This repository contains the Matrix Function Network (MFN) implementation developed by Ilyes Batatia, Lars Schaaf, and Felix Faber. Most of this repo reproduces the MACE repo (https://github.com/ACEsuit/mace). Please refer to the original repo for more information.
Requirements:
- Python >= 3.7
- PyTorch >= 1.8
If you do not have CUDA pre-installed, it is recommended to follow the conda installation process:
# Create a virtual environment and activate it
conda create mfn_env
conda activate mfn_env
# Install PyTorch
conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch-lts -c conda-forge
# Clone and install MACE (and all required packages), use token if still private repo
git clone [email protected]:ilyes319/mfn.git
pip install ./mfnTo install via pip, follow the steps below:
# Create a virtual environment and activate it
python -m venv mfn-venv
source mfn-venv/bin/activate
# Install PyTorch (for example, for CUDA 10.2 [cu102])
pip install torch==1.8.2 --extra-index-url "https://download.pytorch.org/whl/lts/1.8/cu102"
# Clone and install MACE (and all required packages)
git clone [email protected]:ilyes319/mfn.git
pip install ./mfnNote: The homonymous package on PyPI has nothing to do with this one.
To reproduce the MFN model on the cumulene data, you can use the run_train.py script:
python ./mfn/scripts/run_train.py \
--name="mace_mfn_cumulene" \
--train_file="GNL-v0.2/gnl-v0.2-train.xyz" \
--valid_file="GNL-v0.2/gnl-v0.2-val.xyz" \
--test_file="GNL-v0.2/gnl-v0.2-test.xyz" \
--energy_weight=10.0 \
--forces_weight=100.0 \
--config_type_weights="{'Default':1.0}" \
--E0s="average" \
--model="ScaleShiftMFN_MACE" \
--num_features_matrix=16 \
--num_poles=16 \
--num_cutoff_basis=5 \
--num_radial_basis=8 \
--lr=0.01 \
--interaction_first="RealAgnosticResidualInteractionBlock" \
--interaction="RealAgnosticResidualInteractionBlock" \
--num_interactions=2 \
--max_ell=3 \
--hidden_irreps="64x0e + 64x1o" \
--correlation=3 \
--r_max=3.0 \
--scaling="rms_forces_scaling" \
--swa \
--swa_forces_weight=1000.0 \
--swa_energy_weight=1000.0 \
--batch_size=4 \
--max_num_epochs=800 \
--start_swa=400 \
--patience=256 \
--ema \
--ema_decay=0.99 \
--amsgrad \
--default_dtype="float64" \
--clip_grad=10 \
--device=cuda \
--seed=4 \
--restart_latest \You can download the datasets here: https://github.com/LarsSchaaf/Guaranteed-Non-Local-Molecular-Dataset
If you use this code, please cite our papers:
@misc{batatia2024equivariant,
title={Equivariant Matrix Function Neural Networks},
author={Ilyes Batatia and Lars L. Schaaf and Huajie Chen and Gábor Csányi and Christoph Ortner and Felix A. Faber},
year={2024},
eprint={2310.10434},
archivePrefix={arXiv},
primaryClass={stat.ML}
}
@misc{Batatia2022MACE,
title = {MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields},
author = {Batatia, Ilyes and Kov{\'a}cs, D{\'a}vid P{\'e}ter and Simm, Gregor N. C. and Ortner, Christoph and Cs{\'a}nyi, G{\'a}bor},
year = {2022},
number = {arXiv:2206.07697},
eprint = {2206.07697},
eprinttype = {arxiv},
doi = {10.48550/ARXIV.2206.07697},
archiveprefix = {arXiv}
}
If you have any questions, please contact us at [email protected].
MFN is published and distributed under the MIT license.
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