DeePMD

# 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.