+------------------------------------------------------------+
| This is experimental code with lots of bugs intended for   |
| internal use only. Will be ready as soon as we publish the |
| methodology.                                               |
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mbe-automation

Automate the modeling of thermodynamic properties in organic molecular crystals using machine-learning interatomic potentials (MLIPs). The general computational scheme facilitated by this program involves the following steps:

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| Generate training set configurations for a periodic system  |
| using a baseline MLIP (e.g., MACE).                         |
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                           |
                           v
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| Extract finite subsystems composed of one or more molecules.|
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                           |
                           v
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| Use these finite subsystems to generate high-accuracy data  |
| points using quantum chemical wave function methods.        |
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                           |
                           v
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| Perform training of a delta-learning layer on top of the    |
| baseline model.                                             |
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                           |
                           v
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| Carry out the final calculation (e.g., quasi-harmonic       |
| thermodynamics or molecular dynamics) using the newly       |
| trained model.                                              |
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The program integrates several scientific codes into a unified workflow, including:

• MACE: Primary MLIP model for energies and forces.
• phonopy: Phonon calculations and vibrational properties.
• pymatgen: Crystal structure analysis and manipulation.
• ASE: Geometry relaxation and molecular dynamics simulations.
• PySCF and GPU4PySCF: Mean-field electronic structure calculations.
• MRCC and beyond-rpa: High-fidelity data points from correlated wave-function theory.

Table of Contents

Setup

• Installation Guide

Basics

• API Data Classes
• Calculators
• Configuration Classes
• Working with HDF5 Datasets
• Computational Bottlenecks

Workflows

• Quasi-Harmonic Calculation
• Molecular Dynamics
• MD sampling and phonon sampling

Cookbooks

• Extracting frequencies and eigenvectors of the dynamical matrix
• Training Set from MACE MD + r2SCAN Energies & Forces
• Delta Learning Dataset Creation
• Adding Atomic Reference Energies to Existing MACE Training Data