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:
-
more »
- Rutgers Univ., Piscataway, NJ (United States)
- AI for Science Institute, Beijing (China); DP Technology, Beijing (China); Peking Univ., Beijing (China)
- Peking Univ., Beijing (China)
- Hunan Univ., Changsha (China)
- Princeton Univ., NJ (United States)
- Comenius Univ., Bratislava (Slovakia)
- Tsinghua Univ., Beijing (China)
- DP Technology, Beijing (China); Peking Univ., Beijing (China)
- ByteDance Research, Beijing (China)
- AI for Science Institute, Beijing (China)
- Baidu, Inc., Beijing (China)
- Westlake Univ., Hangzhou (China); Westlake AI Therapeutics Lab, Hangzhou (China); Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou (China)
- International School for Advanced Studies (SISSA), Trieste (Italy); Ecole Polytechnique Federale Lausanne (EPFL) (Switzerland)
- National University of Defense Technology, Changsha (China)
- Chinese Academy of Sciences (CAS), Beijing (China). Institute of Computing Technology; University of Chinese Academy of Sciences, Beijing (China)
- Univ. of Tokyo (Japan)
- DP Technology, Beijing (China)
- Univ. of Oslo (Norway)
- AI for Science Institute, Beijing (China); DP Technology, Beijing (China)
- East China Normal Univ. (ECNU), Shanghai (China)
- Queen's Univ., Belfast, Northern Ireland (United Kingdom)
- Xiamen University (China)
- Columbia Univ., New York, NY (United States)
- Independent Researcher, London (United Kingdom)
- Indian Inst. of Technology (IIT), Palakkad (India)
- NVIDIA AI Technology Center (NVAITC), Santa Clara, CA (United States)
- Flatiron Institute, New York, NY (United States)
- AI for Science Institute, Beijing (China); Peking Univ., Beijing (China)
- 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.
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
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.
@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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