1
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Xu Z, Hou S, Wang Z, Xie C. Neural network potentials facilitating accurate complex scaling for molecular resonances: from a model to high dimensional realistic systems. Phys Chem Chem Phys 2024; 26:21861-21873. [PMID: 39104311 DOI: 10.1039/d4cp02452d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/07/2024]
Abstract
Here we propose a neural network based complex scaling (NN-CS) method for computing the complex eigenvalues (Er-iΓ/2) of molecular resonances, in which the CS of the potential part in the non-Hermitian Hamiltonian is effectively achieved by NNs. Taking a two-dimensional (2D) diabatic model including two states coupled by the conical intersection for example, the NN-CS method is shown to reproduce the eigenvalues of the resonance states quite well. Subsequently, this NN-CS method with a 2D Hamiltonian model is utilized to compute the vibronic resonances in the 1nσ*-mediated photodissociation of thioanisole based on a new NN diabatic potential energy matrix. The calculated lifetimes of the vibronic resonances are found to be in good agreement with other theoretical results and available experimental data. Finally, the NN-CS method is applied to treat a much more challenging system, namely, the resonances in the six-dimensional (6D) photodissociation continuum of NH3, due to its high dimensionalities and all three dissociative coordinates needing to be scaled in the complex scaling of the potential part. Again, the calculated energy positions and widths of the 6D resonances by the NN-CS method agree well with other theoretical results. Our calculations show that the NN-CS method is able to accurately treat the vibronic resonances involving multiple coupled electronic states and resonances in high dimensional realistic systems.
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Affiliation(s)
- Zhen Xu
- 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
| | - Zhimo Wang
- 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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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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Guan Y, Chen Q, Varandas AJC. Accurate diabatization based on combined-hyperbolic-inverse-power-representation: 1,2 2A' states of BeH2. J Chem Phys 2024; 160:154105. [PMID: 38624109 DOI: 10.1063/5.0200732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Accepted: 03/28/2024] [Indexed: 04/17/2024] Open
Abstract
A diabatic potential energy matrix (DPEM) for the two lowest states of BeH2+ has been constructed using the combined-hyperbolic-inverse-power-representation (CHIPR) method. By imposing symmetry constraints on the coefficients of polynomials, the complete nuclear permutation inversion symmetry is correctly preserved in the CHIPR functional form. The symmetrized CHIPR functional form is then used in the diabatization by ansatz procedure. The ab initio energies are reproduced with satisfactory accuracy. In addition, the CHIPR-based DPEM also reproduces the local topology of a conical intersection. Future work will focus on a complete four-state diabatic representation with emphasis on the long-range interactions and spin-orbit couplings, which will enable accurate quantum scattering calculations for the Be+(2P) + H2 → BeH+(X1Σ+) + H(2S) reaction.
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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
| | - Qun Chen
- 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
| | - António J C Varandas
- School of Physics and Physical Engineering, Qufu Normal University, 273165 Qufu, People's Republic of China
- Department of Physics, Universidade Federal do Espírito Santo, 29075-910 Vitória, Brazil
- Department of Chemistry and Chemistry Centre, University of Coimbra, 3004-535 Coimbra, Portugal
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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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Akher FB, Shu Y, Varga Z, Bhaumik S, Truhlar DG. Parametrically Managed Activation Function for Fitting a Neural Network Potential with Physical Behavior Enforced by a Low-Dimensional Potential. J Phys Chem A 2023. [PMID: 37307218 DOI: 10.1021/acs.jpca.3c02627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Machine-learned representations of potential energy surfaces generated in the output layer of a feedforward neural network are becoming increasingly popular. One difficulty with neural network output is that it is often unreliable in regions where training data is missing or sparse. Human-designed potentials often build in proper extrapolation behavior by choice of functional form. Because machine learning is very efficient, it is desirable to learn how to add human intelligence to machine-learned potentials in a convenient way. One example is the well-understood feature of interaction potentials that they vanish when subsystems are too far separated to interact. In this article, we present a way to add a new kind of activation function to a neural network to enforce low-dimensional constraints. In particular, the activation function depends parametrically on all of the input variables. We illustrate the use of this step by showing how it can force an interaction potential to go to zero at large subsystem separations without either inputting a specific functional form for the potential or adding data to the training set in the asymptotic region of geometries where the subsystems are separated. In the process of illustrating this, we present an improved set of potential energy surfaces for the 14 lowest 3A' states of O3. The method is more general than this example, and it may be used to add other low-dimensional knowledge or lower-level knowledge to machine-learned potentials. In addition to the O3 example, we present a greater-generality method called parametrically managed diabatization by deep neural network (PM-DDNN) that is an improvement on our previously presented permutationally restrained diabatization by deep neural network (PR-DDNN).
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Affiliation(s)
- Farideh Badichi Akher
- Department of Chemistry and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States
| | - Yinan Shu
- Department of Chemistry and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States
| | - Zoltan Varga
- Department of Chemistry and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States
| | - Suman Bhaumik
- Department of Chemistry and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States
| | - Donald G Truhlar
- Department of Chemistry and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States
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6
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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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7
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Wang J, An F, Chen J, Hu X, Guo H, Xie D. Accurate Full-Dimensional Global Diabatic Potential Energy Matrix for the Two Lowest-Lying Electronic States of the H + O 2 ↔ HO + O Reaction. J Chem Theory Comput 2023; 19:2929-2938. [PMID: 37161259 DOI: 10.1021/acs.jctc.3c00291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
A new and more accurate diabatic potential energy matrix (DPEM) is developed for the two lowest-lying electronic states of HO2, covering both the strong interaction region and reaction asymptotes. The ab initio calculations were performed at the Davidson corrected multireference configuration interaction level with the augmented correlation-consistent polarized valence quintuple-zeta basis set (MRCI+Q/AV5Z). The accuracy of the electronic structure calculations is validated by excellent agreement with the experimental HO2 equilibrium geometry, fundamental vibrational frequencies, and H + O2 ↔ OH + O reaction energy. Through the combination of an electronic angular momentum-method and a configuration interaction vector-based method, the mixing angle between the first two 2A″ states of HO2 was successfully determined. Elements of the 2×2 DPEM were fit to neural networks with a proper account of the complete nuclear permutation inversion symmetry of HO2. The DPEM correctly predicted the properties of conical intersection seams at linear and T-shape geometries, thus providing a reliable platform for studying both the spectroscopy of HO2 and the nonadiabatic dynamics for the H + O2 ↔ OH + O reaction.
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Affiliation(s)
- Junyan Wang
- Institute of Theoretical and Computational Chemistry, Key Laboratory of Mesoscopic Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
| | - Feng An
- Research Center for Graph Computing, Zhejiang Lab, Hangzhou 311121, China
| | - Junjie Chen
- Institute of Theoretical and Computational Chemistry, Key Laboratory of Mesoscopic Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
| | - Xixi Hu
- Kuang Yaming Honors School, Jiangsu Key Laboratory of Vehicle Emissions Control, Center of Modern Analysis, Nanjing University, Nanjing 210023, China
- Hefei National Laboratory, Hefei 230088, China
| | - Hua Guo
- Department of Chemistry and Chemical Biology, 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
- Hefei National Laboratory, Hefei 230088, 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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Shu Y, Varga Z, Kanchanakungwankul S, Zhang L, Truhlar DG. Diabatic States of Molecules. J Phys Chem A 2022; 126:992-1018. [PMID: 35138102 DOI: 10.1021/acs.jpca.1c10583] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Quantitative simulations of electronically nonadiabatic molecular processes require both accurate dynamics algorithms and accurate electronic structure information. Direct semiclassical nonadiabatic dynamics is expensive due to the high cost of electronic structure calculations, and hence it is limited to small systems, limited ensemble averaging, ultrafast processes, and/or electronic structure methods that are only semiquantitatively accurate. The cost of dynamics calculations can be made manageable if analytic fits are made to the electronic structure data, and such fits are most conveniently carried out in a diabatic representation because the surfaces are smooth and the couplings between states are smooth scalar functions. Diabatic representations, unlike the adiabatic ones produced by most electronic structure methods, are not unique, and finding suitable diabatic representations often involves time-consuming nonsystematic diabatization steps. The biggest drawback of using diabatic bases is that it can require large amounts of effort to perform a globally consistent diabatization, and one of our goals has been to develop methods to do this efficiently and automatically. In this Feature Article, we introduce the mathematical framework of diabatic representations, and we discuss diabatization methods, including adiabatic-to-diabatic transformations and recent progress toward the goal of automatization.
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Affiliation(s)
- Yinan Shu
- Department of Chemistry, Chemical Theory Center, and Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States
| | - Zoltan Varga
- Department of Chemistry, Chemical Theory Center, and Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States
| | - Siriluk Kanchanakungwankul
- Department of Chemistry, Chemical Theory Center, and Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States
| | - Linyao Zhang
- Department of Chemistry, Chemical Theory Center, and Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States.,School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, P. R. China
| | - Donald G Truhlar
- Department of Chemistry, Chemical Theory Center, and Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States
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10
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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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11
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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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12
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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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13
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Weight BM, Mandal A, Huo P. Ab initio symmetric quasi-classical approach to investigate molecular Tully models. J Chem Phys 2021; 155:084106. [PMID: 34470343 DOI: 10.1063/5.0061934] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
We perform on-the-fly non-adiabatic molecular dynamics simulations using the symmetrical quasi-classical (SQC) approach with the recently suggested molecular Tully models: ethylene and fulvene. We attempt to provide benchmarks of the SQC methods using both the square and triangle windowing schemes as well as the recently proposed electronic zero-point-energy correction scheme (the so-called γ correction). We use the quasi-diabatic propagation scheme to directly interface the diabatic SQC methods with adiabatic electronic structure calculations. Our results showcase the drastic improvement of the accuracy by using the trajectory-adjusted γ-corrections, which outperform the widely used trajectory surface hopping method with decoherence corrections. These calculations provide useful and non-trivial tests to systematically investigate the numerical performance of various diabatic quantum dynamics approaches, going beyond simple diabatic model systems that have been used as the major workhorse in the quantum dynamics field. At the same time, these available benchmark studies will also likely foster the development of new quantum dynamics approaches based on these techniques.
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Affiliation(s)
- Braden M Weight
- Department of Physics and Astronomy, University of Rochester, Rochester, New York 14627, USA
| | - Arkajit Mandal
- Department of Chemistry, University of Rochester, Rochester, New York 14627, USA
| | - Pengfei Huo
- Department of Chemistry, University of Rochester, Rochester, New York 14627, USA
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14
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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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15
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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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16
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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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17
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Choi S, Vaníček J. How important are the residual nonadiabatic couplings for an accurate simulation of nonadiabatic quantum dynamics in a quasidiabatic representation? J Chem Phys 2021; 154:124119. [PMID: 33810696 DOI: 10.1063/5.0046067] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Diabatization of the molecular Hamiltonian is a standard approach to remove the singularities of nonadiabatic couplings at conical intersections of adiabatic potential energy surfaces. In general, it is impossible to eliminate the nonadiabatic couplings entirely-the resulting "quasidiabatic" states are still coupled by smaller but nonvanishing residual nonadiabatic couplings, which are typically neglected. Here, we propose a general method for assessing the validity of this potentially drastic approximation by comparing quantum dynamics simulated either with or without the residual couplings. To make the numerical errors negligible to the errors due to neglecting the residual couplings, we use the highly accurate and general eighth-order composition of the implicit midpoint method. The usefulness of the proposed method is demonstrated on nonadiabatic simulations in the cubic Jahn-Teller model of nitrogen trioxide and in the induced Renner-Teller model of hydrogen cyanide. We find that, depending on the system, initial state, and employed quasidiabatization scheme, neglecting the residual couplings can result in wrong dynamics. In contrast, simulations with the exact quasidiabatic Hamiltonian, which contains the residual couplings, always yield accurate results.
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Affiliation(s)
- Seonghoon Choi
- Laboratory of Theoretical Physical Chemistry, Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Jiří Vaníček
- Laboratory of Theoretical Physical Chemistry, Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
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18
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Wang Y, Guan Y, Guo H, Yarkony DR. Enabling complete multichannel nonadiabatic dynamics: A global representation of the two-channel coupled, 1,2 1A and 1 3A states of NH 3 using neural networks. J Chem Phys 2021; 154:094121. [PMID: 33685133 DOI: 10.1063/5.0037684] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Global coupled three-state two-channel potential energy and property/interaction (dipole and spin-orbit coupling) surfaces for the dissociation of NH3(Ã) into NH + H2 and NH2 + H are reported. The permutational invariant polynomial-neural network approach is used to simultaneously fit and diabatize the electronic Hamiltonian by fitting the energies, energy gradients, and derivative couplings of the two coupled lowest-lying singlet states as well as fitting the energy and energy gradients of the lowest-lying triplet state. The key issue in fitting property matrix elements in the diabatic basis is that the diabatic surfaces must be smooth, that is, the diabatization must remove spikes in the original adiabatic property surfaces attributable to the switch of electronic wavefunctions at the conical intersection seam. Here, we employ the fit potential energy matrix to transform properties in the adiabatic representation to a quasi-diabatic representation and remove the discontinuity near the conical intersection seam. The property matrix elements can then be fit with smooth neural network functions. The coupled potential energy surfaces along with the dipole and spin-orbit coupling surfaces will enable more accurate and complete treatment of optical transitions, as well as nonadiabatic internal conversion and intersystem crossing.
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Affiliation(s)
- Yuchen Wang
- Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - 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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19
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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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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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21
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Guan Y, Xie C, Guo H, Yarkony DR. Neural Network Based Quasi-diabatic Representation for S0 and S1 States of Formaldehyde. J Phys Chem A 2020; 124:10132-10142. [DOI: 10.1021/acs.jpca.0c08948] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [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
| | - Changjian Xie
- Institute of Modern Physics, Northwest University, Xi’an, Shaanxi 710069, People’s Republic of China
| | - 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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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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Westermayr J, Marquetand P. Deep learning for UV absorption spectra with SchNarc: First steps toward transferability in chemical compound space. J Chem Phys 2020; 153:154112. [DOI: 10.1063/5.0021915] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Affiliation(s)
- J. Westermayr
- Faculty of Chemistry, Institute of Theoretical Chemistry, University of Vienna, Währinger Str. 17, 1090 Vienna, Austria
| | - P. Marquetand
- Faculty of Chemistry, Institute of Theoretical Chemistry, University of Vienna, Währinger Str. 17, 1090 Vienna, Austria
- Vienna Research Platform on Accelerating Photoreaction Discovery, University of Vienna, Währinger Str. 17, 1090 Vienna, Austria
- Faculty of Chemistry, Data Science @ Uni Vienna, University of Vienna, Währinger Str. 29, 1090 Vienna, Austria
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24
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Li J, Zhao B, Xie D, Guo H. Advances and New Challenges to Bimolecular Reaction Dynamics Theory. J Phys Chem Lett 2020; 11:8844-8860. [PMID: 32970441 DOI: 10.1021/acs.jpclett.0c02501] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Dynamics of bimolecular reactions in the gas phase are of foundational importance in combustion, atmospheric chemistry, interstellar chemistry, and plasma chemistry. These collision-induced chemical transformations are a sensitive probe of the underlying potential energy surface(s). Despite tremendous progress in past decades, our understanding is still not complete. In this Perspective, we survey the recent advances in theoretical characterization of bimolecular reaction dynamics, stimulated by new experimental observations, and identify key new challenges.
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Affiliation(s)
- Jun Li
- School of Chemistry and Chemical Engineering & Chongqing Key Laboratory of Theoretical and Computational Chemistry, Chongqing University, Chongqing 401331, China
| | - Bin Zhao
- Theoretische Chemie, Fakultät für Chemie, Universität Bielefeld, Universitätsstraße 25, D-33615 Bielefeld, Germany
| | - Daiqian Xie
- Institute of Theoretical and Computational Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
| | - 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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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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26
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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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27
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Sugisawa H, Ida T, Krems RV. Gaussian process model of 51-dimensional potential energy surface for protonated imidazole dimer. J Chem Phys 2020; 153:114101. [DOI: 10.1063/5.0023492] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Affiliation(s)
- Hiroki Sugisawa
- Department of Chemistry, University of British Columbia, Vancouver, British Columbia V6T 1Z1, Canada
- Division of Material Chemistry, Graduate School of Natural Science and Technology, Kanazawa University, Kakuma, Kanazawa 920-1192, Japan
| | - Tomonori Ida
- Division of Material Chemistry, Graduate School of Natural Science and Technology, Kanazawa University, Kakuma, Kanazawa 920-1192, Japan
| | - R. V. Krems
- Department of Chemistry, University of British Columbia, Vancouver, British Columbia V6T 1Z1, Canada
- Stewart Blusson Quantum Matter Institute, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
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28
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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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29
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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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30
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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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31
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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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32
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Westermayr J, Gastegger M, Marquetand P. Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamics. J Phys Chem Lett 2020; 11:3828-3834. [PMID: 32311258 PMCID: PMC7246974 DOI: 10.1021/acs.jpclett.0c00527] [Citation(s) in RCA: 86] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Accepted: 04/20/2020] [Indexed: 05/26/2023]
Abstract
In recent years, deep learning has become a part of our everyday life and is revolutionizing quantum chemistry as well. In this work, we show how deep learning can be used to advance the research field of photochemistry by learning all important properties-multiple energies, forces, and different couplings-for photodynamics simulations. We simplify such simulations substantially by (i) a phase-free training skipping costly preprocessing of raw quantum chemistry data; (ii) rotationally covariant nonadiabatic couplings, which can either be trained or (iii) alternatively be approximated from only ML potentials, their gradients, and Hessians; and (iv) incorporating spin-orbit couplings. As the deep-learning method, we employ SchNet with its automatically determined representation of molecular structures and extend it for multiple electronic states. In combination with the molecular dynamics program SHARC, our approach termed SchNarc is tested on two polyatomic molecules and paves the way toward efficient photodynamics simulations of complex systems.
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Affiliation(s)
- Julia Westermayr
- Institute
of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 17, 1090 Vienna, Austria
| | - Michael Gastegger
- Machine
Learning Group, Technical University of
Berlin, 10587 Berlin, Germany
| | - Philipp Marquetand
- Institute
of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 17, 1090 Vienna, Austria
- Vienna
Research Platform on Accelerating Photoreaction Discovery, University of Vienna, Währinger Str. 17, 1090 Vienna, Austria
- Data
Science @ Uni Vienna, University of Vienna, Währinger Str. 29, 1090 Vienna, Austria
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33
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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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34
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Westermayr J, Faber FA, Christensen AS, von Lilienfeld OA, Marquetand P. Neural networks and kernel ridge regression for excited states dynamics of CH2NH$_2^+$: From single-state to multi-state representations and multi-property machine learning models. MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2020. [DOI: 10.1088/2632-2153/ab88d0] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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35
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Lischka H, Shepard R, Müller T, Szalay PG, Pitzer RM, Aquino AJA, Araújo do Nascimento MM, Barbatti M, Belcher LT, Blaudeau JP, Borges I, Brozell SR, Carter EA, Das A, Gidofalvi G, González L, Hase WL, Kedziora G, Kertesz M, Kossoski F, Machado FBC, Matsika S, do Monte SA, Nachtigallová D, Nieman R, Oppel M, Parish CA, Plasser F, Spada RFK, Stahlberg EA, Ventura E, Yarkony DR, Zhang Z. The generality of the GUGA MRCI approach in COLUMBUS for treating complex quantum chemistry. J Chem Phys 2020; 152:134110. [PMID: 32268762 DOI: 10.1063/1.5144267] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
The core part of the program system COLUMBUS allows highly efficient calculations using variational multireference (MR) methods in the framework of configuration interaction with single and double excitations (MR-CISD) and averaged quadratic coupled-cluster calculations (MR-AQCC), based on uncontracted sets of configurations and the graphical unitary group approach (GUGA). The availability of analytic MR-CISD and MR-AQCC energy gradients and analytic nonadiabatic couplings for MR-CISD enables exciting applications including, e.g., investigations of π-conjugated biradicaloid compounds, calculations of multitudes of excited states, development of diabatization procedures, and furnishing the electronic structure information for on-the-fly surface nonadiabatic dynamics. With fully variational uncontracted spin-orbit MRCI, COLUMBUS provides a unique possibility of performing high-level calculations on compounds containing heavy atoms up to lanthanides and actinides. Crucial for carrying out all of these calculations effectively is the availability of an efficient parallel code for the CI step. Configuration spaces of several billion in size now can be treated quite routinely on standard parallel computer clusters. Emerging developments in COLUMBUS, including the all configuration mean energy multiconfiguration self-consistent field method and the graphically contracted function method, promise to allow practically unlimited configuration space dimensions. Spin density based on the GUGA approach, analytic spin-orbit energy gradients, possibilities for local electron correlation MR calculations, development of general interfaces for nonadiabatic dynamics, and MRCI linear vibronic coupling models conclude this overview.
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Affiliation(s)
- Hans Lischka
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas 79409, USA
| | - Ron Shepard
- Chemical Sciences and Engineering Division, Argonne National Laboratory, Lemont, Illinois 60439, USA
| | - Thomas Müller
- Institute for Advanced Simulation, Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich 52428, Germany
| | - Péter G Szalay
- ELTE Eötvös Loránd University, Institute of Chemistry, Budapest, Hungary
| | - Russell M Pitzer
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, USA
| | - Adelia J A Aquino
- School of Pharmaceutical Sciences and Technology, Tianjin University, Tianjin 300072, People's Republic of China
| | | | | | - Lachlan T Belcher
- Laser and Optics Research Center, Department of Physics, US Air Force Academy, Colorado 80840, USA
| | | | - Itamar Borges
- Departamento de Química, Instituto Militar de Engenharia, Rio de Janeiro, RJ 22290-270, Brazil
| | - Scott R Brozell
- Chemical Sciences and Engineering Division, Argonne National Laboratory, Lemont, Illinois 60439, USA
| | - Emily A Carter
- Office of the Chancellor and Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Box 951405, Los Angeles, California 90095-1405, USA
| | - Anita Das
- Indian Institute of Engineering Science and Technology, Shibpur, Howrah, India
| | - Gergely Gidofalvi
- Department of Chemistry and Biochemistry, Gonzaga University, Spokane, Washington 99258, USA
| | - Leticia González
- Institute of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Straße 17, 1090 Vienna, Austria
| | - William L Hase
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas 79409, USA
| | - Gary Kedziora
- Air Force Research Laboratory, Wright-Patterson Air Force Base, Ohio 45433, USA
| | - Miklos Kertesz
- Department of Chemistry, Georgetown University, 37th and O Streets, NW, Washington, DC 20057-1227, USA
| | | | - Francisco B C Machado
- Departamento de Química, Instituto Tecnológico de Aeronáutica, São José dos Campos 12228-900, São Paulo, Brazil
| | - Spiridoula Matsika
- Department of Chemistry, Temple University, 1901 N. 13th St., Philadelphia, Pennsylvania 19122, USA
| | | | - Dana Nachtigallová
- Institute of Organic Chemistry and Biochemistry v.v.i., The Czech Academy of Sciences, Flemingovo nám. 2, 160610 Prague 6, Czech Republic
| | - Reed Nieman
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas 79409, USA
| | - Markus Oppel
- Institute of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Straße 17, 1090 Vienna, Austria
| | - Carol A Parish
- Department of Chemistry, Gottwald Center for the Sciences, University of Richmond, Richmond, Virginia 23173, USA
| | - Felix Plasser
- Department of Chemistry, Loughborough University, Loughborough LE11 3TU, United Kingdom
| | - Rene F K Spada
- Departamento de Física, Instituto Tecnológico de Aeronáutica, São José dos Campos 12228-900, São Paulo, Brazil
| | - Eric A Stahlberg
- Biomedical Informatics and Data Science, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21702, USA
| | - Elizete Ventura
- Universidade Federal da Paraíba, 58059-900 João Pessoa, PB, Brazil
| | - David R Yarkony
- Department of Chemistry, Johns Hopkins University, 3400 N. Charles Street, Baltimore, Maryland 21218, USA
| | - Zhiyong Zhang
- Stanford Research Computing Center, Stanford University, 255 Panama Street, Stanford, California 94305, USA
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36
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Neville SP, Seidu I, Schuurman MS. Propagative block diagonalization diabatization of DFT/MRCI electronic states. J Chem Phys 2020; 152:114110. [DOI: 10.1063/1.5143126] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Affiliation(s)
- Simon P. Neville
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, 10 Marie Curie, Ottawa, Ontario K1N 6N5, Canada
| | - Issaka Seidu
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, 10 Marie Curie, Ottawa, Ontario K1N 6N5, Canada
| | - Michael S. Schuurman
- Department of Chemistry and Biomolecular Sciences, University of Ottawa, 10 Marie Curie, Ottawa, Ontario K1N 6N5, Canada
- National Research Council of Canada, 100 Sussex Drive, Ottawa, Ontario K1A 0R6, Canada
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Guan Y, Yarkony DR. Accurate Neural Network Representation of the Ab Initio Determined Spin-Orbit Interaction in the Diabatic Representation Including the Effects of Conical Intersections. J Phys Chem Lett 2020; 11:1848-1858. [PMID: 32062966 DOI: 10.1021/acs.jpclett.0c00074] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
A method for fitting ab initio determined spin-orbit coupling interactions, in the Breit-Pauli approximation, based on quasidiabatic representations using neural network fits is reported. The algorithm generalizes our recently reported neural network approach for representing the dipole interaction. The S0, S1, and T1 states of formaldehyde are used as an example. First, the two singlet states S0 and S1 are diabatized with a modified Boys Localization diabatization method. Second, the spin-orbit coupling between singlet and triplet states is transformed to the diabatic representation. This removes the discontinuities in the adiabatic representation. The diabatized spin-orbit couplings are then fit with smooth neural network functions. The analytic representation of spin-orbit coupling interactions in a diabatic basis by neural networks will make accurate full-dimensional quantum dynamical treatment of both internal conversion and intersystem crossing possible, which will help us to gain better understanding of both processes.
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Affiliation(s)
- 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
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38
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Mangaud E, Lasorne B, Atabek O, Desouter-Lecomte M. Statistical distributions of the tuning and coupling collective modes at a conical intersection using the hierarchical equations of motion. J Chem Phys 2019; 151:244102. [DOI: 10.1063/1.5128852] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Etienne Mangaud
- Physicochimie des Electrolytes et des Nanosystèmes Interfaciaux-UMR 8234 Sorbonne Université, F-75252 Paris, France and Laboratoire Collisions Agrégats Réactivité (IRSAMC), Université Toulouse III Paul Sabatier, UMR 5589, F-31062 Toulouse, France
| | - Benjamin Lasorne
- Institut Charles Gerhardt Montpellier (ICGM), Université de Montpellier, CNRS, ENSCM, F-34095 Montpellier, France
| | - Osman Atabek
- Institut des Sciences Moléculaires d’Orsay (ISMO), Université Paris-Saclay, CNRS, F-91405 Orsay, France
| | - Michèle Desouter-Lecomte
- Institut de Chimie Physique (ICP), Université Paris-Saclay, CNRS, F-91405 Orsay, France and Département de Chimie, Université de Liège, Sart Tilman, B6, B-4000 Liège, Belgium
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39
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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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40
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Wang Y, Guan Y, Yarkony DR. On the Impact of Singularities in the Two-State Adiabatic to Diabatic State Transformation: A Global Treatment. J Phys Chem A 2019; 123:9874-9880. [DOI: 10.1021/acs.jpca.9b08519] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Yuchen 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
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