NEP

# Model Introduction NEP (Neural Evolution Potential) is a neural-network potential training example based on MatPL. It learns material interaction information such as energy, forces, and virial from atomic structure data. # Model Description NEP is based on the MatPL framework and is trained on atomic-system data such as Cu and LiSiC. It performs interatomic potential function training and molecular dynamics simulation for materials systems. # Applicable Scenarios | Scenario | Description | | :---: | :--- | | Cu system NEP training | Train the Cu potential using `demo/nep_Cu/Cu_nep_train.json` | | LiSiC system NEP training | Train the LiSiC potential using `demo/nep_LiSiC/LiSiC_nep_train.json` | | SLURM job submission | Refer to `demo/nep_Cu/submit.sh` to run training on a cluster | | Custom data migration | Organize your data into MatPL-supported formats such as `pwmat/movement` and replace the training path | # 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** - DCU or GPU 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/NEP --local_dir ./nep cd nep ``` ### 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 ``` ### Install MatPL ```bash # Uses the test_pip environment by default; if you use another conda environment name, please specify it first: # export MATCHEM_CONDA_NAME=your_env bash matpl_install.sh ``` ### Training Data Description This repository does not include built-in training data. Taking the MatPL dataset as an example, download it from ModelScope and place it under `data/` in the repository root: ```bash modelscope download --dataset OneScience/MatPL --local_dir ./data ``` After downloading, the data path will be `data/MatPL/`. ### Training Single-GPU: ```bash cd demo/nep_Cu MatPL train Cu_nep_train.json ``` SLURM submission: ```bash cd demo/nep_Cu bash submit.sh ``` ### 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 NEP example code comes from the OneScience repository. This repository retains the source attribution and is organized for OneScience ModelScope automatic execution scenarios. - If you use NEP or MatPL training results in scientific research, we recommend citing the relevant OneScience project information, MatPL/NEP methods, and the sources of the datasets actually used.