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](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.