| --- |
| license: gpl-3.0 |
| tasks: |
| - materials-simulation |
| - energy-prediction |
| - force-prediction |
| - virial-prediction |
| - training |
| frameworks: |
| - pytorch |
| language: |
| - en |
| tags: |
| - OneScience |
| - NEP |
| - MatPL |
| - machine-learning-potential |
| - molecular-simulation |
| - materials-computing |
| - training |
| datasets: |
| - OneScience-Sugon/MatPL |
| --- |
| |
| <p align="center"> |
| <strong> |
| <span style="font-size: 30px;">NEP</span> |
| </strong> |
| </p> |
| |
| # Model Introduction |
|
|
| NEP (Neural Evolution Potential) is a MatPL-based neural-network potential training example that learns material interaction information, including energies, forces, and virials, from atomic-structure data. |
|
|
| # Model Description |
|
|
| NEP is built on the MatPL framework and trained with data from atomic systems such as Cu and LiSiC. It supports interatomic-potential training and molecular dynamics simulations for materials systems. |
|
|
| # Use Cases |
|
|
| | Use case | Description | |
| | :---: | :--- | |
| | NEP training for Cu systems | Train a Cu potential with `demo/nep_Cu/Cu_nep_train.json` | |
| | NEP training for LiSiC systems | Train a LiSiC potential with `demo/nep_LiSiC/LiSiC_nep_train.json` | |
| | SLURM job submission | See `demo/nep_Cu/submit.sh` for cluster training | |
| | Custom data migration | Convert your data to a MatPL-supported format such as `pwmat/movement`, then replace the training path | |
|
|
| # 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 DCU or GPU 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/NEP --local-dir ./nep |
| cd nep |
| ``` |
|
|
| ### 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 |
| ``` |
|
|
| ### Install MatPL |
|
|
| ```bash |
| # The test_pip environment is used by default. To use another conda environment: |
| # export MATCHEM_CONDA_NAME=your_env |
| bash matpl_install.sh |
| ``` |
|
|
| ### Training Data |
|
|
| Training data is not bundled with this repository. Using the MatPL dataset as an example, download it from Hugging Face to `data/` in the repository root: |
|
|
| ```bash |
| hf download --dataset OneScience-Sugon/MatPL --local-dir ./data |
| ``` |
|
|
| After downloading, the data is located at `data/MatPL/`. |
|
|
| ### Training |
|
|
| Single GPU: |
|
|
| ```bash |
| cd demo/nep_Cu |
| MatPL train Cu_nep_train.json |
| ``` |
|
|
| SLURM: |
|
|
| ```bash |
| cd demo/nep_Cu |
| bash submit.sh |
| ``` |
|
|
| ### 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 NEP example code comes from the OneScience repository. This repository retains source attribution and has been organized for automated execution in the OneScience Hugging Face environment. |
| - If you use NEP or MatPL training results in research, please cite the relevant OneScience projects, MatPL/NEP methods, and the datasets used. |
|
|
