DeePMD-kit v2: A software package for deep potential models
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 scien...
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| Published in | The Journal of chemical physics Vol. 159; no. 5 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
American Institute of Physics
07.08.2023
American Institute of Physics (AIP) AIP Publishing LLC |
| Subjects | |
| Online Access | Get full text |
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| 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. |
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| AbstractList | 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. 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.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. |
| Author | Shi, Shaochen Yang, Jiabin Huang, Jiameng Zhang, Duo Li, Yifan Muraoka, Koki Zhang, Yuzhi Goodall, Rhys E. A. Liang, Wenshuo Ye, Haotian Bore, Sigbjørn Løland Tisi, Davide Yao, Sikai Han, Jiequn York, Darrin M. Ding, Ye Rynik, Marián Liu, Jie Wentzcovitch, Renata Chen, Yixiao Zhang, Jingchao Wang, Yingze Jia, Weile E, Weinan Tuo, Ping Lin, Yinnian Mo, Pinghui Xia, Yu Wang, Yibo Cai, Chun Chang, Junhan Zeng, Jinzhe Singh, Anurag Kumar Car, Roberto Zhang, Linfeng Xu, Jiayan Huang, Li’ang Zhu, Jia-Xin Luo, Chenxing Li, Zeyu Wang, Bo Yuan, Fengbo Li, Ziyao Lu, Denghui Bao, Han Wang, Han Zeng, Qiyu |
| Author_xml | – sequence: 1 givenname: Jinzhe surname: Zeng fullname: Zeng, Jinzhe organization: Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University – sequence: 2 givenname: Duo surname: Zhang fullname: Zhang, Duo organization: 37Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Fenghao East Road 2, Beijing 100094, People’s Republic of China – sequence: 3 givenname: Denghui surname: Lu fullname: Lu, Denghui organization: HEDPS, CAPT, College of Engineering, Peking University – sequence: 4 givenname: Pinghui surname: Mo fullname: Mo, Pinghui organization: College of Electrical and Information Engineering, Hunan University – sequence: 5 givenname: Zeyu surname: Li fullname: Li, Zeyu organization: Yuanpei College, Peking University – sequence: 6 givenname: Yixiao surname: Chen fullname: Chen, Yixiao organization: Program in Applied and Computational Mathematics, Princeton University – sequence: 7 givenname: Marián surname: Rynik fullname: Rynik, Marián organization: Department of Experimental Physics, Comenius University – sequence: 8 givenname: Li’ang surname: Huang fullname: Huang, Li’ang organization: Center for Quantum Information, Institute for Interdisciplinary Information Sciences, Tsinghua University – sequence: 9 givenname: Ziyao surname: Li fullname: Li, Ziyao organization: 37Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Fenghao East Road 2, Beijing 100094, People’s Republic of China – sequence: 10 givenname: Shaochen surname: Shi fullname: Shi, Shaochen organization: ByteDance Research, Zhonghang Plaza – sequence: 11 givenname: Yingze surname: Wang fullname: Wang, Yingze organization: 37Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Fenghao East Road 2, Beijing 100094, People’s Republic of China – sequence: 12 givenname: Haotian surname: Ye fullname: Ye, Haotian organization: Yuanpei College, Peking University – sequence: 13 givenname: Ping surname: Tuo fullname: Tuo, Ping organization: AI for Science Institute – sequence: 14 givenname: Jiabin surname: Yang fullname: Yang, Jiabin organization: Baidu, Inc – sequence: 15 givenname: Ye surname: Ding fullname: Ding, Ye organization: 37Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Fenghao East Road 2, Beijing 100094, People’s Republic of China – sequence: 16 givenname: Yifan surname: Li fullname: Li, Yifan organization: Department of Chemistry, Princeton University – sequence: 17 givenname: Davide surname: Tisi fullname: Tisi, Davide organization: 37Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Fenghao East Road 2, Beijing 100094, People’s Republic of China – sequence: 18 givenname: Qiyu surname: Zeng fullname: Zeng, Qiyu organization: Department of Physics, National University of Defense Technology – sequence: 19 givenname: Han surname: Bao fullname: Bao, Han organization: 37Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Fenghao East Road 2, Beijing 100094, People’s Republic of China – sequence: 20 givenname: Yu surname: Xia fullname: Xia, Yu organization: ByteDance Research, Zhonghang Plaza – sequence: 21 givenname: Jiameng surname: Huang fullname: Huang, Jiameng organization: 37Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Fenghao East Road 2, Beijing 100094, People’s Republic of China – sequence: 22 givenname: Koki surname: Muraoka fullname: Muraoka, Koki organization: Department of Chemical System Engineering, The University of Tokyo – sequence: 23 givenname: Yibo surname: Wang fullname: Wang, Yibo organization: DP Technology – sequence: 24 givenname: Junhan surname: Chang fullname: Chang, Junhan organization: 37Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Fenghao East Road 2, Beijing 100094, People’s Republic of China – sequence: 25 givenname: Fengbo surname: Yuan fullname: Yuan, Fengbo organization: DP Technology – sequence: 26 givenname: Sigbjørn Løland surname: Bore fullname: Bore, Sigbjørn Løland organization: Hylleraas Centre for Quantum Molecular Sciences and Department of Chemistry, University of Oslo – sequence: 27 givenname: Chun surname: Cai fullname: Cai, Chun organization: 37Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Fenghao East Road 2, Beijing 100094, People’s Republic of China – sequence: 28 givenname: Yinnian surname: Lin fullname: Lin, Yinnian organization: Wangxuan Institute of Computer Technology, Peking University – sequence: 29 givenname: Bo surname: Wang fullname: Wang, Bo organization: Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Key Laboratory of Green Chemistry and Chemical Process, School of Chemistry and Molecular Engineering, East China Normal University – sequence: 30 givenname: Jiayan surname: Xu fullname: Xu, Jiayan organization: School of Chemistry and Chemical Engineering, Queen’s University Belfast – sequence: 31 givenname: Jia-Xin surname: Zhu fullname: Zhu, Jia-Xin organization: State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, Xiamen University – sequence: 32 givenname: Chenxing surname: Luo fullname: Luo, Chenxing organization: Department of Applied Physics and Applied Mathematics, Columbia University – sequence: 33 givenname: Yuzhi surname: Zhang fullname: Zhang, Yuzhi organization: DP Technology – sequence: 34 givenname: Rhys E. A. surname: Goodall fullname: Goodall, Rhys E. A. organization: Independent Researcher – sequence: 35 givenname: Wenshuo surname: Liang fullname: Liang, Wenshuo organization: DP Technology – sequence: 36 givenname: Anurag Kumar surname: Singh fullname: Singh, Anurag Kumar organization: Department of Data Science, Indian Institute of Technology – sequence: 37 givenname: Sikai surname: Yao fullname: Yao, Sikai organization: DP Technology – sequence: 38 givenname: Jingchao surname: Zhang fullname: Zhang, Jingchao organization: NVIDIA AI Technology Center (NVAITC) – sequence: 39 givenname: Renata surname: Wentzcovitch fullname: Wentzcovitch, Renata organization: 37Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Fenghao East Road 2, Beijing 100094, People’s Republic of China – sequence: 40 givenname: Jiequn surname: Han fullname: Han, Jiequn organization: Center for Computational Mathematics, Flatiron Institute – sequence: 41 givenname: Jie surname: Liu fullname: Liu, Jie organization: College of Electrical and Information Engineering, Hunan University – sequence: 42 givenname: Weile surname: Jia fullname: Jia, Weile organization: 37Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Fenghao East Road 2, Beijing 100094, People’s Republic of China – sequence: 43 givenname: Darrin M. surname: York fullname: York, Darrin M. organization: Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University – sequence: 44 givenname: Weinan surname: E fullname: E, Weinan organization: 37Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Fenghao East Road 2, Beijing 100094, People’s Republic of China – sequence: 45 givenname: Roberto surname: Car fullname: Car, Roberto organization: Department of Chemistry, Princeton University – sequence: 46 givenname: Linfeng surname: Zhang fullname: Zhang, Linfeng email: linfeng.zhang.zlf@gmail.com organization: 37Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Fenghao East Road 2, Beijing 100094, People’s Republic of China – sequence: 47 givenname: Han surname: Wang fullname: Wang, Han organization: 37Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Fenghao East Road 2, Beijing 100094, People’s Republic of China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37526163$$D View this record in MEDLINE/PubMed https://www.osti.gov/servlets/purl/1994160$$D View this record in Osti.gov |
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| CODEN | JCPSA6 |
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| Title | DeePMD-kit v2: A software package for deep potential models |
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