deepmd-kit 3.1.3

A deep learning package for many-body potential energy representation and molecular dynamics

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DeePMD-kit

About DeePMD-kit

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.

For more information, check the documentation.

Highlighted features

• 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.

License and credits

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:

• 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 (2023): 054801.
• 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.

In addition, please follow the bib file to cite the methods you used.

Highlights in major versions

Initial version

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.

The initial proof of concept is in the Deep Potential 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) 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.

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) 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.

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 for more details.

v1

• Code refactor to make it highly modularized.
• GPU support for descriptors.

v2

• 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.

See our v2 paper for details of all features until v2.2.3.

v3

• Multiple backends supported. Add PyTorch and JAX backends.
• The DPA2 and DPA3 models.
• Plugin mechanisms for external models.

See our v3 paper for details of all features until v3.0.

Install and use DeePMD-kit

Just copy and paste in 1s, and let it run.

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

Please read the online documentation for details and alternative installation methods.

Then, read on for a brief overview of the usage of DeePMD-kit. You may start with the first step:

dp

Code structure

The code is organized as follows:

• examples: examples.
• deepmd: DeePMD-kit python modules.
• 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.
• source/gmx: source code of Gromacs plugin.

Contributing

See DeePMD-kit Contributing Guide to become a contributor! 🤓