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Wang G, Wang C, Zhang X, Li Z, Zhou J, Sun Z. Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations. iScience 2024; 27:109673. [PMID: 38646181 PMCID: PMC11033164 DOI: 10.1016/j.isci.2024.109673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2024] Open
Abstract
Machine learning interatomic potential (MLIP) overcomes the challenges of high computational costs in density-functional theory and the relatively low accuracy in classical large-scale molecular dynamics, facilitating more efficient and precise simulations in materials research and design. In this review, the current state of the four essential stages of MLIP is discussed, including data generation methods, material structure descriptors, six unique machine learning algorithms, and available software. Furthermore, the applications of MLIP in various fields are investigated, notably in phase-change memory materials, structure searching, material properties predicting, and the pre-trained universal models. Eventually, the future perspectives, consisting of standard datasets, transferability, generalization, and trade-off between accuracy and complexity in MLIPs, are reported.
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Affiliation(s)
- Guanjie Wang
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
- School of Integrated Circuit Science and Engineering, Beihang University, Beijing 100191, China
| | - Changrui Wang
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
| | - Xuanguang Zhang
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
| | - Zefeng Li
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
| | - Jian Zhou
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
| | - Zhimei Sun
- School of Materials Science and Engineering, Beihang University, Beijing 100191, China
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Zhang J, Guo W, Yao Y. Deep Potential Molecular Dynamics Study of Chapman-Jouguet Detonation Events of Energetic Materials. J Phys Chem Lett 2023; 14:7141-7148. [PMID: 37535980 DOI: 10.1021/acs.jpclett.3c01392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/05/2023]
Abstract
Detonation of energetic materials (EMs) is of great importance for military applications, while the understanding of detailed events and mechanisms for the detonation process is scarce. In this study, the first deep neural network potential NNP_Shock for molecular dynamics (MD) simulation of shock-induced detonation of EMs was generated based on a deep potential model, providing DFT accuracy but 106 times the computational efficiency. On this basis, we employ our deep potential to perform MD simulations of shock-induced detonation of high-performance EM material 2,4,6,8,10,12-hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane (CL-20, C6H6N12O12) and obtain the theoretical Chapman-Jouguet (C-J) detonation velocities and pressures directly by multiscale shock technique (MSST) for the first time, which are in good agreement with experiment. In addition, the Hugoniot curves and initial reaction mechanisms were successfully obtained. Therefore, the NNP_Shock potential is competent in research of the detonation performance and shock sensitivity of CL-20, and the method can also be transplanted to studies of other EMs.
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Affiliation(s)
- Jidong Zhang
- College of Sciences/Xinjiang Production & Construction Corps Key Laboratory of Advanced Energy Storage Materials and Technology, Shihezi University, Shihezi 832000, China
- International Center for Quantum Materials, School of Physics, Peking University, Beijing 100871, P. R. China
| | - Wei Guo
- Frontiers Science Center for High Energy Material (MOE), Beijing Institute of Technology, Beijing 100081, P. R. China
- School of Physics, Beijing Institute of Technology, Beijing 100081, P. R. China
| | - Yugui Yao
- Frontiers Science Center for High Energy Material (MOE), Beijing Institute of Technology, Beijing 100081, P. R. China
- School of Physics, Beijing Institute of Technology, Beijing 100081, P. R. China
- State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, Beijing 100081, China
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Krishnamoorthy A, Nomura KI, Baradwaj N, Shimamura K, Rajak P, Mishra A, Fukushima S, Shimojo F, Kalia R, Nakano A, Vashishta P. Dielectric Constant of Liquid Water Determined with Neural Network Quantum Molecular Dynamics. PHYSICAL REVIEW LETTERS 2021; 126:216403. [PMID: 34114857 DOI: 10.1103/physrevlett.126.216403] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 03/30/2021] [Indexed: 06/12/2023]
Abstract
The static dielectric constant ϵ_{0} and its temperature dependence for liquid water is investigated using neural network quantum molecular dynamics (NNQMD). We compute the exact dielectric constant in canonical ensemble from NNQMD trajectories using fluctuations in macroscopic polarization computed from maximally localized Wannier functions (MLWF). Two deep neural networks are constructed. The first, NNQMD, is trained on QMD configurations for liquid water under a variety of temperature and density conditions to learn potential energy surface and forces and then perform molecular dynamics simulations. The second network, NNMLWF, is trained to predict locations of MLWF of individual molecules using the atomic configurations from NNQMD. Training data for both the neural networks is produced using a highly accurate quantum-mechanical method, DFT-SCAN that yields an excellent description of liquid water. We produce 280×10^{6} configurations of water at 7 temperatures using NNQMD and predict MLWF centers using NNMLWF to compute the polarization fluctuations. The length of trajectories needed for a converged value of the dielectric constant at 0°C is found to be 20 ns (40×10^{6} configurations with 0.5 fs time step). The computed dielectric constants for 0, 15, 30, 45, 60, 75, and 90°C are in good agreement with experiments. Our scalable scheme to compute dielectric constants with quantum accuracy is also applicable to other polar molecular liquids.
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Affiliation(s)
- Aravind Krishnamoorthy
- Collaboratory for Advanced Computing and Simulations, Department of Chemical Engineering and Materials Science, Department of Physics & Astronomy, and Department of Computer Science, University of Southern California, Los Angeles, California 90089, USA
| | - Ken-Ichi Nomura
- Collaboratory for Advanced Computing and Simulations, Department of Chemical Engineering and Materials Science, Department of Physics & Astronomy, and Department of Computer Science, University of Southern California, Los Angeles, California 90089, USA
| | - Nitish Baradwaj
- Collaboratory for Advanced Computing and Simulations, Department of Chemical Engineering and Materials Science, Department of Physics & Astronomy, and Department of Computer Science, University of Southern California, Los Angeles, California 90089, USA
| | - Kohei Shimamura
- Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan
| | - Pankaj Rajak
- Argonne National Laboratory, Lemont, Illinois 60439, USA
| | - Ankit Mishra
- Collaboratory for Advanced Computing and Simulations, Department of Chemical Engineering and Materials Science, Department of Physics & Astronomy, and Department of Computer Science, University of Southern California, Los Angeles, California 90089, USA
| | - Shogo Fukushima
- Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan
| | - Fuyuki Shimojo
- Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan
| | - Rajiv Kalia
- Collaboratory for Advanced Computing and Simulations, Department of Chemical Engineering and Materials Science, Department of Physics & Astronomy, and Department of Computer Science, University of Southern California, Los Angeles, California 90089, USA
| | - Aiichiro Nakano
- Collaboratory for Advanced Computing and Simulations, Department of Chemical Engineering and Materials Science, Department of Physics & Astronomy, and Department of Computer Science, University of Southern California, Los Angeles, California 90089, USA
| | - Priya Vashishta
- Collaboratory for Advanced Computing and Simulations, Department of Chemical Engineering and Materials Science, Department of Physics & Astronomy, and Department of Computer Science, University of Southern California, Los Angeles, California 90089, USA
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Shimamura K, Takeshita Y, Fukushima S, Koura A, Shimojo F. Computational and training requirements for interatomic potential based on artificial neural network for estimating low thermal conductivity of silver chalcogenides. J Chem Phys 2020; 153:234301. [PMID: 33353316 DOI: 10.1063/5.0027058] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
We examined the estimation of thermal conductivity through molecular dynamics simulations for a superionic conductor, α-Ag2Se, using the interatomic potential based on an artificial neural network (ANN potential). The training data were created using the existing empirical potential of Ag2Se to help find suitable computational and training requirements for the ANN potential, with the intent to apply them to first-principles calculations. The thermal conductivities calculated using different definitions of heat flux were compared, and the effect of explicit long-range Coulomb interaction on the conductivities was investigated. We clarified that using a rigorous heat flux formula for the ANN potential, even for highly ionic α-Ag2Se, the resulting thermal conductivity was reasonably consistent with the reference value without explicitly considering Coulomb interaction. It was found that ANN training including the virial term played an important role in reducing the dependency of thermal conductivity on the initial values of the weight parameters of the ANN.
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Affiliation(s)
- Kohei Shimamura
- Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan
| | - Yusuke Takeshita
- Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan
| | - Shogo Fukushima
- Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan
| | - Akihide Koura
- Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan
| | - Fuyuki Shimojo
- Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan
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Manzhos S, Carrington T. Neural Network Potential Energy Surfaces for Small Molecules and Reactions. Chem Rev 2020; 121:10187-10217. [PMID: 33021368 DOI: 10.1021/acs.chemrev.0c00665] [Citation(s) in RCA: 110] [Impact Index Per Article: 27.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
We review progress in neural network (NN)-based methods for the construction of interatomic potentials from discrete samples (such as ab initio energies) for applications in classical and quantum dynamics including reaction dynamics and computational spectroscopy. The main focus is on methods for building molecular potential energy surfaces (PES) in internal coordinates that explicitly include all many-body contributions, even though some of the methods we review limit the degree of coupling, due either to a desire to limit computational cost or to limited data. Explicit and direct treatment of all many-body contributions is only practical for sufficiently small molecules, which are therefore our primary focus. This includes small molecules on surfaces. We consider direct, single NN PES fitting as well as more complex methods that impose structure (such as a multibody representation) on the PES function, either through the architecture of one NN or by using multiple NNs. We show how NNs are effective in building representations with low-dimensional functions including dimensionality reduction. We consider NN-based approaches to build PESs in the sums-of-product form important for quantum dynamics, ways to treat symmetry, and issues related to sampling data distributions and the relation between PES errors and errors in observables. We highlight combinations of NNs with other ideas such as permutationally invariant polynomials or sums of environment-dependent atomic contributions, which have recently emerged as powerful tools for building highly accurate PESs for relatively large molecular and reactive systems.
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Affiliation(s)
- Sergei Manzhos
- Centre Énergie Matériaux Télécommunications, Institut National de la Recherche Scientifique, 1650, Boulevard Lionel-Boulet, Varennes, Québec City, Québec J3X 1S2, Canada
| | - Tucker Carrington
- Chemistry Department, Queen's University, Kingston Ontario K7L 3N6, Canada
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