1
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Chang H, Li W, Sun Z. New Diabatic Potential Energy Surfaces for the Li + H 2 Reaction and Time-Dependent Quantum Wave Packet Studies. J Phys Chem A 2024; 128:4412-4424. [PMID: 38787593 DOI: 10.1021/acs.jpca.4c00539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
New global diabatic potential energy surfaces (DPESs) for the ground (12A') and first excited (22A') states for the Li + H2 system were developed, with more than 30,000 energy points at the IC-MRCI+Q level of theory, utilizing the aug-cc-pV5Z basis set for the H atoms and the cc-pCV5Z basis set for the Li atom, fitted by a single neural network (NN) with symmetry. Product state-resolved quantum dynamics calculations of the nonadiabatic reaction Li (2P) + H2 (X 1 ∑g+, v0 = 0, j0 = 0) → LiH (X 1∑+) + H(2S) were carried out using these new DPESs and also the previous HYLC-DPESs. The numerical results suggested that our newly constructed DPESs provided an accurate description of the LiH2 system.
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Affiliation(s)
- Hanwen Chang
- State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wentao Li
- Weifang University of Science and Technology, Shouguang 262700, China
| | - Zhigang Sun
- State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
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2
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Weike N, Fritsch F, Eisfeld W. Compensation States Approach in the Hybrid Diabatization Scheme: Extension to Multidimensional Data and Properties. J Phys Chem A 2024; 128:4353-4368. [PMID: 38748493 DOI: 10.1021/acs.jpca.4c01134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/31/2024]
Abstract
The diabatization of reactive systems for more than just a couple of states is a very demanding problem and generally requires advanced diabatization techniques. Especially for dissociative processes, the drastic changes in the adiabatic wave functions often would require large diabatic state bases, which quickly become impractical. Recently, we addressed this problem by the compensation states approach developed in the context of our hybrid diabatization scheme. This scheme utilizes wave function as well as energy data in combination with a diabatic potential model. In regions where the initial diabatic state basis becomes insufficient for an appropriate representation of the adiabatic states, new model states are generated. The new model states compensate for the state space not spanned by the initial diabatic basis. Such a compensation state is obtained by projecting the initial diabatic state space out of the adiabatic wave function. This yields a very efficient basis representation of the electronic Hamiltonian. The present work presents two new aspects. First, it is shown how other operators like the spin-orbit operator in the framework of the Effective Relativistic Coupling by Asymptotic Representation (ERCAR) can be evaluated in this compact model state space without losing the correct wave function information and accuracy. Second, the extension of the approach to multidimensional potential energy surface models is presented for methyl iodide including the C-I dissociation coordinate and the angular H3C-I bending coordinates.
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Affiliation(s)
- Nicole Weike
- Theoretische Chemie, Universität Bielefeld, Postfach 100131, D-33501 Bielefeld, Germany
| | - Fabian Fritsch
- Theoretische Chemie, Universität Bielefeld, Postfach 100131, D-33501 Bielefeld, Germany
| | - Wolfgang Eisfeld
- Theoretische Chemie, Universität Bielefeld, Postfach 100131, D-33501 Bielefeld, Germany
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3
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Wang Y, Mazziotti DA. Quantum simulation of conical intersections. Phys Chem Chem Phys 2024; 26:11491-11497. [PMID: 38587679 DOI: 10.1039/d4cp00391h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
We explore the simulation of conical intersections (CIs) on quantum devices, setting the groundwork for potential applications in nonadiabatic quantum dynamics within molecular systems. The intersecting potential energy surfaces of H3+ are computed from a variance-based contracted quantum eigensolver. We show how the CIs can be correctly described on quantum devices using wavefunctions generated by the anti-Hermitian contracted Schrödinger equation ansatz, which is a unitary transformation of wavefunctions that preserves the topography of CIs. A hybrid quantum-classical procedure is used to locate the seam of CIs. Additionally, we discuss the quantum implementation of the adiabatic to diabatic transformation and its relation to the geometric phase effect. Results on noisy intermediate-scale quantum devices showcase the potential of quantum computers in dealing with problems in nonadiabatic chemistry.
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Affiliation(s)
- Yuchen Wang
- Department of Chemistry and The James Franck Institute, The University of Chicago, Chicago, Illinois 60637, USA.
| | - David A Mazziotti
- Department of Chemistry and The James Franck Institute, The University of Chicago, Chicago, Illinois 60637, USA.
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4
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Han S, Xie C, Hu X, Yarkony DR, Guo H, Xie D. Quantum Dynamics of Photodissociation: Recent Advances and Challenges. J Phys Chem Lett 2023; 14:10517-10530. [PMID: 37970789 DOI: 10.1021/acs.jpclett.3c02735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
Recent advances in constructing accurate potential energy surfaces and nonadiabatic couplings from high-level ab initio data have revealed detailed potential landscapes in not only the ground electronic state but also excited ones. They enabled quantitatively accurate characterization of photoexcited reactive systems using quantum mechanical methods. In this Perspective, we survey the recent progress in quantum mechanical studies of adiabatic and nonadiabatic photodissociation dynamics, focusing on initial state control and product energy disposal. These new insights helped to understand quantum effects in small prototypical systems, and the results serve as benchmarks for developing more approximate theoretical methods.
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Affiliation(s)
- Shanyu Han
- International Center for Isotope Effects Research, School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, China
| | - Changjian Xie
- Institute of Modern Physics, Shaanxi Key Laboratory for Theoretical Physics Frontiers, Northwest University, Xi'an 710127, China
| | - Xixi Hu
- Kuang Yaming Honors School, Institute for Brain Sciences, Nanjing University, Nanjing 210023, China
| | - David R Yarkony
- Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Hua Guo
- Department of Chemistry and Chemical Biology, Center for Computational Chemistry, University of New Mexico, Albuquerque, New Mexico 87131, United States
| | - Daiqian Xie
- Institute of Theoretical and Computational Chemistry, Key Laboratory of Mesoscopic Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
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5
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Mutsuji A, Saita K, Maeda S. An energy decomposition and extrapolation scheme for evaluating electron transfer rate constants: a case study on electron self-exchange reactions of transition metal complexes. RSC Adv 2023; 13:32097-32103. [PMID: 37920761 PMCID: PMC10619204 DOI: 10.1039/d3ra05784d] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 10/23/2023] [Indexed: 11/04/2023] Open
Abstract
A simple approach to the analysis of electron transfer (ET) reactions based on energy decomposition and extrapolation schemes is proposed. The present energy decomposition and extrapolation-based electron localization (EDEEL) method represents the diabatic energies for the initial and final states using the adiabatic energies of the donor and acceptor species and their complex. A scheme for the efficient estimation of ET rate constants is also proposed. EDEEL is semi-quantitative by directly evaluating the seam-of-crossing region of two diabatic potentials. In a numerical test, EDEEL successfully provided ET rate constants for electron self-exchange reactions of thirteen transition metal complexes with reasonable accuracy. In addition, its energy decomposition and extrapolation schemes provide all the energy values required for activation-strain model (ASM) analysis. The ASM analysis using EDEEL provided rational interpretations of the variation of the ET rate constants as a function of the transition metal complexes. These results suggest that EDEEL is useful for efficiently evaluating ET rate constants and obtaining a rational understanding of their magnitudes.
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Affiliation(s)
- Akihiro Mutsuji
- Graduate School of Chemical Sciences and Engineering, Hokkaido University Sapporo Hokkaido 060-8628 Japan
| | - Kenichiro Saita
- Department of Chemistry, Graduate School of Science, Hokkaido University Sapporo Hokkaido 060-0810 Japan
| | - Satoshi Maeda
- Department of Chemistry, Graduate School of Science, Hokkaido University Sapporo Hokkaido 060-0810 Japan
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University Sapporo Hokkaido 001-0021 Japan
- ERATO Maeda Artificial Intelligence for Chemical Reaction Design and Discovery Project, Hokkaido University Sapporo Hokkaido 060-0810 Japan
- Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science (NIMS) Tsukuba Ibaraki 305-0044 Japan
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6
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Wang TY, Neville SP, Schuurman MS. Machine Learning Seams of Conical Intersection: A Characteristic Polynomial Approach. J Phys Chem Lett 2023; 14:7780-7786. [PMID: 37615964 PMCID: PMC10494228 DOI: 10.1021/acs.jpclett.3c01649] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 08/15/2023] [Indexed: 08/25/2023]
Abstract
The machine learning of potential energy surfaces (PESs) has undergone rapid progress in recent years. The vast majority of this work, however, has been focused on the learning of ground state PESs. To reliably extend machine learning protocols to excited state PESs, the occurrence of seams of conical intersections between adiabatic electronic states must be correctly accounted for. This introduces a serious problem, for at such points, the adiabatic potentials are not differentiable to any order, complicating the application of standard machine learning methods. We show that this issue may be overcome by instead learning the coordinate-dependent coefficients of the characteristic polynomial of a simple decomposition of the potential matrix. We demonstrate that, through this approach, quantitatively accurate machine learning models of seams of conical intersection may be constructed.
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Affiliation(s)
- Tzu Yu Wang
- Department
of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
| | - Simon P. Neville
- National
Research Council Canada, 100 Sussex Dr., Ottawa, Ontario K1A 0R6, Canada
| | - Michael S. Schuurman
- Department
of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
- National
Research Council Canada, 100 Sussex Dr., Ottawa, Ontario K1A 0R6, Canada
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7
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Li C, Hou S, Xie C. Constructing Diabatic Potential Energy Matrices with Neural Networks Based on Adiabatic Energies and Physical Considerations: Toward Quantum Dynamic Accuracy. J Chem Theory Comput 2023. [PMID: 37216273 DOI: 10.1021/acs.jctc.2c01074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
A permutation invariant polynomial-neural network (PIP-NN) approach for constructing the global diabatic potential energy matrices (PEMs) of the coupled states of molecules is proposed. Specifically, the diabatization scheme is based merely on the adiabatic energy data of the system, which is ideally a most convenient way due to not requiring additional ab initio calculations for the data of the derivative coupling or any other physical properties of the molecule. Considering the permutation and coupling characteristics of the system, particularly in the presence of conical intersections, some vital treatments for the off-diagonal terms in diabatic PEM are essentially needed. Taking the photodissociation of H2O(X~/B~)/NH3(X~/A~) and nonadiabatic reaction Na(3p) + H2 → NaH(Σ+) + H for example, this PIP-NN method is shown to build up the global diabatic PEMs effectively and accurately. The root-mean-square errors of the adiabatic potential energies in the fitting for three different systems are all small (<10 meV). Further quantum dynamic calculations show that the absorption spectra and product branching ratios in both H2O(X~/B~) and NH3(X~/A~) nonadiabatic photodissociation are well reproduced on the new diabatic PEMs, and the nonadiabatic reaction probability of Na(3p) + H2 → NaH(Σ+) + H obtained on the new diabatic PEMs of the 12A1 and 12B2 states is in reasonably good agreement with previous theoretical result as well, validating this new PIP-NN method.
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Affiliation(s)
- Chaofan Li
- Institute of Modern Physics, Shaanxi Key Laboratory for Theoretical Physics Frontiers, Northwest University, Xi'an 710127, China
| | - Siting Hou
- Institute of Modern Physics, Shaanxi Key Laboratory for Theoretical Physics Frontiers, Northwest University, Xi'an 710127, China
| | - Changjian Xie
- Institute of Modern Physics, Shaanxi Key Laboratory for Theoretical Physics Frontiers, Northwest University, Xi'an 710127, China
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8
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Guan Y, Yarkony DR, Zhang DH. Permutation invariant polynomial neural network based diabatic ansatz for the (E + A) × (e + a) Jahn-Teller and Pseudo-Jahn-Teller systems. J Chem Phys 2022; 157:014110. [PMID: 35803819 DOI: 10.1063/5.0096912] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
In this work, the permutation invariant polynomial neural network (PIP-NN) approach is employed to construct a quasi-diabatic Hamiltonian for system with non-Abelian symmetries. It provides a flexible and compact NN-based diabatic ansatz from the related approach of Williams, Eisfeld, and co-workers. The example of H3 + is studied, which is an (E + A) × (e + a) Jahn-Teller and Pseudo-Jahn-Teller system. The PIP-NN diabatic ansatz is based on the symmetric polynomial expansion of Viel and Eisfeld, the coefficients of which are expressed with neural network functions that take permutation-invariant polynomials as input. This PIP-NN-based diabatic ansatz not only preserves the correct symmetry but also provides functional flexibility to accurately reproduce ab initio electronic structure data, thus resulting in excellent fits. The adiabatic energies, energy gradients, and derivative couplings are well reproduced. A good description of the local topology of the conical intersection seam is also achieved. Therefore, this diabatic ansatz completes the PIP-NN based representation of DPEM with correct symmetries and will enable us to diabatize even more complicated systems with complex symmetries.
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Affiliation(s)
- Yafu Guan
- State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical Computational Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, People's Republic of China
| | - David R Yarkony
- Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Dong H Zhang
- State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical Computational Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, People's Republic of China
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9
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Zhang Y, Xu J, Yang H, Xu J. Nonadiabatic dynamics studies of the H( 2S) + RbH(X 1Σ +) reaction: based on new diabatic potential energy surfaces. RSC Adv 2022; 12:19751-19762. [PMID: 35865202 DOI: 10.1039/d2ra03028d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 06/14/2022] [Indexed: 11/21/2022] Open
Abstract
The global diabatic potential energy surfaces (PESs) that correspond to the ground (12A') and first excited states (22A') of the RbH2 system PES are constructed based on 17 786 ab initio points. The neural network method is used to fit the PESs and the topographic features of the new diabatic PESs are discussed in detail. Based on the newly constructed diabatic PESs, the dynamics calculations of the H(2S) + RbH(X1Σ+) → Rb(52S) + H2(X1Σg +)/Rb(52P) + H2(X1Σg +) reactions are performed using the time-dependent wave packet method. The dynamics properties of these two channels such as the reaction probabilities, integral cross sections, and differential cross sections (DCSs) are calculated at state-to-state level of theory. The nonadiabatic effects are discussed in detail, and the results indicate that the adiabatic results are overestimated from the dynamics values. The DCSs of these two channels are forward biased, which indicates that the abstraction mechanism plays a dominant role in the reaction.
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Affiliation(s)
- Yong Zhang
- NEST Lab, Department of Chemistry, Department of Physics, College of Science, Shanghai University Shanghai 200444 China .,Department of Physics, Tonghua Normal University Tonghua Jilin 134002 China
| | - Jinghua Xu
- Department of Physics, Tonghua Normal University Tonghua Jilin 134002 China
| | - Haigang Yang
- Department of Physics, Tonghua Normal University Tonghua Jilin 134002 China
| | - Jiaqiang Xu
- NEST Lab, Department of Chemistry, Department of Physics, College of Science, Shanghai University Shanghai 200444 China
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10
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Modelling Ultrafast Dynamics at a Conical Intersection with Regularized Diabatic States: An Approach Based on Multiplicative Neural Networks. Chem Phys 2022. [DOI: 10.1016/j.chemphys.2022.111542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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11
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Li Y, Liu J, Li J, Zhai Y, Yang J, Qu Z, Li H. A new permutation-symmetry-adapted machine learning diabatization procedure and its application in MgH 2 system. J Chem Phys 2021; 155:214102. [PMID: 34879675 DOI: 10.1063/5.0072004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
This work introduces a new permutation-symmetry-adapted machine learning diabatization procedure, termed the diabatization by equivariant neural network (DENN). In this approach, the permutation symmetric and anti-symmetric elements in diabatic potential energy metrics (DPEMs) were simultaneously simulated by the equivariant neural network. The diabatization by deep neural network scheme was adopted for machine learning diabatization, and non-zero diabatic coupling was included to increase accuracy in the near degenerate region. Based on DENN, the global DPEMs for 11A' and 21A' states of MgH2 have been constructed. To the best of our knowledge, these are the first global DPEMs for the MgH2 system. The root-mean-square-errors (RMSEs) for diagonal elements (H11 and H22) and the off-diagonal element (H12) around the conical intersection region were 5.824, 5.307, and 5.796 meV, respectively. The RMSEs of global adiabatic energies for two adiabatic states were 4.613 and 12.755 meV, respectively. The spectroscopic calculations of the 11A' state in the linear HMgH region are in good agreement with the experiment and previous theoretical results. The differences between calculated frequencies and corresponding experiment values are 1.38 and 1.08 cm-1 for anti-symmetric stretching fundamental vibrational frequency and first overtone. The global DPEMs obtained in this work should be useful for further quantum mechanics dynamic simulations on the MgH2 system.
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Affiliation(s)
- You Li
- Institute of Theoretical Chemistry, College of Chemistry, Jilin University, 2519 Jiefang Road, Changchun 130023, People's Republic of China
| | - Jingmin Liu
- Institute of Theoretical Chemistry, College of Chemistry, Jilin University, 2519 Jiefang Road, Changchun 130023, People's Republic of China
| | - Jiarui Li
- Institute of Theoretical Chemistry, College of Chemistry, Jilin University, 2519 Jiefang Road, Changchun 130023, People's Republic of China
| | - Yu Zhai
- Institute of Theoretical Chemistry, College of Chemistry, Jilin University, 2519 Jiefang Road, Changchun 130023, People's Republic of China
| | - Jitai Yang
- Institute of Theoretical Chemistry, College of Chemistry, Jilin University, 2519 Jiefang Road, Changchun 130023, People's Republic of China
| | - Zexing Qu
- Institute of Theoretical Chemistry, College of Chemistry, Jilin University, 2519 Jiefang Road, Changchun 130023, People's Republic of China
| | - Hui Li
- Institute of Theoretical Chemistry, College of Chemistry, Jilin University, 2519 Jiefang Road, Changchun 130023, People's Republic of China
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12
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Li C, Hou S, Xie C. Three-dimensional diabatic potential energy surfaces of thiophenol with neural networks. CHINESE J CHEM PHYS 2021. [DOI: 10.1063/1674-0068/cjcp2110196] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Chaofan Li
- Institute of Modern Physics, Northwest University, Xi’an 710127, China
- Shaanxi Key Laboratory for Theoretical Physics Frontiers, Xi’an 710127, China
| | - Siting Hou
- Institute of Modern Physics, Northwest University, Xi’an 710127, China
- Shaanxi Key Laboratory for Theoretical Physics Frontiers, Xi’an 710127, China
| | - Changjian Xie
- Institute of Modern Physics, Northwest University, Xi’an 710127, China
- Shaanxi Key Laboratory for Theoretical Physics Frontiers, Xi’an 710127, China
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13
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Guan Y, Xie C, Yarkony DR, Guo H. High-fidelity first principles nonadiabaticity: diabatization, analytic representation of global diabatic potential energy matrices, and quantum dynamics. Phys Chem Chem Phys 2021; 23:24962-24983. [PMID: 34473156 DOI: 10.1039/d1cp03008f] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Nonadiabatic dynamics, which goes beyond the Born-Oppenheimer approximation, has increasingly been shown to play an important role in chemical processes, particularly those involving electronically excited states. Understanding multistate dynamics requires rigorous quantum characterization of both electronic and nuclear motion. However, such first principles treatments of multi-dimensional systems have so far been rather limited due to the lack of accurate coupled potential energy surfaces and difficulties associated with quantum dynamics. In this Perspective, we review recent advances in developing high-fidelity analytical diabatic potential energy matrices for quantum dynamical investigations of polyatomic uni- and bi-molecular nonadiabatic processes, by machine learning of high-level ab initio data. Special attention is paid to methods of diabatization, high fidelity construction of multi-state coupled potential energy surfaces and property surfaces, as well as quantum mechanical characterization of nonadiabatic nuclear dynamics. To illustrate the tremendous progress made by these new developments, several examples are discussed, in which direct comparison with quantum state resolved measurements led to either confirmation of the observation or sometimes reinterpretation of the experimental data. The insights gained in these prototypical systems greatly advance our understanding of nonadiabatic dynamics in chemical systems.
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Affiliation(s)
- Yafu Guan
- Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, USA.
| | - Changjian Xie
- Institute of Modern Physics, Northwest University, Xi'an, Shaanxi 710069, China.
| | - David R Yarkony
- Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, USA.
| | - Hua Guo
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico, 87131, USA.
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14
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Bai X, Guo X, Wang L. Machine Learning Approach to Calculate Electronic Couplings between Quasi-diabatic Molecular Orbitals: The Case of DNA. J Phys Chem Lett 2021; 12:10457-10464. [PMID: 34672582 DOI: 10.1021/acs.jpclett.1c03053] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Diabatization of one-electron states in flexible molecular aggregates is a great challenge due to the presence of surface crossings between molecular orbital (MO) levels and the complex interaction between MOs of neighboring molecules. In this work, we present an efficient machine learning approach to calculate electronic couplings between quasi-diabatic MOs without the need of nonadiabatic coupling calculations. Using MOs of rigid molecules as references, the MOs that can be directly regarded to be quasi-diabatic in molecular dynamics are selected out, state tracked, and phase corrected. On the basis of this information, artificial neural networks are trained to characterize the structure-dependent onsite energies of quasi-diabatic MOs and the intermolecular electronic couplings. A representative sequence of DNA is systematically studied as an illustration. Smooth time evolution of electronic couplings in all base pairs is obtained with quasi-diabatic MOs. In particular, our method can calculate electronic couplings between different quasi-diabatic MOs independently, and thus, this possesses unique advantages in many applications.
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Affiliation(s)
- Xin Bai
- Key Laboratory of Excited-State Materials of Zhejiang Province, Department of Chemistry, Zhejiang University, Hangzhou 310027, China
| | - Xin Guo
- Key Laboratory of Excited-State Materials of Zhejiang Province, Department of Chemistry, Zhejiang University, Hangzhou 310027, China
| | - Linjun Wang
- Key Laboratory of Excited-State Materials of Zhejiang Province, Department of Chemistry, Zhejiang University, Hangzhou 310027, China
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15
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Westermayr J, Marquetand P. Machine Learning for Electronically Excited States of Molecules. Chem Rev 2021; 121:9873-9926. [PMID: 33211478 PMCID: PMC8391943 DOI: 10.1021/acs.chemrev.0c00749] [Citation(s) in RCA: 162] [Impact Index Per Article: 54.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Indexed: 12/11/2022]
Abstract
Electronically excited states of molecules are at the heart of photochemistry, photophysics, as well as photobiology and also play a role in material science. Their theoretical description requires highly accurate quantum chemical calculations, which are computationally expensive. In this review, we focus on not only how machine learning is employed to speed up such excited-state simulations but also how this branch of artificial intelligence can be used to advance this exciting research field in all its aspects. Discussed applications of machine learning for excited states include excited-state dynamics simulations, static calculations of absorption spectra, as well as many others. In order to put these studies into context, we discuss the promises and pitfalls of the involved machine learning techniques. Since the latter are mostly based on quantum chemistry calculations, we also provide a short introduction into excited-state electronic structure methods and approaches for nonadiabatic dynamics simulations and describe tricks and problems when using them in machine learning for excited states of molecules.
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Affiliation(s)
- Julia Westermayr
- Institute
of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Strasse 17, 1090 Vienna, Austria
| | - Philipp Marquetand
- Institute
of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Strasse 17, 1090 Vienna, Austria
- Vienna
Research Platform on Accelerating Photoreaction Discovery, University of Vienna, Währinger Strasse 17, 1090 Vienna, Austria
- Data
Science @ Uni Vienna, University of Vienna, Währinger Strasse 29, 1090 Vienna, Austria
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16
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Abstract
Electronically excited states of molecules are at the heart of photochemistry, photophysics, as well as photobiology and also play a role in material science. Their theoretical description requires highly accurate quantum chemical calculations, which are computationally expensive. In this review, we focus on not only how machine learning is employed to speed up such excited-state simulations but also how this branch of artificial intelligence can be used to advance this exciting research field in all its aspects. Discussed applications of machine learning for excited states include excited-state dynamics simulations, static calculations of absorption spectra, as well as many others. In order to put these studies into context, we discuss the promises and pitfalls of the involved machine learning techniques. Since the latter are mostly based on quantum chemistry calculations, we also provide a short introduction into excited-state electronic structure methods and approaches for nonadiabatic dynamics simulations and describe tricks and problems when using them in machine learning for excited states of molecules.
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Affiliation(s)
- Julia Westermayr
- Institute of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Strasse 17, 1090 Vienna, Austria
| | - Philipp Marquetand
- Institute of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Strasse 17, 1090 Vienna, Austria
- Vienna Research Platform on Accelerating Photoreaction Discovery, University of Vienna, Währinger Strasse 17, 1090 Vienna, Austria
- Data Science @ Uni Vienna, University of Vienna, Währinger Strasse 29, 1090 Vienna, Austria
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17
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Westermayr J, Gastegger M, Schütt KT, Maurer RJ. Perspective on integrating machine learning into computational chemistry and materials science. J Chem Phys 2021; 154:230903. [PMID: 34241249 DOI: 10.1063/5.0047760] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Machine learning (ML) methods are being used in almost every conceivable area of electronic structure theory and molecular simulation. In particular, ML has become firmly established in the construction of high-dimensional interatomic potentials. Not a day goes by without another proof of principle being published on how ML methods can represent and predict quantum mechanical properties-be they observable, such as molecular polarizabilities, or not, such as atomic charges. As ML is becoming pervasive in electronic structure theory and molecular simulation, we provide an overview of how atomistic computational modeling is being transformed by the incorporation of ML approaches. From the perspective of the practitioner in the field, we assess how common workflows to predict structure, dynamics, and spectroscopy are affected by ML. Finally, we discuss how a tighter and lasting integration of ML methods with computational chemistry and materials science can be achieved and what it will mean for research practice, software development, and postgraduate training.
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Affiliation(s)
- Julia Westermayr
- Department of Chemistry, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, United Kingdom
| | - Michael Gastegger
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
| | - Kristof T Schütt
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
| | - Reinhard J Maurer
- Department of Chemistry, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, United Kingdom
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18
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Abstract
Theoretical simulations of electronic excitations and associated processes in molecules are indispensable for fundamental research and technological innovations. However, such simulations are notoriously challenging to perform with quantum mechanical methods. Advances in machine learning open many new avenues for assisting molecular excited-state simulations. In this Review, we track such progress, assess the current state of the art and highlight the critical issues to solve in the future. We overview a broad range of machine learning applications in excited-state research, which include the prediction of molecular properties, improvements of quantum mechanical methods for the calculations of excited-state properties and the search for new materials. Machine learning approaches can help us understand hidden factors that influence photo-processes, leading to a better control of such processes and new rules for the design of materials for optoelectronic applications.
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Yin Z, Braams BJ, Fu B, Zhang DH. Neural Network Representation of Three-State Quasidiabatic Hamiltonians Based on the Transformation Properties from a Valence Bond Model: Three Singlet States of H3+. J Chem Theory Comput 2021; 17:1678-1690. [DOI: 10.1021/acs.jctc.0c01336] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Zhengxi Yin
- State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical and Computational Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, P. R. China
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Bastiaan J. Braams
- Centrum Wiskunde & Informatica (CWI), the Dutch National Center for Mathematics and Computer Science, 1098 XG Amsterdam, Netherlands
| | - Bina Fu
- State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical and Computational Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, P. R. China
| | - Dong H. Zhang
- State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical and Computational Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, P. R. China
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20
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Ha JK, Kim K, Min SK. Machine Learning-Assisted Excited State Molecular Dynamics with the State-Interaction State-Averaged Spin-Restricted Ensemble-Referenced Kohn-Sham Approach. J Chem Theory Comput 2021; 17:694-702. [PMID: 33470100 DOI: 10.1021/acs.jctc.0c01261] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
We present a machine learning-assisted excited state molecular dynamics (ML-ESMD) based on the ensemble density functional theory framework. Since we represent a diabatic Hamiltonian in terms of generalized valence bond ansatz within the state-interaction state-averaged spin-restricted ensemble-referenced Kohn-Sham (SI-SA-REKS) method, we can avoid singularities near conical intersections, which are crucial in excited state molecular dynamics simulations. We train the diabatic Hamiltonian elements and their analytical gradients with the SchNet architecture to construct machine learning models, while the phase freedom of off-diagonal elements of the Hamiltonian is cured by introducing the phase-less loss function. Our machine learning models show reasonable accuracy with mean absolute errors of ∼0.1 kcal/mol and ∼0.5 kcal/mol/Å for the diabatic Hamiltonian elements and their gradients, respectively, for penta-2,4-dieniminium cation. Moreover, by exploiting the diabatic representation, our models can predict correct conical intersection structures and their topologies. In addition, our ML-ESMD simulations give almost identical result with a direct dynamics at the same level of theory.
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Affiliation(s)
- Jong-Kwon Ha
- Department of Chemistry, School of Natural Science, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Ulju-gun, Ulsan 44919, South Korea
| | - Kicheol Kim
- Department of Chemistry, School of Natural Science, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Ulju-gun, Ulsan 44919, South Korea
| | - Seung Kyu Min
- Department of Chemistry, School of Natural Science, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Ulju-gun, Ulsan 44919, South Korea
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21
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Yin Z, Braams BJ, Guan Y, Fu B, Zhang DH. A fundamental invariant-neural network representation of quasi-diabatic Hamiltonians for the two lowest states of H3. Phys Chem Chem Phys 2021; 23:1082-1091. [DOI: 10.1039/d0cp05047d] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The FI-NN approach is capable of representing highly accurate diabatic PESs with particular and complicated symmetry problems.
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Affiliation(s)
- Zhengxi Yin
- State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical and Computational Chemistry
- Dalian Institute of Chemical Physics
- Chinese Academy of Sciences
- Dalian
- P. R. China
| | - Bastiaan J. Braams
- Centrum Wiskunde & Informatica (CWI)
- The Dutch national Center for Mathematics and Computer Science
- The Netherlands
| | - Yafu Guan
- State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical and Computational Chemistry
- Dalian Institute of Chemical Physics
- Chinese Academy of Sciences
- Dalian
- P. R. China
| | - Bina Fu
- State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical and Computational Chemistry
- Dalian Institute of Chemical Physics
- Chinese Academy of Sciences
- Dalian
- P. R. China
| | - Dong H. Zhang
- State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical and Computational Chemistry
- Dalian Institute of Chemical Physics
- Chinese Academy of Sciences
- Dalian
- P. R. China
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22
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Ardiansyah M, Brorsen KR. Mixed Quantum–Classical Dynamics with Machine Learning-Based Potentials via Wigner Sampling. J Phys Chem A 2020; 124:9326-9331. [DOI: 10.1021/acs.jpca.0c07376] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Muhammad Ardiansyah
- Department of Chemistry, University of Missouri, Columbia, Missouri 65203, United States
| | - Kurt R. Brorsen
- Department of Chemistry, University of Missouri, Columbia, Missouri 65203, United States
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23
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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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24
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Han S, Wang Y, Guan Y, Yarkony DR, Guo H. Impact of Diabolical Singular Points on Nonadiabatic Dynamics and a Remedy: Photodissociation of Ammonia in the First Band. J Chem Theory Comput 2020; 16:6776-6784. [DOI: 10.1021/acs.jctc.0c00811] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Shanyu Han
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, United States
| | - Yucheng Wang
- Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Yafu Guan
- Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - David R. Yarkony
- Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Hua Guo
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, United States
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25
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Abstract
Understanding nonadiabatic dynamics is important for chemical and physical processes involving multiple electronic states. Direct nonadiabatic dynamics simulations are often employed to observe such processes on a femtosecond time scale. One often needs to do the simulation on a longer time scale, but direct simulation based on electronic structure calculations of the surfaces and couplings is expensive due to the large number of electronic structure calculations needed for ensemble averaging or simulation of longer-time processes. An alternative approach is to construct an analytical representation of potential energy surfaces (PESs) and couplings, which allows for faster dynamics calculations. Diabatic representations are preferred for such purposes because of the smoothness of the surfaces and couplings and the scalar nature of the couplings. However, many diabatization procedures are complicated by the need to consider orbitals or vector coupling elements, and these can make the process very labor-intensive. To circumvent these difficulties, we here propose diabatization by a deep neural network (DDNN) based on a new architecture for a deep neural network that requires neither orbital input nor vector input. The DDNN method allows convenient and semiautomatic diabatization, and it is demonstrated here for a model problem and for producing diabatic potential energy matrices for thiophenol.
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Affiliation(s)
- Yinan Shu
- Department of Chemistry, Chemical Theory Center, and Minnesota Supercomputing Institute, University of Minnesota, 207 Pleasant Street SE, Minneapolis, Minnesota 55455-0431, United States
| | - Donald G Truhlar
- Department of Chemistry, Chemical Theory Center, and Minnesota Supercomputing Institute, University of Minnesota, 207 Pleasant Street SE, Minneapolis, Minnesota 55455-0431, United States
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26
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Westermayr J, Marquetand P. Machine learning and excited-state molecular dynamics. MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2020. [DOI: 10.1088/2632-2153/ab9c3e] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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27
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Hong Y, Yin Z, Guan Y, Zhang Z, Fu B, Zhang DH. Exclusive Neural Network Representation of the Quasi-Diabatic Hamiltonians Including Conical Intersections. J Phys Chem Lett 2020; 11:7552-7558. [PMID: 32835486 DOI: 10.1021/acs.jpclett.0c02173] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
We propose a numerically simple and straightforward, yet accurate and efficient neural networks-based fitting strategy to construct coupled potential energy surfaces (PESs) in a quasi-diabatic representation. The fundamental invariants are incorporated to account for the complete nuclear permutation inversion symmetry. Instead of derivative couplings or interstate couplings, a so-called modified derivative coupling term is fitted by neural networks, resulting in accurate description of near degeneracy points, such as the conical intersections. The adiabatic energies, energy gradients, and derivative couplings are well reproduced, and the vanishing of derivative couplings as well as the isotropic topography of adiabatic and diabatic energies in asymptotic regions are automatically satisfied. All of these features of the coupled global PESs are requisite for accurate dynamics simulations. Our approach is expected to be very useful in developing highly accurate coupled PESs in a quasi-diabatic representation in an efficient machine learning-based way.
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Affiliation(s)
- Yingyue Hong
- State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical and Computational Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, P.R. China 116023
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Zhengxi Yin
- State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical and Computational Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, P.R. China 116023
- University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Yafu Guan
- State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical and Computational Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, P.R. China 116023
- Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Zhaojun Zhang
- State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical and Computational Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, P.R. China 116023
| | - Bina Fu
- State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical and Computational Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, P.R. China 116023
| | - Dong H Zhang
- State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical and Computational Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, P.R. China 116023
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28
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Williams DMG, Eisfeld W. Complete Nuclear Permutation Inversion Invariant Artificial Neural Network (CNPI-ANN) Diabatization for the Accurate Treatment of Vibronic Coupling Problems. J Phys Chem A 2020; 124:7608-7621. [DOI: 10.1021/acs.jpca.0c05991] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- David M. G. Williams
- Theoretische Chemie, Universität Bielefeld, Postfach 100131, D-33501 Bielefeld, Germany
| | - Wolfgang Eisfeld
- Theoretische Chemie, Universität Bielefeld, Postfach 100131, D-33501 Bielefeld, Germany
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29
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Jiang B, Li J, Guo H. High-Fidelity Potential Energy Surfaces for Gas-Phase and Gas-Surface Scattering Processes from Machine Learning. J Phys Chem Lett 2020; 11:5120-5131. [PMID: 32517472 DOI: 10.1021/acs.jpclett.0c00989] [Citation(s) in RCA: 105] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
In this Perspective, we review recent advances in constructing high-fidelity potential energy surfaces (PESs) from discrete ab initio points, using machine learning tools. Such PESs, albeit with substantial initial investments, provide significantly higher efficiency than direct dynamics methods and/or high accuracy at a level that is not affordable by on-the-fly approaches. These PESs not only are a necessity for quantum dynamical studies because of delocalization of wave packets but also enable the study of low-probability and long-time events in (quasi-)classical treatments. Our focus here is on inelastic and reactive scattering processes, which are more challenging than bound systems because of the involvement of continua. Relevant applications and developments for dynamical processes in both the gas phase and at gas-surface interfaces are discussed.
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Affiliation(s)
- Bin Jiang
- Hefei National Laboratory for Physical Science at the Microscale, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes, Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Jun Li
- School of Chemistry and Chemical Engineering and Chongqing Key Laboratory of Theoretical and Computational Chemistry, Chongqing University, Chongqing 401331, China
| | - Hua Guo
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, United States
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30
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Shen Y, Yarkony DR. Construction of Quasi-diabatic Hamiltonians That Accurately Represent ab Initio Determined Adiabatic Electronic States Coupled by Conical Intersections for Systems on the Order of 15 Atoms. Application to Cyclopentoxide Photoelectron Detachment in the Full 39 Degrees of Freedom. J Phys Chem A 2020; 124:4539-4548. [PMID: 32374600 DOI: 10.1021/acs.jpca.0c02763] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
We present, for systems of moderate dimension, a fitting framework to construct quasi-diabatic Hamiltonians that accurately represent ab initio adiabatic electronic structure data including the effects of conical intersections. The framework introduced here minimizes the difference between the fit prediction and the ab initio data obtained in the adiabatic representation, which is singular at a conical intersection seam. We define a general and flexible merit function to allow arbitrary representations and propose a representation to measure the fit-ab initio difference at geometries near electronic degeneracies. A fit Hamiltonian may behave poorly in insufficiently sampled regions, in which case a machine learning theory analysis of the fit representation suggests a regularization to address the deficiency. Our fitting framework including the regularization is used to construct the full 39-dimensional coupled diabatic potential energy surfaces for cyclopentoxy relevant to cyclopentoxide photoelectron detachment.
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Affiliation(s)
- Yifan Shen
- Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - David R Yarkony
- Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, United States
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31
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Guan Y, Guo H, Yarkony DR. Extending the Representation of Multistate Coupled Potential Energy Surfaces To Include Properties Operators Using Neural Networks: Application to the 1,21A States of Ammonia. J Chem Theory Comput 2019; 16:302-313. [DOI: 10.1021/acs.jctc.9b00898] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Yafu Guan
- Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Hua Guo
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, United States
| | - David R. Yarkony
- Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, United States
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32
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Williams DMG, Viel A, Eisfeld W. Diabatic neural network potentials for accurate vibronic quantum dynamics—The test case of planar NO3. J Chem Phys 2019; 151:164118. [DOI: 10.1063/1.5125851] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- David M. G. Williams
- Theoretische Chemie, Universität Bielefeld, Postfach 100131, D-33501 Bielefeld, Germany
| | - Alexandra Viel
- Univ Rennes, CNRS, IPR (Institut de Physique de Rennes) - UMR 6251, F-35000 Rennes, France
| | - Wolfgang Eisfeld
- Theoretische Chemie, Universität Bielefeld, Postfach 100131, D-33501 Bielefeld, Germany
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33
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Koch W, Bonfanti M, Eisenbrandt P, Nandi A, Fu B, Bowman J, Tannor D, Burghardt I. Two-layer Gaussian-based MCTDH study of the S1 ← S0 vibronic absorption spectrum of formaldehyde using multiplicative neural network potentials. J Chem Phys 2019. [DOI: 10.1063/1.5113579] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Werner Koch
- Weizmann Institute of Science, Rehovot, Israel
- Goethe-Universität Frankfurt, Frankfurt, Germany
| | | | | | | | - Bina Fu
- Dalian Institute of Chemical Physics, Dalian, China
| | - Joel Bowman
- Emory University, Atlanta, Georgia 30322, USA
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34
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Lenzen T, Eisfeld W, Manthe U. Vibronically and spin-orbit coupled diabatic potentials for X(2P) + CH4→ HX + CH3reactions: Neural network potentials for X = Cl. J Chem Phys 2019; 150:244115. [DOI: 10.1063/1.5109877] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Tim Lenzen
- Theoretische Chemie, Fakultät für Chemie, Universität Bielefeld, Universitätsstraße 25, D-33615 Bielefeld, Germany
| | - Wolfgang Eisfeld
- Theoretische Chemie, Fakultät für Chemie, Universität Bielefeld, Universitätsstraße 25, D-33615 Bielefeld, Germany
| | - Uwe Manthe
- Theoretische Chemie, Fakultät für Chemie, Universität Bielefeld, Universitätsstraße 25, D-33615 Bielefeld, Germany
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35
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Guan Y, Guo H, Yarkony DR. Neural network based quasi-diabatic Hamiltonians with symmetry adaptation and a correct description of conical intersections. J Chem Phys 2019; 150:214101. [DOI: 10.1063/1.5099106] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Yafu Guan
- Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Hua Guo
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, USA
| | - David R. Yarkony
- Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, USA
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36
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Xie C, Malbon CL, Xie D, Yarkony DR, Guo H. Nonadiabatic Dynamics in Photodissociation of Hydroxymethyl in the 32A(3px) Rydberg State: A Nine-Dimensional Quantum Study. J Phys Chem A 2019; 123:1937-1944. [DOI: 10.1021/acs.jpca.8b12184] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Changjian Xie
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, United States
| | - Christopher L. Malbon
- Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Daiqian Xie
- Institute of Theoretical and Computational Chemistry, Key Laboratory of Mesoscopic Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
| | - David R. Yarkony
- Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Hua Guo
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, United States
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37
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Lenzen T, Manthe U. Vibronically and spin-orbit coupled diabatic potentials for X(P) + CH4→ HX + CH3reactions: General theory and application for X(P) = F(2P). J Chem Phys 2019; 150:064102. [DOI: 10.1063/1.5063907] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Tim Lenzen
- Theoretische Chemie, Fakultät für Chemie, Universität Bielefeld, Universitätsstraße 25, D-33615 Bielefeld, Germany
| | - Uwe Manthe
- Theoretische Chemie, Fakultät für Chemie, Universität Bielefeld, Universitätsstraße 25, D-33615 Bielefeld, Germany
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38
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Guan Y, Zhang DH, Guo H, Yarkony DR. Representation of coupled adiabatic potential energy surfaces using neural network based quasi-diabatic Hamiltonians: 1,2 2A′ states of LiFH. Phys Chem Chem Phys 2019; 21:14205-14213. [DOI: 10.1039/c8cp06598e] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
A general algorithm for determining diabatic representations from adiabatic energies, energy gradients and derivative couplings using neural networks is introduced.
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Affiliation(s)
- Yafu Guan
- Department of Chemistry
- Johns Hopkins University
- Baltimore
- USA
| | - Dong H. Zhang
- State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical Computational Chemistry
- Dalian Institute of Chemical Physics
- Chinese Academy of Sciences
- Dalian 116023
- People's Republic of China
| | - Hua Guo
- Department of Chemistry and Chemical Biology
- University of New Mexico
- Albuquerque
- USA
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39
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Yin Z, Guan Y, Fu B, Zhang DH. Two-state diabatic potential energy surfaces of ClH2 based on nonadiabatic couplings with neural networks. Phys Chem Chem Phys 2019; 21:20372-20383. [DOI: 10.1039/c9cp03592c] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
A neural network-fitting procedure based on nonadiabatic couplings is proposed to generate two-state diabatic PESs with conical intersections.
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Affiliation(s)
- Zhengxi Yin
- State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical and Computational Chemistry
- Dalian Institute of Chemical Physics
- Chinese Academy of Sciences
- Dalian
- P. R. China
| | - Yafu Guan
- State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical and Computational Chemistry
- Dalian Institute of Chemical Physics
- Chinese Academy of Sciences
- Dalian
- P. R. China
| | - Bina Fu
- State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical and Computational Chemistry
- Dalian Institute of Chemical Physics
- Chinese Academy of Sciences
- Dalian
- P. R. China
| | - Dong H. Zhang
- State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical and Computational Chemistry
- Dalian Institute of Chemical Physics
- Chinese Academy of Sciences
- Dalian
- P. R. China
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40
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Chen WK, Liu XY, Fang WH, Dral PO, Cui G. Deep Learning for Nonadiabatic Excited-State Dynamics. J Phys Chem Lett 2018; 9:6702-6708. [PMID: 30403870 DOI: 10.1021/acs.jpclett.8b03026] [Citation(s) in RCA: 97] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
In this work we show that deep learning (DL) can be used for exploring complex and highly nonlinear multistate potential energy surfaces of polyatomic molecules and related nonadiabatic dynamics. Our DL is based on deep neural networks (DNNs), which are used as accurate representations of the CASSCF ground- and excited-state potential energy surfaces (PESs) of CH2NH. After geometries near conical intersection are included in the training set, the DNN models accurately reproduce excited-state topological structures; photoisomerization paths; and, importantly, conical intersections. We have also demonstrated that the results from nonadiabatic dynamics run with the DNN models are very close to those from the dynamics run with the pure ab initio method. The present work should encourage further studies of using machine learning methods to explore excited-state potential energy surfaces and nonadiabatic dynamics of polyatomic molecules.
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Affiliation(s)
- Wen-Kai Chen
- Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry , Beijing Normal University , Beijing 100875 , China
| | - Xiang-Yang Liu
- Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry , Beijing Normal University , Beijing 100875 , China
| | - Wei-Hai Fang
- Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry , Beijing Normal University , Beijing 100875 , China
| | - Pavlo O Dral
- Max-Planck-Institut für Kohlenforschung , Kaiser-Wilhelm-Platz 1 , 45470 Mülheim an der Ruhr , Germany
| | - Ganglong Cui
- Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry , Beijing Normal University , Beijing 100875 , China
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Williams DMG, Eisfeld W. Neural network diabatization: A new ansatz for accurate high-dimensional coupled potential energy surfaces. J Chem Phys 2018; 149:204106. [DOI: 10.1063/1.5053664] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
- David M. G. Williams
- Theoretische Chemie, Universität Bielefeld, Postfach 100131, D-33501 Bielefeld, Germany
| | - Wolfgang Eisfeld
- Theoretische Chemie, Universität Bielefeld, Postfach 100131, D-33501 Bielefeld, Germany
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42
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Xie C, Zhu X, Yarkony DR, Guo H. Permutation invariant polynomial neural network approach to fitting potential energy surfaces. IV. Coupled diabatic potential energy matrices. J Chem Phys 2018; 149:144107. [DOI: 10.1063/1.5054310] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
- Changjian Xie
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, USA
| | - Xiaolei Zhu
- Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - David R. Yarkony
- Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Hua Guo
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, USA
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43
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Hu D, Xie Y, Li X, Li L, Lan Z. Inclusion of Machine Learning Kernel Ridge Regression Potential Energy Surfaces in On-the-Fly Nonadiabatic Molecular Dynamics Simulation. J Phys Chem Lett 2018; 9:2725-2732. [PMID: 29732893 DOI: 10.1021/acs.jpclett.8b00684] [Citation(s) in RCA: 89] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
We discuss a theoretical approach that employs machine learning potential energy surfaces (ML-PESs) in the nonadiabatic dynamics simulation of polyatomic systems by taking 6-aminopyrimidine as a typical example. The Zhu-Nakamura theory is employed in the surface hopping dynamics, which does not require the calculation of the nonadiabatic coupling vectors. The kernel ridge regression is used in the construction of the adiabatic PESs. In the nonadiabatic dynamics simulation, we use ML-PESs for most geometries and switch back to the electronic structure calculations for a few geometries either near the S1/S0 conical intersections or in the out-of-confidence regions. The dynamics results based on ML-PESs are consistent with those based on CASSCF PESs. The ML-PESs are further used to achieve the highly efficient massive dynamics simulations with a large number of trajectories. This work displays the powerful role of ML methods in the nonadiabatic dynamics simulation of polyatomic systems.
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Affiliation(s)
- Deping Hu
- CAS Key Laboratory of Biobased Materials, Qingdao Institute of Bioenergy and Bioprocess Technology , Chinese Academy of Sciences , Qingdao 266101 , China
- University of Chinese Academy of Sciences , Beijing 100049 , China
| | - Yu Xie
- CAS Key Laboratory of Biobased Materials, Qingdao Institute of Bioenergy and Bioprocess Technology , Chinese Academy of Sciences , Qingdao 266101 , China
| | - Xusong Li
- CAS Key Laboratory of Biobased Materials, Qingdao Institute of Bioenergy and Bioprocess Technology , Chinese Academy of Sciences , Qingdao 266101 , China
- University of Chinese Academy of Sciences , Beijing 100049 , China
| | - Lingyue Li
- University of Chinese Academy of Sciences , Beijing 100049 , China
| | - Zhenggang Lan
- CAS Key Laboratory of Biobased Materials, Qingdao Institute of Bioenergy and Bioprocess Technology , Chinese Academy of Sciences , Qingdao 266101 , China
- University of Chinese Academy of Sciences , Beijing 100049 , China
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Hafizi R, Ghasemi SA, Hashemifar SJ, Akbarzadeh H. A neural-network potential through charge equilibration for WS2: From clusters to sheets. J Chem Phys 2017; 147:234306. [DOI: 10.1063/1.5003904] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Roohollah Hafizi
- Department of Physics, Isfahan University of Technology, 84156-83111 Isfahan, Iran
| | - S. Alireza Ghasemi
- Institute for Advanced Studies in Basic Sciences, P.O. Box 45195-1159, Zanjan, Iran
| | - S. Javad Hashemifar
- Department of Physics, Isfahan University of Technology, 84156-83111 Isfahan, Iran
| | - Hadi Akbarzadeh
- Department of Physics, Isfahan University of Technology, 84156-83111 Isfahan, Iran
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45
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Guan Y, Fu B, Zhang DH. Construction of diabatic energy surfaces for LiFH with artificial neural networks. J Chem Phys 2017; 147:224307. [DOI: 10.1063/1.5007031] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
- Yafu Guan
- State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical Computational Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, People’s Republic of China
| | - Bina Fu
- State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical Computational Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, People’s Republic of China
| | - Dong H. Zhang
- State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical Computational Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, People’s Republic of China
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