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<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 machine-learning potential function training of atomic systems. It provides a minimal runnable training entry point based on PyTorch/TensorFlow backends.

Reference implementation: DeepMD-kit project

# Model Description

DeePMD is based on a deep neural network architecture and is trained on atomic-system data. It performs interatomic potential function training and molecular dynamics simulation for molecular and materials systems.

# Applicable Scenarios

| Scenario | Description |
| :---: | :--- |
| DeePMD water training | Train the water potential using configurations such as `demo/water_se_e2_a_pt/input_torch.json` |
| Multi-GPU SLURM submission | Refer to `demo/water_se_e2_a_pt/submit_4card.sh` and `submit_8card.sh` |
| Environment connectivity check | Run `dp_install.sh` to check whether DeepMD-kit can be installed |
| Custom data migration | Replace the `systems` paths in the configuration file with your own DeepMD npy data |

# 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**

- GPU or DCU is recommended for training.
- CPU can be used for installation checks and small-data connectivity verification; 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/DeePMD --local_dir ./deepmd
cd deepmd
```

### 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 DeepMD-kit

```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 dp_install.sh
```

### Training Data Description

This repository does not include built-in training data. Taking the DeePMD water dataset as an example, download it from ModelScope and place it under `data/` in the repository root:

```bash
modelscope download --dataset OneScience/DeePMD --local_dir ./data
```

After downloading, the data path will be `data/DeePMD/water/data_0..3/`.

### Training

Single-GPU:

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

Multi-GPU:

```bash
cd demo/water_se_e2_a_pt
bash submit_4card.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 DeePMD example code comes from the matchem example implementation in the OneScience project and references the upstream DeepMD-kit project. For upstream DeepMD-kit licensing information, please refer to its official repository.
- If you use DeePMD training results in scientific research, we recommend citing DeepMD-kit, the relevant OneScience project information, and the sources of the datasets actually used.