---
license: lgpl-3.0
tasks:
  - materials-simulation
  - molecular-dynamics
  - energy-prediction
  - force-prediction
  - training
frameworks:
  - pytorch
language:
  - en
tags:
  - OneScience
  - DeePMD
  - machine-learning-potential
  - molecular-simulation
  - materials-computing
  - DeepMD-kit
  - training
datasets:
  - OneScience-Sugon/DeePMD
---
<p align="center">
  <strong>
    <span style="font-size: 30px;">DeePMD</span>
  </strong>
</p>

# Model Introduction

DeePMD is a Deep Potential Molecular Dynamics model ecosystem for training machine-learning potentials for atomic systems. It provides minimal runnable training entry points based on PyTorch and TensorFlow backends.

Reference implementation: DeepMD-kit

# Model Description

DeePMD uses a deep neural network architecture and atomic-system data to train interatomic potentials for molecular and materials systems and to run molecular dynamics simulations.

# Use Cases

| Use case | Description |
| :---: | :--- |
| DeePMD water training | Train a water potential with configurations such as `demo/water_se_e2_a_pt/input_torch.json` |
| Multi-GPU SLURM jobs | See `demo/water_se_e2_a_pt/submit_4card.sh` and `submit_8card.sh` |
| Environment connectivity check | Run `dp_install.sh` to verify that DeepMD-kit can be installed |
| Custom data migration | Replace the `systems` path in the configuration with your own DeepMD NumPy data |

# 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 GPU or DCU is recommended for training.
- A CPU can be used for installation checks and small-data 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/DeePMD --local-dir ./deepmd
cd deepmd
```

### 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 DeepMD-kit

```bash
# The test_pip environment is used by default. To use another conda environment:
# export MATCHEM_CONDA_NAME=your_env
bash dp_install.sh
```

### Training Data

Training data is not bundled with this repository. Using the DeePMD water dataset as an example, download it from Hugging Face to `data/` in the repository root:

```bash
hf download --dataset OneScience-Sugon/DeePMD --local-dir ./data
```

After downloading, the data is located at `data/DeePMD/water/data_0..3/`.

### Training

Single GPU:

```bash
cd demo/water_se_e2_a_pt
dp --pt train input_torch.json
```

Multiple GPUs:

```bash
cd demo/water_se_e2_a_pt
bash submit_4card.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 DeePMD example code comes from the MatChem examples in the OneScience project and refers to the upstream DeepMD-kit project. See the official upstream repository for the DeepMD-kit license.
- If you use DeePMD training results in research, please cite DeepMD-kit, the relevant OneScience projects, and the datasets used.