Equitrain: Training and Fine-Tuning Machine Learning Interatomic Potentials

Equitrain is a Python toolkit for preprocessing atomistic datasets, training machine-learning interatomic potentials (MLIPs), fine-tuning existing checkpoints, and running evaluation or prediction through one CLI/API.

Features

• Unified Torch and JAX training entry points.
• Model wrappers for MACE, SevenNet, ORB, ANI, and M3GNet.
• Native HDF5 preprocessing for large atomistic datasets.
• Torch reaction-relative losses for barrier and reaction energies.
• Fine-tuning adapters for Delta/L²-SP, Freeze, and LoRA workflows.
• ASE calculator helpers for batched prediction and relaxation.

Supported Models

Wrapper	Backends	Upstream / Companion Project	Notes
mace	Torch, JAX	mace-model	Companion repository for MACE model definitions, conversion, and foundation-model export.
sevennet	Torch	MDIL-SNU/SevenNet	Torch SevenNet checkpoints and models.
orb	Torch	orbital-materials/orb-models	Torch ORB force-field models.
ani	Torch, JAX	aiqm/torchani	Torch uses TorchANI; JAX uses a JAX-native bundle.
m3gnet	Torch, JAX	materialsvirtuallab/matgl	Torch uses MatGL; JAX uses a JAX-native bundle.

For MACE, use mace-model for model construction/conversion and equitrain for preprocessing, training, fine-tuning, checkpointing, evaluation, and prediction.

Documentation

Full documentation is published at https://bamescience.github.io/equitrain/:

• Installation
• Quickstart
• Data and Preprocessing
• CLI
• Training Options
• Python API
• Model Wrappers
• JAX Bundles
• Fine-Tuning
• Calculators
• Reaction-Relative Losses
• Resources

The documentation source is in docs/. Build or serve it locally with:

pip install -e '.[docu]'
mkdocs serve

Installation

pip install equitrain

Until the package is fully available on PyPI, install from a local clone:

git clone https://github.com/BAMeScience/equitrain.git
cd equitrain
python3.10 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install uv
uv pip install -e '.[dev,docu]'

Install model/runtime extras as needed:

pip install 'equitrain[torch,mace]'
pip install 'equitrain[jax,mace-jax]'
pip install 'equitrain[torch,ani]'

Minimal Workflow

Preprocess data:

equitrain-preprocess \
    --train-file data-train.xyz \
    --valid-file data-valid.xyz \
    --compute-statistics \
    --atomic-energies average \
    --output-dir data \
    --r-max 4.5

Train a Torch/MACE model:

equitrain -v \
    --train-file data/train.h5 \
    --valid-file data/valid.h5 \
    --output-dir runs/mace \
    --model path/to/mace.model \
    --model-wrapper mace \
    --epochs 10 \
    --tqdm

Evaluate and predict:

equitrain-evaluate -v \
    --test-file data/test.h5 \
    --model path/to/mace.model \
    --model-wrapper mace \
    --output-dir evaluation_mace

equitrain-predict \
    --predict-file data/valid.h5 \
    --model path/to/mace.model \
    --model-wrapper mace \
    --output-dir predictions_mace

See the Quickstart for the full workflow, including JAX bundles and fine-tuned checkpoint export.

Fine-Tuning Note

Equitrain's Delta adapter is a residual-parameter implementation of L²-SP ("Starting Point") regularization from Li, Grandvalet, and Davoine, 2018, Explicit Inductive Bias for Transfer Learning with Convolutional Networks. It parameterizes fine-tuning as theta = theta_0 + delta, so weight decay on trainable deltas regularizes ||delta||_2^2.

Delta combined with freeze_layers is targeted L²-SP (L²-TSP): the L²-SP penalty applies only to selected trainable delta layers while frozen layers remain exactly at their pre-trained starting values. See Fine-Tuning.

Resources

Example data-preparation scripts are in resources/data, training scripts are in resources/training, and initial model examples are in resources/models.