The DPA4 model family traces a Pareto frontier across Matbench Discovery CPS and saturated inference throughput.
## ⚡ Why DeePMD-kit | | Advantage | What it unlocks | | --- | ----------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | 🧬 | **Pretrained-first workflows** | Download [pretrained DPA4 models][dpa4-omat24], fine-tune full models, or adapt supported pretrained representations to downstream properties with [DPA-ADAPT]. | | 🏗️ | **Training from scratch** | Design a model for a new system or physical target, then train it with single-task, multi-task, and distributed workflows across supported backends. | | 🧠 | **Modern model portfolio** | For conservative energy/force interatomic potentials, start with [DPA4] for accuracy or [DPA4C] for simulation throughput and scale. | | 🧲 | **More than energy and force** | Model virials, Hessians, spin and magnetic forces, dipoles, polarizabilities, electronic density of states, atomic populations, and arbitrary intensive or extensive properties. | | 🔄 | **Backend flexibility** | Train or run supported models with [TensorFlow, PyTorch, JAX, or Paddle][backends], with backend-aware model formats and conversion paths for compatible architectures. | | 🚀 | **Performance from training to MD** | Use CPUs, CUDA GPUs, ROCm source builds, distributed training, compiled DPA4 paths, compressed DPA4C CUDA inference, AOTInductor `.pt2` export, and MPI-enabled simulation. | | 🔌 | **Deploy where science happens** | Use the CLI, Python, C, C++, or Node.js, then connect models to LAMMPS, i-PI, ASE, GROMACS, JAX MD, nvalchemi, OpenMM, Amber, CP2K, ABACUS, and more. | | 🧩 | **Open and extensible** | Compose hybrid potentials, add analytical ZBL or long-range corrections, create custom models and operators, or connect external GNNs such as MACE and NequIP through plugins. | > [!TIP] > On supported descriptors and workloads, [model compression][compression] can > deliver more than **10× inference speedup** and reduce memory usage by as much > as **20×**. Actual gains depend on the model, system, and hardware. Backend and interface support varies by model and feature. The [web documentation][documentation] marks compatibility and limitations on each feature page. ## 🧭 Two starting points, one path to dynamics ```mermaid flowchart LR A["Pretrained DPA4 model"] --> C["Fine-tune on target data"] B["Model configuration"] --> D["Train from scratch"] E["Target reference data"] --> C E --> D C --> F["Test, compress, export"] D --> F F --> G["Python and native APIs"] F --> H["Molecular dynamics"] ``` 1. **Choose a starting point:** download a pretrained DPA4 checkpoint for adaptation, or configure DPA4 or DPA4C to train from scratch. 1. **Prepare target data** in DeePMD's NumPy format or convert structures and trajectories with [dpdata][data]. 1. **Fine-tune or train:** adapt the full pretrained DPA4 model, or optimize a new DPA4 or DPA4C model with single-task, multi-task, and distributed training workflows. 1. **Validate and export** with [`dp test`][testing], [`dp freeze`][freeze], backend conversion, embedding extraction, and supported compression paths. 1. **Run simulation** through Python or native APIs, or load the model into a supported molecular-dynamics engine. ## 🚀 Start in minutes DeePMD-kit requires Python 3.10 or later. The fastest installation path is: ```bash curl -fsSL https://dp1s.deepmodeling.com | bash dp --version dp -h ``` The [installation guide][installation] covers pip, conda-forge, containers, offline packages, GPU builds, LAMMPS, i-PI, and source installation. ### Fine-tune a pretrained DPA4 model Download a built-in checkpoint, start from its matching released training configuration, and fine-tune it on your target data. This example uses DPA4-Neo, one of the recommended general-purpose sizes: ```bash dp pretrained download DPA4-Neo-OMat24-v20260805 curl -fsSL \ https://huggingface.co/deepmodelingcommunity/DPA4-OMat24/resolve/main/DPA4-Neo-OMat24-v20260805.json \ -o input_finetune.json ``` The [DPA4 OMat24 release][dpa4-omat24] provides Nano, Mini, Neo, Air, and Plus checkpoints together with their matching training configurations. The downloaded `input_finetune.json` matches the Neo checkpoint above; for another size or version, use the correspondingly named JSON file. Keep its complete `model` section unchanged, including the full-periodic-table `type_map`; replace the training and validation data, and use a smaller learning rate for fine-tuning. Then run: ```bash dp --pt train input_finetune.json \ --finetune ~/.cache/deepmd/pretrained/models/DPA4-Neo-OMat24-v20260805.pt ``` These are PyTorch single-task checkpoints, so no model branch selection is needed. They target inorganic materials in the OMat24 chemical space; validate accuracy before using them outside that domain. The [fine-tuning guide][finetune] covers full-model adaptation. [DPA-ADAPT] reuses supported pretrained DPA representations for downstream property-prediction tasks. Pretrained model names can also be resolved and cached automatically by Python: ```python from deepmd.infer import DeepPot potential = DeepPot("DPA4-Neo-OMat24-v20260805") ``` ### Train a model from scratch Training from scratch remains a first-class workflow for new architectures, fully custom systems, and physical targets without a suitable pretrained checkpoint. Clone the examples and start with the compact water system: ```bash git clone https://github.com/deepmodeling/deepmd-kit.git cd deepmd-kit/examples/water/dpa4 # Accuracy-first DPA4 model dp --pt train input.json # Or the throughput-first DPA4C model cd ../dpa4c dp --pt-expt train input.json ``` Ready-to-run inputs include: - [DPA4 water training](./examples/water/dpa4/input.json) - [DPA4C high-throughput water training](./examples/water/dpa4c/input.json) - [DPA4 multi-task training](./examples/water/dpa4/input_multitask.json) - [DPA-ADAPT property prediction](./examples/dpa_adapt/README.md) For a guided end-to-end example, open the [web quick-start notebook][quick-start]. ## 🧠 Choose a model family For conservative energy/force interatomic potentials, start with the DPA4 family. The choice between its two primary models follows the constraint that matters most for your workload: | Priority | Start with | Why | | ---------------------------------- | ---------- | ---------------------------------------------------------------------------------------------------- | | Highest accuracy | [DPA4] | SO(3)-equivariant message passing targets the accuracy frontier. | | Highest throughput or system scale | [DPA4C] | A compact one-hop descriptor targets the throughput frontier and supports compressed CUDA inference. | DPA4 uses the PyTorch backend (`dp --pt`). DPA4C currently uses the PyTorch Exportable backend (`dp --pt-expt`); its compressed CUDA path requires `float32`. For other physical targets, use the [model guide][model-guide] to select a compatible model and backend. The guide also compares data formats, precision, compression, and deployment constraints.
For energy/force potentials, DPA4 and DPA4C span accuracy–throughput trade-offs for different deployment budgets.
## 🔬 Go beyond conventional force fields | Goal | DeePMD-kit capabilities | | -------------------------------------- | ------------------------------------------------------------------------------------------------------------------ | | **Potential-energy surfaces** | Energy, atomic forces, virials, Hessians, hybrid descriptors, pair tables, and linear model combinations | | **Magnetic systems** | Spin-aware descriptors, atomic and magnetic forces, and spin-capable molecular dynamics | | **Electronic and response properties** | Dipoles, polarizabilities, density of states, atomic charge populations, and custom property heads | | **Long- and short-range physics** | DPLR electrostatics, DPRc range correction for QM/MM, and analytical ZBL bridging | | **Representation learning** | Per-atom descriptors, fitting-network features, structural embeddings, clustering, and downstream auxiliary models | Explore the complete set of [models and physical targets][model-guide] in the web documentation. ## 🔌 Deploy into the scientific ecosystem ### Inference interfaces - [Python][python-inference] - [C and C++][native-inference] - [Node.js][node-inference] - [Model embedding export][embeddings] ### Simulation and workflow integrations - [LAMMPS], [i-PI][ipi], [ASE], [JAX MD][jax-md], and [nvalchemi] - Ecosystem integrations for OpenMM, Amber, CP2K, GROMACS, ABACUS, DP-GEN, and MLatom - External MACE and NequIP models through the DeePMD-GNN plugin See the [integration hub][integrations] for maintained interfaces, third-party projects, supported scope, and installation guidance. The native C and C++ interfaces load machine-learning backends as runtime plugins. Applications can therefore open the backend required by a model without directly linking every framework. > [!NOTE] > Working with an AI coding or scientific agent? Start with > [Install with an AI agent][agent-install], or browse the > [official Agent Skills][agent-skills] for model selection, training, > fine-tuning, Python inference, and LAMMPS workflows. > > ```bash > npx -y skills add https://github.com/deepmodeling/deepmd-kit/tree/master/skills \ > --skill deepmd-install -y > ``` > > If direct GitHub access fails, clone the official Gitee mirror and install > from the local checkout: > > ```bash > git clone --depth 1 \ > https://gitee.com/deepmodeling/deepmd-kit.git \ > deepmd-kit-skill-source > npx -y skills add ./deepmd-kit-skill-source/skills \ > --skill deepmd-install -y > ``` ## 📚 Documentation and community - Read the [full web documentation][documentation]. - Follow hands-on material in the [DeepModeling tutorials][tutorials]. - Browse [examples](./examples) for training, inference, and integrations. - Ask questions or report problems in [GitHub Issues](https://github.com/deepmodeling/deepmd-kit/issues). - Join development through the [contributing guide](./CONTRIBUTING.md). ## Citation If DeePMD-kit contributes to published work, cite the general software paper that matches the version used and the method-specific papers listed in [CITATIONS.bib](./CITATIONS.bib): - Wang et al., “DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics,” *Computer Physics Communications* 228 (2018), 178–184 (describes the initial version). [](https://doi.org/10.1016/j.cpc.2018.03.016) [](https://badge.dimensions.ai/details/doi/10.1016/j.cpc.2018.03.016) - Zeng et al., “DeePMD-kit v2: A software package for Deep Potential models,” *The Journal of Chemical Physics* 159 (2023), 054801 (covers features until v2.2.3). [](https://doi.org/10.1063/5.0155600) [](https://badge.dimensions.ai/details/doi/10.1063/5.0155600) - Zeng et al., “DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials,” *Journal of Chemical Theory and Computation* 21 (2025), 4375–4385 (covers features until v3.0). [](https://doi.org/10.1021/acs.jctc.5c00340) [](https://badge.dimensions.ai/details/doi/10.1021/acs.jctc.5c00340) ## License DeePMD-kit is licensed under the [GNU Lesser General Public License v3.0 or later](./LICENSE). [agent-install]: https://docs.deepmodeling.com/projects/deepmd/en/latest/install/install-with-agent.html [agent-skills]: https://docs.deepmodeling.com/projects/deepmd/en/latest/agent-skills.html [ase]: https://docs.deepmodeling.com/projects/deepmd/en/latest/third-party/ase.html [backends]: https://docs.deepmodeling.com/projects/deepmd/en/latest/backend.html [compression]: https://docs.deepmodeling.com/projects/deepmd/en/latest/freeze/compress.html [data]: https://docs.deepmodeling.com/projects/deepmd/en/latest/data/dpdata.html [documentation]: https://docs.deepmodeling.com/projects/deepmd/en/latest/ [dpa-adapt]: https://docs.deepmodeling.com/projects/deepmd/en/latest/dpa_adapt/overview.html [dpa4]: https://docs.deepmodeling.com/projects/deepmd/en/latest/model/dpa4.html [dpa4-omat24]: https://huggingface.co/deepmodelingcommunity/DPA4-OMat24 [dpa4c]: https://docs.deepmodeling.com/projects/deepmd/en/latest/model/dpa4c.html [embeddings]: https://docs.deepmodeling.com/projects/deepmd/en/latest/inference/embedding.html [finetune]: https://docs.deepmodeling.com/projects/deepmd/en/latest/train/finetuning.html [freeze]: https://docs.deepmodeling.com/projects/deepmd/en/latest/freeze/freeze.html [installation]: https://docs.deepmodeling.com/projects/deepmd/en/latest/install/easy-install.html [integrations]: https://docs.deepmodeling.com/projects/deepmd/en/latest/third-party/index.html [ipi]: https://docs.deepmodeling.com/projects/deepmd/en/latest/third-party/ipi.html [jax-md]: https://docs.deepmodeling.com/projects/deepmd/en/latest/third-party/jaxmd.html [lammps]: https://docs.deepmodeling.com/projects/deepmd/en/latest/third-party/lammps-command.html [logo-guide]: https://docs.deepmodeling.com/projects/deepmd/en/latest/logo.html [model-guide]: https://docs.deepmodeling.com/projects/deepmd/en/latest/model/index.html [native-inference]: https://docs.deepmodeling.com/projects/deepmd/en/latest/inference/cxx.html [node-inference]: https://docs.deepmodeling.com/projects/deepmd/en/latest/inference/nodejs.html [nvalchemi]: https://docs.deepmodeling.com/projects/deepmd/en/latest/third-party/nvalchemi.html [pretrained]: https://docs.deepmodeling.com/projects/deepmd/en/latest/model/pretrained.html [python-inference]: https://docs.deepmodeling.com/projects/deepmd/en/latest/inference/python.html [quick-start]: https://docs.deepmodeling.com/projects/deepmd/en/latest/getting-started/quick_start.html [releases]: https://github.com/deepmodeling/deepmd-kit/releases [testing]: https://docs.deepmodeling.com/projects/deepmd/en/latest/test/test.html [tutorials]: https://tutorials.deepmodeling.com/