OneScience/MACE

MACE

Model Introduction

MACE is a machine-learning interatomic potential (MLIP) model for molecular and materials systems, built on E(3)-equivariant graph neural networks. It predicts energy and forces for atomic structures.

Paper: MACE: Higher order equivariant message passing neural networks for fast and accurate force fields
Reference implementation: https://github.com/ACEsuit/mace

Model Description

MACE is based on an E(3)-equivariant graph neural network architecture and is trained on HDF5/XYZ format data. It performs energy and force prediction and structure optimization for molecular and materials systems.

Applicable Scenarios

Scenario	Description
Interatomic potential training	Train a MACE model using standard configurations that read HDF5/XYZ data
Distributed training pre-check	Check multi-GPU/multi-node training configuration, data paths, and statistics consistency
Validation-set evaluation	Output energy- and force-related error metrics on the validation set during training
Custom data migration	Replace the existing HDF5/XYZ data and statistics files with your own data
Environment connectivity check	Use the pre-check script to verify the OneScience matchem environment, pyyaml, h5py, and data readability

Usage Instructions

1. Using OneCode

You can try out intelligent one-click AI4S programming in the OneCode online environment:

Try intelligent one-click AI4S programming

2. Manual Installation and Usage

Hardware Requirements

• GPU or DCU is recommended for training.
• CPU can be used for import and small-configuration connectivity checks; full training will be slow.
• DCU users need to install DTK in advance. DTK 25.04.2 or above, or the OneScience-recommended version matching the current cluster, is suggested.

Download the Model Package

modelscope download --model OneScience/MACE --local_dir ./mace
cd mace

Install the Runtime Environment

DCU Environment

# Please activate DTK and CONDA first
conda create -n onescience311 python=3.11 -y
conda activate onescience311
# uv installation is also supported
pip install onescience[matchem-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/  --trusted-host mirrors.onescience.ai

GPU Environment

# Please activate CONDA first
conda create -n onescience311 python=3.11 -y libstdcxx-ng=12 libgcc-ng=12 gcc_linux-64=12 gxx_linux-64=12
conda activate onescience311
# uv installation is also supported
pip install onescience[matchem-gpu] -i http://mirrors.onescience.ai:3141/pypi/simple/  --trusted-host mirrors.onescience.ai

Training Data Description

This repository does not include built-in training data. Taking the DMC starter dataset as an example, download it from ModelScope and place it under data/ in the repository root:

modelscope download --dataset OneScience/DMC --local_dir ./data

After downloading, the data path will be data/data/DMC/. scripts/demo/run.sh automatically uses the repository root as ONESCIENCE_DATASETS_DIR, so there is no need to manually set this variable to match the paths in the configuration file.

For other configurations (e.g., ani1x_8dcu.yaml, water_*.yaml, etc.), download the corresponding datasets and adjust the data paths in the YAML file.

Training

Single-GPU:

bash scripts/demo/run.sh --config scripts/demo/configs/DMC.yaml

Multi-GPU:

# Taking 8 GPUs as an example; launch.launcher in the config should be torchrun
bash scripts/demo/run.sh --config scripts/demo/configs/ani1x_8dcu.yaml

SLURM submission:

bash scripts/demo/run.sh --config scripts/demo/configs/ani1x_8dcu.yaml --submit

Training Weights

This repository currently does not include built-in trained weights. Weights can be obtained through the training steps above.

OneScience Official Information

Platform	OneScience Main Repository	Skills Repository
Gitee	https://gitee.com/onescience-ai/onescience	https://gitee.com/onescience-ai/oneskills
GitHub	https://github.com/onescience-ai/OneScience	https://github.com/onescience-ai/oneskills

Citation and License

• The MACE-related code comes from the matchem example implementation in the OneScience project and references the upstream MACE project (https://github.com/ACEsuit/mace). The upstream MACE code is released under the MIT License.
• If you use MACE training results in scientific research, we recommend citing the original MACE paper, the relevant OneScience project information, and the sources of the datasets actually used.