diff --git a/examples/al/.gitignore b/examples/al/.gitignore
new file mode 100644
index 0000000..3659241
--- /dev/null
+++ b/examples/al/.gitignore
@@ -0,0 +1,9 @@
+*.xyz*
+*.extxyz*
+*.out*
+*RESTART*
+outputs
+*.pt
+wandb
+*.ckpt
+RESTART
\ No newline at end of file
diff --git a/examples/al/compare.ipynb b/examples/al/compare.ipynb
new file mode 100644
index 0000000..3dd0e63
--- /dev/null
+++ b/examples/al/compare.ipynb
@@ -0,0 +1,187 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "f1bdd1c2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "156c05e2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "simulations = [\n",
+ " \"simulation-baseline\",\n",
+ " \"simulation-flashmd\",\n",
+ " \"simulation-flashmd-symplectic\",\n",
+ " \"simulation-flashmd-omatpes\",\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "b50538b0",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " time | \n",
+ " conserved | \n",
+ " temperature | \n",
+ "
\n",
+ " \n",
+ " | step | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " 0.000 | \n",
+ " -7.795911 | \n",
+ " 281.497480 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " 0.001 | \n",
+ " -7.795911 | \n",
+ " 341.251899 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 0.002 | \n",
+ " -7.795912 | \n",
+ " 265.825062 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 0.003 | \n",
+ " -7.795913 | \n",
+ " 299.073033 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 0.004 | \n",
+ " -7.795913 | \n",
+ " 346.877868 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " time conserved temperature\n",
+ "step \n",
+ "0 0.000 -7.795911 281.497480\n",
+ "1 0.001 -7.795911 341.251899\n",
+ "2 0.002 -7.795912 265.825062\n",
+ "3 0.003 -7.795913 299.073033\n",
+ "4 0.004 -7.795913 346.877868"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "out_files = {name: np.loadtxt(name + \"/md.out\") for name in simulations}\n",
+ "dfs = {name: pd.DataFrame(frame, columns=[\"step\", \"time\", \"conserved\", \"temperature\"]).astype({\"step\": int}).set_index(\"step\") for name, frame in out_files.items()}\n",
+ "dfs[\"simulation-baseline\"].head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "67a53c45",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, axs = plt.subplots(figsize=(8, 6), nrows=2, sharex=True)\n",
+ "fig.suptitle(\"Simulation Statistics Comparison\")\n",
+ "ax_conserved, ax_temperature = axs\n",
+ "for ax in axs:\n",
+ " ax.grid()\n",
+ "ax_conserved.set(ylabel=\"energy\", title=\"conserved\")\n",
+ "ax_conserved.set_ylim(-8, -6.5)\n",
+ "ax_temperature.set(xlabel=\"time in ps\", ylabel=\"temperature in K\", title=\"kinetic\")\n",
+ "for name, df in dfs.items():\n",
+ " ax_conserved.plot(df[\"time\"], df[\"conserved\"], label=name, lw=2)\n",
+ " ax_temperature.plot(df[\"time\"], df[\"temperature\"], label=name, lw=2)\n",
+ "ax_conserved.legend()\n",
+ "fig.tight_layout()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c4e4acf2",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "default",
+ "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": "3.12.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/al/create-datasets.py b/examples/al/create-datasets.py
new file mode 100644
index 0000000..f847e0d
--- /dev/null
+++ b/examples/al/create-datasets.py
@@ -0,0 +1,67 @@
+import copy
+
+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
+
+
+# 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
+
+time_lag = 32
+spacing = 200
+
+def get_structure_for_dataset_m2d(frame_now, frame_ahead):
+ s = copy.deepcopy(frame_now)
+ s.arrays["delta_positions"] = (
+ frame_ahead.get_positions() - frame_now.get_positions()
+ )
+ s.arrays["delta_momenta"] = frame_ahead.get_momenta() - frame_now.get_momenta()
+ s.set_positions(0.5 * (frame_now.get_positions() + frame_ahead.get_positions()))
+ s.set_momenta(0.5 * (frame_now.get_momenta() + frame_ahead.get_momenta()))
+ return s
+
+def get_structure_for_dataset_s2e(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_m2d = []
+structures_for_dataset_s2e = []
+for i in range(0, len(trajectory) - time_lag, spacing):
+ frame_now = trajectory[i]
+ frame_ahead = trajectory[i + time_lag]
+ s_m2d = get_structure_for_dataset_m2d(frame_now, frame_ahead)
+ s_s2e = get_structure_for_dataset_s2e(frame_now, frame_ahead)
+ structures_for_dataset_m2d.append(s_m2d)
+ structures_for_dataset_s2e.append(s_s2e)
+
+ase.io.write("data/midpoint-to-delta.xyz", structures_for_dataset_m2d)
+ase.io.write("data/start-to-end.xyz", structures_for_dataset_s2e)
diff --git a/examples/al/input.xml b/examples/al/input.xml
new file mode 100644
index 0000000..b00cfb1
--- /dev/null
+++ b/examples/al/input.xml
@@ -0,0 +1,33 @@
+
+ 100
+
+
+ 32123
+
+
+ metatomic
+ {model: ../models/mlip_pet-omatpes-v2.pt, template: ../data/equilibrated.xyz, device: cuda}
+
+
+
+
+
+
+ ../data/equilibrated.xyz
+ 300
+
+
+ 300
+
+
+
+ 32
+ 2
+
+
+
+
\ No newline at end of file
diff --git a/examples/al/options-flashmd-symplectic.yaml b/examples/al/options-flashmd-symplectic.yaml
new file mode 100644
index 0000000..a6fb918
--- /dev/null
+++ b/examples/al/options-flashmd-symplectic.yaml
@@ -0,0 +1,55 @@
+seed: 42
+base_precision: 32
+
+architecture:
+ name: experimental.flashmd_symplectic
+ training:
+ timestep: 32 # in this case 30 (time lag) * 1 fs (timestep of reference MD)
+ batch_size: 8 # to be increased in a production scenario
+ num_epochs: 100 # to be increased (at least 1000-10000) in a production scenario
+ log_interval: 1
+ learning_rate: 3e-4
+ fixed_scaling_weights:
+ positions: 1.0
+ momenta: 1.0
+ loss:
+ positions:
+ type: mse
+ weight: 1.0
+ reduction: mean
+ momenta:
+ type: mse
+ weight: 1.0
+ reduction: mean
+
+training_set:
+ systems:
+ read_from: data/midpoint-to-delta.xyz
+ length_unit: A
+ targets:
+ positions:
+ key: delta_positions
+ quantity: length
+ unit: A
+ type:
+ cartesian:
+ rank: 1
+ per_atom: true
+ momenta:
+ key: delta_momenta
+ quantity: momentum
+ unit: (eV*u)^(1/2)
+ type:
+ cartesian:
+ rank: 1
+ per_atom: true
+
+validation_set: 0.1
+test_set: 0.0
+
+wandb:
+ project: flashmd-variants
+ name: symplectic-flashmd
+ tags:
+ - al
+ - symplectic-flashmd
diff --git a/examples/al/options-flashmd.yaml b/examples/al/options-flashmd.yaml
new file mode 100644
index 0000000..ab54371
--- /dev/null
+++ b/examples/al/options-flashmd.yaml
@@ -0,0 +1,50 @@
+seed: 42
+
+architecture:
+ name: experimental.flashmd
+ training:
+ timestep: 32 # in this case 32 (time lag) * 1 fs (timestep of reference MD)
+ batch_size: 8 # to be increased in a production scenario
+ num_epochs: 100 # 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: data/start-to-end.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
+
+wandb:
+ project: flashmd-variants
+ name: flashmd-baseline
+ tags:
+ - al
+ - flashmd
diff --git a/examples/al/simulation-baseline/baseline.xml b/examples/al/simulation-baseline/baseline.xml
new file mode 100644
index 0000000..3a69456
--- /dev/null
+++ b/examples/al/simulation-baseline/baseline.xml
@@ -0,0 +1,33 @@
+
+ 3200
+
+
+ 32123
+
+
+ metatomic
+ {model: ../models/mlip_pet-omatpes-v2.pt, template: ../data/equilibrated.xyz, device: cuda}
+
+
+
+
+
+
+ ../data/equilibrated.xyz
+ 300
+
+
+ 300
+
+
+
+ 1
+ 2
+
+
+
+
\ No newline at end of file
diff --git a/examples/al/simulation-baseline/run.sh b/examples/al/simulation-baseline/run.sh
new file mode 100644
index 0000000..a7121d6
--- /dev/null
+++ b/examples/al/simulation-baseline/run.sh
@@ -0,0 +1 @@
+pixi run i-pi baseline.xml
\ No newline at end of file
diff --git a/examples/al/simulation-flashmd-omatpes/run.py b/examples/al/simulation-flashmd-omatpes/run.py
new file mode 100644
index 0000000..5fffc1d
--- /dev/null
+++ b/examples/al/simulation-flashmd-omatpes/run.py
@@ -0,0 +1,20 @@
+import torch
+from ipi.utils.scripting import InteractiveSimulation
+from flashmd import get_pretrained
+from flashmd.steppers import FlashMDStepper
+from flashmd.wrappers import wrap_nvt
+from flashmd.vv import flashmd_vv
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+with open("../input.xml", "r") as input_xml:
+ sim = InteractiveSimulation(input_xml)
+
+# replace the motion step with a FlashMD stepper
+_, flashmd_model_32 = get_pretrained("pet-omatpes", 32)
+stepper = FlashMDStepper(flashmd_model_32, device=device)
+step_fn = flashmd_vv(sim, stepper, device=device, dtype=torch.float32, rescale_energy=False)
+step_fn = wrap_nvt(sim, step_fn)
+sim.set_motion_step(step_fn)
+
+sim.run(100)
diff --git a/examples/al/simulation-flashmd-symplectic.py b/examples/al/simulation-flashmd-symplectic.py
new file mode 100644
index 0000000..e69de29
diff --git a/examples/al/simulation-flashmd-symplectic/run.py b/examples/al/simulation-flashmd-symplectic/run.py
new file mode 100644
index 0000000..a287c1f
--- /dev/null
+++ b/examples/al/simulation-flashmd-symplectic/run.py
@@ -0,0 +1,42 @@
+from typing import Callable
+import torch
+from metatomic.torch import load_atomistic_model
+from ipi.utils.scripting import InteractiveSimulation
+from flashmd.steppers import SymplecticStepper, FlashMDStepper
+from flashmd.vv import flashmd_vv
+from flashmd.wrappers import wrap_nvt
+from flashmd.fpi import anderson_solver
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+with open("../input.xml", "r") as input_xml:
+ sim = InteractiveSimulation(input_xml)
+
+# load FlashMD model for initial guess
+flashmd_model_32 = load_atomistic_model("../models/flashmd.pt")
+flashmd_model_32.to(device)
+initial_guess = FlashMDStepper(flashmd_model_32, device=device)
+
+# load FlashMD symplectic model for corrector
+flashmd_symplectic_model_32 = load_atomistic_model("../models/flashmd-symplectic.pt")
+flashmd_symplectic_model_32.to(device)
+
+# create a fixed-point solver and attach a logger to see the convergence behavior
+solver_kwargs = dict(m=0, max_iter=100, tol=1e-3, beta=0.5)
+def solver_with_log(
+ g: Callable[[torch.Tensor], torch.Tensor],
+ x0: torch.Tensor,
+) -> torch.Tensor:
+ x_star, norms = anderson_solver(g, x0, return_residual_norms=True, **solver_kwargs) # type: ignore
+ print("l2 accuracies (converged in %d steps):" % len(norms))
+ for i, n in enumerate(norms):
+ print("iteration", i, "residual norm:", n)
+ return x_star
+
+# replace the motion step with a FlashMD stepper
+stepper = SymplecticStepper(initial_guess, flashmd_symplectic_model_32, solver_with_log)
+step_fn = flashmd_vv(sim, stepper, device=device, dtype=torch.float32, rescale_energy=False, random_rotation=False)
+step_fn = wrap_nvt(sim, step_fn)
+sim.set_motion_step(step_fn)
+
+sim.run(100)
diff --git a/examples/al/simulation-flashmd/run.py b/examples/al/simulation-flashmd/run.py
new file mode 100644
index 0000000..c839227
--- /dev/null
+++ b/examples/al/simulation-flashmd/run.py
@@ -0,0 +1,23 @@
+import torch
+from metatomic.torch import load_atomistic_model
+from ipi.utils.scripting import InteractiveSimulation
+from flashmd.steppers import FlashMDStepper
+from flashmd.vv import flashmd_vv
+from flashmd.wrappers.nvt import wrap_nvt
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+with open("../input.xml", "r") as input_xml:
+ sim = InteractiveSimulation(input_xml)
+
+# load FlashMD model
+flashmd_model_32 = load_atomistic_model("../models/flashmd.pt")
+flashmd_model_32.to(device)
+
+# replace the motion step with a FlashMD stepper
+stepper = FlashMDStepper(flashmd_model_32, device=device)
+step_fn = flashmd_vv(sim, stepper, device=device, dtype=torch.float32, rescale_energy=False)
+step_fn = wrap_nvt(sim, step_fn)
+sim.set_motion_step(step_fn)
+
+sim.run(100)
diff --git a/src/flashmd/ase/velocity_verlet.py b/src/flashmd/ase/velocity_verlet.py
index 087a382..60411d0 100644
--- a/src/flashmd/ase/velocity_verlet.py
+++ b/src/flashmd/ase/velocity_verlet.py
@@ -8,7 +8,7 @@
from metatomic.torch.ase_calculator import _ase_to_torch_data
from scipy.spatial.transform import Rotation
-from ..stepper import FlashMDStepper
+from ..steppers.flashmd import FlashMDStepper
class VelocityVerlet(MolecularDynamics):
diff --git a/src/flashmd/fpi.py b/src/flashmd/fpi.py
new file mode 100644
index 0000000..15a7242
--- /dev/null
+++ b/src/flashmd/fpi.py
@@ -0,0 +1,86 @@
+from typing import Callable
+
+import torch
+
+
+def anderson_solver(
+ f: Callable[[torch.Tensor], torch.Tensor],
+ x0: torch.Tensor,
+ m: int = 5,
+ max_iter: int = 50,
+ tol: float = 1e-5,
+ beta: float = 1.0,
+ lambda_reg: float = 1e-4,
+ return_residual_norms: bool = False,
+) -> torch.Tensor | tuple[torch.Tensor, list[float]]:
+ """
+ Solve fixed-point problem x = f(x) using Anderson acceleration.
+
+ Args:
+ f: Fixed-point mapping.
+ x0: Initial guess.
+ m: Number of previous iterates to use for acceleration.
+ max_iter: Maximum number of iterations.
+ tol: Convergence tolerance based on residual norm.
+ beta: Mixing parameter for the fixed-point step.
+ lambda_reg: Regularization parameter for least-squares solve.
+ return_residual_norms: If True, also return list of residual norms.
+
+ Returns:
+ Approximate solution x, and optionally list of residual norms.
+ """
+ # history buffers
+ delta_xs: list[torch.Tensor] = []
+ delta_gs: list[torch.Tensor] = []
+ residual_norms = []
+
+ # run fixed-pointer iteration
+ x = x0
+ fx = f(x)
+ g = fx - x
+ x_prev, g_prev = None, None
+ for k in range(max_iter):
+ # evaluate residual and compute convergence
+ res_norm = torch.norm(g).item()
+ residual_norms.append(res_norm)
+ if res_norm < tol:
+ break
+
+ # update history
+ if k > 0:
+ assert x_prev is not None and g_prev is not None
+ delta_xs.append(x - x_prev)
+ delta_gs.append(g - g_prev)
+
+ # truncate history to hold at most m elements
+ if len(delta_xs) > m:
+ delta_xs.pop(0)
+ delta_gs.pop(0)
+ x_prev, g_prev = x, g
+
+ # compute Anderson acceleration step
+ if len(delta_xs) > 0:
+ # create matrices from history of shape (features, history_length)
+ X = torch.stack(delta_xs, dim=1) # (n, k)
+ G = torch.stack(delta_gs, dim=1) # (n, k)
+
+ # solve regularized least-squares problem
+ A = G.T @ G + lambda_reg * torch.eye(G.shape[1], device=G.device)
+ b = G.T @ g
+ try:
+ coeffs = torch.linalg.solve(A, b)
+ # update iterate with momentum + Anderson step
+ x = x + beta * g - (X + beta * G) @ coeffs
+ except RuntimeError:
+ x = x + beta * g # fallback to fixed-point step if matrix is singular
+ else:
+ x = x + beta * g # fixed-point step if there is no history
+
+ # update iterate and residual
+ fx = f(x)
+ g = fx - x
+
+ if return_residual_norms:
+ return x, residual_norms
+ else:
+ return x
diff --git a/src/flashmd/ipi.py b/src/flashmd/ipi.py
index 1ba00f6..51817f8 100644
--- a/src/flashmd/ipi.py
+++ b/src/flashmd/ipi.py
@@ -10,7 +10,7 @@
from metatensor.torch import Labels, TensorBlock, TensorMap
from metatomic.torch import System
-from flashmd.stepper import FlashMDStepper
+from flashmd.steppers.flashmd import FlashMDStepper
def get_standard_vv_step(
diff --git a/src/flashmd/steppers/__init__.py b/src/flashmd/steppers/__init__.py
new file mode 100644
index 0000000..357c04a
--- /dev/null
+++ b/src/flashmd/steppers/__init__.py
@@ -0,0 +1,6 @@
+from .core import AtomisticStepper
+from .flashmd import FlashMDStepper
+from .symplectic import SymplecticStepper
+
+
+__all__ = ["AtomisticStepper", "FlashMDStepper", "SymplecticStepper"]
diff --git a/src/flashmd/steppers/core.py b/src/flashmd/steppers/core.py
new file mode 100644
index 0000000..7cfafe5
--- /dev/null
+++ b/src/flashmd/steppers/core.py
@@ -0,0 +1,24 @@
+from abc import ABC, abstractmethod
+
+from metatomic.torch import System
+
+
+class AtomisticStepper(ABC):
+ @abstractmethod
+ def get_timestep(self) -> float:
+ """Get the time step of the stepper in femtoseconds.
+
+ Returns:
+ float: The time step in femtoseconds.
+ """
+
+ @abstractmethod
+ def step(self, system: System) -> System: # type: ignore
+ """Perform a single MD step on the given system.
+
+ Args:
+ system (System): The input system containing positions, momenta, etc.
+
+ Returns:
+ System: The updated system after one MD step.
+ """
diff --git a/src/flashmd/stepper.py b/src/flashmd/steppers/flashmd.py
similarity index 94%
rename from src/flashmd/stepper.py
rename to src/flashmd/steppers/flashmd.py
index a8e46b9..45a08a6 100644
--- a/src/flashmd/stepper.py
+++ b/src/flashmd/steppers/flashmd.py
@@ -1,14 +1,14 @@
-# from ..utils.pretrained import load_pretrained_models
import ase.units
import torch
from metatensor.torch import Labels, TensorBlock, TensorMap
from metatomic.torch import AtomisticModel, ModelEvaluationOptions, ModelOutput, System
from metatrain.utils.neighbor_lists import get_system_with_neighbor_lists
-from .constraints import enforce_physical_constraints
+from ..constraints import enforce_physical_constraints
+from . import AtomisticStepper
-class FlashMDStepper:
+class FlashMDStepper(AtomisticStepper):
def __init__(
self,
model: AtomisticModel,
@@ -17,7 +17,6 @@ def __init__(
self.model = model.to(device)
self.time_step = float(model.module.timestep) * ase.units.fs
- # one of these for each model:
self.evaluation_options = ModelEvaluationOptions(
length_unit="Angstrom",
outputs={
@@ -29,6 +28,9 @@ def __init__(
self.dtype = getattr(torch, self.model.capabilities().dtype)
self.device = device
+ def get_timestep(self) -> float:
+ return self.time_step
+
def step(self, system: System):
if system.device.type != self.device.type:
raise ValueError("System device does not match stepper device.")
diff --git a/src/flashmd/steppers/symplectic.py b/src/flashmd/steppers/symplectic.py
new file mode 100644
index 0000000..161b1ad
--- /dev/null
+++ b/src/flashmd/steppers/symplectic.py
@@ -0,0 +1,153 @@
+from functools import partial
+from typing import Callable
+
+import ase.units
+import torch
+from metatensor.torch import Labels, TensorBlock, TensorMap
+from metatomic.torch import AtomisticModel, ModelEvaluationOptions, ModelOutput, System
+from metatrain.utils.neighbor_lists import get_system_with_neighbor_lists
+
+from flashmd.steppers import AtomisticStepper
+
+
+def system_to_phase_space(system) -> torch.Tensor:
+ # extract positions and momenta from system
+ positions = system.positions
+ momenta = system.get_data("momenta")[0].values
+ # flatten and concatenate
+ return torch.cat([positions.view(-1), momenta.view(-1)], dim=0)
+
+
+def phase_space_to_system(system, x: torch.Tensor):
+ # extract positions and momenta from concatenated tensor and reshape into original shapes
+ positions, momenta = torch.chunk(x, 2)
+ positions = positions.view_as(system.positions)
+ momenta = momenta.view_as(system.get_data("momenta")[0].values)
+
+ # take the types, masses and cell from the original system
+ new_system = System(
+ types=system.types,
+ positions=positions,
+ cell=system.cell,
+ pbc=system.pbc,
+ )
+
+ # copy masses
+ new_system.add_data("masses", system.get_data("masses"))
+
+ # attach momenta
+ device = positions.device
+ new_system.add_data(
+ "momenta",
+ TensorMap(
+ keys=Labels.single().to(device),
+ blocks=[
+ TensorBlock(
+ values=momenta,
+ samples=Labels.range("atom", len(system)).to(device),
+ components=[Labels.range("xyz", 3).to(device)],
+ properties=Labels.single().to(device),
+ )
+ ],
+ ),
+ )
+
+ return new_system
+
+
+class SymplecticStepper(AtomisticStepper):
+ def __init__(
+ self,
+ initial_guess: AtomisticStepper,
+ midpoint_to_delta_model: AtomisticModel,
+ fixed_point_solver: Callable[
+ [Callable[[torch.Tensor], torch.Tensor], torch.Tensor], torch.Tensor
+ ],
+ ):
+ # super().__init__(flashmd, device)
+ self.initial_guess = initial_guess
+ self.midpoint_to_delta_model = midpoint_to_delta_model
+ self.fixed_point_solver = fixed_point_solver
+
+ # self.model = model
+ self.evaluation_options = ModelEvaluationOptions(
+ length_unit="Angstrom",
+ outputs={
+ "positions": ModelOutput(per_atom=True),
+ "momenta": ModelOutput(per_atom=True),
+ },
+ )
+ self.fixed_point_solver = fixed_point_solver
+
+ def get_timestep(self) -> float:
+ timestep: float = self.midpoint_to_delta_model.module.timestep.item() # type: ignore
+ return timestep * ase.units.fs
+
+ def _fixed_point_step(
+ self, system, x_init: torch.Tensor, x_bar: torch.Tensor
+ ) -> torch.Tensor:
+ """
+ Take the current estimate of the midpoint in phase-space representation, update and
+ return it.
+
+ NOTE: The function takes a system as the first argument to allow constructing a
+ metatomic-compatible System object, which unfortunately is required for model
+ evaluation.
+
+ Args:
+ system: The initial system before the step.
+ x_init: The initial system in phase-space representation. For the fixed-point
+ iterations, it has to be of shape (B, D) where B is the batch size (1 here) and
+ D is the dimension of the phase space.
+ x_bar: The current estimate of the midpoint in phase-space representation. Note
+ that this also has to be of shape (B, D).
+
+ Returns:
+ The updated midpoint in phase-space representation.
+ """
+ # convert to system representation
+ midpoint_system = phase_space_to_system(system, x_bar)
+
+ # attach neighbor lists based on the model's requests
+ midpoint_system = get_system_with_neighbor_lists(
+ midpoint_system, self.midpoint_to_delta_model.requested_neighbor_lists()
+ )
+
+ # run the model to get the deltas
+ outputs = self.midpoint_to_delta_model(
+ [midpoint_system], self.evaluation_options, check_consistency=False
+ )
+
+ # depending on the model, extract deltas
+ delta_q = outputs["positions"].block().values.squeeze(-1)
+ delta_p = outputs["momenta"].block().values
+
+ # compute new midpoint in phase space
+ delta_x = torch.cat([delta_q.view(-1), delta_p.view(-1)], dim=0)
+
+ # compute new midpoint
+ x_bar_new = x_init + 0.5 * delta_x
+ return x_bar_new
+
+ def step(self, system: System) -> System: # type: ignore
+ # convert system to phase space representation
+ x_init = system_to_phase_space(system)
+
+ # get initial guess from FlashMD
+ initial_guess = self.initial_guess.step(system)
+ x_prime_init = system_to_phase_space(initial_guess)
+
+ # compute initial midpoint from starting point and initial guess
+ x_bar_init = 0.5 * (x_init + x_prime_init)
+
+ # attach the system to the fixed-point function and call solver
+ f = partial(self._fixed_point_step, system, x_init)
+ x_bar_star = self.fixed_point_solver(f, x_bar_init)
+
+ # compute final updated phase space point
+ x_star = 2 * x_bar_star - x_init
+
+ # convert back to system representation
+ x_prime = phase_space_to_system(system, x_star)
+
+ return x_prime
diff --git a/src/flashmd/vv.py b/src/flashmd/vv.py
new file mode 100644
index 0000000..4310cf6
--- /dev/null
+++ b/src/flashmd/vv.py
@@ -0,0 +1,198 @@
+import ase.data
+import ase.units
+import numpy as np
+import torch
+from ipi.utils.depend import dstrip
+from ipi.utils.mathtools import random_rotation as random_rotation_matrix
+from ipi.utils.messages import info, verbosity
+from metatensor.torch import Labels, TensorBlock, TensorMap
+from metatomic.torch import System
+
+from .steppers.flashmd import AtomisticStepper
+
+
+def standard_vv(sim, rescale_energy: bool = False):
+ """
+ Returns a velocity Verlet stepper function for i-PI simulations.
+
+ Parameters:
+ sim: The i-PI simulation object.
+ rescale_energy: If True, rescales the kinetic energy after the step
+ to maintain energy conservation.
+
+ Returns:
+ A function that performs a velocity Verlet step.
+ """
+
+ def vv_step(motion):
+ old_energy = None
+ if rescale_energy:
+ info("@flashmd: Old energy", verbosity.debug)
+ old_energy = sim.properties("potential") + sim.properties("kinetic_md")
+
+ print(motion.integrator.pdt, motion.integrator.qdt)
+ motion.integrator.pstep(level=0)
+ motion.integrator.pconstraints()
+ motion.integrator.qcstep() # does two steps because qdt is halved in the i-PI integrator
+ motion.integrator.qcstep()
+ motion.integrator.pstep(level=0)
+ motion.integrator.pconstraints()
+
+ if rescale_energy:
+ info("@flashmd: Energy rescale", verbosity.debug)
+ new_energy = sim.properties("potential") + sim.properties("kinetic_md")
+ kinetic_energy = sim.properties("kinetic_md")
+ alpha = np.sqrt(1.0 - (new_energy - old_energy) / kinetic_energy)
+ motion.beads.p[:] = alpha * dstrip(motion.beads.p)
+
+ return vv_step
+
+
+def flashmd_vv(
+ sim,
+ stepper: AtomisticStepper,
+ device: torch.device,
+ dtype: torch.dtype,
+ rescale_energy=True,
+ random_rotation=False,
+):
+ # compare the model's internal timestep with the i-PI one -- they need to match
+ dt = sim.syslist[0].motion.dt * 2.4188843e-17 * ase.units.s
+ timestep = stepper.get_timestep()
+ if not np.allclose(dt, timestep):
+ raise ValueError(
+ f"Mismatch between timestep ({dt}) and model timestep ({timestep})."
+ )
+
+ def flashmd_vv(motion):
+ info("@flashmd: Starting VV", verbosity.debug)
+ old_energy = None
+ if rescale_energy:
+ info("@flashmd: Old energy", verbosity.debug)
+ old_energy = sim.properties("potential") + sim.properties("kinetic_md")
+
+ info("@flashmd: Stepper", verbosity.debug)
+ system = ipi_to_system(motion, device, dtype)
+
+ R = None
+ if random_rotation:
+ # generate a random rotation matrix
+ R = torch.tensor(
+ random_rotation_matrix(motion.prng, improper=True),
+ device=system.positions.device,
+ dtype=system.positions.dtype,
+ )
+ # applies the random rotation
+ system.cell = system.cell @ R.T
+ system.positions = system.positions @ R.T
+ momenta = system.get_data("momenta").block(0).values.squeeze()
+ momenta[:] = momenta @ R.T # does the change in place
+
+ new_system = stepper.step(system)
+
+ if random_rotation:
+ # revert q,p to the original reference frame (`system_to_ipi` ignores the cell)
+ new_system.positions = new_system.positions @ R
+ momenta = new_system.get_data("momenta").block(0).values.squeeze()
+ momenta[:] = momenta @ R
+
+ info("@flashmd: System to ipi", verbosity.debug)
+ system_to_ipi(motion, new_system)
+ info("@flashmd: VV P constraints", verbosity.debug)
+ motion.integrator.pconstraints()
+
+ if rescale_energy:
+ info("@flashmd: Energy rescale", verbosity.debug)
+ new_energy = sim.properties("potential") + sim.properties("kinetic_md")
+ kinetic_energy = sim.properties("kinetic_md")
+ alpha = np.sqrt(1.0 - (new_energy - old_energy) / kinetic_energy)
+ motion.beads.p[:] = alpha * dstrip(motion.beads.p)
+ motion.integrator.pconstraints()
+ info("@flashmd: End of VV step", verbosity.debug)
+
+ return flashmd_vv
+
+
+def ipi_to_system(motion, device, dtype):
+ positions = (
+ dstrip(motion.beads.q).reshape(-1, 3) * ase.units.Bohr / ase.units.Angstrom
+ )
+ positions_torch = torch.tensor(positions, device=device, dtype=dtype)
+ cell = dstrip(motion.cell.h).T * ase.units.Bohr / ase.units.Angstrom
+ cell_torch = torch.tensor(cell, device=device, dtype=dtype)
+ pbc_torch = torch.tensor([True, True, True], device=device, dtype=torch.bool)
+ momenta = (
+ dstrip(motion.beads.p).reshape(-1, 3)
+ * (9.1093819e-31 * ase.units.kg)
+ * (ase.units.Bohr / ase.units.Angstrom)
+ / (2.4188843e-17 * ase.units.s)
+ )
+ momenta_torch = torch.tensor(momenta, device=device, dtype=dtype)
+ masses = dstrip(motion.beads.m) * 9.1093819e-31 * ase.units.kg
+ masses_torch = torch.tensor(masses, device=device, dtype=dtype)
+ types_torch = torch.tensor(
+ [ase.data.atomic_numbers[name] for name in motion.beads.names],
+ device=device,
+ dtype=torch.int32,
+ )
+ system = System(types_torch, positions_torch, cell_torch, pbc_torch)
+ system.add_data(
+ "momenta",
+ TensorMap(
+ keys=Labels.single().to(device),
+ blocks=[
+ TensorBlock(
+ values=momenta_torch.unsqueeze(-1),
+ samples=Labels(
+ names=["system", "atom"],
+ values=torch.tensor(
+ [[0, j] for j in range(len(momenta_torch))], device=device
+ ),
+ ),
+ components=[
+ Labels(
+ names="xyz",
+ values=torch.tensor([[0], [1], [2]], device=device),
+ )
+ ],
+ properties=Labels.single().to(device),
+ )
+ ],
+ ),
+ )
+ system.add_data(
+ "masses",
+ TensorMap(
+ keys=Labels.single().to(device),
+ blocks=[
+ TensorBlock(
+ values=masses_torch.unsqueeze(-1),
+ samples=Labels(
+ names=["system", "atom"],
+ values=torch.tensor(
+ [[0, j] for j in range(len(masses_torch))], device=device
+ ),
+ ),
+ components=[],
+ properties=Labels.single().to(device),
+ )
+ ],
+ ),
+ )
+ return system
+
+
+def system_to_ipi(motion, system):
+ # only needs to convert positions and momenta, it's assumed that the cell won't be changed
+ motion.beads.q[:] = (
+ system.positions.detach().cpu().numpy().flatten()
+ * ase.units.Angstrom
+ / ase.units.Bohr
+ )
+ motion.beads.p[:] = system.get_data("momenta").block().values.detach().squeeze(
+ -1
+ ).cpu().numpy().flatten() / (
+ (9.1093819e-31 * ase.units.kg)
+ * (ase.units.Bohr / ase.units.Angstrom)
+ / (2.4188843e-17 * ase.units.s)
+ )
diff --git a/src/flashmd/wrappers/__init__.py b/src/flashmd/wrappers/__init__.py
new file mode 100644
index 0000000..48861b8
--- /dev/null
+++ b/src/flashmd/wrappers/__init__.py
@@ -0,0 +1,6 @@
+from .npt import wrap_npt
+from .nve import wrap_nve
+from .nvt import wrap_nvt
+
+
+__all__ = ["wrap_npt", "wrap_nve", "wrap_nvt"]
diff --git a/src/flashmd/wrappers/npt.py b/src/flashmd/wrappers/npt.py
new file mode 100644
index 0000000..5e09300
--- /dev/null
+++ b/src/flashmd/wrappers/npt.py
@@ -0,0 +1,83 @@
+from typing import Callable
+
+import numpy as np
+from ipi.engine.motion import Motion
+from ipi.engine.motion.dynamics import NPTIntegrator
+from ipi.engine.simulation import Simulation
+from ipi.utils.messages import info, verbosity
+from ipi.utils.units import Constants
+
+
+def _qbaro(baro):
+ """Propagation step for the cell volume (adjusting atomic positions and momenta)."""
+
+ v = baro.p[0] / baro.m[0]
+ halfdt = (
+ baro.qdt
+ ) # this is set to half the inner loop in all integrators that use a barostat
+ expq, expp = (np.exp(v * halfdt), np.exp(-v * halfdt))
+
+ baro.nm.qnm[0, :] *= expq
+ baro.nm.pnm[0, :] *= expp
+ baro.cell.h *= expq
+
+
+def _pbaro(baro):
+ """Propagation step for the cell momentum (adjusting atomic positions and momenta)."""
+
+ # we are assuming then that p the coupling between p^2 and dp/dt only involves the fast force
+ dt = baro.pdt[0]
+
+ # computes the pressure associated with the forces at the outer level MTS level.
+ press = np.trace(baro.stress_mts(0)) / 3.0
+ # integerates the kinetic part of the pressure with the force at the inner-most level.
+ nbeads = baro.beads.nbeads
+ baro.p += (
+ 3.0
+ * dt
+ * (baro.cell.V * (press - nbeads * baro.pext) + Constants.kb * baro.temp)
+ )
+
+
+def wrap_npt(
+ sim: Simulation,
+ vv_step: Callable[[Motion], None],
+) -> Callable[[Motion], None]:
+ """Wrap a velocity-Verlet stepper into an NPT stepper for i-PI."""
+
+ motion = sim.syslist[0].motion
+ if type(motion.integrator) is not NPTIntegrator:
+ raise TypeError(
+ f"Base i-PI integrator is of type {motion.integrator.__class__.__name__}, use a NPT setup."
+ )
+
+ # The barostat here needs a simpler splitting than for BZP, something as
+ # OAbBbBABbAbPO where Bp and Ap are the cell momentum and volume steps
+ def npt_stepper(motion, *_, **__):
+ info("@flashmd: Starting NPT step", verbosity.debug)
+ info("@flashmd: Particle thermo", verbosity.debug)
+ motion.thermostat.step()
+ info("@flashmd: P constraints", verbosity.debug)
+ motion.integrator.pconstraints()
+ info("@flashmd: Barostat thermo", verbosity.debug)
+ motion.barostat.thermostat.step()
+ info("@flashmd: Barostat q", verbosity.debug)
+ _qbaro(motion.barostat)
+ info("@flashmd: Barostat p", verbosity.debug)
+ _pbaro(motion.barostat)
+ info("@flashmd: FlashVV", verbosity.debug)
+ vv_step(motion)
+ info("@flashmd: Barostat p", verbosity.debug)
+ _pbaro(motion.barostat)
+ info("@flashmd: Barostat q", verbosity.debug)
+ _qbaro(motion.barostat)
+ info("@flashmd: Barostat thermo", verbosity.debug)
+ motion.barostat.thermostat.step()
+ info("@flashmd: Particle thermo", verbosity.debug)
+ motion.thermostat.step()
+ info("@flashmd: P constraints", verbosity.debug)
+ motion.integrator.pconstraints()
+ motion.ensemble.time += motion.dt
+ info("@flashmd: NPT Step finished", verbosity.debug)
+
+ return npt_stepper
diff --git a/src/flashmd/wrappers/nve.py b/src/flashmd/wrappers/nve.py
new file mode 100644
index 0000000..e9d4d94
--- /dev/null
+++ b/src/flashmd/wrappers/nve.py
@@ -0,0 +1,22 @@
+from typing import Callable
+
+from ipi.engine.motion import Motion
+from ipi.engine.motion.dynamics import NVEIntegrator
+from ipi.engine.simulation import Simulation
+
+
+def wrap_nve(
+ sim: Simulation,
+ vv_step: Callable[[Motion], None],
+) -> Callable[[Motion], None]:
+ motion = sim.syslist[0].motion
+ if type(motion.integrator) is not NVEIntegrator:
+ raise TypeError(
+ f"Base i-PI integrator is of type {motion.integrator.__class__.__name__}, use a NVE setup."
+ )
+
+ def nve_stepper(motion, *_, **__):
+ vv_step(motion)
+ motion.ensemble.time += motion.dt
+
+ return nve_stepper
diff --git a/src/flashmd/wrappers/nvt.py b/src/flashmd/wrappers/nvt.py
new file mode 100644
index 0000000..27670aa
--- /dev/null
+++ b/src/flashmd/wrappers/nvt.py
@@ -0,0 +1,26 @@
+from typing import Callable
+
+from ipi.engine.motion import Motion
+from ipi.engine.motion.dynamics import NVTIntegrator
+
+
+def wrap_nvt(
+ sim,
+ vv_step: Callable[[Motion], None],
+) -> Callable[[Motion], None]:
+ motion = sim.syslist[0].motion
+ if type(motion.integrator) is not NVTIntegrator:
+ raise TypeError(
+ f"Base i-PI integrator is of type {motion.integrator.__class__.__name__}, use a NVT setup."
+ )
+
+ def nvt_stepper(motion, *_, **__):
+ # OBABO splitting of a NVT propagator
+ motion.thermostat.step()
+ motion.integrator.pconstraints()
+ vv_step(motion)
+ motion.thermostat.step()
+ motion.integrator.pconstraints()
+ motion.ensemble.time += motion.dt
+
+ return nvt_stepper
diff --git a/tests/test_fpi.py b/tests/test_fpi.py
new file mode 100644
index 0000000..c05e6b2
--- /dev/null
+++ b/tests/test_fpi.py
@@ -0,0 +1,19 @@
+import torch
+
+from flashmd.fpi import anderson_solver
+
+
+def test_anderson_solver_convergence():
+ """Test that the Anderson solver converges on a simple fixed-point problem."""
+
+ def f(x):
+ return 0.5 * x + 1.0
+
+ x0 = torch.tensor([0.0])
+ x_sol, residuals = anderson_solver(
+ f, x0, m=3, max_iter=100, tol=1e-6, return_residual_norms=True
+ )
+ x_exact = torch.tensor([2.0])
+
+ assert torch.allclose(x_sol, x_exact, atol=1e-5)
+ assert all(earlier >= later for earlier, later in zip(residuals, residuals[1:]))