README.md

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  <strong>
    <span style="font-size: 30px;">NEP</span>
  </strong>
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# 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.