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Title: DeePMD-kit v2: A software package for deep potential models

Abstract

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features, such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphical user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments.

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3]; ORCiD logo [4]; ORCiD logo [3]; ORCiD logo [5]; ORCiD logo [6]; ORCiD logo [7]; ORCiD logo [8]; ORCiD logo [9];  [8]; ORCiD logo [3]; ORCiD logo [10];  [11]; ORCiD logo [12]; ORCiD logo [5]; ORCiD logo [13]; ORCiD logo [14]; ORCiD logo [15]; ORCiD logo [9] more »; ORCiD logo [8]; ORCiD logo [16]; ORCiD logo [17]; ORCiD logo [8]; ORCiD logo [17]; ORCiD logo [18]; ORCiD logo [19]; ORCiD logo [3]; ORCiD logo [20]; ORCiD logo [21]; ORCiD logo [22]; ORCiD logo [23]; ORCiD logo [17]; ORCiD logo [24]; ORCiD logo [17]; ORCiD logo [25]; ORCiD logo [17]; ORCiD logo [26]; ORCiD logo [23]; ORCiD logo [27]; ORCiD logo [4]; ORCiD logo [15]; ORCiD logo [1]; ORCiD logo [28]; ORCiD logo [5]; ORCiD logo [19]; ORCiD logo [29] « less
+ Show Author Affiliations
  1. Rutgers Univ., Piscataway, NJ (United States)
  2. AI for Science Institute, Beijing (China); DP Technology, Beijing (China); Peking Univ., Beijing (China)
  3. Peking Univ., Beijing (China)
  4. Hunan Univ., Changsha (China)
  5. Princeton Univ., NJ (United States)
  6. Comenius Univ., Bratislava (Slovakia)
  7. Tsinghua Univ., Beijing (China)
  8. DP Technology, Beijing (China); Peking Univ., Beijing (China)
  9. ByteDance Research, Beijing (China)
  10. AI for Science Institute, Beijing (China)
  11. Baidu, Inc., Beijing (China)
  12. Westlake Univ., Hangzhou (China); Westlake AI Therapeutics Lab, Hangzhou (China); Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou (China)
  13. International School for Advanced Studies (SISSA), Trieste (Italy); Ecole Polytechnique Federale Lausanne (EPFL) (Switzerland)
  14. National University of Defense Technology, Changsha (China)
  15. Chinese Academy of Sciences (CAS), Beijing (China). Institute of Computing Technology; University of Chinese Academy of Sciences, Beijing (China)
  16. Univ. of Tokyo (Japan)
  17. DP Technology, Beijing (China)
  18. Univ. of Oslo (Norway)
  19. AI for Science Institute, Beijing (China); DP Technology, Beijing (China)
  20. East China Normal Univ. (ECNU), Shanghai (China)
  21. Queen's Univ., Belfast, Northern Ireland (United Kingdom)
  22. Xiamen University (China)
  23. Columbia Univ., New York, NY (United States)
  24. Independent Researcher, London (United Kingdom)
  25. Indian Inst. of Technology (IIT), Palakkad (India)
  26. NVIDIA AI Technology Center (NVAITC), Santa Clara, CA (United States)
  27. Flatiron Institute, New York, NY (United States)
  28. AI for Science Institute, Beijing (China); Peking Univ., Beijing (China)
  29. Peking Univ., Beijing (China); Institute of Applied Physics and Computational Mathematics, Beijing (China)
Publication Date:
Research Org.:
Princeton Univ., NJ (United States)
Sponsoring Org.:
USDOE Office of Science (SC); National Institutes of Health (NIH); National Science Foundation (NSF); Slovak Research and Development Agency; Science and Technology Innovation Program of Hunan Province; Research Council of Norway; National Key Research and Development Program of China; National Natural Science Foundation of China (NSFC)
OSTI Identifier:
1994160
Grant/Contract Number:  
SC0019394; GM107485; 2209718; APVV-19-0371; 2021RC4026; 262695; SC0019759; 2022YFA1004300; 12122103; 2138259; 2138286; 2138307; 2137603; 2138296; CHE190067; CHE20002
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Chemical Physics
Additional Journal Information:
Journal Volume: 159; Journal Issue: 5; Journal ID: ISSN 0021-9606
Publisher:
American Institute of Physics (AIP)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; molecular dynamics; computer software; deep learning; artificial neural networks; machine learning; application programming interface; graphical user interface; graphics processing units; tensile properties; peptides

Citation Formats

Zeng, Jinzhe, Zhang, Duo, Lu, Denghui, Mo, Pinghui, Li, Zeyu, Chen, Yixiao, Rynik, Marián, Huang, Li’ang, Li, Ziyao, Shi, Shaochen, Wang, Yingze, Ye, Haotian, Tuo, Ping, Yang, Jiabin, Ding, Ye, Li, Yifan, Tisi, Davide, Zeng, Qiyu, Bao, Han, Xia, Yu, Huang, Jiameng, Muraoka, Koki, Wang, Yibo, Chang, Junhan, Yuan, Fengbo, Bore, Sigbjørn Løland, Cai, Chun, Lin, Yinnian, Wang, Bo, Xu, Jiayan, Zhu, Jia-Xin, Luo, Chenxing, Zhang, Yuzhi, Goodall, Rhys A., Liang, Wenshuo, Singh, Anurag Kumar, Yao, Sikai, Zhang, Jingchao, Wentzcovitch, Renata, Han, Jiequn, Liu, Jie, Jia, Weile, York, Darrin M., E, Weinan, Car, Roberto, Zhang, Linfeng, and Wang, Han. DeePMD-kit v2: A software package for deep potential models. United States: N. p., 2023. Web. doi:10.1063/5.0155600.
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Zeng, Jinzhe, Zhang, Duo, Lu, Denghui, Mo, Pinghui, Li, Zeyu, Chen, Yixiao, Rynik, Marián, Huang, Li’ang, Li, Ziyao, Shi, Shaochen, Wang, Yingze, Ye, Haotian, Tuo, Ping, Yang, Jiabin, Ding, Ye, Li, Yifan, Tisi, Davide, Zeng, Qiyu, Bao, Han, Xia, Yu, Huang, Jiameng, Muraoka, Koki, Wang, Yibo, Chang, Junhan, Yuan, Fengbo, Bore, Sigbjørn Løland, Cai, Chun, Lin, Yinnian, Wang, Bo, Xu, Jiayan, Zhu, Jia-Xin, Luo, Chenxing, Zhang, Yuzhi, Goodall, Rhys A., Liang, Wenshuo, Singh, Anurag Kumar, Yao, Sikai, Zhang, Jingchao, Wentzcovitch, Renata, Han, Jiequn, Liu, Jie, Jia, Weile, York, Darrin M., E, Weinan, Car, Roberto, Zhang, Linfeng, & Wang, Han. DeePMD-kit v2: A software package for deep potential models. United States. https://doi.org/10.1063/5.0155600
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Zeng, Jinzhe, Zhang, Duo, Lu, Denghui, Mo, Pinghui, Li, Zeyu, Chen, Yixiao, Rynik, Marián, Huang, Li’ang, Li, Ziyao, Shi, Shaochen, Wang, Yingze, Ye, Haotian, Tuo, Ping, Yang, Jiabin, Ding, Ye, Li, Yifan, Tisi, Davide, Zeng, Qiyu, Bao, Han, Xia, Yu, Huang, Jiameng, Muraoka, Koki, Wang, Yibo, Chang, Junhan, Yuan, Fengbo, Bore, Sigbjørn Løland, Cai, Chun, Lin, Yinnian, Wang, Bo, Xu, Jiayan, Zhu, Jia-Xin, Luo, Chenxing, Zhang, Yuzhi, Goodall, Rhys A., Liang, Wenshuo, Singh, Anurag Kumar, Yao, Sikai, Zhang, Jingchao, Wentzcovitch, Renata, Han, Jiequn, Liu, Jie, Jia, Weile, York, Darrin M., E, Weinan, Car, Roberto, Zhang, Linfeng, and Wang, Han. Tue . "DeePMD-kit v2: A software package for deep potential models". United States. https://doi.org/10.1063/5.0155600. https://www.osti.gov/servlets/purl/1994160.
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@article{osti_1994160,
title = {DeePMD-kit v2: A software package for deep potential models},
author = {Zeng, Jinzhe and Zhang, Duo and Lu, Denghui and Mo, Pinghui and Li, Zeyu and Chen, Yixiao and Rynik, Marián and Huang, Li’ang and Li, Ziyao and Shi, Shaochen and Wang, Yingze and Ye, Haotian and Tuo, Ping and Yang, Jiabin and Ding, Ye and Li, Yifan and Tisi, Davide and Zeng, Qiyu and Bao, Han and Xia, Yu and Huang, Jiameng and Muraoka, Koki and Wang, Yibo and Chang, Junhan and Yuan, Fengbo and Bore, Sigbjørn Løland and Cai, Chun and Lin, Yinnian and Wang, Bo and Xu, Jiayan and Zhu, Jia-Xin and Luo, Chenxing and Zhang, Yuzhi and Goodall, Rhys A. and Liang, Wenshuo and Singh, Anurag Kumar and Yao, Sikai and Zhang, Jingchao and Wentzcovitch, Renata and Han, Jiequn and Liu, Jie and Jia, Weile and York, Darrin M. and E, Weinan and Car, Roberto and Zhang, Linfeng and Wang, Han},
abstractNote = {DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features, such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphical user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments.},
doi = {10.1063/5.0155600},
journal = {Journal of Chemical Physics},
number = 5,
volume = 159,
place = {United States},
year = {Tue Aug 01 00:00:00 EDT 2023},
month = {Tue Aug 01 00:00:00 EDT 2023}
}
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  • DOI: 10.1021/acs.jctc.8b00908

Machine learning builds full-QM precision protein force fields in seconds
journal, May 2021

  • Han, Yanqiang; Wang, Zhilong; Wei, Zhiyun
  • Briefings in Bioinformatics, Vol. 22, Issue 6
  • DOI: 10.1093/bib/bbab158

CENT2: Improved charge equilibration via neural network technique
journal, April 2022

  • Khajehpasha, Ehsan Rahmatizad; Finkler, Jonas A.; Kühne, Thomas D.
  • Physical Review B, Vol. 105, Issue 14
  • DOI: 10.1103/physrevb.105.144106

Ab Initio Reactive Computer Aided Molecular Design
journal, March 2017

MLatom 2: An Integrative Platform for Atomistic Machine Learning
journal, June 2021

Development of Multimodal Machine Learning Potentials: Toward a Physics-Aware Artificial Intelligence
journal, March 2021

sGDML: Constructing accurate and data efficient molecular force fields using machine learning
journal, July 2019

  • Chmiela, Stefan; Sauceda, Huziel E.; Poltavsky, Igor
  • Computer Physics Communications, Vol. 240
  • DOI: 10.1016/j.cpc.2019.02.007

Condensed Phase Water Molecular Multipole Moments from Deep Neural Network Models Trained on Ab Initio Simulation Data
journal, October 2021

  • Shi, Yu; Doyle, Carrie C.; Beck, Thomas L.
  • The Journal of Physical Chemistry Letters, Vol. 12, Issue 42
  • DOI: 10.1021/acs.jpclett.1c02328

Atom-centered symmetry functions for constructing high-dimensional neural network potentials
journal, February 2011

  • Behler, Jörg
  • The Journal of Chemical Physics, Vol. 134, Issue 7
  • DOI: 10.1063/1.3553717

Recent Advances toward a General Purpose Linear-Scaling Quantum Force Field
journal, June 2014

  • Giese, Timothy J.; Huang, Ming; Chen, Haoyuan
  • Accounts of Chemical Research, Vol. 47, Issue 9
  • DOI: 10.1021/ar500103g

DP Compress: A Model Compression Scheme for Generating Efficient Deep Potential Models
journal, August 2022

  • Lu, Denghui; Jiang, Wanrun; Chen, Yixiao
  • Journal of Chemical Theory and Computation, Vol. 18, Issue 9
  • DOI: 10.1021/acs.jctc.2c00102

Linear Atomic Cluster Expansion Force Fields for Organic Molecules: Beyond RMSE
journal, November 2021

  • Kovács, Dávid Péter; Oord, Cas van der; Kucera, Jiri
  • Journal of Chemical Theory and Computation, Vol. 17, Issue 12
  • DOI: 10.1021/acs.jctc.1c00647

Exploring the Chemical Space of Linear Alkane Pyrolysis via Deep Potential GENerator
journal, December 2020

GPU-Accelerated Molecular Dynamics and Free Energy Methods in Amber18: Performance Enhancements and New Features
journal, September 2018

  • Lee, Tai-Sung; Cerutti, David S.; Mermelstein, Dan
  • Journal of Chemical Information and Modeling, Vol. 58, Issue 10
  • DOI: 10.1021/acs.jcim.8b00462

Efficiently Trained Deep Learning Potential for Graphane
journal, July 2021

  • Achar, Siddarth K.; Zhang, Linfeng; Johnson, J. Karl
  • The Journal of Physical Chemistry C, Vol. 125, Issue 27
  • DOI: 10.1021/acs.jpcc.1c01411

Scalable parallel programming with CUDA
journal, March 2008

The MolSSI Driver Interface Project: A framework for standardized, on-the-fly interoperability between computational molecular sciences codes
journal, April 2021

  • Barnes, Taylor A.; Marin-Rimoldi, Eliseo; Ellis, Samuel
  • Computer Physics Communications, Vol. 261
  • DOI: 10.1016/j.cpc.2020.107688

Toward reliable density functional methods without adjustable parameters: The PBE0 model
journal, April 1999

  • Adamo, Carlo; Barone, Vincenzo
  • The Journal of Chemical Physics, Vol. 110, Issue 13
  • DOI: 10.1063/1.478522

DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models
journal, February 2020

Learning local equivariant representations for large-scale atomistic dynamics
journal, February 2023

Library-Based LAMMPS Implementation of High-Dimensional Neural Network Potentials
journal, January 2019

  • Singraber, Andreas; Behler, Jörg; Dellago, Christoph
  • Journal of Chemical Theory and Computation, Vol. 15, Issue 3
  • DOI: 10.1021/acs.jctc.8b00770

Development of a Robust Indirect Approach for MM → QM Free Energy Calculations That Combines Force-Matched Reference Potential and Bennett’s Acceptance Ratio Methods
journal, September 2019

  • Giese, Timothy J.; York, Darrin M.
  • Journal of Chemical Theory and Computation, Vol. 15, Issue 10
  • DOI: 10.1021/acs.jctc.9b00401

Plastic deformation of superionic water ices
journal, November 2022

  • Matusalem, Filipe; Santos Rego, Jéssica; de Koning, Maurice
  • Proceedings of the National Academy of Sciences, Vol. 119, Issue 45
  • DOI: 10.1073/pnas.2203397119

ω B97M-V: A combinatorially optimized, range-separated hybrid, meta-GGA density functional with VV10 nonlocal correlation
journal, June 2016

  • Mardirossian, Narbe; Head-Gordon, Martin
  • The Journal of Chemical Physics, Vol. 144, Issue 21
  • DOI: 10.1063/1.4952647

Array programming with NumPy
journal, September 2020

  • Harris, Charles R.; Millman, K. Jarrod; van der Walt, Stéfan J.
  • Nature, Vol. 585, Issue 7825
  • DOI: 10.1038/s41586-020-2649-2

Large-scale ab initio simulations based on systematically improvable atomic basis
journal, February 2016

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
journal, January 2017

  • Smith, J. S.; Isayev, O.; Roitberg, A. E.
  • Chemical Science, Vol. 8, Issue 4
  • DOI: 10.1039/c6sc05720a

SchNetPack 2.0: A neural network toolbox for atomistic machine learning
journal, April 2023

  • Schütt, Kristof T.; Hessmann, Stefaan S. P.; Gebauer, Niklas W. A.
  • The Journal of Chemical Physics, Vol. 158, Issue 14
  • DOI: 10.1063/5.0138367

LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales
journal, February 2022

  • Thompson, Aidan P.; Aktulga, H. Metin; Berger, Richard
  • Computer Physics Communications, Vol. 271
  • DOI: 10.1016/j.cpc.2021.108171

ACES: Optimized Alchemically Enhanced Sampling
journal, January 2023

  • Lee, Tai-Sung; Tsai, Hsu-Chun; Ganguly, Abir
  • Journal of Chemical Theory and Computation, Vol. 19, Issue 2
  • DOI: 10.1021/acs.jctc.2c00697

Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation
journal, June 2022

Embedded Atom Neural Network Potentials: Efficient and Accurate Machine Learning with a Physically Inspired Representation
journal, August 2019

E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
journal, May 2022

Multireference Generalization of the Weighted Thermodynamic Perturbation Method
journal, October 2022

  • Giese, Timothy J.; Zeng, Jinzhe; York, Darrin M.
  • The Journal of Physical Chemistry A, Vol. 126, Issue 45
  • DOI: 10.1021/acs.jpca.2c06201

Accurate and efficient molecular dynamics based on machine learning and non von Neumann architecture
journal, May 2022

Effective Approaches to Attention-based Neural Machine Translation
conference, January 2015

  • Luong, Thang; Pham, Hieu; Manning, Christopher D.
  • Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing
  • DOI: 10.18653/v1/D15-1166

Machine Learning Approach Based on a Range-Corrected Deep Potential Model for Efficient Vibrational Frequency Computation
journal, August 2023

  • Yang, Jitai; Cong, Yang; Li, You
  • Journal of Chemical Theory and Computation, Vol. 19, Issue 18
  • DOI: 10.1021/acs.jctc.3c00386
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