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DeePMD-kit v2: A software package for deep potential models
Jinzhe Zeng, Duo Zhang, Denghui Lu and 44 more
The Journal of Chemical Physics · 2023 · CC BY
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.
References 176
- 1.
J. Behler and M. Parrinello, “Generalized neural-network representation of high-dimensional potential-energy surfaces,” Phys. Rev. Lett. 98, 146401 (2007 ).
Not in our library10.1103/physrevlett.98.146401
- 2.
A. P. Bartók, M. C. Payne, R. Kondor, and G. Csányi, “Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons,” Phys. Rev. Lett. 104, 136403 (2010 ).
Not in our library10.1103/physrevlett.104.136403
- 3.
J. Behler, “Atom-centered symmetry functions for constructing high-dimensional neural network potentials,” J. Chem. Phys. 134, 074106 (2011 ).
Not in our library10.1063/1.3553717
- 4.
M. Gastegger, L. Schwiedrzik, M. Bittermann, F. Berzsenyi, and P. Marquetand, “wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials,” J. Chem. Phys. 148, 241709 (2018 ).
Not in our library10.1063/1.5019667
- 5.
S. Chmiela, A. Tkatchenko, H. E. Sauceda, I. Poltavsky, K. T. Schütt, and K.-R. Müller, “Machine learning of accurate energy-conserving molecular force fields,” Sci. Adv. 3, e1603015 (2017 ).
Not in our library10.1126/sciadv.1603015
- 6.
- 7.
K. T. Schütt, H. E. Sauceda, P.-J. Kindermans, A. Tkatchenko, and K.-R. Müller, “SchNet—A deep learning architecture for molecules and materials,” J. Chem. Phys. 148, 241722 (2018 ).
Not in our library10.1063/1.5019779
- 8.
X. Chen, M. S. Jørgensen, J. Li, and B. Hammer, “Atomic energies from a convolutional neural network,” J. Chem. Theory Comput. 14, 3933–3942 (2018 ).
Not in our library10.1021/acs.jctc.8b00149
- 9.
L. Zhang, J. Han, H. Wang, R. Car, and W. E, “Deep potential molecular dynamics: A scalable model with the accuracy of quantum mechanics,” Phys. Rev. Lett. 120, 143001 (2018 ).
Not in our library10.1103/physrevlett.120.143001
- 10.
L. Zhang, J. Han, H. Wang, W. Saidi, R. Car, and W. E, “End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems,” in Advances in Neural Information Processing Systems, edited by S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett (Curran Associates, Inc., 2018 ), Vol. 31, pp. 4436–4446.
Not in our library
- 11.
Y. Zhang, C. Hu, and B. Jiang, “Embedded atom neural network potentials: Efficient and accurate machine learning with a physically inspired representation,” J. Phys. Chem. Lett. 10, 4962–4967 (2019 ).
Not in our library10.1021/acs.jpclett.9b02037
- 12.
J. S. Smith, O. Isayev, and A. E. Roitberg, “ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost,” Chem. Sci. 8, 3192–3203 (2017 ).
Not in our library10.1039/c6sc05720a
- 13.
O. T. Unke and M. Meuwly, “PhysNet: A neural network for predicting energies, forces, dipole moments, and partial charges,” J. Chem. Theory Comput. 15, 3678–3693 (2019 ).
Not in our library10.1021/acs.jctc.9b00181
- 14.
Z. L. Glick, D. P. Metcalf, A. Koutsoukas, S. A. Spronk, D. L. Cheney, and C. D. Sherrill, “AP-Net: An atomic-pairwise neural network for smooth and transferable interaction potentials,” J. Chem. Phys. 153, 044112 (2020 ).
Not in our library10.1063/5.0011521
- 15.
T. Zubatiuk and O. Isayev, “Development of multimodal machine learning potentials: Toward a physics-aware artificial intelligence,” Acc. Chem. Res. 54, 1575–1585 (2021 ).
Not in our library10.1021/acs.accounts.0c00868
- 16.
E. R. Khajehpasha, J. A. Finkler, T. D. Kühne, and S. A. Ghasemi, “CENT2: Improved charge equilibration via neural network technique,” Phys. Rev. B 105, 144106 (2022 ).
Not in our library10.1103/physrevb.105.144106
- 17.
X. Pan, J. Yang, R. Van, E. Epifanovsky, J. Ho, J. Huang, J. Pu, Y. Mei, K. Nam, and Y. Shao, “Machine-learning-assisted free energy simulation of solution-phase and enzyme reactions,” J. Chem. Theory Comput. 17, 5745–5758 (2021 ).
Not in our library10.1021/acs.jctc.1c00565
- 18.
- 19.
- 20.
K. T. Schütt, O. T. Unke, and M. Gastegger, “Equivariant message passing for the prediction of tensorial properties and molecular spectra,” in International Conference on Machine Learning (PMLR, 2021 ), pp. 9377–9388.
Not in our library
- 21.
- 22.
M. Haghighatlari, J. Li, X. Guan, O. Zhang, A. Das, C. J. Stein, F. Heidar-Zadeh, M. Liu, M. Head-Gordon, L. Bertels et al., “NewtonNet: A Newtonian message passing network for deep learning of interatomic potentials and forces,” Digital Discovery 1, 333–343 (2022 ).
Not in our library10.1039/d2dd00008c
- 23.
J. Gasteiger, F. Becker, and S. Günnemann, “GemNet: Universal directional graph neural networks for molecules,” in Advances in Neural Information Processing Systems (Curran Associates, Inc., 2021 ), Vol. 34, pp. 6790–6802.
Not in our library
- 24.
C. Chen and S. P. Ong, “A universal graph deep learning interatomic potential for the periodic table,” Nat. Comput. Sci. 2, 718–728 (2022 ).
Not in our library10.1038/s43588-022-00349-3
- 25.
R. Drautz, “Atomic cluster expansion for accurate and transferable interatomic potentials,” Phys. Rev. B 99, 014104 (2019 ).
Not in our library10.1103/physrevb.99.014104
- 26.
D. P. Kovács, C. van der Oord, J. Kucera, A. E. A. Allen, D. J. Cole, C. Ortner, and G. Csányi, “Linear atomic cluster expansion force fields for organic molecules: Beyond RMSE,” J. Chem. Theory Comput. 17, 7696–7711 (2021 ).
Not in our library10.1021/acs.jctc.1c00647
- 27.
R. Snyder, B. Kim, X. Pan, Y. Shao, and J. Pu, “Facilitating ab initio QM/MM free energy simulations by Gaussian process regression with derivative observations,” Phys. Chem. Chem. Phys. 24, 25134–25143 (2022 ).
Not in our library10.1039/d2cp02820d
- 28.
A. P. Thompson, L. P. Swiler, C. R. Trott, S. M. Foiles, and G. J. Tucker, “Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials,” J. Comput. Phys. 285, 316–330 (2015 ).
Not in our library10.1016/j.jcp.2014.12.018
- 29.
H. Wang, L. Zhang, J. Han, and W. E, “DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics,” Comput. Phys. Commun. 228, 178–184 (2018 ).
Not in our library10.1016/j.cpc.2018.03.016
- 30.
K. T. Schütt, P. Kessel, M. Gastegger, K. A. Nicoli, A. Tkatchenko, and K.-R. Müller, “SchNetPack: A deep learning toolbox for atomistic systems,” J. Chem. Theory Comput. 15, 448–455 (2019 ).
Not in our library10.1021/acs.jctc.8b00908
- 31.
S. Chmiela, H. E. Sauceda, I. Poltavsky, K.-R. Müller, and A. Tkatchenko, “sGDML: Constructing accurate and data efficient molecular force fields using machine learning,” Comput. Phys. Commun. 240, 38–45 (2019 ).
Not in our library10.1016/j.cpc.2019.02.007
- 32.
K. Lee, D. Yoo, W. Jeong, and S. Han, “SIMPLE-NN: An efficient package for training and executing neural- network interatomic potentials,” Comput. Phys. Commun. 242, 95–103 (2019 ).
Not in our library10.1016/j.cpc.2019.04.014
- 33.
X. Gao, F. Ramezanghorbani, O. Isayev, J. S. Smith, and A. E. Roitberg, “TorchANI: A free and open source PyTorch-based deep learning implementation of the ANI neural network potentials,” J. Chem. Inf. Model. 60, 3408–3415 (2020 ).
Not in our library10.1021/acs.jcim.0c00451
- 34.
P. O. Dral, F. Ge, B.-X. Xue, Y.-F. Hou, M. Pinheiro, Jr., J. Huang, and M. Barbatti, “MLatom 2: An integrative platform for atomistic machine learning,” Top. Curr. Chem. 379, 27 (2021 ).
Not in our library10.1007/s41061-021-00339-5
- 35.
A. Singraber, J. Behler, and C. Dellago, “Library-based LAMMPS implementation of high-dimensional neural network potentials,” J. Chem. Theory Comput. 15, 1827–1840 (2019 ).
Not in our library10.1021/acs.jctc.8b00770
- 36.
Y. Zhang, J. Xia, and B. Jiang, “REANN: A PyTorch-based end-to-end multi-functional deep neural network package for molecular, reactive, and periodic systems,” J. Chem. Phys. 156, 114801 (2022 ).
Not in our library10.1063/5.0080766
- 37.
K. T. Schütt, S. S. P. Hessmann, N. W. A. Gebauer, J. Lederer, and M. Gastegger, “SchNetPack 2.0: A neural network toolbox for atomistic machine learning,” J. Chem. Phys. 158, 144801 (2023 ).
Not in our library10.1063/5.0138367
- 38.
Z. Fan, Y. Wang, P. Ying, K. Song, J. Wang, Y. Wang, Z. Zeng, K. Xu, E. Lindgren, J. M. Rahm, A. J. Gabourie, J. Liu, H. Dong, J. Wu, Y. Chen, Z. Zhong, J. Sun, P. Erhart, Y. Su, and T. Ala-Nissila, “GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations,” J. Chem. Phys. 157, 114801 (2022 ).
Not in our library10.1063/5.0106617
- 39.
I. S. Novikov, K. Gubaev, E. V. Podryabinkin, and A. V. Shapeev, “The MLIP package: Moment tensor potentials with MPI and active learning,” Mach. Learn.: Sci. Technol. 2, 025002 (2021 ).
Not in our library10.1088/2632-2153/abc9fe
- 40.
H. Yanxon, D. Zagaceta, B. Tang, D. S. Matteson, and Q. Zhu, “PyXtal_FF: A python library for automated force field generation,” Mach. Learn.: Sci. Technol. 2, 027001 (2021 ).
Not in our library10.1088/2632-2153/abc940
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