---
license: mit
tasks:
  - materials-simulation
  - molecular-dynamics
  - energy-prediction
  - force-prediction
frameworks:
  - pytorch
language:
  - en
tags:
  - OneScience
  - MACE
  - machine-learning-potential
  - molecular-simulation
  - materials-computing
  - graph-neural-network
  - equivariant-neural-network
  - training
  - inference
datasets:
  - OneScience-Sugon/DMC
  - OneScience-Sugon/ANI1x
  - OneScience-Sugon/water
  - OneScience-Sugon/nanotube
---

<p align="center">
  <strong>
    <span style="font-size: 30px;">MACE</span>
  </strong>
</p>

# Model Introduction

MACE is a machine-learning interatomic potential (MLIP) for molecular and materials systems. Built on an E(3)-equivariant graph neural network, it predicts the energies and forces of 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 uses an E(3)-equivariant graph neural network architecture and is trained with HDF5/XYZ data. It supports energy and force prediction and structure optimization for molecular and materials systems.

# Use Cases

| Use case | Description |
| :---: | :--- |
| Interatomic-potential training | Read HDF5/XYZ data with a standard configuration and train a MACE model |
| Distributed-training preflight | Check multi-GPU/multi-node settings and ensure that data paths and statistics are consistent |
| Validation-set evaluation | Report energy- and force-related error metrics on the validation set during training |
| Custom data migration | Adapt an existing configuration to your own HDF5/XYZ data and statistics file |
| Environment connectivity check | Use the preflight script to verify the OneScience MatChem environment, PyYAML, h5py, and data readability |

# Usage

## 1. Using OneCode

Try intelligent, one-click AI4S programming in the OneCode online environment:

[Try intelligent, one-click AI4S programming](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home)

## 2. Manual Installation and Usage

**Hardware requirements**

- A GPU or DCU is recommended for training.
- A CPU can be used for import checks and small-configuration connectivity tests, but full training will be slow.
- DCU users must install DTK in advance. DTK 25.04.2 or later, or the OneScience-recommended version matching the current cluster, is recommended.

### Download the Model Package

```bash
hf download --model OneScience-Sugon/MACE --local-dir ./mace
cd mace
```

### Install the Runtime Environment

**DCU environment**

```bash
# 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**

```bash
# 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

Training data is not bundled with this repository. Using the introductory DMC dataset as an example, download it from Hugging Face to `data/` in the repository root:

```bash
hf download --dataset OneScience-Sugon/DMC --local-dir ./data
```

After downloading, the data is located at `data/data/DMC/`. `scripts/demo/run.sh` automatically sets the repository root as `ONESCIENCE_DATASETS_DIR`, so you do not need to set this variable manually for the paths in the configuration file to resolve.

Other configurations, such as `ani1x_8dcu.yaml` and `water_*.yaml`, require the corresponding datasets and adjusted data paths in the YAML files.

### Training

Single GPU:

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

Multiple GPUs:

```bash
# Eight-GPU example; launch.launcher must be set to torchrun in the configuration
bash scripts/demo/run.sh --config scripts/demo/configs/ani1x_8dcu.yaml
```

SLURM:

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

### Trained Weights

This repository does not currently include trained weights. You can obtain them by following the training procedure above.

# Official OneScience Resources

| 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 examples in the OneScience project and refers to the upstream MACE project (https://github.com/ACEsuit/mace). The upstream MACE code is released under the [MIT License](https://github.com/ACEsuit/mace/blob/main/LICENSE).
- If you use MACE training results in research, please cite the original MACE paper, the relevant OneScience projects, and the datasets used.