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NEP

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NEP
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---
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.