| <p align="center"> |
| <strong> |
| <span style="font-size: 30px;">MACE</span> |
| </strong> |
| </p> |
| |
| # Model Introduction |
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| 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. |
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| Paper: *MACE: Higher order equivariant message passing neural networks for fast and accurate force fields* |
| Reference implementation: https://github.com/ACEsuit/mace |
|
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| # Model Description |
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| 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. |
|
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| # Applicable Scenarios |
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| | 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 | |
|
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| # Usage Instructions |
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| ## 1. Using OneCode |
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| You can try out intelligent one-click AI4S programming in the OneCode online environment: |
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| [Try intelligent one-click AI4S programming](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home) |
|
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| ## 2. Manual Installation and Usage |
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| **Hardware Requirements** |
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| - 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 |
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| ```bash |
| modelscope download --model OneScience/MACE --local_dir ./mace |
| cd mace |
| ``` |
|
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| ### Install the Runtime Environment |
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| **DCU Environment** |
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| ```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 |
| ``` |
|
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| **GPU Environment** |
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| ```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 |
| ``` |
|
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|
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| ### Training Data Description |
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| 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: |
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| ```bash |
| modelscope download --dataset OneScience/DMC --local_dir ./data |
| ``` |
|
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| 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. |
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| For other configurations (e.g., `ani1x_8dcu.yaml`, `water_*.yaml`, etc.), download the corresponding datasets and adjust the data paths in the YAML file. |
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|
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| ### Training |
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| Single-GPU: |
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| ```bash |
| bash scripts/demo/run.sh --config scripts/demo/configs/DMC.yaml |
| ``` |
|
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| Multi-GPU: |
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| ```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 |
| ``` |
|
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| SLURM submission: |
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| ```bash |
| bash scripts/demo/run.sh --config scripts/demo/configs/ani1x_8dcu.yaml --submit |
| ``` |
|
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| ### Training Weights |
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| This repository currently does not include built-in trained weights. Weights can be obtained through the training steps above. |
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| # OneScience Official Information |
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| | 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 | |
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| # Citation and License |
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| - 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. |
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