3BPA molecular dynamics using nequip-eqx.

nequip-eqx

nequip-eqx is a JAX implementation of the neural network interatomic potential NequIP, introduced by Batzner et al. in E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials.

The goal of this repository is to offer a simple (<1000 lines of code) implementation while providing competitive performance to existing implementations.

Usage

Installation

pip install nequip-eqx

Training

Models are trained with the nequip_eqx_train command using a single .yml configuration file:

nequip_eqx_train <config>.yml

See configs/3bpa.yml for an example configuration file for training on the 3BPA dataset. Pretrained weights for 3BPA are available in the models/ directory.

tar -C data -xf data/dataset_3BPA.tar.gz  # decompress data
nequip_eqx_train configs/3bpa.yml # takes ~20 hrs on an NVIDIA RTX A5500

Testing

Models can be evaluated with the nequip_eqx_test command by supplying a path to a pretrained model, and a test .xyz file, e.g.:

nequip_eqx_test \
    --model models/nequip_3bpa.eqx \
    --file data/dataset_3BPA/test_300K.xyz

ASE calculator

Using nequip_eqx.calculator.NequipCalculator, you can perform calculations in ASE with a pre-trained NequIP model.

import ase.io
from ase.md.langevin import Langevin
from ase import units

from nequip_eqx.calculator import NequipCalculator

atoms = ase.io.read("data/dataset_3BPA/test_300K.xyz", index=0, format="extxyz")
atoms.calc = NequipCalculator("models/nequip_3bpa.eqx")

dyn = Langevin(
    atoms,
    timestep=0.5 * units.fs,
    temperature_K=300,
    friction=0.01,
    trajectory="md.traj",
)
dyn.run(steps=1000)

Comparison with other codes

In order to verify correctness of the implementation, we compare performance on the 3BPA dataset to two different PyTorch NequIP implementations:

1. The results from Musaelian et al. in "Learning local equivariant representations for large-scale atomistic dynamics", using a version of their nequip repository.
2. The results from Batatia et al. in "MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields", using a version of their mace repository.

We use the same hyperparameters as [1], which can be viewed in configs/3bpa.yml, with the following exceptions:

• Instead of hidden irreps of 64x0e + 64x0o + 64x1o + 64x1e + 64x2e + 64x2o + 64x3o + 64x3e, we opt for 128x0e + 128x1o + 128x2e + 128x3o for simplicity with the same feature dimensions.
• It is not clear what initialization was originally used for the radial MLP, but we use Kaiming normal, i.e. sampling from $\mathcal{N}(0, \mathrm{std})$ with $\mathrm{std} = \sqrt{\frac{4.0}{\mathrm{fan\_in}}}$.
• Isolated atom energies are added to each predicted node energy.

Resulting energy (E) and force (F) errors in meV and meV/Å respectively.

See also

• mariogeiger/nequip-jax: Another basic implementation of a NequIP style model in JAX.
• e3nn/e3nn-jax: JAX library for E(3)-equivariant neural networks used in this repo.
• patrick-kidger/equinox: JAX library for building neural network architectures, used in this repo.

Citations

@article{batzner20223,
  title={E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials},
  author={Batzner, Simon and Musaelian, Albert and Sun, Lixin and Geiger, Mario and Mailoa, Jonathan P and Kornbluth, Mordechai and Molinari, Nicola and Smidt, Tess E and Kozinsky, Boris},
  journal={Nature communications},
  volume={13},
  number={1},
  pages={2453},
  year={2022},
  publisher={Nature Publishing Group UK London}
}

@article{musaelian2023learning,
  title={Learning local equivariant representations for large-scale atomistic dynamics},
  author={Musaelian, Albert and Batzner, Simon and Johansson, Anders and Sun, Lixin and Owen, Cameron J and Kornbluth, Mordechai and Kozinsky, Boris},
  journal={Nature Communications},
  volume={14},
  number={1},
  pages={579},
  year={2023},
  publisher={Nature Publishing Group UK London}
}

@article{batatia2022mace,
  title={MACE: Higher order equivariant message passing neural networks for fast and accurate force fields},
  author={Batatia, Ilyes and Kovacs, David P and Simm, Gregor and Ortner, Christoph and Cs{\'a}nyi, G{\'a}bor},
  journal={Advances in neural information processing systems},
  volume={35},
  pages={11423--11436},
  year={2022}
}