Eqnorm

Introduction

We propose a novel E(3)-equivariant GNN-based MLIP eqnorm, which employs a dual-branch design: one branch updates high-order tensor features to capture geometric symmetries, while the other one refines scalar embeddings by extracting information from the tensor branch.

Figure 1. Eqnorm architecture.

Trained on the MPtrj dataset, eqnorm ranks second among state-of-the-art models on the Matbench Discovery leaderboard for physical property prediction.

Figure 2. Matbench discovery compliant models leaderboard. See Matbench Discovery.

We used the exact benchmark code found in mace_benchmark with mace-torch v0.3.12 (PyPi) to test MD simulation using different MLIPs. To create a fair comparison, all models are benchmarked using the standard Python (3.10) runtime with Pytorch v2.6.0 and CUDA 11.8. No compile/torchscript was used. Notably, eqnorm achieves a fivefold improvement in inference speed over the leading model, eSEN, while enabling simulations of systems threefold larger in size.

Table 1. Inference efficiency test of MD simulation with 1 fs/step on a single Nvidia A40-48G GPU (speed in ns per day / consumed GPU memory).

Installation

Requirement

python>=3.10
torch>=2.6.0
torch_scatter

for example, install torch-2.6 with cuda 11.8 and torch_scatter:

conda create -n eqnorm python=3.10
conda activate eqnorm
pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu118
pip install torch_scatter -f https://data.pyg.org/whl/torch-2.6.0+cu118.html

After installation of PyTorch, run the following command:

pip install git+https://github.com/yzchen08/eqnorm.git

Usage

ase calculator

from eqnorm.calculator import EqnormCalculator
calc = EqnormCalculator(model_name='eqnorm', model_variant='eqnorm-mptrj', device='cuda')

you can check all available ckpt, for now only eqnorm-mptrj is available:

from eqnorm.calculator import EqnormCalculator
print(EqnormCalculator.url_dict)