diff --git a/CITATIONS.bib b/CITATIONS.bib index 0fd28323dd..954910d83f 100644 --- a/CITATIONS.bib +++ b/CITATIONS.bib @@ -86,6 +86,22 @@ @article{Zeng_JChemTheoryComput_2025_v21_p4375 }, } +@article{Han_CommunComputPhys_2018_v23_p629, + annote = {Deep Potential}, + title = { + {Deep Potential: A General Representation of a Many-Body Potential Energy + Surface} + }, + author = {Jiequn Han and Linfeng Zhang and Roberto Car and Weinan E}, + journal = {Commun. Comput. Phys.}, + year = 2018, + volume = 23, + number = 3, + pages = {629--639}, + doi = {10.4208/cicp.OA-2017-0213}, + url = {https://arxiv.org/abs/1707.01478}, +} + @article{Lu_CompPhysCommun_2021_v259_p107624, annote = {GPU support}, title = { @@ -120,6 +136,18 @@ @article{Zhang_PhysRevLett_2018_v120_p143001 doi = {10.1103/PhysRevLett.120.143001}, } +@article{Zhang_JChemPhys_2018_v149_p34101, + annote = {coarse-grained model (DeePCG)}, + title = {{DeePCG: Constructing Coarse-Grained Models via Deep Neural Networks}}, + author = {Linfeng Zhang and Jiequn Han and Han Wang and Roberto Car and Weinan E}, + journal = {J. Chem. Phys.}, + year = 2018, + volume = 149, + number = 3, + pages = 034101, + doi = {10.1063/1.5027645}, +} + @incollection{Zhang_BookChap_NIPS_2018_v31_p4436, annote = {DeepPot-SE (se\_e2\_a, se\_e2\_r, se\_e3, se\_atten)}, title = { @@ -196,6 +224,36 @@ @article{Zhang_npjComputMater_2024_v10_p293 doi = {10.1038/s41524-024-01493-2}, } +@misc{Zhang_2025_DPA3, + annote = {DPA-3 descriptor (dpa3)}, + author = { + Duo Zhang and Anyang Peng and Chun Cai and Wentao Li and Yuanchang Zhou and + Jinzhe Zeng and Mingyu Guo and Chengqian Zhang and Bowen Li and Hong Jiang + and Tong Zhu and Weile Jia and Linfeng Zhang and Han Wang + }, + title = {{A Graph Neural Network for the Era of Large Atomistic Models}}, + publisher = {arXiv}, + year = 2025, + doi = {10.48550/arXiv.2506.01686}, + url = {https://arxiv.org/abs/2506.01686}, +} + +@misc{Li_2026_DPA4, + annote = {DPA-4 descriptor (dpa4, sezm)}, + author = { + Tiancheng Li and Wentao Li and Anyang Peng and Jianming Xue and Linfeng + Zhang and Duo Zhang and Han Wang + }, + title = { + {{DPA4}: Pushing the Accuracy-Cost Frontier of Interatomic Potentials with + {EMFA} {SO(2)} Convolution} + }, + publisher = {arXiv}, + year = 2026, + doi = {10.48550/arXiv.2606.02419}, + url = {https://arxiv.org/abs/2606.02419}, +} + @article{Zhang_PhysPlasmas_2020_v27_p122704, annote = {frame-specific parameters (e.g. electronic temperature)}, author = { @@ -262,6 +320,18 @@ @article{Sommers_PhysChemChemPhys_2020_v22_p10592 doi = {10.1039/D0CP01893G}, } +@misc{Ahart_2026_DeepPolaron, + annote = {atomic charge population (DeepPopulation)}, + author = {Christian S. Ahart and Denan Li and Jochen Blumberger and Shi Liu}, + title = { + {Polaron Transport in {TiO}$_{2}$ from Machine Learning Molecular Dynamics} + }, + publisher = {arXiv}, + year = 2026, + doi = {10.48550/arXiv.2606.01763}, + url = {https://arxiv.org/abs/2606.01763}, +} + @article{Zeng_JChemTheoryComput_2023_v19_p1261, annote = {fit relative energies}, author = {Jinzhe Zeng and Yujun Tao and Timothy J Giese and Darrin M York}, diff --git a/README.md b/README.md index a07db0cf9c..f320aa36e9 100644 --- a/README.md +++ b/README.md @@ -1,123 +1,277 @@ -[DeePMD-kit logo](./doc/logo.md) - -______________________________________________________________________ +[DeePMD-kit logo][logo-guide] # DeePMD-kit -[![GitHub release](https://img.shields.io/github/release/deepmodeling/deepmd-kit.svg?maxAge=86400)](https://github.com/deepmodeling/deepmd-kit/releases) -[![offline packages](https://img.shields.io/github/downloads/deepmodeling/deepmd-kit/total?label=offline%20packages)](https://github.com/deepmodeling/deepmd-kit/releases) +**Start from a pretrained Deep Potential model, fine-tune it for your system, +and deploy it at simulation scale.** + +[![GitHub release](https://img.shields.io/github/v/release/deepmodeling/deepmd-kit)][releases] +[![offline packages](https://img.shields.io/github/downloads/deepmodeling/deepmd-kit/total?label=offline%20packages)][releases] [![conda-forge](https://img.shields.io/conda/dn/conda-forge/deepmd-kit?color=red&label=conda-forge&logo=conda-forge)](https://anaconda.org/conda-forge/deepmd-kit) -[![pip install](https://img.shields.io/pypi/dm/deepmd-kit?label=pip%20install)](https://pypi.org/project/deepmd-kit) +[![pip install](https://img.shields.io/pypi/dm/deepmd-kit?label=pip%20install)](https://pypi.org/project/deepmd-kit/) [![docker pull](https://img.shields.io/docker/pulls/deepmodeling/deepmd-kit)](https://hub.docker.com/r/deepmodeling/deepmd-kit) -[![Documentation Status](https://readthedocs.org/projects/deepmd/badge/)](https://deepmd.readthedocs.io/) +[![Documentation Status](https://readthedocs.org/projects/deepmd/badge/)][documentation] +[![License](https://img.shields.io/badge/license-LGPL--3.0--or--later-00a98f)](./LICENSE) + +[**Pretrained models**][pretrained] · [**Fine-tuning**][finetune] · +[**Documentation**][documentation] · [**Quick start**][quick-start] · +[**Model guide**][model-guide] · [**Tutorials**][tutorials] · +[**Examples**](./examples) · [**Releases**][releases] + +> [!IMPORTANT] +> **A pretrained model can be your starting point, not just your end result.** +> Download a built-in DPA checkpoint, fine-tune the full model or a LoRA +> adapter on system-specific data, then test, export, and deploy it through the +> same DeePMD-kit workflow. + +DeePMD-kit turns quantum-mechanical reference data into fast, scalable +interatomic potentials. Use it across molecular and materials science—from +finite molecules and covalent systems to periodic solids and metals—and scale +from laptop fine-tuning to distributed training and MPI-parallel molecular +dynamics. + +

+ DPA4 model family Pareto frontier for Matbench Discovery CPS and saturated inference throughput +

+ +

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 DPA models][pretrained], fine-tune full models or LoRA adapters, or adapt learned 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** | Start with efficient DeepPot-SE descriptors or move to [DPA][model-guide] for large atomistic models. | +| 🧲 | **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, model compression, compiled DPA-4 paths, 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 DPA 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"] +``` -## About DeePMD-kit +1. **Choose a starting point:** download a pretrained DPA checkpoint for + adaptation, or configure a model 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 model or LoRA adapters, + or optimize a new 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. -DeePMD-kit is a package written in Python/C++, designed to minimize the effort required to build deep learning-based model of interatomic potential energy and force field and to perform molecular dynamics (MD). This brings new hopes to addressing the accuracy-versus-efficiency dilemma in molecular simulations. Applications of DeePMD-kit span from finite molecules to extended systems and from metallic systems to chemically bonded systems. +## 🚀 Start in minutes -For more information, check the [documentation](https://deepmd.readthedocs.io/). +DeePMD-kit requires Python 3.10 or later. The fastest installation path is: -### Highlighted features +```bash +curl -fsSL https://dp1s.deepmodeling.com | bash +dp --version +dp -h +``` -- **interfaced with multiple backends**, including TensorFlow, PyTorch, JAX, and Paddle, the most popular deep learning frameworks, making the training process highly automatic and efficient. -- **interfaced with high-performance classical MD and quantum (path-integral) MD packages**, including LAMMPS, i-PI, AMBER, CP2K, GROMACS, OpenMM, and ABACUS. -- **implements the Deep Potential series models**, which have been successfully applied to finite and extended systems, including organic molecules, metals, semiconductors, insulators, etc. -- **implements MPI and GPU supports**, making it highly efficient for high-performance parallel and distributed computing. -- **highly modularized**, easy to adapt to different descriptors for deep learning-based potential energy models. -- **adapts pre-trained DPA models to downstream atomistic property prediction tasks with DPA-ADAPT**, a new Python API and CLI that supports frozen-descriptor scikit-learn heads, frozen property-head training, full end-to-end fine-tuning, and multi-task fine-tuning with an auxiliary force-field task. DPA-ADAPT trains on `deepmd/npy` systems and provides conversion pipelines for SMILES tables and structure or calculation files handled through dpdata. See the [DPA-ADAPT guide](doc/dpa_adapt/overview.md) and supported [input formats](doc/dpa_adapt/input_formats.md). +The [installation guide][installation] covers pip, conda-forge, containers, +offline packages, GPU builds, LAMMPS, i-PI, and source installation. -### License and credits +### Fine-tune from a pretrained DPA model -The project DeePMD-kit is licensed under [GNU LGPLv3.0](./LICENSE). -If you use this code in any future publications, please cite the following publications for general purpose: +Download a built-in checkpoint, inspect its branches, and fine-tune the branch +that matches your target system: -- Han Wang, Linfeng Zhang, Jiequn Han, and Weinan E. "DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics." Computer Physics Communications 228 (2018): 178-184. - [![doi:10.1016/j.cpc.2018.03.016](https://img.shields.io/badge/DOI-10.1016%2Fj.cpc.2018.03.016-blue)](https://doi.org/10.1016/j.cpc.2018.03.016) - [![Citations](https://citations.njzjz.win/10.1016/j.cpc.2018.03.016)](https://badge.dimensions.ai/details/doi/10.1016/j.cpc.2018.03.016) -- Jinzhe Zeng, Duo Zhang, Denghui Lu, Pinghui Mo, Zeyu Li, Yixiao Chen, Marián Rynik, Li'ang Huang, Ziyao Li, Shaochen Shi, Yingze Wang, Haotian Ye, Ping Tuo, Jiabin Yang, Ye Ding, Yifan Li, Davide Tisi, Qiyu Zeng, Han Bao, Yu Xia, Jiameng Huang, Koki Muraoka, Yibo Wang, Junhan Chang, Fengbo Yuan, Sigbjørn Løland Bore, Chun Cai, Yinnian Lin, Bo Wang, Jiayan Xu, Jia-Xin Zhu, Chenxing Luo, Yuzhi Zhang, Rhys E. A. Goodall, Wenshuo Liang, Anurag Kumar Singh, Sikai Yao, Jingchao Zhang, Renata Wentzcovitch, Jiequn Han, Jie Liu, Weile Jia, Darrin M. York, Weinan E, Roberto Car, Linfeng Zhang, Han Wang. "DeePMD-kit v2: A software package for deep potential models." J. Chem. Phys. 159 (2023): 054801. - [![doi:10.1063/5.0155600](https://img.shields.io/badge/DOI-10.1063%2F5.0155600-blue)](https://doi.org/10.1063/5.0155600) - [![Citations](https://citations.njzjz.win/10.1063/5.0155600)](https://badge.dimensions.ai/details/doi/10.1063/5.0155600) -- Jinzhe Zeng, Duo Zhang, Anyang Peng, Xiangyu Zhang, Sensen He, Yan Wang, Xinzijian Liu, Hangrui Bi, Yifan Li, Chun Cai, Chengqian Zhang, Yiming Du, Jia-Xin Zhu, Pinghui Mo, Zhengtao Huang, Qiyu Zeng, Shaochen Shi, Xuejian Qin, Zhaoxi Yu, Chenxing Luo, Ye Ding, Yun-Pei Liu, Ruosong Shi, Zhenyu Wang, Sigbjørn Løland Bore, Junhan Chang, Zhe Deng, Zhaohan Ding, Siyuan Han, Wanrun Jiang, Guolin Ke, Zhaoqing Liu, Denghui Lu, Koki Muraoka, Hananeh Oliaei, Anurag Kumar Singh, Haohui Que, Weihong Xu, Zhangmancang Xu, Yong-Bin Zhuang, Jiayu Dai, Timothy J. Giese, Weile Jia, Ben Xu, Darrin M. York, Linfeng Zhang, Han Wang. "DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials." J. Chem. Theory Comput. 21 (2025): 4375-4385. - [![doi:10.1021/acs.jctc.5c00340](https://img.shields.io/badge/DOI-10.1021%2Facs.jctc.5c00340-blue)](https://doi.org/10.1021/acs.jctc.5c00340) - [![Citations](https://citations.njzjz.win/10.1021/acs.jctc.5c00340)](https://badge.dimensions.ai/details/doi/10.1021/acs.jctc.5c00340) +```bash +dp pretrained download DPA-3.2-5M +dp --pt show ~/.cache/deepmd/pretrained/models/DPA-3.2-5M.pt model-branch +dp --pt train input.json \ + --finetune ~/.cache/deepmd/pretrained/models/DPA-3.2-5M.pt \ + --model-branch \ + --use-pretrain-script +``` -In addition, please follow [the bib file](CITATIONS.bib) to cite the methods you used. +`DPA-3.2-5M` is a PyTorch multi-task checkpoint: run the trainer in PyTorch +mode with `dp --pt` and select the branch that matches your system with +`--model-branch` (list them with +`dp --pt show ~/.cache/deepmd/pretrained/models/DPA-3.2-5M.pt model-branch`). The +`--use-pretrain-script` option imports that branch's descriptor and fitting +configuration, so `input.json` does not need to reproduce the DPA-3.2 +architecture. -### Highlights in major versions +The [fine-tuning guide][finetune] covers full-model and LoRA adaptation. +[DPA-ADAPT] reuses pretrained DPA representations for downstream +property-prediction tasks. -#### Initial version +Pretrained model names can also be resolved and cached automatically by +Python: -The goal of Deep Potential is to employ deep learning techniques and realize an inter-atomic potential energy model that is general, accurate, computationally efficient and scalable. The key component is to respect the extensive and symmetry-invariant properties of a potential energy model by assigning a local reference frame and a local environment to each atom. Each environment contains a finite number of atoms, whose local coordinates are arranged in a symmetry-preserving way. These local coordinates are then transformed, through a sub-network, to so-called _atomic energy_. Summing up all the atomic energies gives the potential energy of the system. +```python +from deepmd.infer import DeepPot -The initial proof of concept is in the [Deep Potential][1] paper, which employed an approach that was devised to train the neural network model with the potential energy only. With typical _ab initio_ molecular dynamics (AIMD) datasets this is insufficient to reproduce the trajectories. The Deep Potential Molecular Dynamics ([DeePMD][2]) model overcomes this limitation. In addition, the learning process in DeePMD improves significantly over the Deep Potential method thanks to the introduction of a flexible family of loss functions. The NN potential constructed in this way reproduces accurately the AIMD trajectories, both classical and quantum (path integral), in extended and finite systems, at a cost that scales linearly with system size and is always several orders of magnitude lower than that of equivalent AIMD simulations. +potential = DeepPot("DPA-3.2-5M") +``` -Although highly efficient, the original Deep Potential model satisfies the extensive and symmetry-invariant properties of a potential energy model at the price of introducing discontinuities in the model. This has negligible influence on a trajectory from canonical sampling but might not be sufficient for calculations of dynamical and mechanical properties. These points motivated us to develop the Deep Potential-Smooth Edition ([DeepPot-SE][3]) model, which replaces the non-smooth local frame with a smooth and adaptive embedding network. DeepPot-SE shows great ability in modeling many kinds of systems that are of interest in the fields of physics, chemistry, biology, and materials science. +### Train a model from scratch -In addition to building up potential energy models, DeePMD-kit can also be used to build up coarse-grained models. In these models, the quantity that we want to parameterize is the free energy, or the coarse-grained potential, of the coarse-grained particles. See the [DeePCG paper][4] for more details. +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: -#### v1 +```bash +git clone https://github.com/deepmodeling/deepmd-kit.git +cd deepmd-kit/examples/water/se_e2_a -- Code refactor to make it highly modularized. -- GPU support for descriptors. +# TensorFlow backend +dp train input.json -#### v2 +# Or PyTorch +dp --pt train input_torch.json +``` -- Model compression. Accelerate the efficiency of model inference 4-15 times. -- New descriptors. Including `se_e2_r`, `se_e3`, and `se_atten` (DPA-1). -- Hybridization of descriptors. Hybrid descriptor constructed from the concatenation of several descriptors. -- Atom type embedding. Enable atom-type embedding to decline training complexity and refine performance. -- Training and inference of the dipole (vector) and polarizability (matrix). -- Split of training and validation dataset. -- Optimized training on GPUs, including CUDA and ROCm. -- Non-von-Neumann. -- C API to interface with the third-party packages. +Ready-to-run inputs include: -See [our v2 paper](https://doi.org/10.1063/5.0155600) for details of all features until v2.2.3. +- [DPA-3 water training](./examples/water/dpa3/input_torch.json) +- [DPA-4 water training](./examples/water/dpa4/input.json) +- [Multi-task training](./examples/water_multi_task/pytorch_example/input_torch.json) +- [DPA-ADAPT property prediction](./examples/dpa_adapt/README.md) -#### v3 +For a guided end-to-end example, open the [web quick-start notebook][quick-start]. -- Multiple backends supported. Add PyTorch and JAX backends. -- The DPA2 and DPA3 models. -- Plugin mechanisms for external models. +## 🧠 Choose a model family -See [our v3 paper](https://doi.org/10.1021/acs.jctc.5c00340) for details of all features until v3.0. +DeepPot-SE is a strong default: efficient, established, and broadly supported. +For large atomistic models, start with [DPA-4](https://docs.deepmodeling.com/projects/deepmd/en/latest/model/dpa4.html). -## Install and use DeePMD-kit +Use the [model guide][model-guide] to compare model families, supported backends, +targets, data formats, precision, compression, and deployment constraints. -Just copy and paste in 1s, and let it run. +

+ DPA4 energy and force accuracy versus saturated throughput +

-```sh -curl -fsSL https://dp1s.deepmodeling.com | bash -``` +

DPA4 provides a family of accuracy–throughput trade-offs for different deployment budgets.

-Please read the [online documentation](https://deepmd.readthedocs.io/) for details and alternative installation methods. +## 🔬 Go beyond conventional force fields -Then, read on for a brief overview of the usage of DeePMD-kit. You may start with the first step: +| 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 | -```sh -dp -``` +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. -## Code structure +> [!NOTE] +> Working with an AI coding or scientific agent? DeePMD-kit ships +> [official Agent Skills][agent-skills] for model selection, training, +> fine-tuning, Python inference, and LAMMPS workflows. -The code is organized as follows: +## 📚 Documentation and community -- `examples`: examples. -- `deepmd`: DeePMD-kit python modules. -- `dpa_adapt`: DPA-ADAPT package for adapting pre-trained DPA models; see the [guide](doc/dpa_adapt/overview.md) and [input formats](doc/dpa_adapt/input_formats.md). -- `source/lib`: source code of the core library. -- `source/op`: Operator (OP) implementation. -- `source/api_cc`: source code of DeePMD-kit C++ API. -- `source/api_c`: source code of the C API. -- `source/nodejs`: source code of the Node.js API. -- `source/ipi`: source code of i-PI client. -- `source/lmp`: source code of LAMMPS module. +- 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). -# Contributing +## Citation -See [DeePMD-kit Contributing Guide](CONTRIBUTING.md) to become a contributor! 🤓 +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). + [![doi:10.1016/j.cpc.2018.03.016](https://img.shields.io/badge/DOI-10.1016%2Fj.cpc.2018.03.016-blue)](https://doi.org/10.1016/j.cpc.2018.03.016) + [![Citations](https://citations.njzjz.win/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). + [![doi:10.1063/5.0155600](https://img.shields.io/badge/DOI-10.1063%2F5.0155600-blue)](https://doi.org/10.1063/5.0155600) + [![Citations](https://citations.njzjz.win/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). + [![doi:10.1021/acs.jctc.5c00340](https://img.shields.io/badge/DOI-10.1021%2Facs.jctc.5c00340-blue)](https://doi.org/10.1021/acs.jctc.5c00340) + [![Citations](https://citations.njzjz.win/10.1021/acs.jctc.5c00340)](https://badge.dimensions.ai/details/doi/10.1021/acs.jctc.5c00340) -[1]: https://arxiv.org/abs/1707.01478 -[2]: https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.120.143001 -[3]: https://arxiv.org/abs/1805.09003 -[4]: https://aip.scitation.org/doi/full/10.1063/1.5027645 +## License + +DeePMD-kit is licensed under the +[GNU Lesser General Public License v3.0 or later](./LICENSE). + +[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 +[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/ diff --git a/doc/_static/dpa4-cps-throughput.webp b/doc/_static/dpa4-cps-throughput.webp new file mode 100644 index 0000000000..e559c2a6a2 Binary files /dev/null and b/doc/_static/dpa4-cps-throughput.webp differ diff --git a/doc/_static/dpa4-performance.webp b/doc/_static/dpa4-performance.webp new file mode 100644 index 0000000000..6ef18f1a47 Binary files /dev/null and b/doc/_static/dpa4-performance.webp differ diff --git a/doc/credits.rst b/doc/credits.rst index 2c72a8d2ca..ba661d99a5 100644 --- a/doc/credits.rst +++ b/doc/credits.rst @@ -15,6 +15,24 @@ Cite DeePMD-kit and methods Zeng_JChemPhys_2023_v159_p054801 Zeng_JChemTheoryComput_2025_v21_p4375 +.. _cite-deep-potential: + +- If the original Deep Potential model is used, + +.. bibliography:: + :filter: False + + Han_CommunComputPhys_2018_v23_p629 + +.. _cite-deepcg: + +- If the DeePCG coarse-grained model is used, + +.. bibliography:: + :filter: False + + Zhang_JChemPhys_2018_v149_p34101 + - If GPU version is used, .. bibliography:: @@ -57,6 +75,24 @@ Cite DeePMD-kit and methods Zhang_npjComputMater_2024_v10_p293 +.. _cite-dpa3: + +- If DPA-3 descriptor (`dpa3`) is used, + +.. bibliography:: + :filter: False + + Zhang_2025_DPA3 + +.. _cite-dpa4: + +- If DPA-4 descriptor (`dpa4`, `sezm`) is used, + +.. bibliography:: + :filter: False + + Li_2026_DPA4 + - If frame-specific parameters (`fparam`, e.g. electronic temperature) is used, .. bibliography:: @@ -85,6 +121,15 @@ Cite DeePMD-kit and methods Sommers_PhysChemChemPhys_2020_v22_p10592 +.. _cite-deeppopulation: + +- If fitting atomic charge populations with DeepPopulation, + +.. bibliography:: + :filter: False + + Ahart_2026_DeepPolaron + - If fitting density of states, .. bibliography:: diff --git a/doc/index.rst b/doc/index.rst index 6e698be273..7b2f9f7ede 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -1,30 +1,356 @@ -.. deepmd-kit documentation master file, created by - sphinx-quickstart on Sat Nov 21 18:36:24 2020. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. +=========== +DeePMD-kit +=========== -========================== -DeePMD-kit's documentation -========================== +.. rst-class:: lead -DeePMD-kit is a package written in Python/C++, designed to minimize the effort required to build deep learning-based models of interatomic potential energy and force field and to perform molecular dynamics (MD). This brings new hopes to addressing the accuracy-versus-efficiency dilemma in molecular simulations. Applications of DeePMD-kit span from finite molecules to extended systems and from metallic systems to chemically bonded systems. + Start from a pretrained Deep Potential model, fine-tune it for your system, + and deploy it at simulation scale. -.. Important:: +.. important:: - The project DeePMD-kit is licensed under `GNU LGPLv3.0 `_. - If you use this code in any future publications, please cite the following publications for general purpose: + **A pretrained model can be your starting point, not just your end result.** + Download a built-in DPA checkpoint, fine-tune the full model or a LoRA + adapter on system-specific data, then test, export, and deploy it through + the same DeePMD-kit workflow. - - Han Wang, Linfeng Zhang, Jiequn Han, and Weinan E. "DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics." Computer Physics Communications 228 (2018): 178-184. - - Jinzhe Zeng, Duo Zhang, Denghui Lu, Pinghui Mo, Zeyu Li, Yixiao Chen, Marián Rynik, Li'ang Huang, Ziyao Li, Shaochen Shi, Yingze Wang, Haotian Ye, Ping Tuo, Jiabin Yang, Ye Ding, Yifan Li, Davide Tisi, Qiyu Zeng, Han Bao, Yu Xia, Jiameng Huang, Koki Muraoka, Yibo Wang, Junhan Chang, Fengbo Yuan, Sigbjørn Løland Bore, Chun Cai, Yinnian Lin, Bo Wang, Jiayan Xu, Jia-Xin Zhu, Chenxing Luo, Yuzhi Zhang, Rhys E. A. Goodall, Wenshuo Liang, Anurag Kumar Singh, Sikai Yao, Jingchao Zhang, Renata Wentzcovitch, Jiequn Han, Jie Liu, Weile Jia, Darrin M. York, Weinan E, Roberto Car, Linfeng Zhang, Han Wang. "DeePMD-kit v2: A software package for Deep Potential models." J. Chem. Phys., 159, 054801 (2023). - - Jinzhe Zeng, Duo Zhang, Anyang Peng, Xiangyu Zhang, Sensen He, Yan Wang, Xinzijian Liu, Hangrui Bi, Yifan Li, Chun Cai, Chengqian Zhang, Yiming Du, Jia-Xin Zhu, Pinghui Mo, Zhengtao Huang, Qiyu Zeng, Shaochen Shi, Xuejian Qin, Zhaoxi Yu, Chenxing Luo, Ye Ding, Yun-Pei Liu, Ruosong Shi, Zhenyu Wang, Sigbjørn Løland Bore, Junhan Chang, Zhe Deng, Zhaohan Ding, Siyuan Han, Wanrun Jiang, Guolin Ke, Zhaoqing Liu, Denghui Lu, Koki Muraoka, Hananeh Oliaei, Anurag Kumar Singh, Haohui Que, Weihong Xu, Zhangmancang Xu, Yong-Bin Zhuang, Jiayu Dai, Timothy J. Giese, Weile Jia, Ben Xu, Darrin M. York, Linfeng Zhang, Han Wang. "DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials." J. Chem. Theory Comput. 21 (2025): 4375-4385. +DeePMD-kit turns quantum-mechanical reference data into fast, scalable +interatomic potentials. Use it across molecular and materials science—from +finite molecules and covalent systems to periodic solids and metals—and scale +from laptop fine-tuning to distributed training and MPI-parallel molecular +dynamics. - In addition, please follow :ref:`this page ` to cite the methods you used. +.. figure:: _static/dpa4-cps-throughput.webp + :alt: DPA4 model family Pareto frontier for Matbench Discovery CPS and saturated inference throughput + :width: 100% + :align: center + + The DPA4 model family traces a Pareto frontier across Matbench Discovery CPS + and saturated inference throughput. + +Choose your path +================ + +.. grid:: 1 2 3 3 + :gutter: 3 + + .. grid-item-card:: 🧬 Start from pretrained + :link: model/pretrained + :link-type: doc + :shadow: md + + Download a built-in DPA checkpoint or resolve its model name directly. + + .. grid-item-card:: 🎯 Fine-tune to your data + :link: train/finetuning + :link-type: doc + :shadow: md + + Adapt a full pretrained model or LoRA adapters to your target system. + + .. grid-item-card:: 🏗️ Train from scratch + :link: train/index + :link-type: doc + :shadow: md + + Build a new potential with single-task, multi-task, or distributed + training. + + .. grid-item-card:: 🧠 Choose a model + :link: model/index + :link-type: doc + :shadow: md + + Compare DeepPot-SE, DPA-1, DPA-2, DPA-3, DPA-4, and specialized + physics models. + + .. grid-item-card:: 🔌 Deploy and integrate + :link: third-party/index + :link-type: doc + :shadow: md + + Move models into Python, native APIs, LAMMPS, i-PI, ASE, GROMACS, and + the wider simulation ecosystem. + +Why DeePMD-kit +============== + +.. grid:: 1 2 2 3 + :gutter: 3 + + .. grid-item-card:: Pretrained-first adaptation + :shadow: sm + + Start from built-in DPA checkpoints, fine-tune full models or LoRA + adapters, and reuse learned representations with DPA-ADAPT. + + .. grid-item-card:: Training from scratch + :link: train/index + :link-type: doc + :shadow: sm + + Configure new architectures and physical targets, then train with + single-task, multi-task, or distributed workflows. + + .. grid-item-card:: Modern potential architectures + :shadow: sm + + Use efficient DeepPot-SE descriptors, attention-based DPA models, large + atomic models, and SO(3)-equivariant DPA-4. + + .. grid-item-card:: Broad physical targets + :shadow: sm + + Learn energies, forces, virials, Hessians, spin, dipoles, + polarizabilities, density of states, atomic populations, and custom + properties. + + .. grid-item-card:: Multi-backend framework + :link: backend + :link-type: doc + :shadow: sm + + Work with TensorFlow, PyTorch, JAX, or Paddle and use backend-aware model + formats, conversion, and runtime plugins. + + .. grid-item-card:: Performance at scale + :shadow: sm + + Run on CPUs and GPUs, distribute training, compress supported models, + export compiled ``.pt2`` artifacts, and drive MPI-parallel simulations. + + .. grid-item-card:: Open scientific ecosystem + :link: third-party/index + :link-type: doc + :shadow: sm + + Connect to simulation engines, workflow tools, native applications, and + external GNN models through documented interfaces and plugins. + +.. tip:: + + On supported descriptors and workloads, + :doc:`model 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. + +Two starting points, one path to dynamics +========================================= + +.. grid:: 1 2 3 3 + :gutter: 2 + + .. grid-item-card:: 1 · Pretrained + :link: model/pretrained + :link-type: doc + + Select a built-in DPA model or a compatible checkpoint. + + .. grid-item-card:: 2 · Prepare + :link: data/index + :link-type: doc + + Convert target structures and labels into DeePMD data. + + .. grid-item-card:: 3 · Fine-tune + :link: train/finetuning + :link-type: doc + + Adapt the full model or LoRA adapters to your system. + + .. grid-item-card:: 3 · Train + :link: train/index + :link-type: doc + + Build a new model from scratch when adaptation is not the right fit. + + .. grid-item-card:: 4 · Validate + :link: test/index + :link-type: doc + + Test accuracy, inspect deviation, freeze, and compress. + + .. grid-item-card:: 5 · Simulate + :link: inference/index + :link-type: doc + + Run inference directly or deploy into molecular dynamics. + +Fine-tuning and from-scratch training converge on the same validation, export, +and deployment toolchain. + +Choose a model family +===================== + +.. list-table:: + :header-rows: 1 + :widths: 24 76 + + * - Family + - A strong starting point when you need + * - :doc:`DeepPot-SE ` + - An efficient, established baseline with broad backend and deployment + support. + * - :doc:`DPA-1 ` + - Attention-based local representations and type embedding. + * - :doc:`DPA-2 ` + - Multi-task pretraining, shared representations, and smooth conservative + potentials. + * - :doc:`DPA-3 ` + - Message passing over line-graph representations and broad chemical + coverage. + * - :doc:`DPA-4 ` + - SO(3)-equivariant learning, LoRA fine-tuning, optional ZBL bridging, + spin support, and compiled ``.pt2`` deployment. + +.. figure:: _static/dpa4-performance.webp + :alt: DPA4 energy and force accuracy versus saturated throughput + :width: 100% + :align: center + + DPA4 provides a family of accuracy–throughput trade-offs for different + deployment budgets. + +More than conventional force fields +=================================== + +.. grid:: 1 2 2 3 + :gutter: 3 + + .. grid-item-card:: 🧲 Spin and magnetism + :link: model/train-energy-spin + :link-type: doc + + Train spin-aware potentials with atomic and magnetic force targets. + + .. grid-item-card:: ⚛️ Long- and short-range physics + :link: model/dplr + :link-type: doc + + Combine learned local interactions with DPLR electrostatics, DPRc range + correction, pair tables, or analytical ZBL bridging. + + .. grid-item-card:: 📊 Properties and embeddings + :link: inference/embedding + :link-type: doc + + Predict electronic or structural properties and export learned + representations for analysis or downstream models. + +New and noteworthy +================== + +.. grid:: 1 2 2 4 + :gutter: 3 + + .. grid-item-card:: Pretrained DPA models + :link: model/pretrained + :link-type: doc + :shadow: sm + + Resolve built-in model names directly or download checkpoints to a local + cache. + + .. grid-item-card:: DPA-4 + :link: model/dpa4 + :link-type: doc + :shadow: sm + + Equivariant message passing, LoRA, ZBL, spin, compiled inference, and + LAMMPS deployment. + + .. grid-item-card:: DPA-ADAPT + :link: dpa_adapt/index + :link-type: doc + :shadow: sm + + Adapt pretrained DPA representations to downstream atomistic property + tasks. + + .. grid-item-card:: Official Agent Skills + :link: agent-skills + :link-type: doc + :shadow: sm + + Give AI agents reproducible guidance for training, fine-tuning, + inference, and LAMMPS workflows. + +Documentation map +================= + +* **Getting Started:** :doc:`Installation and first model + `. +* **User Guide:** :doc:`Backends ` · :doc:`Installation + ` · :doc:`Data ` · :doc:`Models ` · + :doc:`Training ` · :doc:`Freeze ` · :doc:`Test + ` · :doc:`Inference ` · :doc:`DPA-ADAPT + ` · :doc:`CLI ` · :doc:`Integrations + ` · :doc:`Agent Skills ` · :doc:`NVNMD + ` · :doc:`Environment ` · :doc:`Troubleshooting + `. +* **Tutorials and Publications:** `Tutorials + `_ · `Publications + `_. +* **Developer Guide:** :doc:`CMake ` · :doc:`TensorFlow + models ` · :doc:`PyTorch models + ` · :doc:`Type embedding + ` · :doc:`Coding conventions + ` · :doc:`CI/CD ` · + :doc:`Python API ` · :doc:`Custom operators ` · + :doc:`C++ API ` · :doc:`C API ` · :doc:`Core API + `. +* **Project Details:** :doc:`License ` · :doc:`Authors and credits + ` · :doc:`Logo `. + +Citation +======== + +If you use DeePMD-kit in published work, cite the general software publication +that matches the version used: + +* Han Wang, Linfeng Zhang, Jiequn Han, and Weinan E. "DeePMD-kit: A deep + learning package for many-body potential energy representation and molecular + dynamics." *Computer Physics Communications* 228 (2018): 178–184. + `DOI: 10.1016/j.cpc.2018.03.016 + `_. +* Jinzhe Zeng, Duo Zhang, Denghui Lu, Pinghui Mo, Zeyu Li, Yixiao Chen, + Marián Rynik, Li'ang Huang, Ziyao Li, Shaochen Shi, Yingze Wang, Haotian Ye, + Ping Tuo, Jiabin Yang, Ye Ding, Yifan Li, Davide Tisi, Qiyu Zeng, Han Bao, + Yu Xia, Jiameng Huang, Koki Muraoka, Yibo Wang, Junhan Chang, Fengbo Yuan, + Sigbjørn Løland Bore, Chun Cai, Yinnian Lin, Bo Wang, Jiayan Xu, Jia-Xin Zhu, + Chenxing Luo, Yuzhi Zhang, Rhys E. A. Goodall, Wenshuo Liang, Anurag Kumar + Singh, Sikai Yao, Jingchao Zhang, Renata Wentzcovitch, Jiequn Han, Jie Liu, + Weile Jia, Darrin M. York, Weinan E, Roberto Car, Linfeng Zhang, and Han + Wang. "DeePMD-kit v2: A software package for Deep Potential models." + *The Journal of Chemical Physics* 159 (2023): 054801. + `DOI: 10.1063/5.0155600 `_. +* Jinzhe Zeng, Duo Zhang, Anyang Peng, Xiangyu Zhang, Sensen He, Yan Wang, + Xinzijian Liu, Hangrui Bi, Yifan Li, Chun Cai, Chengqian Zhang, Yiming Du, + Jia-Xin Zhu, Pinghui Mo, Zhengtao Huang, Qiyu Zeng, Shaochen Shi, Xuejian + Qin, Zhaoxi Yu, Chenxing Luo, Ye Ding, Yun-Pei Liu, Ruosong Shi, Zhenyu Wang, + Sigbjørn Løland Bore, Junhan Chang, Zhe Deng, Zhaohan Ding, Siyuan Han, + Wanrun Jiang, Guolin Ke, Zhaoqing Liu, Denghui Lu, Koki Muraoka, Hananeh + Oliaei, Anurag Kumar Singh, Haohui Que, Weihong Xu, Zhangmancang Xu, + Yong-Bin Zhuang, Jiayu Dai, Timothy J. Giese, Weile Jia, Ben Xu, Darrin M. + York, Linfeng Zhang, and Han Wang. "DeePMD-kit v3: A Multiple-Backend + Framework for Machine Learning Potentials." *Journal of Chemical Theory and + Computation* 21 (2025): 4375–4385. + `DOI: 10.1021/acs.jctc.5c00340 + `_. + +Follow the :doc:`citation guide ` for the method-specific publications +required by the models and features used in your work. + +.. note:: + + DeePMD-kit is licensed under the :doc:`GNU LGPL-3.0-or-later `. .. _getting-started: .. toctree:: :maxdepth: 3 :caption: Getting Started + :hidden: getting-started/index @@ -33,7 +359,8 @@ DeePMD-kit is a package written in Python/C++, designed to minimize the effort r .. toctree:: :maxdepth: 3 :numbered: - :caption: Advanced + :caption: User Guide + :hidden: backend install/index @@ -51,12 +378,12 @@ DeePMD-kit is a package written in Python/C++, designed to minimize the effort r env troubleshooting/index - .. _tutorial: .. toctree:: :maxdepth: 2 - :caption: Tutorial + :caption: Tutorials and Publications + :hidden: Tutorials Publications @@ -66,6 +393,7 @@ DeePMD-kit is a package written in Python/C++, designed to minimize the effort r .. toctree:: :maxdepth: 5 :caption: Developer Guide + :hidden: development/cmake development/create-a-model-tf @@ -79,17 +407,19 @@ DeePMD-kit is a package written in Python/C++, designed to minimize the effort r api_c/api_c api_core/api_core - .. _project-details: .. toctree:: :maxdepth: 3 :caption: Project Details + :hidden: license credits logo +Indices +------- * :ref:`genindex` * :ref:`modindex` diff --git a/doc/model/dpa3.md b/doc/model/dpa3.md index f52cfbdc1f..6c31c12c8a 100644 --- a/doc/model/dpa3.md +++ b/doc/model/dpa3.md @@ -10,7 +10,7 @@ Its architecture provides high fitting accuracy and robust generalization both w DPA3 also preserves energy conservation and the physical symmetries of the potential energy surface, making it a reliable model for a wide range of scientific applications. -Reference: [DPA3 paper](https://arxiv.org/abs/2506.01686). +Reference: {ref}`DPA3 paper ` in the canonical citation guide. Training example: `examples/water/dpa3/input_torch.json`. @@ -92,7 +92,10 @@ Virials are similarly derived from cell tensor gradients, ensuring the model is ### Default Configuration -DPA3 uses LiGS order $K=2$ as the default configuration, which was found effective in prior work ([DPA3 paper](https://arxiv.org/abs/2506.01686)). The model supports scaling through increasing the number of layers $L$ (e.g., DPA3-L3, DPA3-L6, DPA3-L12, DPA3-L24). +DPA3 uses LiGS order $K=2$ as the default configuration, which was found +effective in the {ref}`DPA3 paper `. The model supports scaling +through increasing the number of layers $L$ (e.g., DPA3-L3, DPA3-L6, +DPA3-L12, DPA3-L24). ## Hyperparameter tests diff --git a/doc/model/dpa4.md b/doc/model/dpa4.md index 66ff3363da..7873528156 100644 --- a/doc/model/dpa4.md +++ b/doc/model/dpa4.md @@ -13,7 +13,7 @@ and defaults to the `dpa4_ener` energy fitting network, so `descriptor.type` and `fitting_net.type` may be omitted for energy training. A new energy input then needs only the model type, `type_map`, and a few descriptor options. -Reference: [DPA4 paper](https://arxiv.org/abs/2606.02419). +Reference: {ref}`DPA4 paper ` in the canonical citation guide. ## Quick start @@ -744,20 +744,5 @@ closed over the one-hop neighbor shell. ## Citation -If you use DPA4/SeZM, please cite the [DPA4 paper](https://arxiv.org/abs/2606.02419): - -```bibtex -@article{li2026dpa4, - title = {{DPA4}: Pushing the Accuracy-Cost Frontier of Interatomic - Potentials with {EMFA} {SO(2)} Convolution}, - author = {Li, Tiancheng and Li, Wentao and Peng, Anyang and Xue, Jianming - and Zhang, Linfeng and Zhang, Duo and Wang, Han}, - journal = {arXiv preprint arXiv:2606.02419}, - year = {2026}, - eprint = {2606.02419}, - archivePrefix = {arXiv}, - primaryClass = {physics.chem-ph}, - doi = {10.48550/arXiv.2606.02419}, - url = {https://arxiv.org/abs/2606.02419} -} -``` +If you use DPA4/SeZM, cite the {ref}`DPA4 paper ` from the +canonical citation guide. Its BibTeX record is maintained in `CITATIONS.bib`. diff --git a/doc/model/overall.md b/doc/model/overall.md index 37470f44cb..e7b8b7b628 100644 --- a/doc/model/overall.md +++ b/doc/model/overall.md @@ -1,5 +1,8 @@ # Overall +The original Deep Potential formulation is listed in the +{ref}`canonical citation guide `. + ## Theory A Deep Potential (DP) model, denoted by $\mathcal{M}$, can be generally represented as diff --git a/doc/model/train-fitting-population.md b/doc/model/train-fitting-population.md index e00bfbc9bb..46054b380d 100644 --- a/doc/model/train-fitting-population.md +++ b/doc/model/train-fitting-population.md @@ -5,7 +5,9 @@ Here we present an API to DeepPopulation model, which can be used to fit the atomic charge population. -See the [preprint on arXiv](https://arxiv.org/abs/2606.01763) for details and an example of how the DeepPopulation model was used to study small polaron transport. +See the {ref}`DeepPopulation paper ` in the canonical +citation guide for details and an example of how the model was used to study +small-polaron transport. In this example, we will show you how to train a model to fit the atomic charge population for a titanium dioxide system. A complete training input script of the examples can be found in