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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
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
modelscope download --model OneScience/NEP --local_dir ./nep
cd nep
Install the Runtime Environment
DCU Environment
# 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
# 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
# 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:
modelscope download --dataset OneScience/MatPL --local_dir ./data
After downloading, the data path will be data/MatPL/.
Training
Single-GPU:
cd demo/nep_Cu
MatPL train Cu_nep_train.json
SLURM submission:
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.