README.md

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

# 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](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home)

## 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

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

### Install the Runtime Environment

**DCU Environment**

```bash
# 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**

```bash
# 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:

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

Multi-GPU:

```bash
# 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
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](https://github.com/ACEsuit/mace/blob/main/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.