DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models
In recent years, promising deep learning based interatomic potential energy surface (PES) models have been proposed that can potentially allow us to perform molecular dynamics simulations for large scale systems with quantum accuracy. However, making these models truly reliable and practically usefu...
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| Vydané v: | Computer physics communications Ročník 253; číslo C; s. 107206 |
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| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Netherlands
Elsevier B.V
01.08.2020
Elsevier |
| Predmet: | |
| ISSN: | 0010-4655, 1879-2944 |
| On-line prístup: | Získať plný text |
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| Abstract | In recent years, promising deep learning based interatomic potential energy surface (PES) models have been proposed that can potentially allow us to perform molecular dynamics simulations for large scale systems with quantum accuracy. However, making these models truly reliable and practically useful is still a very non-trivial task. A key component in this task is the generation of datasets used in model training. In this paper, we introduce the Deep Potential GENerator (DP-GEN), an open-source software platform that implements the recently proposed ”on-the-fly” learning procedure (Zhang et al. 2019) and is capable of generating uniformly accurate deep learning based PES models in a way that minimizes human intervention and the computational cost for data generation and model training. DP-GEN automatically and iteratively performs three steps: exploration, labeling, and training. It supports various popular packages for these three steps: LAMMPS for exploration, Quantum Espresso, VASP, CP2K, etc. for labeling, and DeePMD-kit for training. It also allows automatic job submission and result collection on different types of machines, such as high performance clusters and cloud machines, and is adaptive to different job management tools, including Slurm, PBS, and LSF. As a concrete example, we illustrate the details of the process for generating a general-purpose PES model for Cu using DP-GEN.
Program Title: DP-GEN
Program Files doi:http://dx.doi.org/10.17632/sxybkgc5xc.1
Licensing provisions: LGPL
Programming language: Python
Nature of problem: Generating reliable deep learning based potential energy models with minimal human intervention and computational cost.
Solution method: The concurrent learning scheme is implemented. Supports for sampling configuration space with LAMMPS, generating ab initio data with Quantum Espresso, VASP, CP2K and training potential models with DeePMD-kit are provided. Supports for different machines including workstations, high performance clusters and cloud machines are provided. Supports for job management tools including Slurm, PBS, LSF are provided. |
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| AbstractList | In recent years, promising deep learning based interatomic potential energy surface (PES) models have been proposed that can potentially allow us to perform molecular dynamics simulations for large scale systems with quantum accuracy. However, making these models truly reliable and practically useful is still a very non-trivial task. A key component in this task is the generation of datasets used in model training. In this paper, we introduce the Deep Potential GENerator (DP-GEN), an open-source software platform that implements the recently proposed ”on-the-fly” learning procedure (Zhang et al. 2019) and is capable of generating uniformly accurate deep learning based PES models in a way that minimizes human intervention and the computational cost for data generation and model training. DP-GEN automatically and iteratively performs three steps: exploration, labeling, and training. It supports various popular packages for these three steps: LAMMPS for exploration, Quantum Espresso, VASP, CP2K, etc. for labeling, and DeePMD-kit for training. It also allows automatic job submission and result collection on different types of machines, such as high performance clusters and cloud machines, and is adaptive to different job management tools, including Slurm, PBS, and LSF. As a concrete example, we illustrate the details of the process for generating a general-purpose PES model for Cu using DP-GEN.
Program Title: DP-GEN
Program Files doi:http://dx.doi.org/10.17632/sxybkgc5xc.1
Licensing provisions: LGPL
Programming language: Python
Nature of problem: Generating reliable deep learning based potential energy models with minimal human intervention and computational cost.
Solution method: The concurrent learning scheme is implemented. Supports for sampling configuration space with LAMMPS, generating ab initio data with Quantum Espresso, VASP, CP2K and training potential models with DeePMD-kit are provided. Supports for different machines including workstations, high performance clusters and cloud machines are provided. Supports for job management tools including Slurm, PBS, LSF are provided. |
| ArticleNumber | 107206 |
| Author | Wang, Haidi Zeng, Jinzhe Zhang, Yuzhi Chen, Weijie Zhang, Linfeng E, Weinan Wang, Han |
| Author_xml | – sequence: 1 givenname: Yuzhi orcidid: 0000-0002-5841-1107 surname: Zhang fullname: Zhang, Yuzhi organization: Beijing Institute of Big Data Research, Beijing 100871, People’s Republic of China – sequence: 2 givenname: Haidi orcidid: 0000-0003-4768-2136 surname: Wang fullname: Wang, Haidi organization: School of Electronic Science and Applied Physics, Hefei University of Technology, Hefei 230601, People’s Republic of China – sequence: 3 givenname: Weijie orcidid: 0000-0003-3657-2943 surname: Chen fullname: Chen, Weijie organization: Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, People’s Republic of China – sequence: 4 givenname: Jinzhe orcidid: 0000-0002-1515-8172 surname: Zeng fullname: Zeng, Jinzhe organization: School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, People’s Republic of China – sequence: 5 givenname: Linfeng surname: Zhang fullname: Zhang, Linfeng email: linfengz@princeton.edu organization: Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ, USA – sequence: 6 givenname: Han surname: Wang fullname: Wang, Han email: wang_han@iapcm.ac.cn organization: Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Huayuan Road 6, Beijing 100088, People’s Republic of China – sequence: 7 givenname: Weinan surname: E fullname: E, Weinan email: weinan@math.princeton.edu organization: Beijing Institute of Big Data Research, Beijing 100871, People’s Republic of China |
| BackLink | https://www.osti.gov/biblio/1631382$$D View this record in Osti.gov |
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| Cites_doi | 10.1103/PhysRevLett.104.136403 10.1016/j.cpc.2013.10.027 10.1006/jcph.1995.1039 10.1103/PhysRevB.54.11169 10.1103/PhysRevLett.55.2471 10.1103/PhysRev.164.922 10.1080/14786437108217418 10.1016/0927-0256(96)00008-0 10.1103/PhysRev.98.969 10.1103/PhysRevMaterials.3.023804 10.1103/PhysRevB.13.5188 10.1016/j.scriptamat.2015.07.021 10.1039/C7SC04934J 10.1016/j.commatsci.2015.09.013 10.1103/PhysRev.140.A1133 10.4208/cicp.OA-2017-0213 10.1016/j.commatsci.2017.08.031 10.1002/wcms.1159 10.1103/PhysRevLett.120.143001 10.1080/10408436.2013.772503 10.1063/1.5023802 10.1103/PhysRevB.46.2727 10.1103/PhysRevLett.77.3865 10.1016/j.cpc.2018.03.016 10.1103/PhysRevLett.98.146401 10.1103/PhysRevB.12.4634 10.1016/j.commatsci.2015.11.047 10.1021/acs.jctc.8b00908 |
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| References | Kresse, Furthmüller (b20) 1996; 6 Wang, Zhang, Han, E. (b10) 2018; 228 Frisch, Trucks, Schlegel, Scuseria, Robb, Cheeseman, Scalmani, Barone, Petersson, Nakatsuji, Li, Caricato, Marenich, Bloino, Janesko, Gomperts, Mennucci, Hratchian, Ortiz, Izmaylov, Sonnenberg, Williams-Young, Ding, Lipparini, Egidi, Goings, Peng, Petrone, Henderson, Ranasinghe, Zakrzewski, Gao, Rega, Zheng, Liang, Hada, Ehara, Toyota, Fukuda, Hasegawa, Ishida, Nakajima, Honda, Kitao, Nakai, Vreven, Throssell, Montgomery, Peralta, Ogliaro, Bearpark, Heyd, Brothers, Kudin, Staroverov, Keith, Kobayashi, Normand, Raghavachari, Rendell, Burant, Iyengar, Tomasi, Cossi, Millam, Klene, Adamo, Cammi, Ochterski, Martin, Morokuma, Farkas, Foresman, Fox (b23) 2016 Stobbs, Sworn (b36) 1971; 24 Nicklow, Gilat, Smith, Raubenheimer, Wilkinson (b39) 1967; 164 Zhang, Lin, Wang, Car, E (b18) 2019; 3 for phonon calculations using phonopy. Togo, Tanaka (b37) 2015; 108 D. Kingma, J. Ba, Adam: a method for stochastic optimization, in: Proceedings of the International Conference on Learning Representations (ICLR), 2015. Medvedev, Cox, Wagman (b32) 1989 Behler, Parrinello (b5) 2007; 98 Monkhorst, Pack (b29) 1976; 13 Smith, Nebgen, Lubbers, Isayev, Roitberg (b17) 2018; 148 Kohn, Sham (b1) 1965; 140 Overton Jr, Gaffney (b35) 1955; 98 for code implementation. Zhang, Han, Wang, Saidi, Car, E (b8) 2018 Baskes (b40) 1992; 46 Lejaeghere, Van Speybroeck, Van Oost, Cottenier (b33) 2014; 39 Schutt, Kessel, Gastegger, Nicoli, Tkatchenko, Müller (b11) 2018; 15 Yao, Herr, Toth, Mckintyre, Parkhill (b12) 2018; 9 Car, Parrinello (b2) 1985; 55 See LAMMPS interface in Podryabinkin, Shapeev (b16) 2017; 140 Zhang, Han, Wang, Car, E (b7) 2018; 120 . Kresse, Furthmüller (b21) 1996; 54 Abadi, Agarwal, Barham, Brevdo, Chen, Citro, Corrado, Davis, Dean, Devin, Ghemawat, Goodfellow, Harp, Irving, Isard, Jia, Jozefowicz, Kaiser, Kudlur, Levenberg, Mane, Monga, Moore, Murray, Olah, Schuster, Shlens, Steiner, Sutskever, Talwar, Tucker, Vanhoucke, Vasudevan, Viegas, Vinyals, Warden, Wattenberg, Wicke, Yu, Zheng (b13) 2016 See Ceriotti, More, Manolopoulos (b15) 2014; 185 Giannozzi, Andreussi, Brumme, Bunau, Nardelli, Calandra, Car, Cavazzoni, Ceresoli, Cococcioni (b22) 2017; 29 Bartók, Payne, Kondor, Csányi (b4) 2010; 104 Pizzi, Cepellotti, Sabatini, Marzari, Kozinsky (b42) 2016; 111 Marx, Hutter (b3) 2009 Hutter, Iannuzzi, Schiffmann, Vandevondele (b24) 2014; 4 He, Zhang, Ren, Sun (b30) 2016 Plimpton (b14) 1995; 117 Perdew, Burke, Ernzerhof (b28) 1996; 77 Triftshäuser (b34) 1975; 12 Artrith, Urban (b9) 2016; 114 Han, Zhang, Car, E (b6) 2018; 23 10.1016/j.cpc.2020.107206_b19 Perdew (10.1016/j.cpc.2020.107206_b28) 1996; 77 10.1016/j.cpc.2020.107206_b38 Podryabinkin (10.1016/j.cpc.2020.107206_b16) 2017; 140 Stobbs (10.1016/j.cpc.2020.107206_b36) 1971; 24 Marx (10.1016/j.cpc.2020.107206_b3) 2009 Monkhorst (10.1016/j.cpc.2020.107206_b29) 1976; 13 Kresse (10.1016/j.cpc.2020.107206_b20) 1996; 6 Pizzi (10.1016/j.cpc.2020.107206_b42) 2016; 111 Yao (10.1016/j.cpc.2020.107206_b12) 2018; 9 Car (10.1016/j.cpc.2020.107206_b2) 1985; 55 Behler (10.1016/j.cpc.2020.107206_b5) 2007; 98 Han (10.1016/j.cpc.2020.107206_b6) 2018; 23 Wang (10.1016/j.cpc.2020.107206_b10) 2018; 228 Schutt (10.1016/j.cpc.2020.107206_b11) 2018; 15 10.1016/j.cpc.2020.107206_b41 Zhang (10.1016/j.cpc.2020.107206_b7) 2018; 120 10.1016/j.cpc.2020.107206_b25 He (10.1016/j.cpc.2020.107206_b30) 2016 Giannozzi (10.1016/j.cpc.2020.107206_b22) 2017; 29 10.1016/j.cpc.2020.107206_b26 10.1016/j.cpc.2020.107206_b27 Lejaeghere (10.1016/j.cpc.2020.107206_b33) 2014; 39 Triftshäuser (10.1016/j.cpc.2020.107206_b34) 1975; 12 Zhang (10.1016/j.cpc.2020.107206_b8) 2018 Baskes (10.1016/j.cpc.2020.107206_b40) 1992; 46 Bartók (10.1016/j.cpc.2020.107206_b4) 2010; 104 Kohn (10.1016/j.cpc.2020.107206_b1) 1965; 140 Abadi (10.1016/j.cpc.2020.107206_b13) 2016 Zhang (10.1016/j.cpc.2020.107206_b18) 2019; 3 Artrith (10.1016/j.cpc.2020.107206_b9) 2016; 114 Ceriotti (10.1016/j.cpc.2020.107206_b15) 2014; 185 Medvedev (10.1016/j.cpc.2020.107206_b32) 1989 Smith (10.1016/j.cpc.2020.107206_b17) 2018; 148 Kresse (10.1016/j.cpc.2020.107206_b21) 1996; 54 Overton Jr (10.1016/j.cpc.2020.107206_b35) 1955; 98 Nicklow (10.1016/j.cpc.2020.107206_b39) 1967; 164 Hutter (10.1016/j.cpc.2020.107206_b24) 2014; 4 Togo (10.1016/j.cpc.2020.107206_b37) 2015; 108 Plimpton (10.1016/j.cpc.2020.107206_b14) 1995; 117 Frisch (10.1016/j.cpc.2020.107206_b23) 2016 10.1016/j.cpc.2020.107206_b31 |
| References_xml | – volume: 54 start-page: 11169 year: 1996 ident: b21 publication-title: Phys. Rev. B – volume: 185 start-page: 1019 year: 2014 end-page: 1026 ident: b15 publication-title: Comput. Phys. Comm. – reference: for code implementation. – reference: See LAMMPS interface in – volume: 23 start-page: 629 year: 2018 end-page: 639 ident: b6 publication-title: Commun. Comput. Phys. – start-page: 4441 year: 2018 end-page: 4451 ident: b8 publication-title: Advances in Neural Information Processing Systems, Vol. 31 – volume: 24 start-page: 1365 year: 1971 end-page: 1381 ident: b36 publication-title: Phil. Mag. – reference: See – volume: 39 start-page: 1 year: 2014 end-page: 24 ident: b33 publication-title: Crit. Rev. Solid State Mater. Sci. – volume: 120 start-page: 143001 year: 2018 ident: b7 publication-title: Phys. Rev. Lett. – year: 1989 ident: b32 article-title: CODATA Key Values for Thermodynamics – volume: 12 start-page: 4634 year: 1975 ident: b34 publication-title: Phys. Rev. B – reference: for phonon calculations using phonopy. – volume: 15 start-page: 448 year: 2018 end-page: 455 ident: b11 publication-title: J. Chem. Theory Comput. – volume: 6 start-page: 15 year: 1996 end-page: 50 ident: b20 publication-title: Comput. Mater. Sci. – volume: 98 start-page: 969 year: 1955 ident: b35 publication-title: Phys. Rev. – volume: 228 start-page: 178 year: 2018 end-page: 184 ident: b10 publication-title: Comput. Phys. Comm. – volume: 3 start-page: 023804 year: 2019 ident: b18 publication-title: Phys. Rev. Mater. – volume: 164 start-page: 922 year: 1967 ident: b39 publication-title: Phys. Rev. – volume: 77 start-page: 3865 year: 1996 end-page: 3868 ident: b28 publication-title: Phys. Rev. Lett. – year: 2016 ident: b23 article-title: Gaussian16 Revision C.01 – year: 2009 ident: b3 article-title: Ab Initio Molecular Dynamics: Basic Theory and Advanced Methods – volume: 148 start-page: 241733 year: 2018 ident: b17 publication-title: J. Chem. Phys. – volume: 104 start-page: 136403 year: 2010 ident: b4 publication-title: Phys. Rev. Lett. – volume: 98 start-page: 146401 year: 2007 ident: b5 publication-title: Phys. Rev. Lett. – reference: . – reference: D. Kingma, J. Ba, Adam: a method for stochastic optimization, in: Proceedings of the International Conference on Learning Representations (ICLR), 2015. – volume: 140 start-page: 171 year: 2017 end-page: 180 ident: b16 publication-title: Comput. Mater. Sci. – year: 2016 ident: b13 article-title: Tensorflow: Large-scale machine learning on heterogeneous distributed systems – volume: 108 start-page: 1 year: 2015 end-page: 5 ident: b37 publication-title: Scr. Mater. – volume: 114 start-page: 135 year: 2016 end-page: 150 ident: b9 publication-title: Comput. Mater. Sci. – volume: 111 start-page: 218 year: 2016 end-page: 230 ident: b42 publication-title: Comput. Mater. Sci. – volume: 55 start-page: 2471 year: 1985 ident: b2 publication-title: Phys. Rev. Lett. – volume: 13 start-page: 5188 year: 1976 ident: b29 publication-title: Phys. Rev. B – volume: 9 start-page: 2261 year: 2018 end-page: 2269 ident: b12 publication-title: Chem. Sci. – volume: 46 start-page: 2727 year: 1992 ident: b40 publication-title: Phys. Rev. B – volume: 4 start-page: 15 year: 2014 end-page: 25 ident: b24 publication-title: Wiley Interdiscip. Rev. Comput. Mol. Sci. – volume: 29 start-page: 465901 year: 2017 ident: b22 publication-title: J. Phys.: Condens. Matter – year: 2016 ident: b30 publication-title: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) – volume: 140 start-page: A1133 year: 1965 ident: b1 publication-title: Phys. Rev. – volume: 117 start-page: 1 year: 1995 end-page: 19 ident: b14 publication-title: J. Comput. Phys. – volume: 104 start-page: 136403 issue: 13 year: 2010 ident: 10.1016/j.cpc.2020.107206_b4 publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.104.136403 – volume: 185 start-page: 1019 year: 2014 ident: 10.1016/j.cpc.2020.107206_b15 publication-title: Comput. Phys. Comm. doi: 10.1016/j.cpc.2013.10.027 – volume: 117 start-page: 1 issue: 1 year: 1995 ident: 10.1016/j.cpc.2020.107206_b14 publication-title: J. Comput. Phys. doi: 10.1006/jcph.1995.1039 – volume: 54 start-page: 11169 issue: 16 year: 1996 ident: 10.1016/j.cpc.2020.107206_b21 publication-title: Phys. Rev. B doi: 10.1103/PhysRevB.54.11169 – volume: 55 start-page: 2471 issue: 22 year: 1985 ident: 10.1016/j.cpc.2020.107206_b2 publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.55.2471 – volume: 164 start-page: 922 issue: 3 year: 1967 ident: 10.1016/j.cpc.2020.107206_b39 publication-title: Phys. Rev. doi: 10.1103/PhysRev.164.922 – volume: 24 start-page: 1365 issue: 192 year: 1971 ident: 10.1016/j.cpc.2020.107206_b36 publication-title: Phil. Mag. doi: 10.1080/14786437108217418 – ident: 10.1016/j.cpc.2020.107206_b38 – volume: 6 start-page: 15 issue: 1 year: 1996 ident: 10.1016/j.cpc.2020.107206_b20 publication-title: Comput. Mater. Sci. doi: 10.1016/0927-0256(96)00008-0 – ident: 10.1016/j.cpc.2020.107206_b19 – volume: 98 start-page: 969 issue: 4 year: 1955 ident: 10.1016/j.cpc.2020.107206_b35 publication-title: Phys. Rev. doi: 10.1103/PhysRev.98.969 – year: 2016 ident: 10.1016/j.cpc.2020.107206_b13 – volume: 3 start-page: 023804 issue: 2 year: 2019 ident: 10.1016/j.cpc.2020.107206_b18 publication-title: Phys. Rev. Mater. doi: 10.1103/PhysRevMaterials.3.023804 – volume: 13 start-page: 5188 issue: 12 year: 1976 ident: 10.1016/j.cpc.2020.107206_b29 publication-title: Phys. Rev. B doi: 10.1103/PhysRevB.13.5188 – year: 2009 ident: 10.1016/j.cpc.2020.107206_b3 – volume: 108 start-page: 1 year: 2015 ident: 10.1016/j.cpc.2020.107206_b37 publication-title: Scr. Mater. doi: 10.1016/j.scriptamat.2015.07.021 – volume: 9 start-page: 2261 issue: 8 year: 2018 ident: 10.1016/j.cpc.2020.107206_b12 publication-title: Chem. Sci. doi: 10.1039/C7SC04934J – ident: 10.1016/j.cpc.2020.107206_b26 – year: 2016 ident: 10.1016/j.cpc.2020.107206_b23 – volume: 111 start-page: 218 year: 2016 ident: 10.1016/j.cpc.2020.107206_b42 publication-title: Comput. Mater. Sci. doi: 10.1016/j.commatsci.2015.09.013 – volume: 140 start-page: A1133 issue: 4A year: 1965 ident: 10.1016/j.cpc.2020.107206_b1 publication-title: Phys. Rev. doi: 10.1103/PhysRev.140.A1133 – volume: 23 start-page: 629 issue: 3 year: 2018 ident: 10.1016/j.cpc.2020.107206_b6 publication-title: Commun. Comput. Phys. doi: 10.4208/cicp.OA-2017-0213 – volume: 140 start-page: 171 year: 2017 ident: 10.1016/j.cpc.2020.107206_b16 publication-title: Comput. Mater. Sci. doi: 10.1016/j.commatsci.2017.08.031 – volume: 4 start-page: 15 issue: 1 year: 2014 ident: 10.1016/j.cpc.2020.107206_b24 publication-title: Wiley Interdiscip. Rev. Comput. Mol. Sci. doi: 10.1002/wcms.1159 – volume: 120 start-page: 143001 year: 2018 ident: 10.1016/j.cpc.2020.107206_b7 publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.120.143001 – volume: 39 start-page: 1 issue: 1 year: 2014 ident: 10.1016/j.cpc.2020.107206_b33 publication-title: Crit. Rev. Solid State Mater. Sci. doi: 10.1080/10408436.2013.772503 – ident: 10.1016/j.cpc.2020.107206_b41 – volume: 148 start-page: 241733 issue: 24 year: 2018 ident: 10.1016/j.cpc.2020.107206_b17 publication-title: J. Chem. Phys. doi: 10.1063/1.5023802 – volume: 46 start-page: 2727 issue: 5 year: 1992 ident: 10.1016/j.cpc.2020.107206_b40 publication-title: Phys. Rev. B doi: 10.1103/PhysRevB.46.2727 – volume: 77 start-page: 3865 year: 1996 ident: 10.1016/j.cpc.2020.107206_b28 publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.77.3865 – start-page: 4441 year: 2018 ident: 10.1016/j.cpc.2020.107206_b8 – ident: 10.1016/j.cpc.2020.107206_b31 – volume: 228 start-page: 178 year: 2018 ident: 10.1016/j.cpc.2020.107206_b10 publication-title: Comput. Phys. Comm. doi: 10.1016/j.cpc.2018.03.016 – year: 2016 ident: 10.1016/j.cpc.2020.107206_b30 – volume: 98 start-page: 146401 issue: 14 year: 2007 ident: 10.1016/j.cpc.2020.107206_b5 publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.98.146401 – ident: 10.1016/j.cpc.2020.107206_b25 – ident: 10.1016/j.cpc.2020.107206_b27 – year: 1989 ident: 10.1016/j.cpc.2020.107206_b32 – volume: 29 start-page: 465901 issue: 46 year: 2017 ident: 10.1016/j.cpc.2020.107206_b22 publication-title: J. Phys.: Condens. Matter – volume: 12 start-page: 4634 issue: 11 year: 1975 ident: 10.1016/j.cpc.2020.107206_b34 publication-title: Phys. Rev. B doi: 10.1103/PhysRevB.12.4634 – volume: 114 start-page: 135 year: 2016 ident: 10.1016/j.cpc.2020.107206_b9 publication-title: Comput. Mater. Sci. doi: 10.1016/j.commatsci.2015.11.047 – volume: 15 start-page: 448 issue: 1 year: 2018 ident: 10.1016/j.cpc.2020.107206_b11 publication-title: J. Chem. Theory Comput. doi: 10.1021/acs.jctc.8b00908 |
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| Title | DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models |
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