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MatterTune: A Unified Platform for Atomistic Foundation Model Fine-Tuning

Documentation Status

📚 Documentation | 🔧 Installation Guide

MatterTune is a flexible and powerful machine learning library designed specifically for fine-tuning state-of-the-art chemistry foundation models. It provides intuitive interfaces for computational chemists and materials scientists to fine-tune pre-trained models on their specific use cases.

Features

  • Pre-trained model support: JMP, EquiformerV2, M3GNet, ORB, and more to be added.
  • Multiple property predictions: energy, forces, stress, and custom properties.
  • Various supported dataset formats: XYZ, ASE databases, Materials Project, Matbench, and more.
  • Comprehensive training features with automated data splitting and logging.

Quick Start

import mattertune as mt
from pathlib import Path

# Phase 1: Fine-tuning the model
# -----------------------------

# Define the configuration for model, data, and training
config = mt.configs.MatterTunerConfig(
    # Configure the model: using JMP backbone with energy prediction
    model=mt.configs.JMPBackboneConfig(
        ckpt_path=Path("YOUR_CHECKPOINT_PATH"),  # Path to pre-trained model
        properties=[
            mt.configs.EnergyPropertyConfig(  # Configure energy prediction
                loss=mt.configs.MAELossConfig(),  # Using MAE loss
                loss_coefficient=1.0  # Weight for this property's loss
            )
        ],
    ),
    # Configure the data: loading from XYZ file with automatic train/val split
    data=mt.configs.AutoSplitDataModuleConfig(
        dataset=mt.configs.XYZDatasetConfig(
            src=Path("YOUR_XYZFILE_PATH")  # Path to your XYZ data
        ),
        train_split=0.8,  # Use 80% of data for training
        batch_size=32  # Process 32 structures per batch
    ),
    # Configure the training process
    trainer=mt.configs.TrainerConfig(
        max_epochs=10,  # Train for 10 epochs
        accelerator="gpu",  # Use GPU for training
        devices=[0]  # Use first GPU
    ),
)

# Create tuner and start training
tuner = mt.MatterTune(config)
model, trainer = tuner.tune()

# Save the fine-tuned model
trainer.save_checkpoint("finetuned_model.ckpt")

# Phase 2: Using the fine-tuned model
# ----------------------------------

from ase.optimize import BFGS
from ase import Atoms

# Load the fine-tuned model
model = mt.backbones.JMPBackboneModule.load_from_checkpoint("finetuned_model.ckpt")

# Create an ASE calculator from the model
calculator = model.ase_calculator()

# Set up an atomic structure
atoms = Atoms('H2O',
             positions=[[0, 0, 0], [0, 0, 1], [0, 1, 0]],
             cell=[10, 10, 10],
             pbc=True)
atoms.calc = calculator

# Run geometry optimization
opt = BFGS(atoms)
opt.run(fmax=0.01)

# Get results
print("Final energy:", atoms.get_potential_energy())
print("Final forces:", atoms.get_forces())

License

MatterTune's core framework is licensed under the MIT License. Note that each supported model backbone is subject to its own licensing terms - see our license information page of the documentation for more details.

Citation

Coming soon.