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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
- 41.
W. Jia, H. Wang, M. Chen, D. Lu, L. Lin, R. Car, W. E, and L. Zhang, “Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning,” in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC’20 (IEEE Press, 2020 ).
Not in our library
- 42.
Z. Guo, D. Lu, Y. Yan, S. Hu, R. Liu, G. Tan, N. Sun, W. Jiang, L. Liu, Y. Chen, L. Zhang, M. Chen, H. Wang, and W. Jia, “Extending the limit of molecular dynamics with ab initio accuracy to 10 billion atoms,” in PPoPP’22: Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (Association for Computing Machinery, New York, 2022 ), pp. 205–218.
Not in our library
- 43.
J. Behler, “Perspective: Machine learning potentials for atomistic simulations,” J. Chem. Phys. 145, 170901 (2016 ).
Not in our library10.1063/1.4966192
- 44.
K. T. Butler, D. W. Davies, H. Cartwright, O. Isayev, and A. Walsh, “Machine learning for molecular and materials science,” Nature 559, 547–555 (2018 ).
Not in our library10.1038/s41586-018-0337-2
- 45.
F. Noé, A. Tkatchenko, K.-R. Müller, and C. Clementi, “Machine learning for molecular simulation,” Annu. Rev. Phys. Chem. 71, 361–390 (2020 ).
Not in our library10.1146/annurev-physchem-042018-052331
- 46.
O. T. Unke, S. Chmiela, H. E. Sauceda, M. Gastegger, I. Poltavsky, K. T. Schütt, A. Tkatchenko, and K.-R. Müller, “Machine learning force fields,” Chem. Rev. 121, 10142–10186 (2021 ).
Not in our library10.1021/acs.chemrev.0c01111
- 47.
M. Pinheiro, Jr., F. Ge, N. Ferré, P. O. Dral, and M. Barbatti, “Choosing the right molecular machine learning potential,” Chem. Sci. 12, 14396–14413 (2021 ).
Not in our library10.1039/d1sc03564a
- 48.
S. Manzhos and T. Carrington, Jr., “Neural network potential energy surfaces for small molecules and reactions,” Chem. Rev. 121, 10187–10217 (2021 ).
Not in our library10.1021/acs.chemrev.0c00665
- 49.
J. Zeng, L. Cao, and T. Zhu, “Neural network potentials,” in Quantum Chemistry in the Age of Machine Learning, edited by P. O. Dral (Elsevier, 2022 ), Chap. 12, pp. 279–294.
Not in our library
- 50.
X. Wang, Y. Wang, L. Zhang, F. Dai, and H. Wang, “A tungsten deep neural-network potential for simulating mechanical property degradation under fusion service environment,” Nucl. Fusion 62, 126013 (2022 ).
Not in our library10.1088/1741-4326/ac888b
- 51.
D. Zhang, H. Bi, F.-Z. Dai, W. Jiang, L. Zhang, and H. Wang, “DPA-1: Pretraining of attention-based deep potential model for molecular simulation,” arXiv.2208.08236 (preprint) (2022 ).
Not in our library
- 52.
J. Zeng, T. J. Giese, Ş. Ekesan, and D. M. York, “Development of range-corrected deep learning potentials for fast, accurate quantum mechanical/molecular mechanical simulations of chemical reactions in solution,” J. Chem. Theory Comput. 17, 6993–7009 (2021 ).
Not in our library10.1021/acs.jctc.1c00201
- 53.
L. Zhang, H. Wang, M. C. Muniz, A. Z. Panagiotopoulos, R. Car, and W. E, “A deep potential model with long-range electrostatic interactions,” J. Chem. Phys. 156, 124107 (2022 ).
Not in our library10.1063/5.0083669
- 54.
W. Liang, J. Zeng, D. M. York, L. Zhang, and H. Wang, “Learning DeePMD-kit: A guide to building deep potential models,” in A Practical Guide to Recent Advances in Multiscale Modeling and Simulation of Biomolecules, edited by Y. Wang and R. Zhou (AIP Publishing, 2023 ), Chap. 6, pp. 1–20.
Not in our library
- 55.
T. Wen, L. Zhang, H. Wang, W. E, and D. J. Srolovitz, “Deep potentials for materials science,” Mater. Futures 1, 022601 (2022 ).
Not in our library10.1088/2752-5724/ac681d
- 56.
S. K. Achar, L. Zhang, and J. K. Johnson, “Efficiently trained deep learning potential for graphane,” J. Phys. Chem. C 125, 14874–14882 (2021 ).
Not in our library10.1021/acs.jpcc.1c01411
- 57.
L. Bonati and M. Parrinello, “Silicon liquid structure and crystal nucleation from ab initio deep metadynamics,” Phys. Rev. Lett. 121, 265701 (2018 ).
Not in our library10.1103/physrevlett.121.265701
- 58.
J. Wang, H. Shen, R. Yang, K. Xie, C. Zhang, L. Chen, K.-M. Ho, C.-Z. Wang, and S. Wang, “A deep learning interatomic potential developed for atomistic simulation of carbon materials,” Carbon 186, 1–8 (2022 ).
Not in our library10.1016/j.carbon.2021.09.062
- 59.
R. Li, E. Lee, and T. Luo, “A unified deep neural network potential capable of predicting thermal conductivity of silicon in different phases,” Mater. Today Phys. 12, 100181 (2020 ).
Not in our library10.1016/j.mtphys.2020.100181
- 60.
I. A. Balyakin, S. V. Rempel, R. E. Ryltsev, and A. A. Rempel, “Deep machine learning interatomic potential for liquid silica,” Phys. Rev. E 102, 052125 (2020 ).
Not in our library10.1103/physreve.102.052125
- 61.
H.-Y. Ko, L. Zhang, B. Santra, H. Wang, W. E, R. A. DiStasio, Jr., and R. Car, “Isotope effects in liquid water via deep potential molecular dynamics,” Mol. Phys. 117, 3269–3281 (2019 ).
Not in our library10.1080/00268976.2019.1652366
- 62.
J. Xu, C. Zhang, L. Zhang, M. Chen, B. Santra, and X. Wu, “Isotope effects in molecular structures and electronic properties of liquid water via deep potential molecular dynamics based on the SCAN functional,” Phys. Rev. B 102, 214113 (2020 ).
Not in our library10.1103/physrevb.102.214113
- 63.
C. Andreani, G. Romanelli, A. Parmentier, R. Senesi, A. I. Kolesnikov, H.-Y. Ko, M. F. Calegari Andrade, and R. Car, “Hydrogen dynamics in supercritical water probed by neutron scattering and computer simulations,” J. Phys. Chem. Lett. 11, 9461–9467 (2020 ).
Not in our library10.1021/acs.jpclett.0c02547
- 64.
C. Zhang, L. Zhang, J. Xu, F. Tang, B. Santra, and X. Wu, “Isotope effects in x-ray absorption spectra of liquid water,” Phys. Rev. B 102, 115155 (2020 ).
Not in our library10.1103/physrevb.102.115155
- 65.
T. E. Gartner III, L. Zhang, P. M. Piaggi, R. Car, A. Z. Panagiotopoulos, and P. G. Debenedetti, “Signatures of a liquid–liquid transition in an ab initio deep neural network model for water,” Proc. Natl. Acad. Sci. U. S. A. 117, 26040–26046 (2020 ).
Not in our library10.1073/pnas.2015440117
- 66.
D. Tisi, L. Zhang, R. Bertossa, H. Wang, R. Car, and S. Baroni, “Heat transport in liquid water from first-principles and deep neural network simulations,” Phys. Rev. B 104, 224202 (2021 ).
Not in our library10.1103/physrevb.104.224202
- 67.
C. Malosso, L. Zhang, R. Car, S. Baroni, and D. Tisi, “Viscosity in water from first-principles and deep-neural-network simulations,” npj Comput. Mater. 8, 139 (2022 ).
Not in our library10.1038/s41524-022-00830-7
- 68.
Y. Shi, C. C. Doyle, and T. L. Beck, “Condensed phase water molecular multipole moments from deep neural network models trained on ab initio simulation data,” J. Phys. Chem. Lett. 12, 10310–10317 (2021 ).
Not in our library10.1021/acs.jpclett.1c02328
- 69.
F. Matusalem, J. Santos Rego, and M. de Koning, “Plastic deformation of superionic water ices,” Proc. Natl. Acad. Sci. U. S. A. 119, e2203397119 (2022 ).
Not in our library10.1073/pnas.2203397119
- 70.
Y. Zhai, A. Caruso, S. L. Bore, Z. Luo, and F. Paesani, “A ‘short blanket’ dilemma for a state-of-the-art neural network potential for water: Reproducing experimental properties or the physics of the underlying many-body interactions?,” J. Chem. Phys. 158, 084111 (2023 ).
Not in our library10.1063/5.0142843
- 71.
S. L. Bore and F. Paesani, “Realistic phase diagram of water from “first principles” data-driven quantum simulations,” Nat Commun 14, 3349 (2023 ).
Not in our library10.1038/s41467-023-38855-1
- 72.
J. Zeng, Y. Tao, T. J. Giese, and D. M. York, “QDπ: A quantum deep potential interaction model for drug discovery,” J. Chem. Theory Comput. 19, 1261–1275 (2023 ).
Not in our library10.1021/acs.jctc.2c01172
- 73.
- 74.
M. Yang, L. Bonati, D. Polino, and M. Parrinello, “Using metadynamics to build neural network potentials for reactive events: The case of urea decomposition in water,” Catal. Today 387, 143–149 (2022 ).
Not in our library10.1016/j.cattod.2021.03.018
- 75.
T. J. Giese, J. Zeng, Ş. Ekesan, and D. M. York, “Combined QM/MM, machine learning path integral approach to compute free energy profiles and kinetic isotope effects in RNA cleavage reactions,” J. Chem. Theory Comput. 18, 4304–4317 (2022 ).
Not in our library10.1021/acs.jctc.2c00151
- 76.
J. Liu, R. Liu, Y. Cao, and M. Chen, “Solvation structures of calcium and magnesium ions in water with the presence of hydroxide: A study by deep potential molecular dynamics,” Phys. Chem. Chem. Phys. 25, 983–993 (2023 ).
Not in our library10.1039/d2cp04105g
- 77.
- 78.
J. Zeng, L. Zhang, H. Wang, and T. Zhu, “Exploring the chemical space of linear alkane pyrolysis via deep potential GENerator,” Energy Fuels 35, 762–769 (2021 ).
Not in our library10.1021/acs.energyfuels.0c03211
- 79.
Q. Chu, K. H. Luo, and D. Chen, “Exploring complex reaction networks using neural network-based molecular dynamics simulation,” J. Phys. Chem. Lett. 13, 4052–4057 (2022 ).
Not in our library10.1021/acs.jpclett.2c00647
- 80.
B. Wang, J. Zeng, L. Cao, C.-H. Chin, D. York, T. Zhu, and J. Zhang, “Growth of polycyclic aromatic hydrocarbon and soot inception by in silico simulation,” chemrxiv-2022-qp8fc (2022 ).
Not in our library
- 81.
Z. Wang, Y. Han, J. Li, and X. He, “Combining the fragmentation approach and neural network potential energy surfaces of fragments for accurate calculation of protein energy,” J. Phys. Chem. B 124, 3027–3035 (2020 ).
Not in our library10.1021/acs.jpcb.0c01370
- 82.
Y. Han, Z. Wang, Z. Wei, J. Liu, and J. Li, “Machine learning builds full-QM precision protein force fields in seconds,” Briefings Bioinf. 22, bbab158 (2021 ).
Not in our library10.1093/bib/bbab158
- 83.
M. F. Calegari Andrade, H.-Y. Ko, L. Zhang, R. Car, and A. Selloni, “Free energy of proton transfer at the water-TiO2 interface from ab initio deep potential molecular dynamics,” Chem. Sci. 11, 2335–2341 (2020 ).
Not in our library10.1039/c9sc05116c
- 84.
M. Galib and D. T. Limmer, “Reactive uptake of N2O5 by atmospheric aerosol is dominated by interfacial processes,” Science 371, 921–925 (2021 ).
Not in our library10.1126/science.abd7716
- 85.
Y.-B. Zhuang, R.-H. Bi, and J. Cheng, “Resolving the odd–even oscillation of water dissociation at rutile TiO2(110)–water interface by machine learning accelerated molecular dynamics,” J. Chem. Phys. 157, 164701 (2022 ).
Not in our library10.1063/5.0126333
- 86.
M. de la Puente, R. David, A. Gomez, and D. Laage, “Acids at the edge: Why nitric and formic acid dissociations at air–water interfaces depend on depth and on interface specific area,” J. Am. Chem. Soc. 144, 10524–10529 (2022 ).
Not in our library10.1021/jacs.2c03099
- 87.
S. P. Niblett, M. Galib, and D. T. Limmer, “Learning intermolecular forces at liquid-vapor interfaces,” J. Chem. Phys. 155, 164101 (2021 ).
Not in our library10.1063/5.0067565
- 88.
L. Zhang, H. Wang, R. Car, and W. E, “Phase diagram of a deep potential water model,” Phys. Rev. Lett. 126, 236001 (2021 ).
Not in our library10.1103/physrevlett.126.236001
- 89.
J. Zeng, Y. Tao, T. J. Giese, and D. M. York, “Modern semiempirical electronic structure methods and machine learning potentials for drug discovery: Conformers, tautomers, and protonation states,” J. Chem. Phys. 158, 124110 (2023 ).
Not in our library10.1063/5.0139281
- 90.
W.-K. Chen, X.-Y. Liu, W.-H. Fang, P. O. Dral, and G. Cui, “Deep learning for nonadiabatic excited-state dynamics,” J. Phys. Chem. Lett. 9, 6702–6708 (2018 ).
Not in our library10.1021/acs.jpclett.8b03026
- 91.
C. Vega and J. L. F. Abascal, “Simulating water with rigid non-polarizable models: A general perspective,” Phys. Chem. Chem. Phys. 13, 19663–19688 (2011 ).
Not in our library10.1039/c1cp22168j
- 92.
E. Sanz, C. Vega, J. L. F. Abascal, and L. G. MacDowell, “Phase diagram of water from computer simulation,” Phys. Rev. Lett. 92, 255701 (2004 ).
Not in our library10.1103/physrevlett.92.255701
- 93.
T. Darden, D. York, and L. Pedersen, “Particle mesh Ewald: An N·log(N) method for Ewald sums in large systems,” J. Chem. Phys. 98, 10089–10092 (1993 ).
Not in our library10.1063/1.464397
- 94.
T. J. Giese, M. T. Panteva, H. Chen, and D. M. York, “Multipolar Ewald methods, 1: Theory, accuracy, and performance,” J. Chem. Theory Comput. 11, 436–450 (2015 ).
Not in our library10.1021/ct5007983
- 95.
T. J. Giese, M. T. Panteva, H. Chen, and D. M. York, “Multipolar Ewald methods, 2: Applications using a quantum mechanical force field,” J. Chem. Theory Comput. 11, 451–461 (2015 ).
Not in our library10.1021/ct500799g
- 96.
K. Nam, J. Gao, and D. M. York, “An efficient linear-scaling Ewald method for long-range electrostatic interactions in combined QM/MM calculations,” J. Chem. Theory Comput. 1, 2–13 (2005 ).
Not in our library10.1021/ct049941i
- 97.
T. J. Giese and D. M. York, “Ambient-potential composite Ewald method for ab initio quantum mechanical/molecular mechanical molecular dynamics simulation,” J. Chem. Theory Comput. 12, 2611–2632 (2016 ).
Not in our library10.1021/acs.jctc.6b00198
- 98.
T. J. Giese, M. Huang, H. Chen, and D. M. York, “Recent advances toward a general purpose linear-scaling quantum force field,” Acc. Chem. Res. 47, 2812–2820 (2014 ).
Not in our library10.1021/ar500103g
- 99.
T. J. Giese and D. M. York, “Quantum mechanical force fields for condensed phase molecular simulations,” J. Phys.: Condens. Matter 29, 383002 (2017 ).
Not in our library10.1088/1361-648x/aa7c5c
- 100.
T. J. Giese, J. Zeng, and D. M. York, “Multireference generalization of the weighted thermodynamic perturbation method,” J. Phys. Chem. A 126, 8519–8533 (2022 ).
Not in our library10.1021/acs.jpca.2c06201
- 101.
T. J. Martínez, “Ab initio reactive computer aided molecular design,” Acc. Chem. Res. 50, 652–656 (2017 ).
Not in our library10.1021/acs.accounts.7b00010
- 102.
J. Zeng, L. Cao, C.-H. Chin, H. Ren, J. Z. H. Zhang, and T. Zhu, “ReacNetGenerator: An automatic reaction network generator for reactive molecular dynamics simulations,” Phys. Chem. Chem. Phys. 22, 683–691 (2020 ).
Not in our library10.1039/c9cp05091d
- 103.
L. Zhang, D.-Y. Lin, H. Wang, R. Car, and W. E, “Active learning of uniformly accurate interatomic potentials for materials simulation,” Phys. Rev. Mater. 3, 023804 (2019 ).
Not in our library10.1103/physrevmaterials.3.023804
- 104.
L. Cao, J. Zeng, B. Wang, T. Zhu, and J. Z. Zhang, “Ab initio neural network MD simulation of thermal decomposition of high energy material CL-20/TNT,” Phys. Chem. Chem. Phys. 24, 11801–11811 (2022 ).
Not in our library10.1039/d2cp00710j
- 105.
L. Zhang, M. Chen, X. Wu, H. Wang, W. E, and R. Car, “Deep neural network for the dielectric response of insulators,” Phys. Rev. B 102, 041121 (2020 ).
Not in our library10.1103/physrevb.102.041121
- 106.
G. M. Sommers, M. F. C. Andrade, L. Zhang, H. Wang, and R. Car, “Raman spectrum and polarizability of liquid water from deep neural networks,” Phys. Chem. Chem. Phys. 22, 10592–10602 (2020 ).
Not in our library10.1039/d0cp01893g
- 107.
Y. Zhang, H. Wang, W. Chen, J. Zeng, L. Zhang, W. Han, and W. E, “DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models,” Comput. Phys. Commun. 253, 107206 (2020 ).
Not in our library10.1016/j.cpc.2020.107206
- 108.
D. Lu, H. Wang, M. Chen, L. Lin, R. Car, W. E, W. Jia, and L. Zhang, “86 PFLOPS deep potential molecular dynamics simulation of 100 million atoms with ab initio accuracy,” Comput. Phys. Commun. 259, 107624 (2021 ).
Not in our library10.1016/j.cpc.2020.107624
- 109.
D. Lu, W. Jiang, Y. Chen, L. Zhang, W. Jia, H. Wang, and M. Chen, “DP compress: A model compression scheme for generating efficient deep potential models,” J. Chem. Theory Comput. 18, 5559–5567 (2022 ).
Not in our library10.1021/acs.jctc.2c00102
- 110.
P. Mo, C. Li, D. Zhao, Y. Zhang, M. Shi, J. Li, and J. Liu, “Accurate and efficient molecular dynamics based on machine learning and non von Neumann architecture,” npj Comput. Mater. 8, 107 (2022 ).
Not in our library10.1038/s41524-022-00773-z
- 111.
K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in Computer Vision–ECCV 2016 (Springer International Publishing, 2016 ), pp. 630–645.
Not in our library
- 112.
V. Nair and G. E. Hinton, “Rectified linear units improve restricted Boltzmann machines,” in Proceedings of the 27th International Conference on International Conference on Machine Learning (ICML’10) (Omnipress, Madison, WI, 2010 ), pp. 807–814.
Not in our library
- 113.
X. Glorot, A. Bordes, and Y. Bengio, “Deep sparse rectifier neural networks,” in Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, edited by G. Gordon, D. Dunson, and M. Dudík (Proceedings of Machine Learning Research, Fort Lauderdale, FL, 2011 ), Vol. 15, pp. 315–323.
Not in our library
- 114.
D. Hendrycks and K. Gimpel, “Gaussian error linear units (GELUs),” arXiv:1606.08415 [cs.LG] (2020 ).
Not in our library
- 115.
L. Chanussot, A. Das, S. Goyal, T. Lavril, M. Shuaibi, M. Riviere, K. Tran, J. Heras-Domingo, C. Ho, W. Hu, A. Palizhati, A. Sriram, B. Wood, J. Yoon, D. Parikh, C. L. Zitnick, and Z. Ulissi, “Open catalyst 2020 (OC20) dataset and community challenges,” ACS Catal. 11, 6059–6072 (2021 ).
Not in our library10.1021/acscatal.0c04525
- 116.
J. Gasteiger, M. Shuaibi, A. Sriram, S. Günnemann, Z. Ulissi, C. L. Zitnick, and A. Das, “GemNet-OC: Developing graph neural networks for large and diverse molecular simulation datasets,” Transactions on Machine Learning Research (published online, 2022); available at https://dblp.org/db/journals/tmlr/tmlr2022.html [cs.LG] (2022 ).
Not in our library
- 117.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. u. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems, edited by I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Curran Associates, Inc., 2017 ), Vol. 30.
Not in our library
- 118.
M.-T. Luong, H. Pham, and C. D. Manning, “Effective approaches to attention-based neural machine translation,” in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (Association for Computational Linguistics, Lisbon, Portugal, 2015 ), pp. 1412–1421
Not in our library10.18653/v1/d15-1166
- 119.
Y. Zhang, C. Gao, Q. Liu, L. Zhang, H. Wang, and M. Chen, “Warm dense matter simulation via electron temperature dependent deep potential molecular dynamics,” Phys. Plasmas 27, 122704 (2020 ).
Not in our library10.1063/5.0023265
- 120.
T.-S. Lee, D. S. Cerutti, D. Mermelstein, C. Lin, S. LeGrand, T. J. Giese, A. Roitberg, D. A. Case, R. C. Walker, and D. M. York, “GPU-accelerated molecular dynamics and free energy methods in Amber18: Performance enhancements and new features,” J. Chem. Inf. Model. 58, 2043–2050 (2018 ).
Not in our library10.1021/acs.jcim.8b00462
- 121.
J. Yang, Y. Cong, and H. Li, “A new machine learning approach based on range corrected deep potential model for efficient vibrational frequency computation,” arXiv:2303.15969 (2023 ).
Not in our library10.1021/acs.jctc.3c00386
- 122.
J. F. Ziegler and J. P. Biersack, “The stopping and range of ions in matter,” in Treatise on Heavy-Ion Science: Volume 6: Astrophysics, Chemistry, and Condensed Matter, edited by D. A. Bromley (Springer, Boston, MA, 1985 ), pp. 93–129.
Not in our library
- 123.
H. Wang, X. Guo, L. Zhang, H. Wang, and J. Xue, “Deep learning inter-atomic potential model for accurate irradiation damage simulations,” Appl. Phys. Lett. 114, 244101 (2019 ).
Not in our library10.1063/1.5098061
- 124.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in Proceedings of the 3rd International Conference for Learning Representations (ICLR), 2015.
Not in our library
- 125.
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, TensorFlow: Large-scale machine learning on heterogeneous systems, 2015, software available from tensorflow.org.
Not in our library
- 126.
J. Nickolls, I. Buck, M. Garland, and K. Skadron, “Scalable parallel programming with CUDA: Is CUDA the parallel programming model that application developers have been waiting for?,” Queue 6, 40–53 (2008 ).
Not in our library10.1145/1365490.1365500
- 127.
AMD, Inc., ROCm—Open Source Platform for HPC and Ultrascale GPU Computing, 2023.
Not in our library
- 128.
- 129.
A. Collette, Python and HDF5: Unlocking Scientific Data (O’Reilly Media, Inc., 2013 ).
Not in our library
- 130.
K. Martin and B. Hoffman, Mastering CMake: Version 3.1, Kitware Incorporated, 2015.
Not in our library
- 131.
J.-C. Fillion-Robin, M. McCormick, O. Padron, M. Smolens, M. Grauer, and M. Sarahan, jcfr/scipy_2018_scikit-build_talk: Scipy 2018 talk—Scikit-build: A build system generator for cpython c/c++/fortran/cython extensions, 2018.
Not in our library
- 132.
G. Van Rossum and Python Development Team, The Python Library Reference, 12th Media Services, Suwanee, GA, 2018.
Not in our library
- 133.
Google, Inc., GoogleTest–Google Testing and Mocking Framework, 2023.
Not in our library
- 134.
L. Dagum and R. Menon, “OpenMP: An industry standard API for shared-memory programming,” IEEE Comput. Sci. Eng. 5, 46–55 (1998 ).
Not in our library10.1109/99.660313
- 135.
E. Gabriel, G. E. Fagg, G. Bosilca, T. Angskun, J. J. Dongarra, J. M. Squyres, V. Sahay, P. Kambadur, B. Barrett, A. Lumsdaine, R. H. Castain, D. J. Daniel, R. L. Graham, and T. S. Woodall, “Open MPI: Goals, concept, and design of a next generation MPI implementation,” in Recent Advances in Parallel Virtual Machine and Message Passing Interface, edited by D. Kranzlmüller, P. Kacsuk, and J. Dongarra (Springer, Berlin, Heidelberg, 2004 ), pp. 97–104.
Not in our library
- 136.
W. Gropp, “MPICH2: A new start for MPI implementations,” in Recent Advances in Parallel Virtual Machine and Message Passing Interface (Springer, Berlin, Heidelberg, 2002 ), p. 7.
Not in our library
- 137.
A. Sergeev and M. Del Balso, “Horovod: Fast and easy distributed deep learning in TensorFlow,” arXiv:1802.05799 (2018 ).
Not in our library
- 138.
P. Goyal, P. Dollár, R. Girshick, P. Noordhuis, L. Wesolowski, A. Kyrola, A. Tulloch, Y. Jia, and K. He, “Accurate, large minibatch SGD: Training ImageNet in 1 hour,” arXiv:1706.02677 (2017 ).
Not in our library
- 139.
L. Dalcin and Y.-L. L. Fang, “mpi4py: Status update after 12 years of development,” Comput. Sci. Eng. 23, 47–54 (2021 ).33967632
Not in our library10.1109/mcse.2021.3083216
- 140.
A. P. Thompson, H. M. Aktulga, R. Berger, D. S. Bolintineanu, W. M. Brown, P. S. Crozier, P. J. in’t Veld, A. Kohlmeyer, S. G. Moore, T. D. Nguyen, R. Shan, M. J. Stevens, J. Tranchida, C. Trott, and S. J. Plimpton, “LAMMPS—A flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales,” Comput. Phys. Commun. 271, 108171 (2022 ).
Not in our library10.1016/j.cpc.2021.108171
- 141.
D. A. Case, K. Belfon, I. Y. Ben-Shalom, S. R. Brozell, D. S. Cerutti, T. E. Cheatham III, V. W. D. Cruzeiro, T. A. Darden, R. E. Duke, G. Giambasu, M. K. Gilson, H. Gohlke, A. W. Goetz, R. Harris, S. Izadi, S. A. Izmailov, K. Kasavajhala, K. Kovalenko, R. Krasny, T. Kurtzman, T. Lee, S. Le-Grand, P. Li, C. Lin, J. Liu, T. Luchko, R. Luo, V. Man, K. Merz, Y. Miao, O. Mikhailovskii, G. Monard,, H. Nguyen, A. Onufriev, F. Pan, S. Pantano, R. Qi, D. R. Roe, A. Roitberg, C. Sagui, S. Schott-Verdugo, J. Shen, C. L. Simmerling, N. Skrynnikov, J. Smith, J. Swails, R. C. Walker, J. Wang, R. M. Wilson, R. M. Wolf, X. Wu, Y. Xiong, Y. Xue, D. M. York, and P. A. Kollman, AMBER 20, University of California, San Francisco, CA, 2020.
Not in our library
- 142.
T. Komiya, G. Brandl, T. Shimizukawa, J. L. Andersen, A. Turner, S. Finucane, R. Lehmann, T. Kampik, J. Magin, J. Dufresne, J. Waltman, J. L. C. Rodríguez, A. Ronacher, M. Geier, D. Shachnev, R. Ruana, P. Virtanen, F. Freitag, L. Maddox, M. Liška, H. Xu, E. Wieser, J. Maitin-Shepard, N. Kaneko, and cocoatomo, sphinx-doc/sphinx: v7.0.0, 2023.
Not in our library
- 143.
D. van Heesch, Doxygen: Source Code Documentation Generator Tool, 2022.
Not in our library
- 144.
Conda-Forge Community, The Conda-Forge Project: Community-Based Software Distribution Built on the Conda Package Format and Ecosystem, 2015.
Not in our library
- 145.
F. Pezoa, J. L. Reutter, F. Suarez, M. Ugarte, and D. Vrgoč, “Foundations of JSON schema,” in Proceedings of the 25th International Conference on World Wide Web (International World Wide Web Conferences Steering Committee, 2016 ), pp. 263–273.
Not in our library
- 146.
O. Ben-Kiki, C. Evans, and I. döt Net, “YAML Ain’t Markup Language (YAMLTM) version 1.2, 2009.
Not in our library
- 147.
Q. Koziol and D. Robinson, HDF5, 2018.
Not in our library
- 148.
A. H. Larsen, J. J. Mortensen, J. Blomqvist, I. E. Castelli, R. Christensen, M. Dułak, J. Friis, M. N. Groves, B. Hammer, C. Hargus, E. D. Hermes, P. C. Jennings, P. B. Jensen, J. Kermode, J. R. Kitchin, E. L. Kolsbjerg, J. Kubal, K. Kaasbjerg, S. Lysgaard, J. B. Maronsson, T. Maxson, T. Olsen, L. Pastewka, A. Peterson, C. Rostgaard, J. Schiøtz, O. Schütt, M. Strange, K. S. Thygesen, T. Vegge, L. Vilhelmsen, M. Walter, Z. Zeng, and K. W. Jacobsen, “The atomic simulation environment—A python library for working with atoms,” J. Phys.: Condens. Matter 29, 273002 (2017 ).
Not in our library10.1088/1361-648x/aa680e
- 149.
W. Laosi, H. Gao, Y. Han, C. Ding, P. Shuning, Y. Wang, J. Qiuhan, H.-T. Wang, D. Xing, and J. Sun, “MAGUS: Machine learning and graph theory assisted universal structure searcher,” Natl. Sci. Rev. 10, nwad128 (2023 ).
Not in our library10.1093/nsr/nwad128
- 150.
S. Plimpton, “Fast parallel algorithms for short-range molecular dynamics,” J. Comput. Phys. 117, 1–19 (1995 ).
Not in our library10.1006/jcph.1995.1039
- 151.
V. Kapil, M. Rossi, O. Marsalek, R. Petraglia, Y. Litman, T. Spura, B. Cheng, A. Cuzzocrea, R. H. Meißner, D. M. Wilkins, B. A. Helfrecht, P. Juda, S. P. Bienvenue, W. Fang, J. Kessler, I. Poltavsky, S. Vandenbrande, J. Wieme, C. Corminboeuf, T. D. Kühne, D. E. Manolopoulos, T. E. Markland, J. O. Richardson, A. Tkatchenko, G. A. Tribello, V. V. Speybroeck, and M. Ceriotti, “i-PI 2.0: A universal force engine for advanced molecular simulations,” Comput. Phys. Commun. 236, 214–223 (2019 ).
Not in our library10.1016/j.cpc.2018.09.020
- 152.
M. J. Abraham, T. Murtola, R. Schulz, S. Páll, J. C. Smith, B. Hess, and E. Lindahl, “GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers,” SoftwareX 1–2, 19–25 (2015 ).
Not in our library10.1016/j.softx.2015.06.001
- 153.
- 154.
Y. Ding and J. Huang (2023 ). “Implementation and validation of an openmm plugin for the deep potential representation of potential energy,” https://github.com/JingHuangLab/openmm_deepmd_plugin.
Not in our library10.3390/ijms25031448
- 155.
P. Li, X. Liu, M. Chen, P. Lin, X. Ren, L. Lin, C. Yang, and L. He, “Large-scale ab initio simulations based on systematically improvable atomic basis,” Comput. Mater. Sci. 112, 503–517 (2016 ).
Not in our library10.1016/j.commatsci.2015.07.004
- 156.
P. M. Piaggi, J. Weis, A. Z. Panagiotopoulos, P. G. Debenedetti, and R. Car, “Homogeneous ice nucleation in an ab initio machine-learning model of water,” Proc. Natl. Acad. Sci. U. S. A. 119, e2207294119 (2022 ).
Not in our library10.1073/pnas.2207294119
- 157.
S. K. Achar, J. J. Wardzala, L. Bernasconi, L. Zhang, and J. K. Johnson, “Combined deep learning and classical potential approach for modeling diffusion in UiO-66,” J. Chem. Theory Comput. 18, 3593–3606 (2022 ).
Not in our library10.1021/acs.jctc.2c00010
- 158.
P. P. Ewald, “Die berechnung optischer und elektrostatischer gitterpotentiale,” Ann. Phys. 369, 253–287 (1921 ).
Not in our library10.1002/andp.19213690304
- 159.
T. A. Barnes, E. Marin-Rimoldi, S. Ellis, and T. D. Crawford, “The MolSSI driver interface project: A framework for standardized, on-the-fly interoperability between computational molecular sciences codes,” Comput. Phys. Commun. 261, 107688 (2021 ).
Not in our library10.1016/j.cpc.2020.107688
- 160.
H.-C. Tsai, T.-S. Lee, A. Ganguly, T. J. Giese, M. C. Ebert, P. Labute, K. M. Merz, Jr., and D. M. York, “AMBER free energy tools: A new framework for the design of optimized alchemical transformation pathways,” J. Chem. Theory Comput. 19, 640–658 (2023 ).
Not in our library10.1021/acs.jctc.2c00725
- 161.
T.-S. Lee, H.-C. Tsai, A. Ganguly, and D. M. York, “ACES: Optimized alchemically enhanced sampling,” J. Chem. Theory Comput. 19, 472–487 (2023 ).
Not in our library10.1021/acs.jctc.2c00697
- 162.
A. Ganguly, H.-C. Tsai, M. Fernández-Pendás, T.-S. Lee, T. J. Giese, and D. M. York, “AMBER drug discovery boost tools: Automated workflow for production free-energy simulation setup and analysis (ProFESSA),” J. Chem. Inf. Model. 62, 6069–6083 (2022 ).
Not in our library10.1021/acs.jcim.2c00879
- 163.
T. J. Giese and D. M. York, “Development of a robust indirect approach for MM → QM free energy calculations that combines force-matched reference potential and Bennett’s acceptance ratio methods,” J. Chem. Theory Comput. 15, 5543–5562 (2019 ).
Not in our library10.1021/acs.jctc.9b00401
- 164.
Y. Chen, L. Zhang, H. Wang, and W. E, “DeePKS-kit: A package for developing machine learning-based chemically accurate energy and density functional models,” Comput. Phys. Commun. 282, 108520 (2023 ).
Not in our library10.1016/j.cpc.2022.108520
- 165.
X. Wang, J. Li, L. Yang, F. Chen, Y. Wang, J. Chang, J. Chen, L. Zhang, and K. Yu, “DMFF: An open-source automatic differentiable platform for molecular force field development and molecular dynamics simulation,” Physical Chemistry (published online, 2022 ).
Not in our library10.26434/chemrxiv-2022-2c7gv
- 166.
H. Li, Z. Wang, N. Zou, M. Ye, R. Xu, X. Gong, W. Duan, and Y. Xu, “Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation,” Nat. Comput. Sci. 2, 367–377 (2022 ).
Not in our library10.1038/s43588-022-00265-6
- 167.
R. Mao, M. Lin, Y. Zhang, T. Zhang, Z.-Q. J. Xu, and Z. X. Chen, “DeepFlame: A deep learning empowered open-source platform for reacting flow simulations,” Computer Physics Communications (to be published).
Not in our library10.1016/j.cpc.2023.108842
- 168.
- 169.
E. You, Vue.js—The Progressive JavaScript Framework, 2023.
Not in our library
- 170.
L. Zhang, D.-Y. Lin, H. Wang, R. Car, and W. E, “Active learning of uniformly accurate interatomic potentials for materials simulation,” Phys. Rev. Mater. 3, 023804 (2019 ).
Not in our library10.1103/physrevmaterials.3.023804
- 171.
W. Jiang, D. Zhang, S. Yao, L. Zhang, H. Wang, and F. Dai, “Hybrid Monte Carlo-molecular dynamics simulation of order-disorder transition in refractory high entropy alloys using deep potential model reliable in the full concentration space” (unpublished) (2023 ).
Not in our library
- 172.
C. Adamo and V. Barone, “Toward reliable density functional methods without adjustable parameters: The PBE0 model,” J. Chem. Phys. 110, 6158–6170 (1999 ).
Not in our library10.1063/1.478522
- 173.
P. E. Blöchl, “Projector augmented-wave method,” Phys. Rev. B 50, 17953 (1994 ).
Not in our library10.1103/physrevb.50.17953
- 174.
J. P. Perdew, K. Burke, and M. Ernzerhof, “Generalized gradient approximation made simple,” Phys. Rev. Lett. 77, 3865 (1996 ).
Not in our library10.1103/physrevlett.77.3865
- 175.
N. Mardirossian and M. Head-Gordon, “ωB97M-V: A combinatorially optimized, range-separated hybrid, meta-GGA density functional with VV10 nonlocal correlation,” J. Chem. Phys. 144, 214110 (2016 ).
Not in our library10.1063/1.4952647
- 176.
A. Najibi and L. Goerigk, “DFT-D4 counterparts of leading meta-generalized-gradient approximation and hybrid density functionals for energetics and geometries,” J. Comput. Chem. 41, 2562–2572 (2020 ).
Not in our library10.1002/jcc.26411
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