171819 -2.33824298 0.01034748 -12 -Properties=species:S:1:pos:R:3 A=28.29567 B=4.14901 C=3.88349 mu=1.0363 alpha=39.34 epsHOMO=-0.2503 epsLUMO=0.0925 epsgap=0.3428 =368.9331 zpve=0.107895 U0=-194.254127 U=-194.248585 H=-194.247641 G=-194.281899 Cv=17.888 pbc="F F F" -C -0.00519551 1.50868224 0.03308760 -C 0.01316580 -0.01037819 0.01988643 -O 0.69758944 -0.46344140 1.16868979 -C 0.76688491 -1.86508183 1.24602669 -H 1.01445476 1.90423799 0.02996018 -H -0.52990866 1.89184059 -0.84726911 -H -0.51263765 1.87805843 0.92875424 -H -1.01599507 -0.40998729 0.00714282 -H 0.51093143 -0.38369435 -0.89219395 -H -0.23415841 -2.32451489 1.28977780 -H 1.30129276 -2.29853663 0.38486095 -H 1.30990287 -2.11811998 2.16013206 -10 -Properties=species:S:1:pos:R:3 A=14.79671 B=5.6956 C=4.58846 mu=0.0075 alpha=31.42 epsHOMO=-0.2594 epsLUMO=0.0584 epsgap=0.3179 =297.8398 zpve=0.085172 U0=-230.183076 U=-230.177723 H=-230.176779 G=-230.211195 Cv=16.837 pbc="F F F" -O -0.01418735 1.42642242 -0.05423651 -C -0.00391839 0.00717714 0.03496062 -C -1.42317019 -0.55560600 -0.01256639 -O -2.11229413 -0.10563875 -1.17228313 -H -0.18987882 1.64154705 -0.97555148 -H 0.59659504 -0.43315430 -0.77409095 -H 0.47311116 -0.24903733 0.98747216 -H -1.39494859 -1.64975279 -0.06283188 -H -1.96497417 -0.27216915 0.90131972 -H -2.35925991 0.80944396 -1.00614597 -12 -Properties=species:S:1:pos:R:3 A=15.61472 B=6.31363 C=5.54514 mu=0.1136 alpha=41.96 epsHOMO=-0.2727 epsLUMO=0.1012 epsgap=0.3738 =298.6061 zpve=0.109284 U0=-157.116735 U=-157.11209 H=-157.111146 G=-157.143262 Cv=16.49 pbc="F F F" -C -0.03602245 1.52092949 0.01569161 -C 0.01938487 0.01004403 0.04814953 -C 1.30891671 -0.71251995 -0.24920416 -C 0.75347423 -0.70267714 1.15564676 -H 0.90105691 1.95389291 0.38283338 -H -0.19640365 1.89437369 -1.00208569 -H -0.84685273 1.90588930 0.64451322 -H -0.88416809 -0.48001889 -0.30566335 -H 1.26900901 -1.63903068 -0.81150371 -H 2.19010738 -0.11131209 -0.44927513 -H 1.26248862 -0.09487455 1.89690028 -H 0.33540836 -1.62248918 1.54980695 -10 -Properties=species:S:1:pos:R:3 A=18.21931 B=6.63877 C=5.92459 mu=1.812 alpha=35.01 epsHOMO=-0.2633 epsLUMO=0.1052 epsgap=0.3685 =267.2979 zpve=0.085275 U0=-193.039603 U=-193.035186 H=-193.034242 G=-193.065979 Cv=14.764 pbc="F F F" -C -0.01646343 1.51830705 0.00771445 -C -0.02743227 0.01657694 -0.10889741 -C 0.77296553 -0.70131886 -1.10863115 -O 1.17212293 -0.66702969 0.26278829 -H 0.91865980 1.92914217 -0.38285880 -H -0.85158014 1.95456506 -0.55109460 -H -0.11487702 1.82596470 1.05397777 -H -0.93582598 -0.47350600 0.24682793 -H 0.44443922 -1.67201495 -1.47810622 -H 1.40538284 -0.13600339 -1.79271491 -11 -Properties=species:S:1:pos:R:3 A=16.58914 B=7.18798 C=6.11415 mu=1.1353 alpha=39.02 epsHOMO=-0.2304 epsLUMO=0.0968 epsgap=0.3271 =270.5508 zpve=0.097671 U0=-173.147782 U=-173.143343 H=-173.142399 G=-173.174073 Cv=15.298 pbc="F F F" -C -0.05275541 1.47419574 0.00999108 -N 0.01940516 0.02189947 0.06334866 -C 1.32511048 -0.56446850 -0.21420725 -C 0.78051759 -0.54057993 1.17242760 -H 0.86784202 1.97176076 0.36297285 -H -0.23845155 1.79095005 -1.02142866 -H -0.88683448 1.81938959 0.62950398 -H 1.31992909 -1.48186782 -0.79602038 -H 2.15202820 0.11020204 -0.43285248 -H 1.24563570 0.14996098 1.87499245 -H 0.37013358 -1.44020447 1.62233365 -10 -Properties=species:S:1:pos:R:3 A=16.79477 B=6.71015 C=5.86781 mu=1.3894 alpha=34.64 epsHOMO=-0.239 epsLUMO=0.0775 epsgap=0.3166 =263.966 zpve=0.085106 U0=-193.034988 U=-193.030356 H=-193.029411 G=-193.061689 Cv=15.679 pbc="F F F" -O 0.21577583 1.35386859 -0.10695107 -C 0.01628099 -0.02832718 -0.02915694 -C -0.39081465 -0.71497009 1.24315669 -C -1.35118138 -0.64280380 0.06273589 -H -0.43642338 1.79062849 0.45006761 -H 0.74981167 -0.52404050 -0.65624767 -H 0.05959375 -1.66840483 1.49729237 -H -0.61369353 -0.08848006 2.10170640 -H -2.19821932 0.03058430 0.15411451 -H -1.56395912 -1.54640426 -0.49826515 -12 -Properties=species:S:1:pos:R:3 A=10.66107 B=10.66107 C=6.30452 mu=0 alpha=41.83 epsHOMO=-0.2982 epsLUMO=0.0956 epsgap=0.3938 =268.4432 zpve=0.110511 U0=-157.115484 U=-157.111322 H=-157.110378 G=-157.141657 Cv=14.696 pbc="F F F" -C -0.02542957 1.54032578 -0.04215944 -C 1.52564399 1.50419989 0.04121276 -C 1.42146727 0.06146790 0.60844885 -C -0.02638833 -0.01070956 0.04935088 -H -0.46591372 1.99639894 0.84962894 -H -0.47900239 2.00028207 -0.92365389 -H 1.98248278 1.51375334 -0.95303183 -H 2.02191365 2.25986579 0.65522129 -H 2.14291679 -0.67956358 0.25531480 -H 1.43105853 0.05751479 1.70261879 -H -0.79053440 -0.48530296 0.66996573 -H -0.05233245 -0.47238446 -0.94236394 -10 -Properties=species:S:1:pos:R:3 A=12.06545 B=11.77731 C=6.71997 mu=1.7978 alpha=34.56 epsHOMO=-0.2424 epsLUMO=0.0859 epsgap=0.3283 =236.9759 zpve=0.086675 U0=-193.034094 U=-193.029968 H=-193.029024 G=-193.060777 Cv=12.915 pbc="F F F" -C -0.03489789 1.55259455 -0.08507691 -C 1.49349183 1.45459700 0.10114677 -O 1.42725884 0.02348990 0.27254886 -C -0.00634758 0.02236494 0.10944630 -H -0.56579210 2.11301721 0.68591527 -H -0.36628352 1.89393380 -1.06692327 -H 2.10031724 1.73525720 -0.76898349 -H 1.90023090 1.95506611 0.98896748 -H -0.51987849 -0.35595179 1.00235948 -H -0.31970062 -0.57567421 -0.75559275 -12 -Properties=species:S:1:pos:R:3 A=8.63483 B=4.11977 C=2.88825 mu=0.6875 alpha=45.37 epsHOMO=-0.2392 epsLUMO=0.0192 epsgap=0.2584 =452.5112 zpve=0.100501 U0=-248.375248 U=-248.368823 H=-248.367879 G=-248.405354 Cv=21.616 pbc="F F F" -C -0.10255370 1.49907127 -0.00133642 -C -0.00519774 -0.00343114 -0.02723470 -C -0.00688536 -0.76890740 1.26434744 -N 0.08186799 -0.69202791 -1.10087306 -O 0.07439067 0.13215394 -2.24195258 -H 0.78342093 1.94690862 -0.46354050 -H -0.96150430 1.83614082 -0.59021965 -H -0.19854173 1.87462989 1.01931607 -H -0.92620012 -0.57297946 1.82982479 -H 0.07256154 -1.84064782 1.07516863 -H 0.82937178 -0.45553340 1.90133089 -H 0.13975924 -0.51254299 -2.95389260 -10 -Properties=species:S:1:pos:R:3 A=9.17136 B=9.04195 C=4.5531 mu=1.8689 alpha=43.14 epsHOMO=-0.2029 epsLUMO=0.0499 epsgap=0.2528 =303.9808 zpve=0.082433 U0=-210.101789 U=-210.097816 H=-210.096872 G=-210.12818 Cv=14.821 pbc="F F F" -N -0.00829694 1.35362838 0.00995971 -C 1.28033254 1.82457421 -0.00022459 -C 2.12216639 0.73698898 -0.01445600 -C 1.30849904 -0.43109580 -0.01284154 -C -0.00342782 -0.01836884 0.00235736 -H -0.83243234 1.92772668 0.02122995 -H 1.48471820 2.88312630 0.00354313 -H 3.20066918 0.77343077 -0.02488416 -H 1.64810599 -1.45539683 -0.02180394 -H -0.92551647 -0.57697054 0.00839402 -9 -Properties=species:S:1:pos:R:3 A=9.77329 B=9.41192 C=4.7946 mu=3.6193 alpha=39.13 epsHOMO=-0.2253 epsLUMO=0.0332 epsgap=0.2585 =283.6817 zpve=0.071145 U0=-226.160842 U=-226.157088 H=-226.156144 G=-226.187104 Cv=13.371 pbc="F F F" -N -0.00996090 1.35636172 0.01000855 -C 1.29535422 1.79575578 -0.00047017 -C 2.05550519 0.65637438 -0.01422927 -N 1.25385745 -0.46275132 -0.01260894 -C 0.02301170 -0.00706382 0.00200146 -H -0.83195718 1.93618436 0.02132575 -H 1.54553961 2.84344998 0.00289054 -H 3.13177434 0.57569766 -0.02524155 -H -0.87667864 -0.60414531 0.00750794 -9 -Properties=species:S:1:pos:R:3 A=9.50228 B=9.29497 C=4.69874 mu=0.5571 alpha=39.2 epsHOMO=-0.2246 epsLUMO=0.0199 epsgap=0.2445 =289.005 zpve=0.069883 U0=-229.969129 U=-229.965414 H=-229.96447 G=-229.995393 Cv=13.358 pbc="F F F" -O -0.03255819 1.35065130 0.00967376 -C 1.25319237 1.79167710 -0.00030351 -C 2.11664681 0.74375623 -0.01441494 -C 1.30397796 -0.43852387 -0.01283316 -C 0.01629568 -0.00777185 0.00191294 -H 1.37951089 2.86161135 0.00427433 -H 3.19376406 0.79865501 -0.02458604 -H 1.63858893 -1.46383271 -0.02156949 -H -0.93736295 -0.50899925 0.00843891 -8 -Properties=species:S:1:pos:R:3 A=10.10926 B=9.69372 C=4.94857 mu=1.5081 alpha=35.17 epsHOMO=-0.2509 epsLUMO=0.001 epsgap=0.2519 =269.2444 zpve=0.058593 U0=-246.02915 U=-246.025614 H=-246.024669 G=-246.055309 Cv=12.04 pbc="F F F" -O -0.03808298 1.35643965 0.00975193 -C 1.26496982 1.76855319 -0.00061744 -C 2.04955750 0.66630033 -0.01406047 -N 1.24227075 -0.46732066 -0.01237541 -C 0.04019820 0.00561577 0.00183569 -H 1.43513907 2.83095265 0.00340823 -H 3.12532898 0.59783015 -0.02480757 -H -0.89387707 -0.53342154 0.00796935 -17 -Properties=species:S:1:pos:R:3 A=4.4178 B=4.41753 C=4.41717 mu=0.0003 alpha=56.01 epsHOMO=-0.3145 epsLUMO=0.0737 epsgap=0.3882 =486.2719 zpve=0.158836 U0=-197.632222 U=-197.625241 H=-197.624297 G=-197.661411 Cv=26.084 pbc="F F F" -C -0.01859029 1.53486266 0.01039904 -C 0.00210320 -0.00413672 0.00196980 -C 0.74643954 -0.51390760 1.24887638 -C -1.44190166 -0.53673615 0.01311457 -C 0.72234971 -0.50049606 -1.26445632 -H 0.99776928 1.94420529 0.00435474 -H -0.54181883 1.92841415 -0.86819911 -H -0.52758402 1.91888376 0.90148530 -H 0.25549576 -0.17602728 2.16833067 -H 0.77815389 -1.60892372 1.27088904 -H 1.77964786 -0.14975834 1.26944035 -H -1.99871895 -0.18888794 -0.86408066 -H -1.45956909 -1.63227235 0.00649916 -H -1.98088827 -0.19979639 0.90561580 -H 1.75482473 -0.13554018 -1.30118083 -H 0.75396989 -1.59524036 -1.29822280 -H 0.21358221 -0.15343900 -2.17067181 -15 -Properties=species:S:1:pos:R:3 A=4.69474 B=4.66938 C=4.47909 mu=1.384 alpha=49.04 epsHOMO=-0.2601 epsLUMO=0.0664 epsgap=0.3265 =449.0573 zpve=0.134977 U0=-233.560626 U=-233.553779 H=-233.552834 G=-233.589759 Cv=25.128 pbc="F F F" -C -0.00855999 1.54270147 0.00152717 -C 0.00506780 0.00702635 0.01856160 -C 0.76137731 -0.51839196 1.24766443 -C -1.42012774 -0.54933183 -0.02116008 -O 0.62590477 -0.48363745 -1.17657057 -H 1.01361406 1.94020776 0.00281128 -H -0.51459669 1.90702466 -0.89705781 -H -0.52214815 1.94882514 0.87918344 -H 0.28130613 -0.20165548 2.17943598 -H 0.79965506 -1.61131746 1.23022549 -H 1.79096916 -0.14072935 1.26083003 -H -1.93854829 -0.19910724 -0.91828281 -H -1.39937632 -1.64259062 -0.04563833 -H -1.98850725 -0.23059656 0.85726520 -H 1.53111188 -0.15488046 -1.19210222 -9 -Properties=species:S:1:pos:R:3 A=10.21876 B=4.04324 C=2.94978 mu=2.8579 alpha=42.02 epsHOMO=-0.2654 epsLUMO=-0.0575 epsgap=0.2079 =416.7799 zpve=0.065175 U0=-229.927277 U=-229.921622 H=-229.920677 G=-229.955843 Cv=18.723 pbc="F F F" -C -0.00693851 1.49559567 -0.01063358 -C -0.02986170 -0.01594623 0.06248673 -O -0.08815239 -0.63215062 1.10334812 -C 0.02336981 -0.70256747 -1.22062379 -C 0.06738132 -1.26058341 -2.28623129 -H 0.90429806 1.83281887 -0.51655384 -H -0.85338524 1.85556926 -0.60552819 -H -0.05237764 1.91099807 0.99695949 -H 0.10466868 -1.77036047 -3.21832617 -8 -Properties=species:S:1:pos:R:3 A=10.16063 B=4.15847 C=3.0056 mu=3.3351 alpha=37.08 epsHOMO=-0.3007 epsLUMO=-0.0889 epsgap=0.2118 =397.7757 zpve=0.05451 U0=-246.027383 U=-246.022024 H=-246.02108 G=-246.055769 Cv=16.963 pbc="F F F" -C -0.01171097 1.49973776 -0.01213268 -C -0.02919740 -0.00338745 0.07151218 -O -0.09164028 -0.64592927 1.08732241 -C 0.03831104 -0.69690258 -1.23748557 -N 0.09136286 -1.22982650 -2.26228068 -H 0.90093132 1.83370670 -0.51814928 -H -0.85663933 1.84864400 -0.61618135 -H -0.06416540 1.92131542 0.99203818 -8 -Properties=species:S:1:pos:R:3 A=10.68423 B=4.3198 C=3.07993 mu=5.1815 alpha=38.31 epsHOMO=-0.2739 epsLUMO=-0.0438 epsgap=0.2301 =384.5574 zpve=0.056619 U0=-242.19573 U=-242.190591 H=-242.189646 G=-242.223513 Cv=17.465 pbc="F F F" -N -0.05232108 1.34991769 0.15535538 -C 0.08549378 -0.01476951 0.02668890 -N 1.14555215 -0.71993906 -0.03966037 -C -1.19426556 -0.71152088 0.01802151 -N -2.22747610 -1.22790543 0.00132148 -H 0.75474469 1.91998737 -0.04411334 -H -0.94458527 1.76600473 -0.05932293 -H 1.98312429 -0.13415500 -0.02878551 -9 -Properties=species:S:1:pos:R:3 A=9.08854 B=4.42607 C=3.03169 mu=0.9461 alpha=36.51 epsHOMO=-0.2538 epsLUMO=-0.0964 epsgap=0.1574 =399.222 zpve=0.064992 U0=-267.10335 U=-267.097658 H=-267.096714 G=-267.132534 Cv=17.806 pbc="F F F" -C -0.43126066 1.30586664 -0.12758585 -C 0.25285715 -0.01026988 0.13138867 -O 0.69872099 -0.36890333 1.19604690 -C 0.38978943 -0.96773680 -1.06226208 -O -0.01611268 -0.71564293 -2.16583718 -H 0.10006565 1.85324490 -0.91332597 -H -1.44270433 1.13069361 -0.50923371 -H -0.46757554 1.89548158 0.78836479 -H 0.90329886 -1.91558845 -0.79531972 -9 -Properties=species:S:1:pos:R:3 A=10.08927 B=4.54115 C=3.13487 mu=2.7707 alpha=38.09 epsHOMO=-0.2561 epsLUMO=-0.064 epsgap=0.192 =384.4827 zpve=0.067668 U0=-263.278851 U=-263.273589 H=-263.272645 G=-263.306835 Cv=17.972 pbc="F F F" -N -0.16032929 1.08528962 -0.45398652 -C 0.18649127 -0.08762327 0.14758021 -N 1.28979627 -0.46427423 0.67927030 -C -0.94204384 -1.08440264 0.20423621 -O -2.01802205 -0.89007687 -0.30583149 -H 0.54574437 1.73837930 -0.74689246 -H -1.04684920 1.09570857 -0.93607796 -H 2.01738195 0.24991828 0.59881811 -H -0.67740148 -2.01003995 0.74746441 -13 -Properties=species:S:1:pos:R:3 A=7.92874 B=3.8272 C=2.82581 mu=0.6578 alpha=51.2 epsHOMO=-0.2589 epsLUMO=0.0571 epsgap=0.316 =481.9854 zpve=0.112471 U0=-195.186772 U=-195.180446 H=-195.179502 G=-195.215658 Cv=22.569 pbc="F F F" -C -0.01763434 1.54859468 -0.00253584 -C -0.01719180 0.00735789 0.03269604 -C 0.75818463 -0.52820155 1.25278862 -C -1.38213303 -0.51768438 0.00775516 -C -2.50796392 -0.94005629 0.00478552 -H 1.00944396 1.92639613 -0.02632830 -H -0.54514614 1.92217052 -0.88393459 -H -0.51243000 1.95339892 0.88560642 -H 0.49366179 -0.34904155 -0.87265890 -H 0.28968458 -0.19345372 2.18334679 -H 0.77833392 -1.62102506 1.25783727 -H 1.78952492 -0.16181727 1.23574954 -H -3.49998244 -1.31810165 -0.00723572 -12 -Properties=species:S:1:pos:R:3 A=7.90009 B=3.95466 C=2.88643 mu=3.9512 alpha=46.23 epsHOMO=-0.318 epsLUMO=0.0365 epsgap=0.3545 =460.694 zpve=0.102281 U0=-211.295796 U=-211.289821 H=-211.288877 G=-211.324525 Cv=20.467 pbc="F F F" -C -0.00242350 1.55035837 0.01056187 -C -0.01856600 0.01004352 -0.02261770 -C -1.45045318 -0.55766178 0.01293347 -C 0.70125749 -0.48583168 -1.20172764 -N 1.26190946 -0.87126766 -2.13619740 -H 1.01959496 1.93673215 0.00349668 -H -0.52972844 1.96141639 -0.85498386 -H -0.50134251 1.90346839 0.91755862 -H 0.52481877 -0.36222456 0.85525462 -H -2.02427903 -0.21428716 -0.85251869 -H -1.44438296 -1.65026927 0.00754107 -H -1.95805494 -0.21725882 0.91996760 -11 -Properties=species:S:1:pos:R:3 A=8.30095 B=3.97615 C=2.93764 mu=2.7429 alpha=42.87 epsHOMO=-0.2704 epsLUMO=0.0278 epsgap=0.2983 =440.0738 zpve=0.091554 U0=-227.338075 U=-227.332253 H=-227.331309 G=-227.366638 Cv=19.946 pbc="F F F" -C -0.01064295 1.54128691 0.00722087 -C -0.00203811 0.00756015 0.03863463 -N 0.76268677 -0.47101822 1.19250856 -C -1.38541954 -0.52099256 -0.00194097 -N -2.45680847 -0.95636072 0.00694969 -H 1.02043523 1.90106589 0.00392365 -H -0.52873161 1.91193452 -0.88031480 -H -0.51849536 1.94150431 0.89013197 -H 0.50418828 -0.35805500 -0.86276072 -H 0.30070681 -0.18364934 2.05155305 -H 0.78148992 -1.48670299 1.20428183 -11 -Properties=species:S:1:pos:R:3 A=8.55919 B=3.89952 C=2.91869 mu=1.3582 alpha=44.03 epsHOMO=-0.2665 epsLUMO=0.0336 epsgap=0.3001 =444.6452 zpve=0.088908 U0=-231.108368 U=-231.102292 H=-231.101348 G=-231.137061 Cv=21.306 pbc="F F F" -C -0.00754278 1.55566144 0.08650633 -C 0.01207541 0.02096553 0.04935916 -O -1.29150251 -0.52785128 0.22126386 -C 0.65093362 -0.47718651 -1.17430507 -C 1.13902673 -0.86577343 -2.20205518 -H 1.00824243 1.95969458 0.04929733 -H -0.56272245 1.95192010 -0.76978085 -H -0.49328161 1.88929438 1.00704123 -H 0.57811967 -0.34464907 0.91458864 -H -1.78516532 -0.35903262 -0.58909400 -H 1.58078877 -1.22365060 -3.09919447 -10 -Properties=species:S:1:pos:R:3 A=8.55908 B=4.01671 C=2.97882 mu=3.269 alpha=39.28 epsHOMO=-0.3051 epsLUMO=0.0115 epsgap=0.3166 =424.3395 zpve=0.078602 U0=-247.214861 U=-247.209162 H=-247.208218 G=-247.243338 Cv=19.252 pbc="F F F" -C -0.00992462 1.54951908 0.00732200 -C 0.00740485 0.01722020 0.05031270 -O 0.72085722 -0.49630479 1.15864583 -C -1.36995095 -0.52355929 0.00894432 -N -2.45081572 -0.93233138 0.00716912 -H 1.01892314 1.91630447 0.02724149 -H -0.50261490 1.90969362 -0.89997951 -H -0.54800426 1.95377611 0.87053664 -H 0.53602846 -0.36494195 -0.82970945 -H 0.27702643 -0.20644534 1.96421773 -13 -Properties=species:S:1:pos:R:3 A=7.66289 B=3.70925 C=2.79937 mu=2.6921 alpha=46.58 epsHOMO=-0.2469 epsLUMO=-0.0188 epsgap=0.2281 =482.0475 zpve=0.112328 U0=-232.364952 U=-232.358577 H=-232.357633 G=-232.394589 Cv=21.434 pbc="F F F" -C -0.02629647 1.53877116 -0.00783747 -C 0.01468904 0.00095310 0.00166037 -C 0.74737862 -0.53388859 1.24420471 -C -1.40211264 -0.53859257 -0.01557820 -O -1.87620455 -1.23582351 -0.87399559 -H 0.98823696 1.94625080 0.03705522 -H -0.50725090 1.92153422 -0.91285565 -H -0.57756514 1.92360883 0.85796386 -H 0.50584680 -0.36421091 -0.90619124 -H 0.22958380 -0.23909403 2.16431052 -H 0.81692736 -1.62559499 1.22973438 -H 1.76267153 -0.12846566 1.29050755 -H -2.00861510 -0.22792742 0.87121666 -11 -Properties=species:S:1:pos:R:3 A=8.31096 B=4.07963 C=2.92918 mu=2.8354 alpha=39.16 epsHOMO=-0.255 epsLUMO=-0.0279 epsgap=0.2271 =432.1489 zpve=0.088443 U0=-268.287661 U=-268.281505 H=-268.280561 G=-268.316982 Cv=20.344 pbc="F F F" -C -0.05483118 1.46032843 -0.17068647 -C 0.07105714 -0.03524485 0.11225841 -O 0.58204486 -0.33316656 1.40176256 -C -1.27639279 -0.74383027 0.03051185 -O -2.11855775 -0.49643175 -0.79163412 -H 0.93158072 1.93091398 -0.23353204 -H -0.57713995 1.61290939 -1.11835562 -H -0.62444315 1.94776119 0.62573670 -H 0.69687521 -0.50071620 -0.67159471 -H 1.45603316 0.06066659 1.47858584 -H -1.40723217 -1.53388627 0.80211181 -12 -Properties=species:S:1:pos:R:3 A=8.9331 B=4.19405 C=2.96046 mu=3.7163 alpha=44.42 epsHOMO=-0.2424 epsLUMO=0.0327 epsgap=0.2751 =441.85 zpve=0.10227 U0=-248.430371 U=-248.424309 H=-248.423365 G=-248.459383 Cv=19.918 pbc="F F F" -C -0.09776499 1.44917268 0.04561984 -N 0.01043053 0.00373818 -0.00070600 -C 0.05323028 -0.71432112 1.25264891 -C 0.06728716 -0.64067225 -1.20060778 -O 0.03636432 -0.11006515 -2.29109228 -H 0.75650660 1.88747109 0.57647421 -H -0.11648577 1.81632103 -0.98049172 -H -1.01641106 1.75521548 0.56151415 -H -0.85453556 -0.53657675 1.84390168 -H 0.13297722 -1.78696336 1.05851351 -H 0.91637348 -0.40737846 1.85761345 -H 0.14762240 -1.73801339 -1.06805139 -11 -Properties=species:S:1:pos:R:3 A=10.13982 B=3.81346 C=2.86965 mu=2.9514 alpha=39.33 epsHOMO=-0.2699 epsLUMO=-0.0262 epsgap=0.2437 =440.1727 zpve=0.088924 U0=-268.301176 U=-268.295084 H=-268.29414 G=-268.331307 Cv=19.824 pbc="F F F" -C -0.01708808 1.44281136 0.03225501 -C -0.05544542 -0.06560572 -0.02375999 -O -0.17379913 -0.76196233 0.96223129 -C 0.06068107 -0.73615179 -1.38825252 -O 0.00877771 -2.12574390 -1.27749486 -H 0.92062950 1.81463913 -0.39522215 -H -0.83050523 1.86542281 -0.56780350 -H -0.10911975 1.78049045 1.06493675 -H -0.75015067 -0.35235060 -2.03056074 -H 1.00312898 -0.40389029 -1.85592942 -H -0.09063255 -2.29450960 -0.32608019 -13 -Properties=species:S:1:pos:R:3 A=9.55216 B=3.57844 C=2.7341 mu=2.6168 alpha=46.19 epsHOMO=-0.2423 epsLUMO=-0.0072 epsgap=0.2351 =489.8518 zpve=0.112006 U0=-232.377706 U=-232.371073 H=-232.370129 G=-232.408256 Cv=21.716 pbc="F F F" -C -0.04384102 1.54099793 -0.03913814 -C 0.02765132 0.01885817 0.04778773 -C 0.76814644 -0.48842218 1.28000847 -C 0.87850208 -1.99746312 1.42393323 -O 1.24179142 0.26651340 2.09788702 -H 0.95796454 1.97690198 -0.06816012 -H -0.58447717 1.85356031 -0.93690369 -H -0.55173626 1.95737759 0.83440529 -H -0.97692824 -0.42709877 0.05353178 -H 0.51853486 -0.40806507 -0.83809409 -H 1.39924980 -2.42678188 0.56053177 -H 1.42001187 -2.24449367 2.33776426 -H -0.11750602 -2.45360651 1.45141965 -12 -Properties=species:S:1:pos:R:3 A=9.78802 B=3.78441 C=2.84416 mu=3.499 alpha=42.54 epsHOMO=-0.2438 epsLUMO=0.0355 epsgap=0.2793 =457.447 zpve=0.101847 U0=-248.448467 U=-248.441988 H=-248.441044 G=-248.478935 Cv=21.374 pbc="F F F" -C -0.00220144 1.53344329 -0.05960369 -C -0.03235837 0.01206167 0.06840265 -C 0.69666086 -0.48196462 1.31828322 -N 0.98104007 -1.82034162 1.31984698 -O 1.00392124 0.24606232 2.24136749 -H 1.02111190 1.89525169 -0.19182672 -H -0.59707204 1.86206427 -0.91645604 -H -0.39728839 1.99920712 0.84545201 -H -1.06936083 -0.34552935 0.13040806 -H 0.39947165 -0.46906818 -0.81777020 -H 0.63281951 -2.43917647 0.60874785 -H 1.35708014 -2.22217793 2.16303522 -12 -Properties=species:S:1:pos:R:3 A=10.01185 B=3.84195 C=2.87635 mu=3.5402 alpha=43.61 epsHOMO=-0.2418 epsLUMO=0.0387 epsgap=0.2805 =458.9803 zpve=0.101735 U0=-248.443503 U=-248.436899 H=-248.435955 G=-248.474272 Cv=20.967 pbc="F F F" -C 0.00619941 1.45933202 -0.03893859 -N -0.06411616 0.01422963 -0.12046901 -C -1.20595394 -0.68196796 0.16117120 -C -1.08982704 -2.19284899 0.02095775 -O -2.24482911 -0.14443077 0.49994418 -H 0.75053989 1.78372520 0.69715573 -H 0.25692001 1.90395263 -1.00876215 -H -0.97856026 1.81121438 0.27082194 -H 0.75820982 -0.49543660 -0.39464024 -H -0.09909703 -2.53543104 -0.28970003 -H -1.34097671 -2.65395851 0.97972885 -H -1.82903100 -2.53483935 -0.70813076 -11 -Properties=species:S:1:pos:R:3 A=10.44045 B=4.04692 C=2.98547 mu=3.5648 alpha=39.51 epsHOMO=-0.2436 epsLUMO=0.0599 epsgap=0.3034 =428.404 zpve=0.091713 U0=-264.504487 U=-264.498452 H=-264.497508 G=-264.533633 Cv=20.274 pbc="F F F" -C 0.00922630 1.45021814 -0.03194543 -N -0.01429488 -0.00054646 -0.01782667 -C -1.21576579 -0.67252403 0.04421896 -N -1.09733352 -2.04644898 -0.15997741 -O -2.28599299 -0.12893711 0.24196027 -H 0.89659038 1.81885022 0.49120949 -H 0.00233950 1.86388504 -1.04914155 -H -0.88562684 1.79943346 0.48353298 -H 0.75257305 -0.47506631 -0.46719265 -H -0.24882333 -2.48772194 0.16375654 -H -1.93419610 -2.53898393 0.11482880 -12 -Properties=species:S:1:pos:R:3 A=9.69683 B=4.1533 C=3.01703 mu=1.1876 alpha=43.57 epsHOMO=-0.2595 epsLUMO=0.0352 epsgap=0.2948 =443.1687 zpve=0.102062 U0=-248.416462 U=-248.410358 H=-248.409414 G=-248.445651 Cv=20.256 pbc="F F F" -C -0.09160695 1.37540046 -0.00729426 -O -0.06964534 -0.04831581 -0.11148812 -C 0.68114625 -0.56142673 -1.10732458 -C 0.60405298 -2.06701550 -1.08766892 -N 1.32645533 0.19266343 -1.89967731 -H 0.91463916 1.76843655 0.16257460 -H -0.48562826 1.82624615 -0.92235216 -H -0.73971430 1.60007423 0.84110605 -H -0.43430065 -2.39242162 -1.20325205 -H 1.20627110 -2.50049977 -1.88745640 -H 0.95651337 -2.44976670 -0.12501838 -H 1.83504115 -0.36104361 -2.58486055 -11 -Properties=species:S:1:pos:R:3 A=10.20392 B=4.16834 C=3.07281 mu=1.7569 alpha=39.33 epsHOMO=-0.2685 epsLUMO=0.0174 epsgap=0.2859 =427.6606 zpve=0.089436 U0=-268.32127 U=-268.315051 H=-268.314106 G=-268.351214 Cv=19.501 pbc="F F F" -C 0.07295254 1.38840919 0.06442046 -O 0.21049114 -0.03740028 0.04612736 -C 0.50102805 -0.57224288 -1.15975346 -C 0.62218715 -2.07378323 -1.06030684 -O 0.63922154 0.08224418 -2.16157325 -H 1.00029720 1.87062570 -0.25532064 -H -0.73338957 1.70782089 -0.60080454 -H -0.15751280 1.65154060 1.09692954 -H -0.31330001 -2.50202851 -0.68941906 -H 0.85646588 -2.48430275 -2.04157352 -H 1.40651685 -2.34055496 -0.34634301 -10 -Properties=species:S:1:pos:R:3 A=10.68182 B=4.39532 C=3.17969 mu=2.329 alpha=35.5 epsHOMO=-0.2669 epsLUMO=0.0575 epsgap=0.3244 =398.5908 zpve=0.079271 U0=-284.385189 U=-284.379361 H=-284.378417 G=-284.414085 Cv=19.084 pbc="F F F" -C -0.02645771 1.38305753 0.02085660 -O 0.04139566 -0.04439503 -0.00277452 -C 0.66775338 -0.56001625 -1.09000344 -N 0.72605063 -1.92219983 -0.98822954 -O 1.12952381 0.09648143 -1.99322180 -H 0.97529779 1.82044307 0.02387306 -H -0.57085698 1.76214621 -0.84790425 -H -0.55410722 1.63835731 0.94043594 -H 0.12662696 -2.38401590 -0.32566929 -H 0.97230070 -2.41798421 -1.82732573 -10 -Properties=species:S:1:pos:R:3 A=10.20696 B=3.93135 C=2.9219 mu=4.5676 alpha=35.87 epsHOMO=-0.246 epsLUMO=0.0273 epsgap=0.2733 =418.5967 zpve=0.078222 U0=-284.360325 U=-284.354148 H=-284.353204 G=-284.390143 Cv=20.089 pbc="F F F" -N -0.08115607 1.31807026 0.01276742 -C 0.05442259 -0.04477733 0.08155388 -O 1.12163074 -0.59783950 0.20940553 -C -1.28687786 -0.79011642 0.06178435 -O -1.04605114 -2.13954195 -0.23957158 -H 0.76623366 1.85991128 -0.04483425 -H -0.93021346 1.74567447 -0.31565615 -H -1.74559696 -0.65521334 1.05526700 -H -1.96026061 -0.31149222 -0.67026256 -H -1.85637296 -2.63017646 -0.07625478 -10 -Properties=species:S:1:pos:R:3 A=10.14389 B=4.0714 C=3.02223 mu=5.3004 alpha=35.19 epsHOMO=-0.2527 epsLUMO=0.0208 epsgap=0.2735 =408.0279 zpve=0.080317 U0=-284.372483 U=-284.367172 H=-284.366228 G=-284.40095 Cv=17.931 pbc="F F F" -N 0.01838100 0.93057357 0.04943058 -H -0.70674506 0.56116798 -0.55550134 -H 0.68248307 0.18430002 0.21487099 -H 0.21928084 2.79858558 -0.32670585 -C -0.54586914 1.44688585 1.30274586 -C -0.80466975 2.96030569 1.21052835 -O -0.21468822 3.53648884 0.15531687 -O -1.45295676 3.56338618 2.02143528 -H -1.47662579 0.96512183 1.62072169 -H 0.17781209 1.30324286 2.11157176 -13 -Properties=species:S:1:pos:R:3 A=6.6508 B=4.19317 C=3.39441 mu=2.2854 alpha=42.01 epsHOMO=-0.2597 epsLUMO=0.0631 epsgap=0.3228 =438.3028 zpve=0.11345 U0=-269.479234 U=-269.472993 H=-269.472049 G=-269.508213 Cv=22.072 pbc="F F F" -C -0.02775167 1.53421433 0.00651151 -C 0.08343246 0.01218277 0.05258986 -O 0.95856392 -0.48200710 -0.96694113 -C 0.69767574 -0.49516451 1.35948812 -O 2.02247062 -0.02842530 1.52222186 -H 0.94333763 1.98841434 0.21841589 -H -0.37320516 1.87354041 -0.97716908 -H -0.74773817 1.89256394 0.74926853 -H -0.91671848 -0.43697036 -0.06040528 -H 0.76726710 -0.01412177 -1.78502043 -H 0.11650002 -0.13351914 2.21392431 -H 0.65814685 -1.59563445 1.36274474 -H 2.46378566 -0.22762476 0.68698431 -17 -Properties=species:S:1:pos:R:3 A=7.26511 B=3.32874 C=2.54491 mu=0.0618 alpha=56.26 epsHOMO=-0.3085 epsLUMO=0.085 epsgap=0.3934 =565.8412 zpve=0.159632 U0=-197.629387 U=-197.622325 H=-197.621381 G=-197.659365 Cv=25.169 pbc="F F F" -C 0.05879035 1.56174169 -0.01945240 -C -0.01544024 0.03182306 0.00308332 -C -1.44103237 -0.54862028 0.05028629 -C -1.40777007 -2.07231030 -0.13012010 -C -2.18400529 -0.17659759 1.34114303 -H 1.09435638 1.90233607 -0.11900072 -H -0.50689566 1.97217319 -0.86388098 -H -0.34407951 2.00628082 0.89579522 -H 0.48926758 -0.36011214 -0.88953902 -H 0.55254938 -0.35139446 0.86269887 -H -2.00031575 -0.12153682 -0.79559684 -H -0.85501131 -2.55065810 0.68775438 -H -2.41787513 -2.49543628 -0.13720127 -H -0.91928762 -2.35363802 -1.06914014 -H -3.19100197 -0.60699559 1.35137051 -H -1.65197294 -0.56010584 2.22063481 -H -2.28793759 0.90575132 1.46079572 -15 -Properties=species:S:1:pos:R:3 A=8.11598 B=3.43678 C=2.66869 mu=1.3894 alpha=49.45 epsHOMO=-0.2617 epsLUMO=0.0733 epsgap=0.335 =521.8605 zpve=0.136091 U0=-233.555951 U=-233.549143 H=-233.548199 G=-233.585602 Cv=23.99 pbc="F F F" -C -0.03841227 1.55621396 0.00935837 -C 0.01907135 0.02716079 -0.02074203 -C 0.73141506 -0.52709328 -1.25454422 -C 0.83975868 -2.05206973 -1.22872655 -O -0.01226470 -0.08514394 -2.38973758 -H 0.96845664 1.98863424 0.03526234 -H -0.54377006 1.93824134 -0.88074407 -H -0.57652955 1.91383386 0.89256698 -H 0.53166867 -0.34991486 0.87240999 -H -0.99727186 -0.38638654 -0.00185289 -H 1.74909346 -0.09943025 -1.28826184 -H 1.34143968 -2.42487137 -2.12957624 -H 1.41822012 -2.39229108 -0.36340871 -H -0.15616765 -2.50443859 -1.18308875 -H 0.43090825 -0.40901616 -3.17979642 -15 -Properties=species:S:1:pos:R:3 A=7.55335 B=3.78323 C=2.80566 mu=1.0758 alpha=49.8 epsHOMO=-0.2476 epsLUMO=0.086 epsgap=0.3336 =507.9614 zpve=0.135681 U0=-233.545899 U=-233.539034 H=-233.53809 G=-233.57582 Cv=23.573 pbc="F F F" -C -0.00857605 1.38097833 0.09454670 -O -0.02255106 -0.00739461 -0.13062576 -C -1.30243341 -0.61997805 -0.02088309 -C -1.18687286 -1.97885751 -0.70303918 -C -1.75587824 -0.74662760 1.43714508 -H 1.00321550 1.73258087 -0.12443496 -H -0.71511468 1.90838275 -0.56674280 -H -0.24909065 1.64967237 1.13384681 -H -2.03737978 -0.00432241 -0.56884198 -H -0.44792779 -2.59945605 -0.18596870 -H -2.14751041 -2.50225525 -0.69102581 -H -0.86524212 -1.85860679 -1.74077836 -H -2.73321482 -1.23674908 1.49205117 -H -1.03621350 -1.34483828 2.00544671 -H -1.84898410 0.22935483 1.92198087 -15 -Properties=species:S:1:pos:R:3 A=6.14183 B=5.17679 C=3.79297 mu=0.1068 alpha=52.92 epsHOMO=-0.2633 epsLUMO=0.0893 epsgap=0.3526 =439.1643 zpve=0.137025 U0=-196.409349 U=-196.403398 H=-196.402453 G=-196.437676 Cv=22.258 pbc="F F F" -C -0.03913204 1.50456305 0.04978913 -C 0.00985856 -0.01113880 0.01248619 -C -1.24167981 -0.66663044 -0.53989979 -C 1.33803387 -0.66895748 -0.27400430 -C 0.75126442 -0.72221586 1.11862571 -H 0.88633040 1.92490040 0.45579358 -H -0.18183878 1.92045743 -0.95489291 -H -0.86832076 1.85815718 0.67439648 -H -1.43570890 -0.34344387 -1.56976411 -H -1.15326757 -1.75757800 -0.54433832 -H -2.12221416 -0.40570820 0.05951361 -H 1.34398374 -1.58235208 -0.86005084 -H 2.20196897 -0.03329084 -0.43929326 -H 1.21949576 -0.12246018 1.89246580 -H 0.36153319 -1.67153717 1.47171932 -13 -Properties=species:S:1:pos:R:3 A=6.49534 B=5.52329 C=3.9108 mu=1.8235 alpha=45.7 epsHOMO=-0.2596 epsLUMO=0.091 epsgap=0.3505 =405.521 zpve=0.112851 U0=-232.335768 U=-232.329981 H=-232.329037 G=-232.364091 Cv=20.719 pbc="F F F" -C -0.03970330 1.50954998 -0.04112233 -C -0.03023556 -0.00335329 -0.06498361 -C -1.24613189 -0.68356648 0.52531190 -C 0.78937201 -0.71129193 -1.06027559 -O 1.22546798 -0.60059036 0.29712283 -H 0.88992772 1.90948478 -0.45296558 -H -0.87952723 1.90350108 -0.62376642 -H -0.14448576 1.87590604 0.98604429 -H -2.15218727 -0.41012616 -0.02629495 -H -1.13412584 -1.77005431 0.49747032 -H -1.38586631 -0.38055453 1.56882757 -H 0.50712690 -1.71386874 -1.38085018 -H 1.37088104 -0.14369322 -1.78640853 -13 -Properties=species:S:1:pos:R:3 A=6.57648 B=5.31603 C=3.98453 mu=1.3822 alpha=45.85 epsHOMO=-0.2494 epsLUMO=0.0776 epsgap=0.327 =401.4888 zpve=0.113237 U0=-232.33341 U=-232.327734 H=-232.32679 G=-232.36147 Cv=21.17 pbc="F F F" -C -0.03714052 1.53215920 0.01332310 -C 0.01344335 0.02117019 0.01922005 -O -1.17476104 -0.59735284 -0.40931260 -C 1.30744798 -0.69902106 -0.27389650 -C 0.70844749 -0.71373504 1.12226318 -H 0.89026861 1.96240279 0.40276135 -H -0.17846151 1.91561262 -1.00518522 -H -0.87011232 1.88955201 0.62730953 -H -1.28352265 -0.40225391 -1.34690641 -H 1.23973813 -1.61642115 -0.84783774 -H 2.20543993 -0.11523820 -0.44744474 -H 1.19539159 -0.13889834 1.90240284 -H 0.24735439 -1.63670241 1.45378480 -10 -Properties=species:S:1:pos:R:3 A=12.18399 B=5.1384 C=3.78868 mu=2.5732 alpha=40.19 epsHOMO=-0.263 epsLUMO=0.0277 epsgap=0.2907 =352.0378 zpve=0.080558 U0=-247.201165 U=-247.196699 H=-247.195755 G=-247.22841 Cv=15.51 pbc="F F F" -N -0.04445152 1.25623533 0.31681915 -C -0.03914559 0.04913710 -0.01670558 -C -1.08749619 -1.04164446 -0.11488033 -C 0.09869688 -1.90754844 -0.58225894 -O 0.99647508 -0.76306629 -0.44028010 -H 0.88935980 1.66481350 0.24916893 -H -1.54324114 -1.32235812 0.83572172 -H -1.86910036 -0.85713839 -0.85325910 -H 0.39739753 -2.72227350 0.08158721 -H 0.06945080 -2.25418345 -1.61796084 -11 -Properties=species:S:1:pos:R:3 A=10.79571 B=4.78732 C=3.54292 mu=2.7119 alpha=42.77 epsHOMO=-0.2415 epsLUMO=-0.0194 epsgap=0.2222 =379.6371 zpve=0.090544 U0=-231.15578 U=-231.150804 H=-231.149859 G=-231.184401 Cv=17.076 pbc="F F F" -O -0.00101746 0.01846628 -0.20114742 -C -0.01405811 1.19658059 0.00205306 -C -1.13542911 2.22979148 0.21957980 -C -0.03500884 3.29097637 0.53052223 -C 1.08469664 2.25947795 0.18994331 -H -1.71642814 2.41772662 -0.68901078 -H -1.83392757 1.98415059 1.02444244 -H -0.05534741 4.17585264 -0.10670963 -H -0.02541477 3.61399048 1.57278917 -H 1.81074389 2.03288534 0.97578973 -H 1.63600801 2.46255380 -0.73376244 -10 -Properties=species:S:1:pos:R:3 A=12.23436 B=4.97895 C=3.70561 mu=3.6671 alpha=39.73 epsHOMO=-0.25 epsLUMO=0.038 epsgap=0.288 =355.0934 zpve=0.080442 U0=-247.225618 U=-247.220897 H=-247.219953 G=-247.253218 Cv=16.1 pbc="F F F" -O 0.03184134 -0.03461682 0.00849836 -C -0.02440334 1.16440533 0.02160811 -C -1.14541322 2.23712809 0.03012901 -C 0.00928805 3.27945878 0.04557758 -N 0.93843838 2.14652109 0.03554396 -H -1.77030529 2.23779730 -0.86428437 -H -1.77632809 2.21765386 0.92007934 -H 0.07170564 3.91863516 -0.84072193 -H 0.06622603 3.89826625 0.94657952 -H 1.94616840 2.09535713 0.03855945 -9 -Properties=species:S:1:pos:R:3 A=12.44643 B=5.23328 C=3.86599 mu=3.9339 alpha=35.4 epsHOMO=-0.2788 epsLUMO=0.0089 epsgap=0.2878 =336.0792 zpve=0.068574 U0=-267.106213 U=-267.101929 H=-267.100985 G=-267.13332 Cv=14.483 pbc="F F F" -O 0.04424004 0.03933243 -0.33071104 -C -0.04854479 1.17416230 0.00410076 -C -1.14211656 2.23611850 0.15338361 -C 0.02332538 3.12357010 0.61681625 -O 0.95663808 2.01190122 0.42603746 -H -1.62573755 2.51482212 -0.78401102 -H -1.90009213 1.99553386 0.90028663 -H 0.28368077 3.96172751 -0.03229287 -H 0.00782250 3.43942655 1.66162725 -10 -Properties=species:S:1:pos:R:3 A=11.54185 B=4.90368 C=3.63309 mu=2.5257 alpha=39.34 epsHOMO=-0.2437 epsLUMO=-0.0258 epsgap=0.2179 =359.152 zpve=0.07965 U0=-247.190194 U=-247.185543 H=-247.184599 G=-247.217688 Cv=16.083 pbc="F F F" -O 0.00053891 0.00665475 -0.09743049 -C -0.01371990 1.19213881 0.03139936 -C -1.10668681 2.26612284 0.17247913 -N -0.03543445 3.25123475 0.48533693 -C 1.05328615 2.29521506 0.14383904 -H -1.65047970 2.42796175 -0.76886054 -H -1.83606786 2.11370530 0.97567410 -H -0.05434600 4.07193609 -0.10727051 -H 1.80754206 2.16277968 0.92736250 -H 1.56737937 2.47130277 -0.81152688 -9 -Properties=species:S:1:pos:R:3 A=12.18264 B=4.94228 C=3.68162 mu=0.8477 alpha=35.94 epsHOMO=-0.2647 epsLUMO=-0.0352 epsgap=0.2295 =344.5473 zpve=0.066989 U0=-267.068488 U=-267.063976 H=-267.063032 G=-267.095887 Cv=15.115 pbc="F F F" -O -0.00031692 0.00676321 -0.17139774 -C -0.01417098 1.18672865 -0.00980279 -C -1.08283120 2.27653381 0.15574094 -O -0.03856115 3.26460423 0.27513986 -C 1.02857270 2.30515693 0.12774311 -H -1.72923855 2.43647507 -0.71671274 -H -1.70219898 2.19346725 1.05800754 -H 1.67366781 2.23923293 1.01324179 -H 1.64705068 2.48224548 -0.76148355 -15 -Properties=species:S:1:pos:R:3 A=9.63416 B=4.10254 C=3.28665 mu=0.095 alpha=52.94 epsHOMO=-0.2896 epsLUMO=0.0927 epsgap=0.3823 =455.0215 zpve=0.138424 U0=-196.407957 U=-196.40245 H=-196.401505 G=-196.436159 Cv=20.299 pbc="F F F" -C -0.47005934 1.44206547 -0.06684548 -C 0.14287306 0.05485144 0.03747984 -C 1.37254693 -0.30295273 -0.84679168 -C 1.95947994 -1.09364561 0.35326039 -C 0.99887433 -0.30768787 1.28563099 -H 0.29210839 2.21844829 0.06844100 -H -0.93614604 1.60094605 -1.04612799 -H -1.24148161 1.59707353 0.69627593 -H -0.63517341 -0.70560132 -0.10055095 -H 1.19338376 -0.84118177 -1.78129390 -H 1.96867777 0.58857708 -1.07004184 -H 1.69873597 -2.15500718 0.30521100 -H 3.03253510 -1.01077235 0.54148058 -H 1.48290386 0.58242428 1.70211026 -H 0.51304296 -0.84979686 2.10121062 -13 -Properties=species:S:1:pos:R:3 A=9.93129 B=4.56362 C=3.63704 mu=1.6826 alpha=45.72 epsHOMO=-0.241 epsLUMO=0.0915 epsgap=0.3325 =407.6909 zpve=0.11459 U0=-232.33123 U=-232.325877 H=-232.324933 G=-232.359589 Cv=18.582 pbc="F F F" -C -0.32308228 1.53751451 0.01947624 -C 0.11847214 0.08900172 -0.00523140 -C 0.87489284 -0.43763303 -1.24721738 -C 1.97841048 -0.80657483 -0.23740356 -O 1.27344926 -0.15419108 0.83794040 -H 0.49211985 2.19658795 -0.29527664 -H -1.17615835 1.68923490 -0.65134808 -H -0.62776963 1.82747912 1.03013205 -H -0.71410200 -0.56389187 0.29597305 -H 0.41033314 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zpve=0.115063 U0=-232.333258 U=-232.328097 H=-232.327153 G=-232.361103 Cv=19.023 pbc="F F F" -O 0.54622847 1.03270424 -0.58398083 -C -0.00112269 -0.11036621 0.01898238 -C 0.13945950 -0.34310429 1.54777117 -C -1.26863784 -0.99876517 1.51632654 -C -1.53844651 -0.26418236 0.17423510 -H 0.20332379 1.80433874 -0.12071219 -H 0.41296647 -0.95857955 -0.53560402 -H 0.98890738 -0.92719626 1.90921867 -H 0.11786549 0.61323034 2.08356053 -H -1.20828813 -2.08191064 1.38036699 -H -1.94143614 -0.79119746 2.35014116 -H -2.00495682 0.71307933 0.34581493 -H -2.09160311 -0.78230202 -0.61249171 -11 -Properties=species:S:1:pos:R:3 A=10.85394 B=4.52867 C=3.62328 mu=2.4158 alpha=38.58 epsHOMO=-0.2465 epsLUMO=0.0604 epsgap=0.3069 =374.4087 zpve=0.091005 U0=-268.248371 U=-268.243234 H=-268.24229 G=-268.276572 Cv=17.344 pbc="F F F" -O 0.07721714 1.41738724 -0.14439016 -C 0.01675156 0.02484791 0.03233817 -C 0.11193823 -0.54392541 1.47000744 -O -1.16387804 -1.20093191 1.32441605 -C -1.41930178 -0.52040930 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zr?z=ukpy>#kHM?W!^t35adcHaL+nM9Bl3eskJ7j6cTv)_K69vl94L@>-V&PGnvlb4 zbJTf1{G&kbfFy66XDS7SY}G)lnm!MRwL{ZJn}P*1H8#IYLCsSHQ#55c6a+0)4J8!? zZ!*x4tjWl=s)Obv#9J*$=3ZY1wbHDq@WWPcXu*HGWchNQw&F#4L~f*W{OAqpdkPm_ z%vF`P>P(&oCLr^aWxCQn%VMX@tb5Kpat){(C+T)Q30uMvEQ3BKd`3$>I7J$N3Z;q7H>^X=dbXhe5EY-z z83*B3vMxfx#~ar5G9U89lM3z8(S*1#obF+MIt}bFv-|hEp81?m>P>fxlN+&)2CJxa z#yOmv)(zVI$~LB;xxQ9}m3>~JO)uV(U>Ede2~Y>n&vszW)1U*;=~^1JrmdG+p~FLZ z93tl}BqeS09d8^bgO2nHBXgy~2x+G_)4wmylzw1QVKndr3^uGp^ur}an#vm9l~Lbwr|L0(c10v*^ zNfa6)`uVOU3iQMga~G`V7X!QWy43GfEw*>tOob=uuiqcFKGUVFB_hWdjFT~$dUBxl z4?|AE^uDc10I6ph?C=cA3eB#_e*4WyTsRDfASqhS?`td` - to download the full example code. - -.. rst-class:: sphx-glr-example-title - -.. _sphx_glr_examples_programmatic_use_architectures_outside_use_outside.py: - - -Using metatrain architectures outside of metatrain -================================================== - -This tutorial demonstrates how to use one of metatrain's implemented architectures -outside of metatrain. This will be done by taking internal representations of a -NanoPET model (as an example) and using them inside a user-defined torch ``Module``. - -Only architectures which can output internal representations ("features" output) can -be used in this way. - -.. GENERATED FROM PYTHON SOURCE LINES 15-28 - -.. code-block:: Python - - - import torch - from metatomic.torch import ModelOutput - - from metatrain.pet import PET - from metatrain.utils.architectures import get_default_hypers - from metatrain.utils.data import DatasetInfo, read_systems - from metatrain.utils.neighbor_lists import ( - get_requested_neighbor_lists, - get_system_with_neighbor_lists, - ) - - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 29-31 - -Read some sample systems. Metatrain always reads systems in float64, while torch -uses float32 by default. We will convert the systems to float32. - -.. GENERATED FROM PYTHON SOURCE LINES 32-37 - -.. code-block:: Python - - - systems = read_systems("qm9_reduced_100.xyz") - systems = [s.to(torch.float32) for s in systems] - - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 38-41 - -Define the custom model using the PET architecture as a building block. -The dummy architecture here adds a linear layer and a tanh activation function -on top of the PET model. - -.. GENERATED FROM PYTHON SOURCE LINES 42-68 - -.. code-block:: Python - - - - class PETWithTanh(torch.nn.Module): - def __init__(self): - super().__init__() - self.pet = PET( - get_default_hypers("pet")["model"], - DatasetInfo( - length_unit="angstrom", - atomic_types=[1, 6, 7, 8, 9], - targets={}, - ), - ) - self.linear = torch.nn.Linear(384, 1) - self.tanh = torch.nn.Tanh() - - def forward(self, systems): - model_outputs = self.pet( - systems, - {"features": ModelOutput()}, - # ModelOutput(per_atom=True) would give per-atom features - ) - features = model_outputs["features"].block().values - return self.tanh(self.linear(features)) - - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 69-71 - -Now we can train the custom model. Here is one training step executed with -some random targets. - -.. GENERATED FROM PYTHON SOURCE LINES 72-91 - -.. code-block:: Python - - my_targets = torch.randn(100, 1) - - # instantiate the model - model = PETWithTanh() - - # all metatrain models require neighbor lists to be present in the input systems - systems = [ - get_system_with_neighbor_lists(sys, get_requested_neighbor_lists(model)) - for sys in systems - ] - - # define an optimizer - optimizer = torch.optim.SGD(model.parameters(), lr=1e-3) - - # this is one training step - predictions = model(systems) - loss = torch.nn.functional.mse_loss(predictions, my_targets) - loss.backward() - optimizer.step() - - - - - - - - -.. _sphx_glr_download_examples_programmatic_use_architectures_outside_use_outside.py: - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-example - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: use_outside.ipynb ` - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: use_outside.py ` - - .. container:: sphx-glr-download sphx-glr-download-zip - - :download:`Download zipped: use_outside.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/programmatic/use_architectures_outside/use_outside.zip b/docs/src/examples/programmatic/use_architectures_outside/use_outside.zip deleted file mode 100644 index 6a075f505bc2699e4a479fa393b86291801c082d..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 7022 zcmdT}+iv8<77dpJl93QcLP9!EB}WKb>M;`6-#@B#bE zvbkUhF`RFh;TNQ33ND4h`Gr_PJ22%Yg&=e$cRuGTKmPE9WqBeb^SEYQG4Yg>06sn^ z5Rtf;rO3dFY{)o?UEE2;B2^F&>cva;=+S3+QcCgYQQ$aVD7hI5&eqEmR57?ig;il(KvosDW zDQgOdD&!Yd4yQ**u@I~TWUCm#FyqzIq*4dNc&Vw+g^6GjUP==!H!D%--J26`cyq!7 zeAdEzq89lC4&q6qH`<77&sHVU^W{7*BAIGq!-Vwp9EOXIFT~?gV1E(~DJLyO#Xef6 zX1PVyTQ}V zcdp-~KGpEQXG0*Ux%q677YsI3%~5B4^&i;awvxo9HWW%WL)vU$lr{Z0V3a#-UZygk 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- -.. only:: html - - .. image:: /examples/train_from_scratch/images/thumb/sphx_glr_train_from_scratch_thumb.png - :alt: - - :ref:`sphx_glr_examples_train_from_scratch_train_from_scratch.py` - -.. raw:: html - -
Training a model from scratch
-
- - -.. thumbnail-parent-div-close - -.. raw:: html - - - - -.. toctree:: - :hidden: - - /examples/train_from_scratch/train_from_scratch - - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-gallery - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download all examples in Python source code: train_from_scratch_python.zip ` - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download all examples in Jupyter notebooks: train_from_scratch_jupyter.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/train_from_scratch/options.yaml b/docs/src/examples/train_from_scratch/options.yaml deleted file mode 100644 index ceef2551ed..0000000000 --- a/docs/src/examples/train_from_scratch/options.yaml +++ /dev/null @@ -1,22 +0,0 @@ -architecture: - name: pet # the architecture name for PET, or the name for other architectures, if you want to train other models - model: - cutoff: 4.5 # the cutoff - training: - num_epochs: 10 # this is for a reasonable time of a tutorial, for a good model, consider increasing the number - batch_size: 10 # the size of the training data feed to the model per batch, determining the GPU memory usage during the training - log_interval: 1 - checkpoint_interval: 10 # it saves checkpoints of the model every 10 epochs - -# this needs specifying based on the specific dataset -training_set: - systems: - read_from: ./ethanol_reduced_100.xyz - length_unit: Angstrom - targets: - energy: - key: energy # name of the target value - unit: eV # unit of the target value - -test_set: 0.1 # 10 % of the training_set are randomly split and taken for test set -validation_set: 0.1 # 10 % of the training_set are randomly split and for validation set diff --git a/docs/src/examples/train_from_scratch/train_from_scratch.ipynb b/docs/src/examples/train_from_scratch/train_from_scratch.ipynb deleted file mode 100644 index d0f0dadee6..0000000000 --- a/docs/src/examples/train_from_scratch/train_from_scratch.ipynb +++ /dev/null @@ -1,32 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n\n# Training a model from scratch\nThis tutorial explains how to train a model with ``metatrain`` from scratch and evaluate\nit. [This dataset](./ethanol_reduced_100.xyz) is used here as an example of the\npreferred dataset format. If you have your own dataset, you can simply replace the\ndataset file name with yours.\n\n## Train the model\n\n### Configure the ``options.yaml`` and run the training\nBelow is an example ``options.yaml`` for training a PET model. In order to train other\nmodels, simply replace the architecture name with other models' architecture name. For\nthe supported models, please check `Available Architectures`_ .\n\n\n.. literalinclude:: ./options.yaml\n :language: yaml\n :linenos:\n\nStart the training by running\n\n```bash\nmtt train options.yaml\n```\nIt will start training. ``metatrain`` will automatically read the atomic forces from the\ntraining set, if they are stored in it and named as \"forces\". The model can also be\ntrained to learn other properties through transfer learning. For this, please refer to\nthis `transfer learning tutorial`_.\n\n\nOnce the training is started, a folder named ``outputs`` will be created automatically\nunder the folder where you run the command. Under this ``outputs`` folder, there is a\nfolder with the timestamp. Below is a normal structure of that folder of a successful\ntraining run.\n\n```bash\noutputs/2025-10-07/17-08-25/\n\u251c\u2500\u2500 indices # the results of dataset-spliting\n\u2502 \u251c\u2500\u2500 test.txt\n\u2502 \u251c\u2500\u2500 training.txt\n\u2502 \u2514\u2500\u2500 validation.txt\n\u251c\u2500\u2500 model_0.ckpt # the intermediate model saved at the 0th training step\n\u251c\u2500\u2500 model.ckpt # the final model\n\u251c\u2500\u2500 model.pt # the final model , usable directly by ASE and LAMMPS\n\u251c\u2500\u2500 options_restart.yaml # an expanded options file\n\u251c\u2500\u2500 train.csv # structured log of training metrics (loss, MAE, RMSE,...)\n\u2514\u2500\u2500 train.log # a human-friendly output\n```\nThe ``train.log`` provides information of the training procedure. For example, by\nchecking the following:\n\n```bash\n[2025-10-07 17:08:25][INFO] - Setting up training set [2025-10-07 17:08:25][INFO] -\nForces found in section 'energy', we will use this gradient to train the model\n[2025-10-07 17:08:25][WARNING] - No stress found in section 'energy'.\n```\nYou can know that the forces are identified by ``metatrain`` and are used during the\ntraining, and it fails to find stress. The following provides some statistical of the\ntraining, validation, and the test set\n\n```bash\n[2025-10-07 17:08:25][INFO] - Training dataset:\n Dataset containing 80 structures Mean and standard deviation of targets: -\n energy:\n - mean -9.708e+04 eV\n - std 3.97 eV\n[2025-10-07 17:08:25][INFO] - Validation dataset:\n Dataset containing 10 structures Mean and standard deviation of targets: -\n energy:\n - mean -9.708e+04 eV\n - std 3.73 eV\n[2025-10-07 17:08:25][INFO] - Test dataset:\n Dataset containing 10 structures Mean and standard deviation of targets: -\n energy:\n - mean -9.708e+04 eV\n - std 3.535 eV\n```\nThe training metrics are outputted every epoch, like\n\n```bash\n[2025-10-07 17:08:28][INFO] - Epoch: 0 | learning rate: 0.000e+00 | training\nloss: 6.305e+03 | training energy RMSE (per atom): 884.08 meV | training energy MAE\n(per atom): 773.44 meV | training forces RMSE: 28059.9 meV/A | training forces MAE:\n20581.1 meV/A | validation loss: 7.725e+02 | validation energy RMSE (per atom):\n877.08 meV | validation energy MAE (per atom): 772.04 meV | validation forces RMSE:\n27779.2 meV/A | validation forces MAE: 20201.9 meV/A\n```\nThese metrics are also outputted into ``train.csv`` in a formatted way, which can be\nused for plotting graph like loss curve.\n\nIt is easy to restart the training from the last step, by running\n\n```bash\nmtt train options.yaml --restart model.ckpt\n```\n## Evaluate the trained model\nIn order to evaluate the model on the test set, we can use the mtt eval sub-command.\nFirst, create the input file ``eval.yaml`` with the following options:\n\n.. literalinclude:: ./eval.yaml\n :language: yaml\n :linenos:\n\nand run\n\n```bash\nmtt eval PATH_TO_YOUR_MODEL/model.pt eval.yaml # be sure to replace the path\n```\nAfter this, a file named ``output.xyz`` will be created, with the atom positions and the\npredicted forces recorded in it. Also, you should see these statistical on your screen\n\n```bash\n[2025-10-07 17:11:47][INFO] - energy RMSE (per atom): 436.50 meV | energy MAE (per\natom): 341.32 meV | forces RMSE: 27823.1 meV/A | forces MAE: 20392.7 meV/A\n[2025-10-07 17:11:47][INFO] - Evaluation time: 1.10 s [1.2185 \u00b1 1.2768 ms per atom]\n```\nFurther analysis can be performed now that the model is trained. We provide a `Python\nscript`_ that can be used to generate a parity plot of the target vs predicted energies,\nbut otherwise leave this open-ended.\n\n\nTo run the script, download it from the repository, modify the paths as necessary\n(indicated with a #TODO), and run. This will generate a plot saved at parity_plot.png.\n\n\n## Use the model\nWith the trained model, you can run molecular dynamics. Please refer to these two\ntutorials for `ASE`_ and `LAMMPS`_ to see how to do that.\n\n\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/src/examples/train_from_scratch/train_from_scratch.py b/docs/src/examples/train_from_scratch/train_from_scratch.py deleted file mode 100644 index a639da7f55..0000000000 --- a/docs/src/examples/train_from_scratch/train_from_scratch.py +++ /dev/null @@ -1,151 +0,0 @@ -""" -Training a model from scratch -============================= - -This tutorial explains how to train a model with ``metatrain`` from scratch and evaluate -it. :download:`This dataset ` is used here as an example of the -preferred dataset format. If you have your own dataset, you can simply replace the -dataset file name with yours. - -Train the model ---------------- - -Configure the ``options.yaml`` and run the training -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -Below is an example ``options.yaml`` for training a PET model. In order to train other -models, simply replace the architecture name with other models' architecture name. For -the supported models, please check `Available Architectures`_ . - -.. _`Available Architectures`: https://metatensor.github.io/metatrain/latest/architectures/index.html - -.. literalinclude:: options-scratch.yaml - :language: yaml - :linenos: - -Start the training by running - -.. code-block:: bash - - mtt train options-scratch.yaml - -It will start training. ``metatrain`` will automatically read the atomic forces from the -training set, if they are stored in it and named as "forces". The model can also be -trained to learn other properties through transfer learning. For this, please refer to -this `transfer learning tutorial`_. - -.. _`transfer learning tutorial`: https://metatensor.github.io/metatrain/latest/advanced-concepts/transfer-learning.html - -Once the training is started, a folder named ``outputs`` will be created automatically -under the folder where you run the command. Under this ``outputs`` folder, there is a -folder with the timestamp. Below is a normal structure of that folder of a successful -training run. - -.. code-block:: bash - - outputs/2025-10-07/17-08-25/ - ├── indices # the results of dataset-spliting - │ ├── test.txt - │ ├── training.txt - │ └── validation.txt - ├── model_0.ckpt # the intermediate model saved at the 0th training step - ├── model.ckpt # the final model - ├── model.pt # the final model , usable directly by ASE and LAMMPS - ├── options_restart.yaml # an expanded options file - ├── train.csv # structured log of training metrics (loss, MAE, RMSE,...) - └── train.log # a human-friendly output - -The ``train.log`` provides information of the training procedure. For example, by -checking the following: - -.. code-block:: bash - - [2025-10-07 17:08:25][INFO] - Setting up training set [2025-10-07 17:08:25][INFO] - - Forces found in section 'energy', we will use this gradient to train the model - [2025-10-07 17:08:25][WARNING] - No stress found in section 'energy'. - -You can know that the forces are identified by ``metatrain`` and are used during the -training, and it fails to find stress. The following provides some statistical of the -training, validation, and the test set - -.. code-block:: bash - - [2025-10-07 17:08:25][INFO] - Training dataset: - Dataset containing 80 structures Mean and standard deviation of targets: - - energy: - - mean -9.708e+04 eV - - std 3.97 eV - [2025-10-07 17:08:25][INFO] - Validation dataset: - Dataset containing 10 structures Mean and standard deviation of targets: - - energy: - - mean -9.708e+04 eV - - std 3.73 eV - [2025-10-07 17:08:25][INFO] - Test dataset: - Dataset containing 10 structures Mean and standard deviation of targets: - - energy: - - mean -9.708e+04 eV - - std 3.535 eV - -The training metrics are outputted every epoch, like - -.. code-block:: bash - - [2025-10-07 17:08:28][INFO] - Epoch: 0 | learning rate: 0.000e+00 | training - loss: 6.305e+03 | training energy RMSE (per atom): 884.08 meV | training energy MAE - (per atom): 773.44 meV | training forces RMSE: 28059.9 meV/A | training forces MAE: - 20581.1 meV/A | validation loss: 7.725e+02 | validation energy RMSE (per atom): - 877.08 meV | validation energy MAE (per atom): 772.04 meV | validation forces RMSE: - 27779.2 meV/A | validation forces MAE: 20201.9 meV/A - -These metrics are also outputted into ``train.csv`` in a formatted way, which can be -used for plotting graph like loss curve. - -It is easy to restart the training from the last step, by running - -.. code-block:: bash - - mtt train options-scratch.yaml --restart model.ckpt - -Evaluate the trained model --------------------------- - -In order to evaluate the model on the test set, we can use the mtt eval sub-command. -First, create the input file ``eval-scratch.yaml`` with the following options: - -.. literalinclude:: ./eval-scratch.yaml - :language: yaml - :linenos: - -and run (be sure to replace the path to the ``model.pt``) - -.. code-block:: bash - - mtt eval model.pt eval-scratch.yaml - -After this, a file named ``output.xyz`` will be created, with the atom positions and the -predicted forces recorded in it. Also, you should see these statistical on your screen - -.. code-block:: bash - - [2025-10-07 17:11:47][INFO] - energy RMSE (per atom): 436.50 meV | energy MAE (per - atom): 341.32 meV | forces RMSE: 27823.1 meV/A | forces MAE: 20392.7 meV/A - [2025-10-07 17:11:47][INFO] - Evaluation time: 1.10 s [1.2185 ± 1.2768 ms per atom] - -Further analysis can be performed now that the model is trained. We provide a `Python -script`_ that can be used to generate a parity plot of the target vs predicted energies, -but otherwise leave this open-ended. - -.. _`Python script`: https://raw.githubusercontent.com/metatensor/Workshop-spring-2025/refs/heads/main/training-custom-models/part-1-gap/parity_plot.py - -To run the script, download it from the repository, modify the paths as necessary -(indicated with a #TODO), and run. This will generate a plot saved at parity_plot.png. - -Use the model -------------- - -With the trained model, you can run molecular dynamics. Please refer to these two -tutorials for `ASE`_ and `LAMMPS`_ to see how to do that. - -.. _`ASE`: https://docs.metatensor.org/metatomic/latest/examples/2-running-ase-md.html - -.. _`LAMMPS`: https://atomistic-cookbook.org/examples/pet-mad-nc/pet-mad-nc.html#running-lammps-on-gpus-with-kokkos -""" diff --git a/docs/src/examples/train_from_scratch/train_from_scratch.py.md5 b/docs/src/examples/train_from_scratch/train_from_scratch.py.md5 deleted file mode 100644 index d77b561efc..0000000000 --- a/docs/src/examples/train_from_scratch/train_from_scratch.py.md5 +++ /dev/null @@ -1 +0,0 @@ -6dabc07a5102263b98df5d6e7b15c10a \ No newline at end of file diff --git a/docs/src/examples/train_from_scratch/train_from_scratch.rst b/docs/src/examples/train_from_scratch/train_from_scratch.rst deleted file mode 100644 index c57f518ceb..0000000000 --- a/docs/src/examples/train_from_scratch/train_from_scratch.rst +++ /dev/null @@ -1,195 +0,0 @@ - -.. DO NOT EDIT. -.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. -.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: -.. "examples/train_from_scratch/train_from_scratch.py" -.. LINE NUMBERS ARE GIVEN BELOW. - -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - :ref:`Go to the end ` - to download the full example code. - -.. rst-class:: sphx-glr-example-title - -.. _sphx_glr_examples_train_from_scratch_train_from_scratch.py: - - -.. _train-from-scratch: - -Training a model from scratch -############################# -This tutorial explains how to train a model with ``metatrain`` from scratch and evaluate -it. `This dataset <./ethanol_reduced_100.xyz>`_ is used here as an example of the -preferred dataset format. If you have your own dataset, you can simply replace the -dataset file name with yours. - -Train the model ---------------- - -Configure the ``options.yaml`` and run the training -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -Below is an example ``options.yaml`` for training a PET model. In order to train other -models, simply replace the architecture name with other models' architecture name. For -the supported models, please check `Available Architectures`_ . - -.. _`Available Architectures`: https://metatensor.github.io/metatrain/latest/architectures/index.html - -.. literalinclude:: ./options.yaml - :language: yaml - :linenos: - -Start the training by running - -.. code-block:: bash - - mtt train options.yaml - -It will start training. ``metatrain`` will automatically read the atomic forces from the -training set, if they are stored in it and named as "forces". The model can also be -trained to learn other properties through transfer learning. For this, please refer to -this `transfer learning tutorial`_. - -.. _`transfer learning tutorial`: https://metatensor.github.io/metatrain/latest/advanced-concepts/transfer-learning.html - -Once the training is started, a folder named ``outputs`` will be created automatically -under the folder where you run the command. Under this ``outputs`` folder, there is a -folder with the timestamp. Below is a normal structure of that folder of a successful -training run. - -.. code-block:: bash - - outputs/2025-10-07/17-08-25/ - ├── indices # the results of dataset-spliting - │ ├── test.txt - │ ├── training.txt - │ └── validation.txt - ├── model_0.ckpt # the intermediate model saved at the 0th training step - ├── model.ckpt # the final model - ├── model.pt # the final model , usable directly by ASE and LAMMPS - ├── options_restart.yaml # an expanded options file - ├── train.csv # structured log of training metrics (loss, MAE, RMSE,...) - └── train.log # a human-friendly output - -The ``train.log`` provides information of the training procedure. For example, by -checking the following: - -.. code-block:: bash - - [2025-10-07 17:08:25][INFO] - Setting up training set [2025-10-07 17:08:25][INFO] - - Forces found in section 'energy', we will use this gradient to train the model - [2025-10-07 17:08:25][WARNING] - No stress found in section 'energy'. - -You can know that the forces are identified by ``metatrain`` and are used during the -training, and it fails to find stress. The following provides some statistical of the -training, validation, and the test set - -.. code-block:: bash - - [2025-10-07 17:08:25][INFO] - Training dataset: - Dataset containing 80 structures Mean and standard deviation of targets: - - energy: - - mean -9.708e+04 eV - - std 3.97 eV - [2025-10-07 17:08:25][INFO] - Validation dataset: - Dataset containing 10 structures Mean and standard deviation of targets: - - energy: - - mean -9.708e+04 eV - - std 3.73 eV - [2025-10-07 17:08:25][INFO] - Test dataset: - Dataset containing 10 structures Mean and standard deviation of targets: - - energy: - - mean -9.708e+04 eV - - std 3.535 eV - -The training metrics are outputted every epoch, like - -.. code-block:: bash - - [2025-10-07 17:08:28][INFO] - Epoch: 0 | learning rate: 0.000e+00 | training - loss: 6.305e+03 | training energy RMSE (per atom): 884.08 meV | training energy MAE - (per atom): 773.44 meV | training forces RMSE: 28059.9 meV/A | training forces MAE: - 20581.1 meV/A | validation loss: 7.725e+02 | validation energy RMSE (per atom): - 877.08 meV | validation energy MAE (per atom): 772.04 meV | validation forces RMSE: - 27779.2 meV/A | validation forces MAE: 20201.9 meV/A - -These metrics are also outputted into ``train.csv`` in a formatted way, which can be -used for plotting graph like loss curve. - -It is easy to restart the training from the last step, by running - -.. code-block:: bash - - mtt train options.yaml --restart model.ckpt - -Evaluate the trained model --------------------------- -In order to evaluate the model on the test set, we can use the mtt eval sub-command. -First, create the input file ``eval.yaml`` with the following options: - -.. literalinclude:: ./eval.yaml - :language: yaml - :linenos: - -and run - -.. code-block:: bash - - mtt eval PATH_TO_YOUR_MODEL/model.pt eval.yaml # be sure to replace the path - -After this, a file named ``output.xyz`` will be created, with the atom positions and the -predicted forces recorded in it. Also, you should see these statistical on your screen - -.. code-block:: bash - - [2025-10-07 17:11:47][INFO] - energy RMSE (per atom): 436.50 meV | energy MAE (per - atom): 341.32 meV | forces RMSE: 27823.1 meV/A | forces MAE: 20392.7 meV/A - [2025-10-07 17:11:47][INFO] - Evaluation time: 1.10 s [1.2185 ± 1.2768 ms per atom] - -Further analysis can be performed now that the model is trained. We provide a `Python -script`_ that can be used to generate a parity plot of the target vs predicted energies, -but otherwise leave this open-ended. - -.. _`Python script`: https://raw.githubusercontent.com/metatensor/Workshop-spring-2025/refs/heads/main/training-custom-models/part-1-gap/parity_plot.py - -To run the script, download it from the repository, modify the paths as necessary -(indicated with a #TODO), and run. This will generate a plot saved at parity_plot.png. - - -Use the model -------------------------- -With the trained model, you can run molecular dynamics. Please refer to these two -tutorials for `ASE`_ and `LAMMPS`_ to see how to do that. - -.. _`ASE`: https://docs.metatensor.org/metatomic/latest/examples/2-running-ase-md.html - -.. _`LAMMPS`: https://atomistic-cookbook.org/examples/pet-mad-nc/pet-mad-nc.html#running-lammps-on-gpus-with-kokkos - - -.. _sphx_glr_download_examples_train_from_scratch_train_from_scratch.py: - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-example - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: train_from_scratch.ipynb ` - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: train_from_scratch.py ` - - .. container:: sphx-glr-download sphx-glr-download-zip - - :download:`Download zipped: train_from_scratch.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git 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- -.. only:: html - - .. image:: /examples/validation/images/thumb/sphx_glr_parity_plot_thumb.png - :alt: - - :ref:`sphx_glr_examples_validation_parity_plot.py` - -.. raw:: html - -
Model validation with parity plots for energies and forces
-
- - -.. thumbnail-parent-div-close - -.. raw:: html - - - - -.. toctree:: - :hidden: - - /examples/validation/parity_plot - - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-gallery - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download all examples in Python source code: validation_python.zip ` - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download all examples in Jupyter notebooks: validation_jupyter.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/validation/output.xyz b/docs/src/examples/validation/output.xyz deleted file mode 100644 index a07f3fddcc..0000000000 --- a/docs/src/examples/validation/output.xyz +++ /dev/null @@ -1,1100 +0,0 @@ -9 -Properties=species:S:1:pos:R:3:forces:R:3 energy=-97079.8125 pbc="F F F" -H 0.16712676 -1.19985032 0.84958249 -7.49372673 12.43364716 -17.29284668 -H -0.93496192 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+0,0 @@ -{ - "ase.io.read": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase.io", - "module_short": "ase.io", - "name": "read" - } - ], - "axs": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray" - } - ], - "chemiscope.ase_vectors_to_arrows": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "chemiscope", - "module_short": "chemiscope", - "name": "ase_vectors_to_arrows" - } - ], - "chemiscope.quick_settings": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "chemiscope", - "module_short": "chemiscope", - "name": "quick_settings" - } - ], - "chemiscope.show": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "chemiscope", - "module_short": "chemiscope", - "name": "show" - } - ], - "cs": [ - { - "is_class": false, - "is_explicit": false, - "module": "chemiscope.jupyter", - "module_short": "chemiscope.jupyter", - "name": "ChemiscopeWidget" - }, - { - "is_class": false, - "is_explicit": false, - "module": "chemiscope", - "module_short": "chemiscope", - "name": "ChemiscopeWidget" - } - ], - "e_predictions": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray" - } - ], - "e_targets": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ndarray" - } - ], - "f_predictions": [ - { - "is_class": false, - "is_explicit": 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"module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.get_forces" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.get_forces" - } - ], - "frame.get_total_energy": [ - { - "is_class": false, - "is_explicit": false, - "module": "ase.atoms", - "module_short": "ase", - "name": "Atoms.get_total_energy" - }, - { - "is_class": false, - "is_explicit": false, - "module": "ase", - "module_short": "ase", - "name": "Atoms.get_total_energy" - } - ], - "max_e": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "float64" - } - ], - "max_f": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "float64" - } - ], - "min_e": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "float64" - } - ], - "min_f": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "float64" - } - ], - "np.array": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "builtin_function_or_method" - }, - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "array" - } - ], - "np.max": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "_ArrayFunctionDispatcher" - }, - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "max" - } - ], - "np.mean": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "_ArrayFunctionDispatcher" - }, - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "mean" - } - ], - "np.min": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "_ArrayFunctionDispatcher" - }, - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "min" - } - ], - "np.sqrt": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "ufunc" - }, - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": "sqrt" - } - ], - "plt.show": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot", - "module_short": "matplotlib.pyplot", - "name": "show" - } - ], - "plt.subplots": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot", - "module_short": "matplotlib.pyplot", - "name": "subplots" - } - ], - "plt.tight_layout": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot", - "module_short": "matplotlib.pyplot", - "name": "tight_layout" - } - ], - "predictions": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "list" - } - ], - "targets": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "list" - } - ] -} diff --git a/docs/src/examples/validation/parity_plot.ipynb b/docs/src/examples/validation/parity_plot.ipynb deleted file mode 100644 index 7b3b971834..0000000000 --- a/docs/src/examples/validation/parity_plot.ipynb +++ /dev/null @@ -1,154 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# Model validation with parity plots for energies and forces\n\nThis tutorial shows how to visualise your model output using parity plots. In the\n`train-from-scratch` we learned how to evaluate a trained model on a test set and\nsave the results to an output file. Here we will show how to create parity plots from\nthese results.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Import necessary libraries\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import ase.io\nimport chemiscope\nimport matplotlib.pyplot as plt\nimport numpy as np" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load the target and predicted data\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "targets = ase.io.read(\n \"../train_from_scratch/ethanol_reduced_100.xyz\", \":\"\n) # reference data (ground truth)\npredictions = ase.io.read(\"output.xyz\", \":\") # predicted data from the model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Extract the energies from the loaded frames\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "e_targets = np.array(\n [frame.get_total_energy() / len(frame) for frame in targets]\n) # target energies\ne_predictions = np.array(\n [frame.get_total_energy() / len(frame) for frame in predictions]\n) # predicted energies\nf_targets = np.array(\n [frame.get_forces().flatten() for frame in targets]\n).flatten() # target forces\nf_predictions = np.array(\n [frame.get_forces().flatten() for frame in predictions]\n).flatten() # predicted forces" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create parity plots to compare predicted vs. target energies and forces\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(1, 2, figsize=(12, 5))\n\n# Parity plot for energies\naxs[0].scatter(e_targets, e_predictions)\naxs[0].axline((np.min(e_targets), np.min(e_targets)), slope=1, ls=\"--\", color=\"red\")\naxs[0].set_xlabel(\"Target energy / kcal\")\naxs[0].set_ylabel(\"Predicted energy / kcal\")\nmin_e = np.min(np.array([e_targets, e_predictions])) - 2\nmax_e = np.max(np.array([e_targets, e_predictions])) + 2\naxs[0].set_xlim([min_e, max_e])\naxs[0].set_ylim([min_e, max_e])\naxs[0].set_title(\"Energy Parity Plot\")\n\n# Parity plot for forces\naxs[1].scatter(f_targets, f_predictions, alpha=0.5)\naxs[1].axline((np.min(f_targets), np.min(f_targets)), slope=1, ls=\"--\", color=\"red\")\naxs[1].set_xlabel(\"Target force / kcal/\u00c5\")\naxs[1].set_ylabel(\"Predicted force / kcal/\u00c5\")\nmin_f = np.min(np.array([f_targets, f_predictions])) - 2\nmax_f = np.max(np.array([f_targets, f_predictions])) + 2\naxs[1].set_xlim([min_f, max_f])\naxs[1].set_ylim([min_f, max_f])\naxs[1].set_title(\"Force Parity Plot\")\n\nplt.tight_layout()\nplt.show()\n\nprint(\n \"RMSE energy (per atom):\",\n np.sqrt(np.mean((e_targets - e_predictions) ** 2)),\n \"kcal\",\n)\nprint(\"RMSE forces:\", np.sqrt(np.mean((f_targets - f_predictions) ** 2)), \"kcal/\u00c5 \")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The results are a bit poor here because the model was not trained well enough and\nwas created only for demonstration purposes. In the case of a well-trained model, the\npoints should be closer to the diagonal line.\n\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Check outliers with ``Chemiscope``\nWith the approach above, you can inspect the whole dataset, but it might be difficult\nto identify outliers. `Chemiscope ` is a\nvisualisation tool, allowing you to explore the dataset interactively. The following\nexample shows how to use it to check the structure of probable outliers and the atomic\nforces.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "for frame in targets + predictions:\n frame.arrays[\"forces\"] = frame.get_forces()\n# a workaround, because the chemiscope interface for getting forces is broken with ASE\n# 3.23" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot the energy parity plot with Chemiscope. This can be rendered as a widget in a\nJupyter notebook.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "cs = chemiscope.show(\n targets, # reading structures from the dataset\n properties={\n \"Target energy\": {\"values\": e_targets, \"target\": \"structure\", \"units\": \"kcal\"},\n \"Predicted energy\": {\n \"values\": e_predictions,\n \"target\": \"structure\",\n \"units\": \"kcal\",\n },\n }, # plotting the energy parity plot\n mode=\"default\",\n shapes={\n \"target_forces\": chemiscope.ase_vectors_to_arrows(\n targets, key=\"forces\", scale=0.05, radius=0.15\n ),\n \"predicted_forces\": chemiscope.ase_vectors_to_arrows(\n predictions, key=\"forces\", scale=0.05, radius=0.15\n ),\n }, # plotting the atomic forces\n settings=chemiscope.quick_settings(\n trajectory=True,\n map_settings={\"joinPoints\": False},\n structure_settings={\n \"unitCell\": True,\n \"environments\": {\"activated\": False},\n \"shape\": \"predicted_forces\", # show predicted forces by defalut\n },\n ),\n)\ncs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can check the structures by clicking the red dots on the parity plot, or\ndragging the bar in the bottom right corner. Currently, plotting the diagonal line is\nnot supported in chemiscope, so please check the parity plot above to see the\noutliers.\n\nThe atomic forces are shown in arrows. The predicted forces are shown here. The target\nforces can be toggled by clicking the \"target_forces\" option in the menu in the upper\nright corner of the right panel.\n\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/src/examples/validation/parity_plot.py b/docs/src/examples/validation/parity_plot.py deleted file mode 100644 index d5d23cf12e..0000000000 --- a/docs/src/examples/validation/parity_plot.py +++ /dev/null @@ -1,147 +0,0 @@ -""" -Model validation with parity plots -================================== - -This tutorial shows how to visualise your model output using parity plots. In the -:ref:`sphx_glr_generated_examples_0-beginner_03-train_from_scratch.py` we learned how to -evaluate a trained model on a test set and save the results to an output file. Here we -will show how to create parity plots from these results. - -""" - -# %% -# Import necessary libraries -import ase.io -import chemiscope -import matplotlib.pyplot as plt -import numpy as np - - -# %% -# Load the reference data (ground truth) -targets = ase.io.read("ethanol_reduced_100.xyz", ":") - -# %% -# predicted data from a model -predictions = ase.io.read("ethanol_reduced_100_predicted.xyz", ":") - -# %% -# Extract the energies from the loaded frames -e_targets = np.array( - [frame.get_total_energy() / len(frame) for frame in targets] -) # target energies -e_predictions = np.array( - [frame.get_total_energy() / len(frame) for frame in predictions] -) # predicted energies -f_targets = np.array( - [frame.get_forces().flatten() for frame in targets] -).flatten() # target forces -f_predictions = np.array( - [frame.get_forces().flatten() for frame in predictions] -).flatten() # predicted forces - - -# %% -# Create parity plots to compare predicted vs. target energies and forces -# ----------------------------------------------------------------------- - -fig, axs = plt.subplots(1, 2, figsize=(12, 5)) - -# Parity plot for energies -axs[0].scatter(e_targets, e_predictions) -axs[0].axline((np.min(e_targets), np.min(e_targets)), slope=1, ls="--", color="red") -axs[0].set_xlabel("Target energy / kcal") -axs[0].set_ylabel("Predicted energy / kcal") -min_e = np.min(np.array([e_targets, e_predictions])) - 2 -max_e = np.max(np.array([e_targets, e_predictions])) + 2 -axs[0].set_xlim([min_e, max_e]) -axs[0].set_ylim([min_e, max_e]) -axs[0].set_title("Energy Parity Plot") - -# Parity plot for forces -axs[1].scatter(f_targets, f_predictions, alpha=0.5) -axs[1].axline((np.min(f_targets), np.min(f_targets)), slope=1, ls="--", color="red") -axs[1].set_xlabel("Target force / kcal/Ã…") -axs[1].set_ylabel("Predicted force / kcal/Ã…") -min_f = np.min(np.array([f_targets, f_predictions])) - 2 -max_f = np.max(np.array([f_targets, f_predictions])) + 2 -axs[1].set_xlim([min_f, max_f]) -axs[1].set_ylim([min_f, max_f]) -axs[1].set_title("Force Parity Plot") - -fig.tight_layout() -plt.show() - -# %% -# We can also compute and print RMSE for energies and forces - -print( - "RMSE energy (per atom):", - np.sqrt(np.mean((e_targets - e_predictions) ** 2)), - "kcal", -) -print("RMSE forces:", np.sqrt(np.mean((f_targets - f_predictions) ** 2)), "kcal/Ã… ") - -# %% -# -# The results are a bit poor here because the model was not trained well enough and -# was created only for demonstration purposes. In the case of a well-trained model, the -# points should be closer to the diagonal line. -# -# Check outliers with ``Chemiscope`` -# ---------------------------------- -# With the approach above, you can inspect the whole dataset, but it might be difficult -# to identify outliers. `Chemiscope ` is a -# visualisation tool, allowing you to explore the dataset interactively. The following -# example shows how to use it to check the structure of probable outliers and the atomic -# forces. - -for frame in targets + predictions: - frame.arrays["forces"] = frame.get_forces() - -# a workaround, because the chemiscope interface for getting forces is broken with ASE -# 3.23 - -# %% -# Plot the energy parity plot with Chemiscope. This can be rendered as a widget in a -# Jupyter notebook. - -chemiscope.show( - targets, # reading structures from the dataset - properties={ - "Target energy": {"values": e_targets, "target": "structure", "units": "kcal"}, - "Predicted energy": { - "values": e_predictions, - "target": "structure", - "units": "kcal", - }, - }, # plotting the energy parity plot - mode="default", - shapes={ - "target_forces": chemiscope.ase_vectors_to_arrows( - targets, key="forces", scale=0.05, radius=0.15 - ), - "predicted_forces": chemiscope.ase_vectors_to_arrows( - predictions, key="forces", scale=0.05, radius=0.15 - ), - }, # plotting the atomic forces - settings=chemiscope.quick_settings( - trajectory=True, - map_settings={"joinPoints": False}, - structure_settings={ - "unitCell": True, - "environments": {"activated": False}, - "shape": "predicted_forces", # show predicted forces by defalut - }, - ), -) - -# %% -# You can check the structures by clicking the red dots on the parity plot, or -# dragging the bar in the bottom right corner. Currently, plotting the diagonal line is -# not supported in chemiscope, so please check the parity plot above to see the -# outliers. -# -# The atomic forces are shown in arrows. The predicted forces are shown here. The target -# forces can be toggled by clicking the "target_forces" option in the menu in the upper -# right corner of the right panel. diff --git a/docs/src/examples/validation/parity_plot.py.md5 b/docs/src/examples/validation/parity_plot.py.md5 deleted file mode 100644 index c1a3ea37a7..0000000000 --- a/docs/src/examples/validation/parity_plot.py.md5 +++ /dev/null @@ -1 +0,0 @@ -4381fe7e0c0bc39cf97ff93007ea6114 \ No newline at end of file diff --git a/docs/src/examples/validation/parity_plot.rst b/docs/src/examples/validation/parity_plot.rst deleted file mode 100644 index de747cd147..0000000000 --- a/docs/src/examples/validation/parity_plot.rst +++ /dev/null @@ -1,281 +0,0 @@ - -.. DO NOT EDIT. -.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. -.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: -.. "examples/validation/parity_plot.py" -.. LINE NUMBERS ARE GIVEN BELOW. - -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - :ref:`Go to the end ` - to download the full example code. - -.. rst-class:: sphx-glr-example-title - -.. _sphx_glr_examples_validation_parity_plot.py: - - -Model validation with parity plots for energies and forces -========================================================== - -This tutorial shows how to visualise your model output using parity plots. In the -:ref:`train-from-scratch` we learned how to evaluate a trained model on a test set and -save the results to an output file. Here we will show how to create parity plots from -these results. - -.. GENERATED FROM PYTHON SOURCE LINES 13-14 - -Import necessary libraries - -.. GENERATED FROM PYTHON SOURCE LINES 14-20 - -.. code-block:: Python - - import ase.io - import chemiscope - import matplotlib.pyplot as plt - import numpy as np - - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 21-22 - -Load the target and predicted data - -.. GENERATED FROM PYTHON SOURCE LINES 22-28 - -.. code-block:: Python - - targets = ase.io.read( - "../train_from_scratch/ethanol_reduced_100.xyz", ":" - ) # reference data (ground truth) - predictions = ase.io.read("output.xyz", ":") # predicted data from the model - - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 29-30 - -Extract the energies from the loaded frames - -.. GENERATED FROM PYTHON SOURCE LINES 30-44 - -.. code-block:: Python - - e_targets = np.array( - [frame.get_total_energy() / len(frame) for frame in targets] - ) # target energies - e_predictions = np.array( - [frame.get_total_energy() / len(frame) for frame in predictions] - ) # predicted energies - f_targets = np.array( - [frame.get_forces().flatten() for frame in targets] - ).flatten() # target forces - f_predictions = np.array( - [frame.get_forces().flatten() for frame in predictions] - ).flatten() # predicted forces - - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 45-47 - -Create parity plots to compare predicted vs. target energies and forces ------------------------------------------------------------------------ - -.. GENERATED FROM PYTHON SOURCE LINES 47-81 - -.. code-block:: Python - - - fig, axs = plt.subplots(1, 2, figsize=(12, 5)) - - # Parity plot for energies - axs[0].scatter(e_targets, e_predictions) - axs[0].axline((np.min(e_targets), np.min(e_targets)), slope=1, ls="--", color="red") - axs[0].set_xlabel("Target energy / kcal") - axs[0].set_ylabel("Predicted energy / kcal") - min_e = np.min(np.array([e_targets, e_predictions])) - 2 - max_e = np.max(np.array([e_targets, e_predictions])) + 2 - axs[0].set_xlim([min_e, max_e]) - axs[0].set_ylim([min_e, max_e]) - axs[0].set_title("Energy Parity Plot") - - # Parity plot for forces - axs[1].scatter(f_targets, f_predictions, alpha=0.5) - axs[1].axline((np.min(f_targets), np.min(f_targets)), slope=1, ls="--", color="red") - axs[1].set_xlabel("Target force / kcal/Ã…") - axs[1].set_ylabel("Predicted force / kcal/Ã…") - min_f = np.min(np.array([f_targets, f_predictions])) - 2 - max_f = np.max(np.array([f_targets, f_predictions])) + 2 - axs[1].set_xlim([min_f, max_f]) - axs[1].set_ylim([min_f, max_f]) - axs[1].set_title("Force Parity Plot") - - plt.tight_layout() - plt.show() - - print( - "RMSE energy (per atom):", - np.sqrt(np.mean((e_targets - e_predictions) ** 2)), - "kcal", - ) - print("RMSE forces:", np.sqrt(np.mean((f_targets - f_predictions) ** 2)), "kcal/Ã… ") - - - - -.. rst-class:: sphx-glr-script-out - - .. code-block:: none - - RMSE energy (per atom): 0.3662913918764794 kcal - RMSE forces: 15.042111936421486 kcal/Ã… - - - - -.. GENERATED FROM PYTHON SOURCE LINES 82-85 - -The results are a bit poor here because the model was not trained well enough and -was created only for demonstration purposes. In the case of a well-trained model, the -points should be closer to the diagonal line. - -.. GENERATED FROM PYTHON SOURCE LINES 87-94 - -Check outliers with ``Chemiscope`` ----------------------------------- -With the approach above, you can inspect the whole dataset, but it might be difficult -to identify outliers. `Chemiscope ` is a -visualisation tool, allowing you to explore the dataset interactively. The following -example shows how to use it to check the structure of probable outliers and the atomic -forces. - -.. GENERATED FROM PYTHON SOURCE LINES 94-100 - -.. code-block:: Python - - - for frame in targets + predictions: - frame.arrays["forces"] = frame.get_forces() - # a workaround, because the chemiscope interface for getting forces is broken with ASE - # 3.23 - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 101-103 - -Plot the energy parity plot with Chemiscope. This can be rendered as a widget in a -Jupyter notebook. - -.. GENERATED FROM PYTHON SOURCE LINES 103-134 - -.. code-block:: Python - - cs = chemiscope.show( - targets, # reading structures from the dataset - properties={ - "Target energy": {"values": e_targets, "target": "structure", "units": "kcal"}, - "Predicted energy": { - "values": e_predictions, - "target": "structure", - "units": "kcal", - }, - }, # plotting the energy parity plot - mode="default", - shapes={ - "target_forces": chemiscope.ase_vectors_to_arrows( - targets, key="forces", scale=0.05, radius=0.15 - ), - "predicted_forces": chemiscope.ase_vectors_to_arrows( - predictions, key="forces", scale=0.05, radius=0.15 - ), - }, # plotting the atomic forces - settings=chemiscope.quick_settings( - trajectory=True, - map_settings={"joinPoints": False}, - structure_settings={ - "unitCell": True, - "environments": {"activated": False}, - "shape": "predicted_forces", # show predicted forces by defalut - }, - ), - ) - cs - - - -.. chemiscope:: _datasets/fig_parity_plot_001.json.gz - :mode: default - :warning_timeout: 2000 - - - -.. raw:: html - -
- -
-
-
- -.. GENERATED FROM PYTHON SOURCE LINES 135-143 - -You can check the structures by clicking the red dots on the parity plot, or -dragging the bar in the bottom right corner. Currently, plotting the diagonal line is -not supported in chemiscope, so please check the parity plot above to see the -outliers. - -The atomic forces are shown in arrows. The predicted forces are shown here. The target -forces can be toggled by clicking the "target_forces" option in the menu in the upper -right corner of the right panel. - - -.. _sphx_glr_download_examples_validation_parity_plot.py: - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-example - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: parity_plot.ipynb ` - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: parity_plot.py ` - - .. container:: sphx-glr-download sphx-glr-download-zip - - :download:`Download zipped: parity_plot.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/validation/parity_plot.zip b/docs/src/examples/validation/parity_plot.zip deleted file mode 100644 index f3d9dbd83d2f478cceb628d85b1850ddb7b94f50..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 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"metatomic.torch.ase_calculator", - "name": "MetatomicCalculator" - }, - { - "is_class": false, - "is_explicit": false, - "module": "metatomic.torch", - "module_short": "metatomic.torch", - "name": "MetatomicCalculator" - }, - { - "is_class": false, - "is_explicit": false, - "module": "metatomic", - "module_short": "metatomic", - "name": "MetatomicCalculator" - } - ], - "calc_zbl": [ - { - "is_class": false, - "is_explicit": false, - "module": "metatomic.torch.ase_calculator", - "module_short": "metatomic.torch.ase_calculator", - "name": "MetatomicCalculator" - }, - { - "is_class": false, - "is_explicit": false, - "module": "metatomic.torch", - "module_short": "metatomic.torch", - "name": "MetatomicCalculator" - }, - { - "is_class": false, - "is_explicit": false, - "module": "metatomic", - "module_short": "metatomic", - "name": "MetatomicCalculator" - } - ], - "distance": [ - { - "is_class": false, - "is_explicit": false, - "module": "numpy", - "module_short": "numpy", - "name": 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"module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot", - "module_short": "matplotlib.pyplot", - "name": "plot" - } - ], - "plt.show": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot", - "module_short": "matplotlib.pyplot", - "name": "show" - } - ], - "plt.tight_layout": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot", - "module_short": "matplotlib.pyplot", - "name": "tight_layout" - } - ], - "plt.title": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot", - "module_short": "matplotlib.pyplot", - "name": "title" - } - ], - "plt.xlabel": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot", - "module_short": "matplotlib.pyplot", - "name": "xlabel" - } - ], - "plt.ylabel": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "matplotlib.pyplot", - "module_short": "matplotlib.pyplot", - "name": "ylabel" - } - ], - "structures": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "list" - } - ], - "structures.append": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "builtin_function_or_method" - } - ], - "structures_for_pair": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "list" - } - ], - "torch.jit.optimized_execution": [ - { - "is_class": false, - "is_explicit": false, - "module": "builtins", - "module_short": "builtins", - "name": "function" - }, - { - "is_class": false, - "is_explicit": false, - "module": "torch.jit", - "module_short": "torch.jit", - "name": "optimized_execution" - } - ] -} diff --git a/docs/src/examples/zbl/dimers.ipynb b/docs/src/examples/zbl/dimers.ipynb deleted file mode 100644 index b241500ab9..0000000000 --- a/docs/src/examples/zbl/dimers.ipynb +++ /dev/null @@ -1,140 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# Training a model with ZBL corrections\n\nThis tutorial demonstrates how to train a model with ZBL corrections.\n\nThe training set for this example consists of a\nsubset of the ethanol moleculs from the [rMD17 dataset](https://iopscience.iop.org/article/10.1088/2632-2153/abba6f/meta).\n\nThe models are trained using the following training options, respectively:\n\n.. literalinclude:: options_no_zbl.yaml\n :language: yaml\n\n.. literalinclude:: options_zbl.yaml\n :language: yaml\n\nAs you can see, they are identical, except for the ``zbl`` key in the\n``model`` section.\nYou can train the same models yourself with\n\n.. literalinclude:: train.sh\n :language: bash\n\nA detailed step-by-step introduction on how to train a model is provided in\nthe `label_basic_usage` tutorial.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, we start by importing the necessary libraries, including the integration of ASE\ncalculators for metatensor atomistic models.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import ase\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\nfrom metatomic.torch.ase_calculator import MetatomicCalculator" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setting up the dimers\n\nWe set up a series of dimers with different atom pairs and distances. We will\ncalculate the energies of these dimers using the models trained with and without ZBL\ncorrections.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "distances = np.linspace(0.5, 6.0, 200)\npairs = {}\nfor pair in [(\"H\", \"H\"), (\"H\", \"C\"), (\"C\", \"C\"), (\"C\", \"O\"), (\"O\", \"O\"), (\"H\", \"O\")]:\n structures = []\n for distance in distances:\n atoms = ase.Atoms(\n symbols=[pair[0], pair[1]],\n positions=[[0, 0, 0], [0, 0, distance]],\n )\n structures.append(atoms)\n pairs[pair] = structures" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now load the two exported models, one with and one without ZBL corrections\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "calc_no_zbl = MetatomicCalculator(\"model_no_zbl.pt\", extensions_directory=\"extensions/\")\ncalc_zbl = MetatomicCalculator(\"model_zbl.pt\", extensions_directory=\"extensions/\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Calculate and plot energies without ZBL\n\nWe calculate the energies of the dimer curves for each pair of atoms and\nplot the results, using the non-ZBL-corrected model.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "for pair, structures_for_pair in pairs.items():\n energies = []\n for atoms in structures_for_pair:\n atoms.set_calculator(calc_no_zbl)\n with torch.jit.optimized_execution(False):\n energies.append(atoms.get_potential_energy())\n energies = np.array(energies) - energies[-1]\n plt.plot(distances, energies, label=f\"{pair[0]}-{pair[1]}\")\nplt.title(\"Dimer curves - no ZBL\")\nplt.xlabel(\"Distance (\u00c5)\")\nplt.ylabel(\"Energy (eV)\")\nplt.legend()\nplt.tight_layout()\nplt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Calculate and plot energies from the ZBL-corrected model\n\nWe repeat the same procedure as above, but this time with the ZBL-corrected model.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "for pair, structures_for_pair in pairs.items():\n energies = []\n for atoms in structures_for_pair:\n atoms.set_calculator(calc_zbl)\n with torch.jit.optimized_execution(False):\n energies.append(atoms.get_potential_energy())\n energies = np.array(energies) - energies[-1]\n plt.plot(distances, energies, label=f\"{pair[0]}-{pair[1]}\")\nplt.title(\"Dimer curves - with ZBL\")\nplt.xlabel(\"Distance (\u00c5)\")\nplt.ylabel(\"Energy (eV)\")\nplt.legend()\nplt.tight_layout()\nplt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It can be seen that all the dimer curves include a strong repulsion\nat short distances, which is due to the ZBL contribution. Even the H-H dimer,\nwhose ZBL correction is very weak due to the small covalent radii of hydrogen,\nwould show a strong repulsion closer to the origin (here, we only plotted\nstarting from a distance of 0.5 \u00c5). Let's zoom in on the H-H dimer to see\nthis effect more clearly.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "new_distances = np.linspace(0.1, 2.0, 200)\n\nstructures = []\nfor distance in new_distances:\n atoms = ase.Atoms(\n symbols=[\"H\", \"H\"],\n positions=[[0, 0, 0], [0, 0, distance]],\n )\n structures.append(atoms)\n\nfor atoms in structures:\n atoms.set_calculator(calc_zbl)\nwith torch.jit.optimized_execution(False):\n energies = [atoms.get_potential_energy() for atoms in structures]\nenergies = np.array(energies) - energies[-1]\nplt.plot(new_distances, energies, label=\"H-H\")\nplt.title(\"Dimer curve - H-H with ZBL\")\nplt.xlabel(\"Distance (\u00c5)\")\nplt.ylabel(\"Energy (eV)\")\nplt.legend()\nplt.tight_layout()\nplt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/src/examples/zbl/dimers.py b/docs/src/examples/zbl/dimers.py deleted file mode 100644 index 3fa6b5acdc..0000000000 --- a/docs/src/examples/zbl/dimers.py +++ /dev/null @@ -1,147 +0,0 @@ -""" -Training a model with ZBL corrections -===================================== - -This tutorial demonstrates how to train a model with ZBL corrections. - -The training set for this example consists of a -subset of the ethanol moleculs from the `rMD17 dataset -`_. - -The models are trained using the following training options, respectively: - -.. literalinclude:: options-no-zbl.yaml - :language: yaml - -.. literalinclude:: options_zbl.yaml - :language: yaml - -As you can see, they are identical, except for the ``zbl`` key in the ``model`` section. -A detailed step-by-step introduction on how to train a model is provided in the -:ref:`label_basic_usage` tutorial. -""" - -# %% -# -# First, we start by importing the necessary libraries, including the integration of ASE -# calculators for metatensor atomistic models. - -import subprocess - -import ase -import matplotlib.pyplot as plt -import numpy as np -import torch -from metatomic.torch.ase_calculator import MetatomicCalculator - - -# %% - -subprocess.run(["mtt", "train", "options-no-zbl.yaml", "-o", "model_no_zbl.pt"]) -subprocess.run(["mtt", "train", "options_zbl.yaml", "-o", "model_zbl.pt"]) - -# %% -# -# Setting up the dimers -# --------------------- -# -# We set up a series of dimers with different atom pairs and distances. We will -# calculate the energies of these dimers using the models trained with and without ZBL -# corrections. - -distances = np.linspace(0.5, 6.0, 200) -pairs = {} -for pair in [("H", "H"), ("H", "C"), ("C", "C"), ("C", "O"), ("O", "O"), ("H", "O")]: - structures = [] - for distance in distances: - atoms = ase.Atoms( - symbols=[pair[0], pair[1]], - positions=[[0, 0, 0], [0, 0, distance]], - ) - structures.append(atoms) - pairs[pair] = structures - -# %% -# -# We now load the two exported models, one with and one without ZBL corrections - -calc_no_zbl = MetatomicCalculator("model_no_zbl.pt", extensions_directory="extensions/") -calc_zbl = MetatomicCalculator("model_zbl.pt", extensions_directory="extensions/") - - -# %% -# -# Calculate and plot energies without ZBL -# --------------------------------------- -# -# We calculate the energies of the dimer curves for each pair of atoms and -# plot the results, using the non-ZBL-corrected model. - -for pair, structures_for_pair in pairs.items(): - energies = [] - for atoms in structures_for_pair: - atoms.set_calculator(calc_no_zbl) - with torch.jit.optimized_execution(False): - energies.append(atoms.get_potential_energy()) - energies = np.array(energies) - energies[-1] - plt.plot(distances, energies, label=f"{pair[0]}-{pair[1]}") -plt.title("Dimer curves - no ZBL") -plt.xlabel("Distance (Ã…)") -plt.ylabel("Energy (eV)") -plt.legend() -plt.tight_layout() -plt.show() - -# %% -# -# Calculate and plot energies from the ZBL-corrected model -# -------------------------------------------------------- -# -# We repeat the same procedure as above, but this time with the ZBL-corrected model. - -for pair, structures_for_pair in pairs.items(): - energies = [] - for atoms in structures_for_pair: - atoms.set_calculator(calc_zbl) - with torch.jit.optimized_execution(False): - energies.append(atoms.get_potential_energy()) - energies = np.array(energies) - energies[-1] - plt.plot(distances, energies, label=f"{pair[0]}-{pair[1]}") -plt.title("Dimer curves - with ZBL") -plt.xlabel("Distance (Ã…)") -plt.ylabel("Energy (eV)") -plt.legend() -plt.tight_layout() -plt.show() - -# %% -# -# It can be seen that all the dimer curves include a strong repulsion -# at short distances, which is due to the ZBL contribution. Even the H-H dimer, -# whose ZBL correction is very weak due to the small covalent radii of hydrogen, -# would show a strong repulsion closer to the origin (here, we only plotted -# starting from a distance of 0.5 Ã…). Let's zoom in on the H-H dimer to see -# this effect more clearly. - -new_distances = np.linspace(0.1, 2.0, 200) - -structures = [] -for distance in new_distances: - atoms = ase.Atoms( - symbols=["H", "H"], - positions=[[0, 0, 0], [0, 0, distance]], - ) - structures.append(atoms) - -for atoms in structures: - atoms.set_calculator(calc_zbl) -with torch.jit.optimized_execution(False): - energies = [atoms.get_potential_energy() for atoms in structures] -energies = np.array(energies) - energies[-1] -plt.plot(new_distances, energies, label="H-H") -plt.title("Dimer curve - H-H with ZBL") -plt.xlabel("Distance (Ã…)") -plt.ylabel("Energy (eV)") -plt.legend() -plt.tight_layout() -plt.show() diff --git a/docs/src/examples/zbl/dimers.py.md5 b/docs/src/examples/zbl/dimers.py.md5 deleted file mode 100644 index 2d0c746956..0000000000 --- a/docs/src/examples/zbl/dimers.py.md5 +++ /dev/null @@ -1 +0,0 @@ -fc55a8030c89ecfae84a5d1c4dce758f \ No newline at end of file diff --git a/docs/src/examples/zbl/dimers.rst b/docs/src/examples/zbl/dimers.rst deleted file mode 100644 index c529edc435..0000000000 --- a/docs/src/examples/zbl/dimers.rst +++ /dev/null @@ -1,287 +0,0 @@ - -.. DO NOT EDIT. -.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. -.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: -.. "examples/zbl/dimers.py" -.. LINE NUMBERS ARE GIVEN BELOW. - -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - :ref:`Go to the end ` - to download the full example code. - -.. rst-class:: sphx-glr-example-title - -.. _sphx_glr_examples_zbl_dimers.py: - - -Training a model with ZBL corrections -===================================== - -This tutorial demonstrates how to train a model with ZBL corrections. - -The training set for this example consists of a -subset of the ethanol moleculs from the `rMD17 dataset -`_. - -The models are trained using the following training options, respectively: - -.. literalinclude:: options_no_zbl.yaml - :language: yaml - -.. literalinclude:: options_zbl.yaml - :language: yaml - -As you can see, they are identical, except for the ``zbl`` key in the -``model`` section. -You can train the same models yourself with - -.. literalinclude:: train.sh - :language: bash - -A detailed step-by-step introduction on how to train a model is provided in -the :ref:`label_basic_usage` tutorial. - -.. GENERATED FROM PYTHON SOURCE LINES 31-33 - -First, we start by importing the necessary libraries, including the integration of ASE -calculators for metatensor atomistic models. - -.. GENERATED FROM PYTHON SOURCE LINES 34-42 - -.. code-block:: Python - - - import ase - import matplotlib.pyplot as plt - import numpy as np - import torch - from metatomic.torch.ase_calculator import MetatomicCalculator - - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 43-49 - -Setting up the dimers ---------------------- - -We set up a series of dimers with different atom pairs and distances. We will -calculate the energies of these dimers using the models trained with and without ZBL -corrections. - -.. GENERATED FROM PYTHON SOURCE LINES 50-63 - -.. code-block:: Python - - - distances = np.linspace(0.5, 6.0, 200) - pairs = {} - for pair in [("H", "H"), ("H", "C"), ("C", "C"), ("C", "O"), ("O", "O"), ("H", "O")]: - structures = [] - for distance in distances: - atoms = ase.Atoms( - symbols=[pair[0], pair[1]], - positions=[[0, 0, 0], [0, 0, distance]], - ) - structures.append(atoms) - pairs[pair] = structures - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 64-65 - -We now load the two exported models, one with and one without ZBL corrections - -.. GENERATED FROM PYTHON SOURCE LINES 66-71 - -.. code-block:: Python - - - calc_no_zbl = MetatomicCalculator("model_no_zbl.pt", extensions_directory="extensions/") - calc_zbl = MetatomicCalculator("model_zbl.pt", extensions_directory="extensions/") - - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 72-77 - -Calculate and plot energies without ZBL ---------------------------------------- - -We calculate the energies of the dimer curves for each pair of atoms and -plot the results, using the non-ZBL-corrected model. - -.. GENERATED FROM PYTHON SOURCE LINES 78-94 - -.. code-block:: Python - - - for pair, structures_for_pair in pairs.items(): - energies = [] - for atoms in structures_for_pair: - atoms.set_calculator(calc_no_zbl) - with torch.jit.optimized_execution(False): - energies.append(atoms.get_potential_energy()) - energies = np.array(energies) - energies[-1] - plt.plot(distances, energies, label=f"{pair[0]}-{pair[1]}") - plt.title("Dimer curves - no ZBL") - plt.xlabel("Distance (Ã…)") - plt.ylabel("Energy (eV)") - plt.legend() - plt.tight_layout() - plt.show() - - - - - -.. rst-class:: sphx-glr-script-out - - .. code-block:: none - - /Users/philiploche/repos/lab-cosmo/metatrain/examples/zbl/dimers.py:82: FutureWarning: Please use atoms.calc = calc - atoms.set_calculator(calc_no_zbl) - - - - -.. GENERATED FROM PYTHON SOURCE LINES 95-99 - -Calculate and plot energies from the ZBL-corrected model --------------------------------------------------------- - -We repeat the same procedure as above, but this time with the ZBL-corrected model. - -.. GENERATED FROM PYTHON SOURCE LINES 100-116 - -.. code-block:: Python - - - for pair, structures_for_pair in pairs.items(): - energies = [] - for atoms in structures_for_pair: - atoms.set_calculator(calc_zbl) - with torch.jit.optimized_execution(False): - energies.append(atoms.get_potential_energy()) - energies = np.array(energies) - energies[-1] - plt.plot(distances, energies, label=f"{pair[0]}-{pair[1]}") - plt.title("Dimer curves - with ZBL") - plt.xlabel("Distance (Ã…)") - plt.ylabel("Energy (eV)") - plt.legend() - plt.tight_layout() - plt.show() - - - - - -.. rst-class:: sphx-glr-script-out - - .. code-block:: none - - /Users/philiploche/repos/lab-cosmo/metatrain/examples/zbl/dimers.py:104: FutureWarning: Please use atoms.calc = calc - atoms.set_calculator(calc_zbl) - - - - -.. GENERATED FROM PYTHON SOURCE LINES 117-123 - -It can be seen that all the dimer curves include a strong repulsion -at short distances, which is due to the ZBL contribution. Even the H-H dimer, -whose ZBL correction is very weak due to the small covalent radii of hydrogen, -would show a strong repulsion closer to the origin (here, we only plotted -starting from a distance of 0.5 Ã…). Let's zoom in on the H-H dimer to see -this effect more clearly. - -.. GENERATED FROM PYTHON SOURCE LINES 124-147 - -.. code-block:: Python - - - new_distances = np.linspace(0.1, 2.0, 200) - - structures = [] - for distance in new_distances: - atoms = ase.Atoms( - symbols=["H", "H"], - positions=[[0, 0, 0], [0, 0, distance]], - ) - structures.append(atoms) - - for atoms in structures: - atoms.set_calculator(calc_zbl) - with torch.jit.optimized_execution(False): - energies = [atoms.get_potential_energy() for atoms in structures] - energies = np.array(energies) - energies[-1] - plt.plot(new_distances, energies, label="H-H") - plt.title("Dimer curve - H-H with ZBL") - plt.xlabel("Distance (Ã…)") - plt.ylabel("Energy (eV)") - plt.legend() - plt.tight_layout() - plt.show() - - - - -.. rst-class:: sphx-glr-script-out - - .. code-block:: none - - /Users/philiploche/repos/lab-cosmo/metatrain/examples/zbl/dimers.py:136: FutureWarning: Please use atoms.calc = calc - atoms.set_calculator(calc_zbl) - - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** (0 minutes 15.025 seconds) - - -.. _sphx_glr_download_examples_zbl_dimers.py: - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-example - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: dimers.ipynb ` - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: dimers.py ` - - .. container:: sphx-glr-download sphx-glr-download-zip - - :download:`Download zipped: dimers.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/zbl/dimers.zip b/docs/src/examples/zbl/dimers.zip deleted file mode 100644 index 6fc0c82974246cb822013bf756b95c1fb876a0d3..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 11424 zcmeHNTW{OQ6;3y8fwJvGfqiKoiUEVg%78@Mag%O^xH{CL^J)Fy#GiT0izTwkHH^2M-4SH_= z{*Qwne;$1Jx1Zd&K|eo0-6WlhqKxuY7>2>Kf~P7~r;M|CHW897Q$1sU{_SIyWJMto 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- -.. only:: html - - .. image:: /examples/zbl/images/thumb/sphx_glr_dimers_thumb.png - :alt: - - :ref:`sphx_glr_examples_zbl_dimers.py` - -.. raw:: html - -
Training a model with ZBL corrections
-
- - -.. thumbnail-parent-div-close - -.. raw:: html - - - - -.. toctree:: - :hidden: - - /examples/zbl/dimers - - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-gallery - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download all examples as source code: zbl.zip ` - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download all examples in Jupyter notebooks: zbl_jupyter.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/src/examples/zbl/options_no_zbl.yaml b/docs/src/examples/zbl/options_no_zbl.yaml deleted file mode 100644 index efc0bf57ce..0000000000 --- a/docs/src/examples/zbl/options_no_zbl.yaml +++ /dev/null @@ -1,22 +0,0 @@ -seed: 42 - -architecture: - name: soap_bpnn - model: - zbl: false - training: - num_epochs: 10 - batch_size: 10 - -# training set section -training_set: - systems: - read_from: ethanol_reduced_100.xyz - length_unit: angstrom - targets: - energy: - key: energy - unit: eV # very important to run simulations - -validation_set: 0.1 -test_set: 0.0 diff --git a/docs/src/examples/zbl/options_zbl.yaml b/docs/src/examples/zbl/options_zbl.yaml deleted file mode 100644 index f7969f9d90..0000000000 --- a/docs/src/examples/zbl/options_zbl.yaml +++ /dev/null @@ -1,22 +0,0 @@ -seed: 42 - -architecture: - name: soap_bpnn - model: - zbl: true - training: - num_epochs: 10 - batch_size: 10 - -# training set section -training_set: - systems: - read_from: ethanol_reduced_100.xyz - length_unit: angstrom - targets: - energy: - key: energy - unit: eV # very important to run simulations - -validation_set: 0.1 -test_set: 0.0 diff --git a/docs/src/examples/zbl/train.sh b/docs/src/examples/zbl/train.sh deleted file mode 100644 index 03b6baab24..0000000000 --- a/docs/src/examples/zbl/train.sh +++ /dev/null @@ -1,4 +0,0 @@ -#!/bin/bash - -mtt train options_no_zbl.yaml -o model_no_zbl.pt -mtt train options_zbl.yaml -o model_zbl.pt diff --git a/docs/src/examples/zbl/zbl.zip b/docs/src/examples/zbl/zbl.zip deleted file mode 100644 index 1e804b005e3441c902c5f04b4b933aeec40abd00..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 4789 zcmd^D-;WbF5WbeSYPaeG73#Aagh-@hb9V@eqF$vdKSTwHt3c2;Iwj7olel=-tF`xX zf%3pZpZibnpY$K=H}>vk!z~nEP^AWPalH1-c;@?N#yPrs?vo2U^!oDWU&c?)e{)G< zOz+R}wmFw+XqFeh9nNGroN==Zg2YFT3qZNiN^@>?tp7?FLLJ}kVLbA&U%jP;~mSl@l zo@7eEw$w;t4byYR15?as6~-1jg<{+$u3j1P*^FPC4-;Ye57S9$ zt&5dmJTDCwF)IvNPX^7kQhMdSRn9I$-eBUQv=G-`uK zz!)1To-PW$5F$Bg!zK)VEv0&#lP?TUga?|_nH!=W3mNDYRVOn`hDm0kn@y-o&9SHpzcOlb?HU^$KO zkY&nN&(k8w5Mjg8teS$ZXc;)FIBn1pg>Ev0;K|0K@`&s{*+i>u)W!x4-#ir7xuM7$ z2Yk-K(FScV=glW@BUp?L90T$dImB=DSt*wDxyS)Y=LME=_@D7K#v%j&^fe)al$BJf z;Y3i9vO+5IMJW^Cji@AVcv_O!VGgKpiIAkzKo^#hB8lCkMfkqf%nsnp5VSQJk3@Gj zyxM2i!reaG-`(v6-qZv3{6#?Vq_0Gqakq1)(`We8>$7rv!;f#!d~^K3&p)8~a_shE zd^~czMydtq3#1enGd^~6q^+`%G*)Bdw@^BuDAEpuIXt9s*X^W7^fK#Yrj{vm`j55cuu|LH)T_5|3=eshi8SsyoBRUjI;Z>?MsAii zu!fkYC@)I$*u~neH1ZV@5n=RE?IRHoGEmznq}7))tj(YX0b#wYyU^Hz^KQop-PC5p zM4S>qT^)}l)lHqR4?3IKVW;N>BX7Q4s;}YiD6-*&iig6T(6PP-{WgT#DA+Prv9jWR zE0H{nSXAUEz#YL&@M!4~PQ}@Q3oZw9oJqt*Ahb{xsc^%+RHp;*Yf!SQiW?YK@ulC0 z{RESfibM_|A*v>j+w-ul&%MP1ZwpAU0Z}dJH9CaIM2(!+Z9rlJTZbl}B7c`Qq?(?{ zXCj`6Q#2mLh3;*xj3|xrbYgWT+QNFcfWk}zLl&*m#2sMW?RjO5;~>ZIJm>3fHP>T< zTH<)H=UIg%Nc4u(M=j=Y@t(F>{5S-DdfF$Zf zpl4kP{cwbCf-=xP(ukaA2Kr>-Tmm}3+LYan-PAeaj#b|>7%*_H+QxDwBz^e e?7VvU7l!`sge&0D-A_NGM<3%~g3tZ0>DxaLdIuN) diff --git a/docs/src/examples/zbl/zbl_jupyter.zip b/docs/src/examples/zbl/zbl_jupyter.zip deleted file mode 100644 index a9dbfb39d4d3302871790d89a169d8616113ede9..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6854 zcmeHMTaO#J72Y&yAJRVNsSgDqKU51yBgtF4$pZV}CJtQKK^-LMO%k-!3?;GVkOPvl z+Ogq>{+Ira{G|Mbeuw0Yq+QwVB1K>qkuT!)@bK`Q?_Bi!>4zVEazdY9e*M?!cfaX> z|LoT%C-m=E7%OBctjlE8=;f=A8SCXjDc3t^Q*;=kA^L>jg>Nc>sb0zLi^6PmZ_rMc z!t;WAj@ehQyHRIqo70X+ED)OMd+b}wrIvclI4eyd6x&L_VSoR_GnN}`Mee21Zl-7Y z+l_S0*WOsk6)QxEac{X7j&01AdBf0?`mJ=bNJzw`bgC1ct&C-!sEcb}R!ZPx?WFUL znHA$R?dl~hL&t9f6Mn;+chZLwpfFE4{QFzTXuKumv2bfXH8R2(lw)TnT(bwaIzZoNqgVQc0#H?O|rG&a+Z zpmUx}1=%|}ejplOg@@ZSYHJogc6*B<7)&YzaY^m4SXH_0l32&T-p7ah8@_Dck^eAmwS7 zKm=k9Q-rCKl|1_9@l4}2VI3Ui3JZaOkwSZ+9hw*{VO}y%?}N+U>AE>^Vh1qfwG5K^Sa?S56dIzT(P>G_}JkQ~Rp+0sZD3v11?>rNv(^4$VmmlagsFyu3uqFA1tH~G7|lR@u@h)|AodlBlvaJ- z$@)i9ntS)yH^K)MsjC1&(Ra{8{so!@w8S_^gVcp|BF;x}FXU<^EKELV0;@Pwka1mL z5p)nR=`sc?Td9CU?X`h?1io6>bs~wr6D{E#944#`<_v2K?u3X_1CL6s1ypyw<^9&a zPGsx?%9H`sUBz?JA7>8+>|r(@u>0fj=}bo7o--? 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This can be rendered as a widget in a # Jupyter notebook. -<<<<<<< HEAD chemiscope.show( -======= -cs = chemiscope.show( ->>>>>>> a77e21e8 (Reorganized example files) targets, # reading structures from the dataset properties={ "Target energy": {"values": e_targets, "target": "structure", "units": "kcal"}, @@ -142,10 +135,6 @@ }, ), ) -<<<<<<< HEAD -======= -cs ->>>>>>> a77e21e8 (Reorganized example files) # %% # You can check the structures by clicking the red dots on the parity plot, or diff --git a/examples/1-advanced/05-multi-gpu.py b/examples/1-advanced/05-multi-gpu.py index 2272f0b3f7..64864938e8 100644 --- a/examples/1-advanced/05-multi-gpu.py +++ b/examples/1-advanced/05-multi-gpu.py @@ -1,8 +1,4 @@ """ -<<<<<<< HEAD -======= -================== ->>>>>>> a77e21e8 (Reorganized example files) Multi-GPU training ================== @@ -16,7 +12,6 @@ The different gradients obtained on each device are then summed. This approach allows the user to reduce the time it takes to train models. -<<<<<<< HEAD To know if the model supports multi-GPU training, please check :ref:`available-architectures` and see if the default hyperparameters have the ``distributed`` option. @@ -28,18 +23,18 @@ for the training. Let's take the :ref:`sphx_glr_generated_examples_0-beginner_03-train_from_scratch.py` example and adjust the ``options.yaml`` file. -======= -To know if the model supports multi-GPU training, please check `Available Architectures -<../../docs/src/architectures/index.rst>`_ and see if the default hyperparameters have -the ``distributed`` option. + +To know if the model supports multi-GPU training, please check +:ref:`available-architectures` and see if the default hyperparameters have the +``distributed`` option. Input file ---------- + To do this, you only need to switch on the ``distributed`` option in the ``.yaml`` file -for the training. Let's take `this tutorial -<../beginner_tutorials/train-from-scratch.rst>`_ as an example. Now, the -``options.yaml`` is ->>>>>>> a77e21e8 (Reorganized example files) +for the training. Let's take the +:ref:`sphx_glr_generated_examples_0-beginner_03-train_from_scratch.py` example and +adjust the ``options.yaml`` file. .. literalinclude:: options-distributed.yaml :language: yaml @@ -52,13 +47,8 @@ configurations vary from clusters to clusters, so you have to modify it. Different scheduler will require similar options. ``metatrain`` will automatically use all the GPUs that you have asked for. You should make a single GPU visible for each process -<<<<<<< HEAD (setting ``--gpus-per-node`` equal to the number of GPUs, or setting ``--gpus-per-task=1``, depending on your cluster configuration). -======= -(setting `--gpus-per-node` equal to the number of GPUs, or setting `--gpus-per-task=1`, -depending on your cluster configuration). ->>>>>>> a77e21e8 (Reorganized example files) .. code-block:: bash @@ -106,14 +96,8 @@ Multi-GPU fine-tuning --------------------- -<<<<<<< HEAD You can use multi-GPU for fine-tuning too, by writing ``distributed: True`` in the ``.yaml`` input. For information about fine-tuning, please refer to the :ref:`sphx_glr_generated_examples_0-beginner_02-fine-tuning.py` example. -======= -You can use multi-GPU for fine-tuning too, by writing ``distributed: True`` in the -``.yaml`` input. For information about fine-tuning, please refer to `this tutorial on -fine-tuning <../../getting-started/finetuning-example.rst>`_. ->>>>>>> a77e21e8 (Reorganized example files) """ diff --git a/tox.ini b/tox.ini index 5b8eca68f9..a5395a98c4 100644 --- a/tox.ini +++ b/tox.ini @@ -223,11 +223,7 @@ extras = # these architectures are used in the documentation flashmd soap-bpnn commands = -<<<<<<< HEAD uv pip install --no-build-isolation sphericart torch-spex>=0.1,<0.2 wigners -======= - {env:ENV_INSTALLER:python -m pip} install --no-build-isolation sphericart torch-spex>=0.1,<0.2 wigners ->>>>>>> a77e21e8 (Reorganized example files) # Run all .sh files in the example folder (including subfolders) bash -c "set -e && for f in $(find examples -name '*.sh'); do cd $(dirname $f); bash $(basename $f); cd -; done" From 75506268e0e6d881cf927fe7d935518a1b889fb2 Mon Sep 17 00:00:00 2001 From: Philip Loche Date: Mon, 27 Oct 2025 13:26:59 +0100 Subject: [PATCH 6/7] trigger CI --- examples/0-beginner/04-parity_plot.py | 1 + examples/0-beginner/data.xyz | 1201 ------------------------- 2 files changed, 1 insertion(+), 1201 deletions(-) delete mode 100644 examples/0-beginner/data.xyz diff --git a/examples/0-beginner/04-parity_plot.py b/examples/0-beginner/04-parity_plot.py index d5d23cf12e..3375fa35e5 100644 --- a/examples/0-beginner/04-parity_plot.py +++ b/examples/0-beginner/04-parity_plot.py @@ -90,6 +90,7 @@ # # Check outliers with ``Chemiscope`` # ---------------------------------- +# # With the approach above, you can inspect the whole dataset, but it might be difficult # to identify outliers. `Chemiscope ` is a # visualisation tool, allowing you to explore the dataset interactively. 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mu=1.1502 alpha=28.13 epsHOMO=-0.2525 epsLUMO=0.091 epsgap=0.3435 =187.1015 zpve=0.079534 U0=-100.0 U=-154.956045 H=-154.9551 G=-154.985747 Cv=12.934 pbc="F F F" -C -0.01482147 1.39241234 0.00567104 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -O -0.00471537 -0.01360660 0.01459658 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 0.63794893 -0.55329676 -1.11358232 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 1.00528358 1.81015807 0.00465631 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.54689560 1.79343530 -0.87251051 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 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0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 1.30299118 0.77886562 -0.00617842 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 0.00867174 0.00767042 0.00201032 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.30541464 2.01702117 0.92533239 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.32275498 2.02680156 -0.89347839 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 1.88493396 0.79145020 -0.92119393 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 1.90226631 0.78165391 0.89766373 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.27088043 -0.51289125 0.91137474 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.28821983 -0.50310409 -0.90740824 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -7 -Properties=species:S:1:pos:R:3:forces:R:3:my_tensor:R:9 A=25.77385 B=22.12109 C=14.18066 mu=1.7675 alpha=24.04 epsHOMO=-0.2682 epsLUMO=0.1042 epsgap=0.3724 =129.891 zpve=0.057289 U0=-100.0 U=-153.73941 H=-153.738466 G=-153.766642 Cv=9.176 pbc="F F F" -C 0.01537720 1.41764380 0.00956287 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 1.26478161 0.64923984 -0.00655044 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 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B=8.49011 C=4.89615 mu=2.7362 alpha=35.53 epsHOMO=-0.2431 epsLUMO=-0.0087 epsgap=0.2344 =292.4367 zpve=0.083382 U0=-100.0 U=-193.082969 H=-193.082024 G=-193.116476 Cv=16.893 pbc="F F F" -C -0.00310189 1.48050328 -0.17259867 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C -0.04527202 -0.02887059 0.00139493 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 1.29705800 -0.72194415 0.16889042 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -O -1.08643172 -0.64273343 0.00630393 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 0.59403529 1.74668579 -1.05225043 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -1.01540407 1.87034023 -0.28382265 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 0.48099600 1.95070605 0.69099376 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 1.82512144 -0.32470731 1.04328658 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 1.14892507 -1.79597689 0.28554393 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 1.93682635 -0.52949692 -0.69995403 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -9 -Properties=species:S:1:pos:R:3:forces:R:3:my_tensor:R:9 A=10.78886 B=9.27509 C=5.14977 mu=3.6367 alpha=31.83 epsHOMO=-0.2436 epsLUMO=0.0347 epsgap=0.2783 =267.6148 zpve=0.07319 U0=-100.0 U=-209.15402 H=-209.153076 G=-209.187468 Cv=16.561 pbc="F F F" -C 0.00679889 1.49969736 -0.02567030 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C -0.02740385 -0.01752051 -0.14894811 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -N 1.17826667 -0.63324298 0.05691721 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -O -1.03721965 -0.63980649 -0.40727572 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 0.91390280 1.92923755 -0.46100067 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 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0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 1.22985272 1.91339997 0.00028781 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 2.23625322 2.57430970 -0.00529277 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.91670701 1.79467546 0.01781352 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 3.13429662 3.14370267 -0.01037656 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -5 -Properties=species:S:1:pos:R:3:forces:R:3:my_tensor:R:9 A=67.88408 B=5.00823 C=4.66413 mu=2.3112 alpha=26.25 epsHOMO=-0.3166 epsLUMO=-0.11 epsgap=0.2066 =251.0007 zpve=0.02654 U0=-100.0 U=-206.717875 H=-206.716931 G=-206.747625 Cv=11.329 pbc="F F F" -O -0.04055978 -0.03799144 0.00266513 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C -0.01768870 1.16193251 0.00746008 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 1.23315534 1.93381336 0.00043371 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -N 2.19857378 2.57055097 -0.00483320 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.92627534 1.78959196 0.01764859 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -6 -Properties=species:S:1:pos:R:3:forces:R:3:my_tensor:R:9 A=56.25048 B=4.77441 C=4.40088 mu=0.002 alpha=26.12 epsHOMO=-0.2668 epsLUMO=-0.1113 epsgap=0.1555 =266.8164 zpve=0.036943 U0=-100.0 U=-227.79457 H=-227.793626 G=-227.825074 Cv=12.147 pbc="F F F" -O 0.00337122 -0.03146586 0.00157206 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C -0.01713174 1.17081409 0.00676444 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 1.26555783 1.99719410 -0.00007703 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -O 1.24507069 3.19946589 0.00681275 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.94690483 1.77678142 0.01852668 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 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Cv=17.806 pbc="F F F" -C -0.43126066 1.30586664 -0.12758585 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 0.25285715 -0.01026988 0.13138867 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -O 0.69872099 -0.36890333 1.19604690 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 0.38978943 -0.96773680 -1.06226208 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -O -0.01611268 -0.71564293 -2.16583718 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 0.10006565 1.85324490 -0.91332597 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 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U=-232.32857 H=-232.327626 G=-232.363064 Cv=20.451 pbc="F F F" -C -0.03345961 1.54674842 -0.13373520 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C -0.04966080 0.05914273 0.10861437 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -O 1.17261123 -0.65888877 -0.09925231 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 0.62653087 -0.62548063 1.22492030 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -C 1.43849738 0.05647689 2.29618065 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 0.84261573 2.02417904 0.30895947 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.02020603 1.75197977 -1.20938503 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.93419215 2.01073729 0.28317577 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H -0.92262410 -0.44789053 -0.31001063 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 0.19593793 -1.58039168 1.53656778 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 1.84118250 1.01302927 1.95813013 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 0.82514514 0.22925358 3.18745348 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 -H 2.28152757 -0.57796414 2.58961605 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 From 852b9f0d32d9b9e640a32fee46a3bad5cca229f7 Mon Sep 17 00:00:00 2001 From: Philip Loche Date: Mon, 27 Oct 2025 15:14:15 +0100 Subject: [PATCH 7/7] update QM9 link --- examples/0-beginner/00-basic-usage.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/0-beginner/00-basic-usage.sh b/examples/0-beginner/00-basic-usage.sh index cc35dfc1e2..6fd2a32e53 100644 --- a/examples/0-beginner/00-basic-usage.sh +++ b/examples/0-beginner/00-basic-usage.sh @@ -14,7 +14,7 @@ mtt --help # # We now demonstrate how to ``train`` and ``evaluate`` a model from the command line. # For this example we use the :ref:`architecture-soap-bpnn` architecture and a subset of -# the `QM9 dataset `_. You can obtain the +# the `QM9 dataset `_. You can obtain the # dataset for this example here: :download:`qm9_reduced_100.xyz `. # #

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