DeePMD / README.md

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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

DeePMD

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

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

hf download --model OneScience-Sugon/DeePMD --local-dir ./deepmd
cd deepmd

Install the Runtime Environment

DCU environment

# 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

# 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

# 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:

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:

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

Multiple GPUs:

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.