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Jiang J, Yang J, Hong Q, Sun Q, Li J. Global Potential Energy Surfaces by Compressed-State Multistate Pair-Density Functional Theory for Hyperthermal Collisions in the O 2+O 2 System. Chemphyschem 2024; 25:e202400078. [PMID: 38526528 DOI: 10.1002/cphc.202400078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 03/02/2024] [Accepted: 03/25/2024] [Indexed: 03/26/2024]
Abstract
Interactions between oxygen molecules play an important role in atmospheric chemistry and hypersonic flow chemistry in atmospheric entries. Recently, high-quality ab initio potential energy surface (PES) of the quintet O4 was reported by Paukku et al. [J. Chem. Phys. 147, 034301 (2017)]. 10543 configurations were sampled and calculated at the level of MS-CASPT2/maug-cc-pVTZ with scaled external correlation. The PES was fitted to a many-body (MB) form with the many-body part described by the permutationally invariant polynomial approach (MB-PIP). In this work, the PIP-Neural Network (PIP-NN) and MB-PIP-NN methods were used to refit the PES based on the same data by Paukku et al. Three PESs were compared. It was found that the performances differ significantly in the O+O3 region as well as in the long-range region. Therefore, additional 1300 points were sampled, and the efficient compressed-state multistate pair-density functional theory (CMS-PDFT) was used to calculate the electronic structure of these 1300 points and 10543 points by Paukku et al. Then, a completely new quintet PES was fitted using the MB-PIP-NN method. Based on this PES, the quasi-classical trajectory (QCT) approach was used to reveal all possible reaction channels for hyperthermal O2-O2 collisions.
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Affiliation(s)
- Jie Jiang
- School of Chemistry and Chemical Engineering & Chongqing Key Laboratory of Chemical Theory and Mechanism, Chongqing University, Chongqing, 401331, China
| | - Jiawei Yang
- School of Chemistry and Chemical Engineering & Chongqing Key Laboratory of Chemical Theory and Mechanism, Chongqing University, Chongqing, 401331, China
| | - Qizhen Hong
- State Key Laboratory of High Temperature Gas Dynamics, Institute of Mechanics, Chinese Academy of Sciences, 100190, Beijing, China
| | - Quanhua Sun
- State Key Laboratory of High Temperature Gas Dynamics, Institute of Mechanics, Chinese Academy of Sciences, 100190, Beijing, China
- School of Engineering Science, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jun Li
- School of Chemistry and Chemical Engineering & Chongqing Key Laboratory of Chemical Theory and Mechanism, Chongqing University, Chongqing, 401331, China
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2
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Yang Y, Zhang S, Ranasinghe KD, Isayev O, Roitberg AE. Machine Learning of Reactive Potentials. Annu Rev Phys Chem 2024; 75:371-395. [PMID: 38941524 DOI: 10.1146/annurev-physchem-062123-024417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/30/2024]
Abstract
In the past two decades, machine learning potentials (MLPs) have driven significant developments in chemical, biological, and material sciences. The construction and training of MLPs enable fast and accurate simulations and analysis of thermodynamic and kinetic properties. This review focuses on the application of MLPs to reaction systems with consideration of bond breaking and formation. We review the development of MLP models, primarily with neural network and kernel-based algorithms, and recent applications of reactive MLPs (RMLPs) to systems at different scales. We show how RMLPs are constructed, how they speed up the calculation of reactive dynamics, and how they facilitate the study of reaction trajectories, reaction rates, free energy calculations, and many other calculations. Different data sampling strategies applied in building RMLPs are also discussed with a focus on how to collect structures for rare events and how to further improve their performance with active learning.
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Affiliation(s)
- Yinuo Yang
- Department of Chemistry, University of Florida, Gainesville, Florida;
| | - Shuhao Zhang
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania;
| | | | - Olexandr Isayev
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania;
| | - Adrian E Roitberg
- Department of Chemistry, University of Florida, Gainesville, Florida;
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3
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Jiang X, Liu L, Peng Y, Zhu H. A New Ab Initio Potential Energy Surface and Rovibrational Spectra for the CO-N 2O Complex. J Phys Chem A 2024; 128:2743-2751. [PMID: 38557005 DOI: 10.1021/acs.jpca.4c00055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
We constructed a new ab initio potential energy surface (PES) for CO-N2O which includes the intramolecular Q3 normal coordinate for the N2O ν3 antisymmetric stretching vibration. The intermolecular potential was evaluated employing the supermolecular method at the [CCSD(T)]-F12a level, with the aug-cc-pVTZ basis set plus bond functions. By integral over the intramolecular Q3 coordinate, we obtained the vibrationally averaged PESs for the CO-N2O system in the ground and ν3 excited states of N2O. Each PES features one nearly T-shaped global minimum and one skewed T-shaped local minimum. Based on these obtained PESs of CO-N2O, the radial discrete variable representation/angle finite base representation method and the Lanczos algorithm were applied for the calculations of bound states and rovibrational energy levels. The calculated ν3 vibrational band origin shift of the N2O monomer in CO-N2O is 2.7570 cm-1, matching well with the observed value of 2.9048 cm-1. The computed microwave and infrared transition frequencies, as well as the rotational parameters, are consistent with the experimental observations.
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Affiliation(s)
- Xuedan Jiang
- School of Chemistry, Sichuan University, Chengdu 610064, China
| | - Li Liu
- School of Chemistry, Sichuan University, Chengdu 610064, China
| | - Yang Peng
- School of Chemistry, Sichuan University, Chengdu 610064, China
| | - Hua Zhu
- School of Chemistry, Sichuan University, Chengdu 610064, China
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4
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Peng Y, Jiang X, Liu L, Liu G, Zhu H. A new six-dimensional ab initio potential energy surface and rovibrational spectra for the N2-CO2 complex. J Chem Phys 2023; 159:244304. [PMID: 38146833 DOI: 10.1063/5.0182188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 12/04/2023] [Indexed: 12/27/2023] Open
Abstract
New six-dimensional ab initio potential energy surfaces (PESs) for the N2-CO2 complex, which involve the stretching vibration of N2 and the Q3 normal mode for the ν3 asymmetric stretching vibration of CO2, were constructed using the CCSD(T)-F12/AVTZ method with midpoint bond functions. Two vibrational averaged 4D interaction potentials were obtained by integrating over the two intramolecular coordinates. It was found that both PESs possess two equivalent T-shaped global minima as well as two in-plane and one out-of-plane saddle points. Based on these PESs, rovibrational bound states and energy levels were calculated applying the radial discrete variable representation/angular finite basis representation method and the Lanczos algorithm. The splitting of the energy levels between oN2-CO2 and pN2-CO2 for the intermolecular vibrational ground state is determined to be only 0.000 09 cm-1 due to the higher barriers. The obtained band origin shift is about +0.471 74 cm-1 in the N2-CO2 infrared spectra with CO2 at the ν3 zone, which coincides with the experimental data of +0.483 74 cm-1. The frequencies of the in-plane geared-bending for N2-CO2 at the ν3 = 0 and 1 states of CO2 turn out to be 21.6152 and 21.4522 cm-1, the latter reproduces the available experimental 21.3793 cm-1 value with CO2 at the ν3 zone. The spectral parameters fitted from the rovibrational energy levels show that this dimer is a near prolate asymmetric rotor. The computed microwave transitions as well as the infrared fundamental and combination bands for the complex agree well with the observed data.
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Affiliation(s)
- Yang Peng
- School of Chemistry, Sichuan University, Chengdu 610064, China
| | - Xuedan Jiang
- School of Chemistry, Sichuan University, Chengdu 610064, China
| | - Li Liu
- School of Chemistry, Sichuan University, Chengdu 610064, China
| | - Guangliang Liu
- School of Chemistry, Sichuan University, Chengdu 610064, China
| | - Hua Zhu
- School of Chemistry, Sichuan University, Chengdu 610064, China
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5
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Stark W, Westermayr J, Douglas-Gallardo OA, Gardner J, Habershon S, Maurer RJ. Machine Learning Interatomic Potentials for Reactive Hydrogen Dynamics at Metal Surfaces Based on Iterative Refinement of Reaction Probabilities. THE JOURNAL OF PHYSICAL CHEMISTRY. C, NANOMATERIALS AND INTERFACES 2023; 127:24168-24182. [PMID: 38148847 PMCID: PMC10749455 DOI: 10.1021/acs.jpcc.3c06648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 11/12/2023] [Accepted: 11/15/2023] [Indexed: 12/28/2023]
Abstract
The reactive chemistry of molecular hydrogen at surfaces, notably dissociative sticking and hydrogen evolution, plays a crucial role in energy storage and fuel cells. Theoretical studies can help to decipher underlying mechanisms and reaction design, but studying dynamics at surfaces is computationally challenging due to the complex electronic structure at interfaces and the high sensitivity of dynamics to reaction barriers. In addition, ab initio molecular dynamics, based on density functional theory, is too computationally demanding to accurately predict reactive sticking or desorption probabilities, as it requires averaging over tens of thousands of initial conditions. High-dimensional machine learning-based interatomic potentials are starting to be more commonly used in gas-surface dynamics, yet robust approaches to generate reliable training data and assess how model uncertainty affects the prediction of dynamic observables are not well established. Here, we employ ensemble learning to adaptively generate training data while assessing model performance with full uncertainty quantification (UQ) for reaction probabilities of hydrogen scattering on different copper facets. We use this approach to investigate the performance of two message-passing neural networks, SchNet and PaiNN. Ensemble-based UQ and iterative refinement allow us to expose the shortcomings of the invariant pairwise-distance-based feature representation in the SchNet model for gas-surface dynamics.
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Affiliation(s)
- Wojciech
G. Stark
- Department
of Chemistry, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, U.K.
| | - Julia Westermayr
- Department
of Chemistry, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, U.K.
| | | | - James Gardner
- Department
of Chemistry, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, U.K.
| | - Scott Habershon
- Department
of Chemistry, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, U.K.
| | - Reinhard J. Maurer
- Department
of Chemistry, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, U.K.
- Department
of Physics, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, U.K.
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6
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Chen J, Zhang H, Zhou L, Hu X, Xie D. New accurate diabatic potential energy surfaces for the two lowest 1A'' states of H 2S and photodissociation dynamics in its first absorption band. Phys Chem Chem Phys 2023; 25:26032-26042. [PMID: 37750311 DOI: 10.1039/d3cp03026a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/27/2023]
Abstract
In this work, state-to-state photodissociation dynamics of H2S in its first absorption band has been studied quantum mechanically with a new set of coupled potential energy surfaces (PESs) for the first two 1A'' excited states, which were developed at the explicitly correlated internally contracted multi-reference configuration interaction level with the cc-pVQZ-F12 basis set and a large active space. The calculated absorption spectrum, product state distributions, and angular distributions are in excellent agreement with available experimental data, validating the accuracy of the PESs and the non-adiabatic couplings. Detailed analysis of the dynamics reveals that there are strong non-adiabatic couplings between the bound 11B1 and dissociative 11A2 states around the Franck-Condon region, leading to very fast predissociation to ro-vibrationally cold SH(X̃) fragments, during which marginal angular anisotropy of the PESs is involved. This study provides quantitatively accurate characterization of the electronic structure and detailed fragmentation dynamics of this prototypical photodissociation system, which is desirable for improving astrochemical modelling.
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Affiliation(s)
- Junjie Chen
- Institute of Theoretical and Computational Chemistry, Key Laboratory of Mesoscopic Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
| | - Hanzi Zhang
- Institute of Theoretical and Computational Chemistry, Key Laboratory of Mesoscopic Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
| | - Linsen Zhou
- Institute of Materials, China Academy of Engineering Physics, Mianyang 621907, China.
| | - Xixi Hu
- Kuang Yaming Honors School, Institute for Brain Sciences, Nanjing University, Nanjing 210023, China.
- Hefei National Laboratory, Hefei 230088, China
| | - 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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7
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Fu L, Yang S, Zhang DH. Neural network potential energy surfaces and dipole moment surfaces for SO 2(H 2O) and SO 2(H 2O) 2 complexes. Phys Chem Chem Phys 2023; 25:22804-22812. [PMID: 37584113 DOI: 10.1039/d3cp03113f] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/17/2023]
Abstract
Full-dimensional, ab initio-based many-body potential energy surfaces and dipole moment surfaces constructed using the neural network method for SO2(H2O)n (n = 1,2) complexes are reported. The database of the SO2 1-body PES, SO2(H2O) 2-body PES and SO2(H2O)2 3-body PES consists of 11 952, 79 882 and 84 159 ab initio energies, respectively. All 1-body energies were calculated at the CCSD(T)/CBS(AVTZ:AVQZ) level and all 2,3-body energies were calculated at the DSD-PBEP86/AVTZ level. The database of DMSs is the same as that of PESs and all dipole moments were calculated at the MP2/AVTZ level. Harmonic frequencies and dissociation energies of SO2(H2O) and SO2(H2O)2 were calculated on these PESs and compared with ab initio results to examine the fidelity of these PESs.
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Affiliation(s)
- Liangfei Fu
- State Key Laboratory of Molecular Reaction Dynamics, 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
| | - Shuo Yang
- State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, P. R. China.
| | - Dong H Zhang
- State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, P. R. China.
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8
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Zhang L, Zuo J, Suleimanov YV, Guo H. Ring Polymer Molecular Dynamics Approach to Quantum Dissociative Chemisorption Rates. J Phys Chem Lett 2023; 14:7118-7125. [PMID: 37531595 DOI: 10.1021/acs.jpclett.3c01848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/04/2023]
Abstract
A ring polymer molecular dynamics (RPMD) method is proposed for the calculation of the dissociative chemisorption rate coefficient on surfaces. The RPMD rate theory is capable of handling quantum effects such as the zero-point energy and tunneling in dissociative chemisorption, while it relies on classical trajectories for the simulation. Applications to H2 dissociative chemisorption are demonstrated. For the highly activated process on Ag(111), strong deviations from Arrhenius behavior are found at low temperatures and attributed to tunneling. On Pt(111), where the dissociation has a barrierless pathway, the RPMD rate coefficient is found to agree with the experimentally derived thermal sticking coefficient within a factor of 2 over a large temperature range. Significant quantum effects are also found.
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Affiliation(s)
- Liang Zhang
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, United States
| | - Junxiang Zuo
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, United States
| | - Yury V Suleimanov
- American Association for the Advancement of Science, 1200 New York Ave NW, Washington, D.C. 20005, United States
| | - Hua Guo
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, United States
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9
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Riera M, Knight C, Bull-Vulpe EF, Zhu X, Agnew H, Smith DGA, Simmonett AC, Paesani F. MBX: A many-body energy and force calculator for data-driven many-body simulations. J Chem Phys 2023; 159:054802. [PMID: 37526156 PMCID: PMC10550339 DOI: 10.1063/5.0156036] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 07/11/2023] [Indexed: 08/02/2023] Open
Abstract
Many-Body eXpansion (MBX) is a C++ library that implements many-body potential energy functions (PEFs) within the "many-body energy" (MB-nrg) formalism. MB-nrg PEFs integrate an underlying polarizable model with explicit machine-learned representations of many-body interactions to achieve chemical accuracy from the gas to the condensed phases. MBX can be employed either as a stand-alone package or as an energy/force engine that can be integrated with generic software for molecular dynamics and Monte Carlo simulations. MBX is parallelized internally using Open Multi-Processing and can utilize Message Passing Interface when available in interfaced molecular simulation software. MBX enables classical and quantum molecular simulations with MB-nrg PEFs, as well as hybrid simulations that combine conventional force fields and MB-nrg PEFs, for diverse systems ranging from small gas-phase clusters to aqueous solutions and molecular fluids to biomolecular systems and metal-organic frameworks.
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Affiliation(s)
- Marc Riera
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
| | - Christopher Knight
- Argonne National Laboratory, Computational Science Division, Lemont, Illinois 60439, USA
| | - Ethan F. Bull-Vulpe
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
| | - Xuanyu Zhu
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
| | - Henry Agnew
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
| | | | - Andrew C. Simmonett
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
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10
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Zhang Y, Lin Q, Jiang B. Atomistic neural network representations for chemical dynamics simulations of molecular, condensed phase, and interfacial systems: Efficiency, representability, and generalization. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Affiliation(s)
- Yaolong Zhang
- Department of Chemical Physics, School of Chemistry and Materials Science, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes University of Science and Technology of China Hefei Anhui China
| | - Qidong Lin
- Department of Chemical Physics, School of Chemistry and Materials Science, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes University of Science and Technology of China Hefei Anhui China
| | - Bin Jiang
- Department of Chemical Physics, School of Chemistry and Materials Science, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes University of Science and Technology of China Hefei Anhui China
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Bull-Vulpe EF, Riera M, Bore SL, Paesani F. Data-Driven Many-Body Potential Energy Functions for Generic Molecules: Linear Alkanes as a Proof-of-Concept Application. J Chem Theory Comput 2022. [PMID: 36113028 DOI: 10.1021/acs.jctc.2c00645] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
We present a generalization of the many-body energy (MB-nrg) theoretical/computational framework that enables the development of data-driven potential energy functions (PEFs) for generic covalently bonded molecules, with arbitrary quantum mechanical accuracy. The "nearsightedness of electronic matter" is exploited to define monomers as "natural building blocks" on the basis of their distinct chemical identity. The energy of generic molecules is then expressed as a sum of individual many-body energies of incrementally larger subsystems. The MB-nrg PEFs represent the low-order n-body energies, with n = 1-4, using permutationally invariant polynomials derived from electronic structure data carried out at an arbitrary quantum mechanical level of theory, while all higher-order n-body terms (n > 4) are represented by a classical many-body polarization term. As a proof-of-concept application of the general MB-nrg framework, we present MB-nrg PEFs for linear alkanes. The MB-nrg PEFs are shown to accurately reproduce reference energies, harmonic frequencies, and potential energy scans of alkanes, independently of their length. Since, by construction, the MB-nrg framework introduced here can be applied to generic covalently bonded molecules, we envision future computer simulations of complex molecular systems using data-driven MB-nrg PEFs, with arbitrary quantum mechanical accuracy.
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Affiliation(s)
- Ethan F. Bull-Vulpe
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States
| | - Marc Riera
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States
| | - Sigbjørn L. Bore
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States
| | - Francesco Paesani
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States
- Materials Science and Engineering, University of California San Diego, La Jolla, California 92093, United States
- San Diego Supercomputer Center, University of California San Diego, La Jolla, California 92093, United States
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12
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Peng Y, Zhu F, Zhu H. A new potential energy surface and rovibrational spectra of the CO-CO 2 complex: Dependence on the antisymmetric stretching vibration of CO 2. J Chem Phys 2022; 157:084310. [DOI: 10.1063/5.0100613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
We present a new ab initio five-dimensional potential energy surface for the CO-CO2 complex containing the Q3 normal mode for the ν3 asymmetric stretching vibration of the CO2 unit. The potential was calculated by supermolecular approach at the CCSD(T)-F12 level with aug-cc-pVTZ basis set plus midpoint bond functions. Two vibrationally averaged four-dimensional potentials for CO-CO2 with CO2 in the ground and ν3 excited states were generated by the integration of the five-dimensional potential over the Q3 intramolecular coordinate. Each potential displays a T-shaped global minimum with the C end in the CO unit pointing toward the C atom in the CO2 unit and a T-shaped local minimum but with the CO monomer rotated by 180º. The rovibrational bound states and energy levels for the CO-CO2 dimer were obtained employing the radial discrete variable representation (DVR)/angular finite basis representation (FBR) method in conjunction with the Lanczos algorithm. The vibrational ground and some lower excited states for CO-CO2 are localized around the global minimum because of the higher potential barriers. The band origin is blueshifted by 0.2089 cm-1 for CO-CO2 in the CO2 ν3 range, which is consistent with the experimental result of 0.211 cm-1. The geared bending vibrational frequencies for CO-CO2 are 24.7101 and 24.5549 cm-1 at the ground and ν3 excited states of CO2, respectively. The predicted rovibrational frequencies as well as spectral constants coincide with the available observations, and these parameters show the CO-CO2 complex is a nearly prolate asymmetric rotor.
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Affiliation(s)
- Yang Peng
- Sichuan University College of Chemistry, China
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13
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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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14
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Meng Q, Chen J, Ma J, Zhang X, Chen J. Adiabatic models for the quantum dynamics of surface scattering with lattice effects. Phys Chem Chem Phys 2022; 24:16415-16436. [PMID: 35766107 DOI: 10.1039/d2cp01560a] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
In this contribution, we review models for the lattice effects in quantum dynamics calculations on surface scattering, which is important to modeling heterogeneous catalysis for achieving an interpretation of experimental measurements. Unlike dynamics models for reactions in the gas phase, those for heterogeneous reactions have to include the effects of the surface. For manageable computational costs in calculations, the effects of static surface (SS) are firstly modeled as this is simply and easily implemented. Then, the SS model has to be improved to include the effects of the flexible surface, that is the lattice effects. To do this, various surface models have been designed where the coordinates of the surface atoms are introduced in the Hamiltonian operator, especially those of the top surface atom. Based on this model Hamiltonian operator, extensive multi-dimension quantum dynamics calculations can be performed to recover the lattice effects. Here, we first review an overview of the techniques in constructing the Hamiltonian operator, which is a sum of the kinetic energy operator (KEO) and potential energy surface (PES). Since the PES containing the coordinates of the surface atoms in a cell is still expensive, the SS model is often accepted. We consider a mathematical model, called the coupled harmonic oscillator (CHO) model, to introduce the concepts of adiabatic and diabatic representations for separating the molecule and surface. Under the adiabatic model, we further introduce the expansion model where the potential function is Taylor expanded around the optimized geometry of the surface. By an expansion model truncated at the first and second order, various coupling surface models between the molecule and surface are derived. Moreover, by further and deeply understanding the adiabatic representation, an effective Hamiltonian operator is obtained by optimizing the total wave function in factorized form. By this factorized form of wave function and effective Hamiltonian operator, the geometry phase of the surface wave function is theoretically found. This theoretical prediction may be measured by carefully designing experiments. Finally, discussions on the adiabatic representation, the PES construction, and possibility of the classical-dynamics solutions are given. Based on these discussions, a simple outlook on the dynamics of photocatalytics is finally given.
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Affiliation(s)
- Qingyong Meng
- Department of Chemistry, Northwestern Polytechnical University, West Youyi Road 127, 710072 Xi'an, China.
| | - Junbo Chen
- Department of Chemistry, Northwestern Polytechnical University, West Youyi Road 127, 710072 Xi'an, China. .,Xi'an Modern Chemistry Research Institute, China North Industries Group Corp., Ltd., East Zhangba Road 168, 710065 Xi'an, China
| | - Jianxing Ma
- Department of Chemistry, Northwestern Polytechnical University, West Youyi Road 127, 710072 Xi'an, China.
| | - Xingyu Zhang
- Department of Chemistry, Northwestern Polytechnical University, West Youyi Road 127, 710072 Xi'an, China.
| | - Jun Chen
- State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Yangqiao Road West 155, 350002 Fuzhou, China.,Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Optoelectronic Industry Base at High-tech Zone, 350108 Fuzhou, China
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15
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Díaz C, Gravielle MS. Grazing incidence fast atom and molecule diffraction: theoretical challenges. Phys Chem Chem Phys 2022; 24:15628-15656. [PMID: 35730987 DOI: 10.1039/d2cp01246d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
This perspective article reviews the state-of-the-art of grazing incidence fast atom and molecule diffraction (GIFAD and GIFMD) simulations and addresses the main challenges that theorists, aiming to provide useful inputs in this topic, are facing. We first discuss briefly the methods used to build accurate potential energy surfaces describing the interaction between the projectile and the surface. Subsequently, we focus on the dynamics simulation methods for GIFAD, a phenomenon that has received a lot of experimental attention since 2007, when the first measurements were published. Following this experimental effort, theorists have developed and adapted a bunch of methods able to simulate, analyze and extract information from the experimental outputs. We review these methods, from the very simple ones based on classical dynamics to the full quantum ones, paying special attention to more versatile semiclassical approaches, which include quantum ingredients in the dynamics at a computational cost only slightly higher than that required in classical dynamics. Within the semiclassical framework it is possible, for example, to include in the dynamics the surface phonons and the projectile coherence, two factors that may have a relevant influence on the experimental measurements, at a reasonable computational cost. Finally, we address GIFMD, a phenomenon that has received much less attention and for which there is still a lot of room for research. We review the few examples of GIFMD available in the literature, and we discuss new phenomena associated with the molecular internal degrees of freedom, which may have some impact in other closely related fields, such as molecular reactivity on metal surfaces. Finally, we point out opened questions, raised from the comparisons between theoretical and experimental results, which claim for further experimental efforts.
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Affiliation(s)
- Cristina Díaz
- Departamento de Química Física, Facultad de CC. Químicas, Universidad Complutense de Madrid, 28040 Madrid, Spain.
| | - María Silvia Gravielle
- Instituto de Astronomía y Física del Espacio (IAFE, UBA-CONICET), Ciudad Universitaria, C1428EGA, Buenos Aires, Argentina.
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16
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Li H, Zhou M, Sebastian J, Wu J, Gu M. Efficient force field and energy emulation through partition of permutationally equivalent atoms. J Chem Phys 2022; 156:184304. [PMID: 35568561 DOI: 10.1063/5.0088017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Gaussian process (GP) emulator has been used as a surrogate model for predicting force field and molecular potential, to overcome the computational bottleneck of ab initio molecular dynamics simulation. Integrating both atomic force and energy in predictions was found to be more accurate than using energy alone, yet it requires O((NM)3) computational operations for computing the likelihood function and making predictions, where N is the number of atoms and M is the number of simulated configurations in the training sample due to the inversion of a large covariance matrix. The high computational cost limits its applications to the simulation of small molecules. The computational challenge of using both gradient information and function values in GPs was recently noticed in machine learning communities, whereas conventional approximation methods may not work well. Here, we introduce a new approach, the atomized force field model, that integrates both force and energy in the emulator with many fewer computational operations. The drastic reduction in computation is achieved by utilizing the naturally sparse covariance structure that satisfies the constraints of the energy conservation and permutation symmetry of atoms. The efficient machine learning algorithm extends the limits of its applications on larger molecules under the same computational budget, with nearly no loss of predictive accuracy. Furthermore, our approach contains an uncertainty assessment of predictions of atomic forces and energies, useful for developing a sequential design over the chemical input space.
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Affiliation(s)
- Hao Li
- Department of Statistics and Applied Probability, University of California, Santa Barbara, California 93106, USA
| | - Musen Zhou
- Department of Chemical and Environmental Engineering, University of California, Riverside, California 92521, USA
| | - Jessalyn Sebastian
- Department of Statistics and Applied Probability, University of California, Santa Barbara, California 93106, USA
| | - Jianzhong Wu
- Department of Chemical and Environmental Engineering, University of California, Riverside, California 92521, USA
| | - Mengyang Gu
- Department of Statistics and Applied Probability, University of California, Santa Barbara, California 93106, USA
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17
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Shi H, Liu T, Fu Y, Wu H, Fu B, Zhang DH. Fundamental invariant-neural network potential energy surface and dissociative chemisorption dynamics of N2 on rigid Ni(111). COMPUT THEOR CHEM 2022. [DOI: 10.1016/j.comptc.2022.113679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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18
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Mattsson S, Paulus B. First principle calculations including ab initio molecular dynamics studies for the activation of hydrogen fluoride on Ni(111). Chem Phys 2022. [DOI: 10.1016/j.chemphys.2022.111469] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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19
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Effects of vibrational and rotational excitations on the dissociative chemisorption dynamics of N 2 on Fe(111). CHINESE J CHEM PHYS 2022. [DOI: 10.1063/1674-0068/cjcp2201009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
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20
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Formulation of temperature dependent effective Hartree potential incorporating quadratic over linear molecular DOFs-surface modes couplings and its effect on quantum dynamics of D2 (v = 0, j = 0)/D2 (v = 0, j = 2) on Cu(111) metal surface. Chem Phys 2022. [DOI: 10.1016/j.chemphys.2021.111371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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21
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Yang Z, Chen H, Chen M. Representing Globally Accurate Reactive Potential Energy Surfaces with Complex Topography by Combining Gaussian Process Regression and Neural Network. Phys Chem Chem Phys 2022; 24:12827-12836. [DOI: 10.1039/d2cp00719c] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
There has been increasing attention in using machine learning technologies, such as neural network (NN) and Gaussian process regression (GPR), to model multidimensional potential energy surfaces (PESs). NN PES features...
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22
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Yang S, Zhang Z, Zhang DH. A full-dimensional ab initio potential energy and dipole moment surfaces for (NH 3) 2. J Chem Phys 2021; 155:164306. [PMID: 34717358 DOI: 10.1063/5.0072063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
A full-dimensional ab initio potential energy surface (PES) and dipole moment surface (DMS) for the ammonia dimer (NH3)2 are reported. The database of the PES consists of 27 736 ab initio energy points and all of these points were calculated at the UCCSD(T)-F12a/AVTZ level. The PES was fitted by using the fundamental invariant neural network (FI-NN) method that satisfies the permutational symmetry of identical atoms, and the root mean square fitting error for the PES is very small as low as 0.562 meV. The geometries for the (NH3)2 DMS are the same as those used for the PES and are calculated at the XYG3/AVTZ level. This PES can describe a variety of internal floppy motions, including all kinds of vibrational modes no matter intermolecular or intramolecular. The CCSD(T)-PES can dissociate correctly to two NH3 monomers, with De = 1135.55 cm-1 (13.58 kJ/mol) which agrees accurately with the 13.5 ± 0.3 kJ/mol predicted by previous work.
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Affiliation(s)
- Shuo Yang
- 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, People's Republic of China
| | - 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 116023, People's Republic of 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, People's Republic of China
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23
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Zhang Y, Xia J, Jiang B. Physically Motivated Recursively Embedded Atom Neural Networks: Incorporating Local Completeness and Nonlocality. PHYSICAL REVIEW LETTERS 2021; 127:156002. [PMID: 34677998 DOI: 10.1103/physrevlett.127.156002] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 09/07/2021] [Indexed: 06/13/2023]
Abstract
Recent advances in machine-learned interatomic potentials largely benefit from the atomistic representation and locally invariant many-body descriptors. It was, however, recently argued that including three-body (or even four-body) features is incomplete to distinguish specific local structures. Utilizing an embedded density descriptor made by linear combinations of neighboring atomic orbitals and realizing that each orbital coefficient physically depends on its own local environment, we propose a recursively embedded atom neural network model. We formally prove that this model can efficiently incorporate complete many-body correlations without explicitly computing high-order terms. This model not only successfully addresses challenges regarding local completeness and nonlocality in representative systems, but also provides an easy and general way to update local many-body descriptors to have a message-passing form without changing their basic structures.
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Affiliation(s)
- Yaolong Zhang
- 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
| | - Junfan Xia
- 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
| | - 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
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24
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Bull-Vulpe EF, Riera M, Götz AW, Paesani F. MB-Fit: Software infrastructure for data-driven many-body potential energy functions. J Chem Phys 2021; 155:124801. [PMID: 34598567 DOI: 10.1063/5.0063198] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Many-body potential energy functions (MB-PEFs), which integrate data-driven representations of many-body short-range quantum mechanical interactions with physics-based representations of many-body polarization and long-range interactions, have recently been shown to provide high accuracy in the description of molecular interactions from the gas to the condensed phase. Here, we present MB-Fit, a software infrastructure for the automated development of MB-PEFs for generic molecules within the TTM-nrg (Thole-type model energy) and MB-nrg (many-body energy) theoretical frameworks. Besides providing all the necessary computational tools for generating TTM-nrg and MB-nrg PEFs, MB-Fit provides a seamless interface with the MBX software, a many-body energy and force calculator for computer simulations. Given the demonstrated accuracy of the MB-PEFs, particularly within the MB-nrg framework, we believe that MB-Fit will enable routine predictive computer simulations of generic (small) molecules in the gas, liquid, and solid phases, including, but not limited to, the modeling of quantum isomeric equilibria in molecular clusters, solvation processes, molecular crystals, and phase diagrams.
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Affiliation(s)
- Ethan F Bull-Vulpe
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
| | - Marc Riera
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
| | - Andreas W Götz
- San Diego Supercomputer Center, University of California San Diego, La Jolla, California 92093, USA
| | - Francesco Paesani
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
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25
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Lambros E, Dasgupta S, Palos E, Swee S, Hu J, Paesani F. General Many-Body Framework for Data-Driven Potentials with Arbitrary Quantum Mechanical Accuracy: Water as a Case Study. J Chem Theory Comput 2021; 17:5635-5650. [PMID: 34370954 DOI: 10.1021/acs.jctc.1c00541] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
We present a general framework for the development of data-driven many-body (MB) potential energy functions (MB-QM PEFs) that represent the interactions between small molecules at an arbitrary quantum-mechanical (QM) level of theory. As a demonstration, a family of MB-QM PEFs for water is rigorously derived from density functionals belonging to different rungs across Jacob's ladder of approximations within density functional theory (MB-DFT) and from Møller-Plesset perturbation theory (MB-MP2). Through a systematic analysis of individual MB contributions to the interaction energies of water clusters, we demonstrate that all MB-QM PEFs preserve the same accuracy as the corresponding ab initio calculations, with the exception of those derived from density functionals within the generalized gradient approximation (GGA). The differences between the DFT and MB-DFT results are traced back to density-driven errors that prevent GGA functionals from accurately representing the underlying molecular interactions for different cluster sizes and hydrogen-bonding arrangements. We show that this shortcoming may be overcome, within the MB formalism, by using density-corrected functionals (DC-DFT) that provide a more consistent representation of each individual MB contribution. This is demonstrated through the development of a MB-DFT PEF derived from DC-PBE-D3 data, which more accurately reproduce the corresponding ab initio results.
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Affiliation(s)
- Eleftherios Lambros
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States
| | - Saswata Dasgupta
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States
| | - Etienne Palos
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States
| | - Steven Swee
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States
| | - Jie Hu
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States
| | - Francesco Paesani
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States.,Materials Science and Engineering, University of California San Diego, La Jolla, California 92093, United States.,San Diego Supercomputer Center, University of California San Diego, La Jolla, California 92093, United States
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26
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Zhou X, Zhang Y, Yin R, Hu C, Jiang B. Neural Network Representations for Studying
Gas‐Surface
Reaction Dynamics: Beyond the
Born‐Oppenheimer
Static Surface Approximation
†. CHINESE J CHEM 2021. [DOI: 10.1002/cjoc.202100303] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Affiliation(s)
- Xueyao Zhou
- 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
| | - Yaolong Zhang
- 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
| | - Rongrong Yin
- 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
| | - Ce Hu
- 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
| | - 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
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27
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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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28
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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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29
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Lin Q, Zhang L, Zhang Y, Jiang B. Searching Configurations in Uncertainty Space: Active Learning of High-Dimensional Neural Network Reactive Potentials. J Chem Theory Comput 2021; 17:2691-2701. [PMID: 33904718 DOI: 10.1021/acs.jctc.1c00166] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Neural network (NN) potential energy surfaces (PESs) have been widely used in atomistic simulations with ab initio accuracy. While constructing NN PESs, their training data points are often sampled by molecular dynamics trajectories. This strategy can be however inefficient for reactive systems involving rare events. Here, we develop an uncertainty-driven active learning strategy to automatically and efficiently generate high-dimensional NN-based reactive potentials, taking a gas-surface reaction as an example. The difference between two independent NN models is used as a simple and differentiable uncertainty metric, allowing us to quickly search in the uncertainty space and place new samples at which the PES is less reliable. By interfacing this algorithm with the first-principles simulation package, we demonstrate that a globally accurate NN potential of the H2 + Ag(111) system can be constructed with merely ∼150 data points. This PES can be further refined to describe H2 dissociation on Ag(100) by adding ∼130 more configurations on this facet. The entire process is completely automatic and self-terminated once the relative error criterion is fulfilled. Impressively, data points sampled by this uncertainty-driven strategy are substantially fewer than by the traditional trajectory-based sampling. The final NN PES not only converges well the quantum dissociation probability of the molecule but also well-reproduces the phonon properties of the substrate and is capable of describing surface temperature effects. These results show the potential of this active learning approach in developing high-dimensional NN reactive potentials in gas and condensed phases.
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Affiliation(s)
- Qidong Lin
- Hefei National Laboratory for Physical Science at the Microscale, Department of Chemical Physics, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Liang Zhang
- Hefei National Laboratory for Physical Science at the Microscale, Department of Chemical Physics, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Yaolong Zhang
- Hefei National Laboratory for Physical Science at the Microscale, Department of Chemical Physics, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Bin Jiang
- Hefei National Laboratory for Physical Science at the Microscale, Department of Chemical Physics, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui 230026, China
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30
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Kroes GJ. Computational approaches to dissociative chemisorption on metals: towards chemical accuracy. Phys Chem Chem Phys 2021; 23:8962-9048. [PMID: 33885053 DOI: 10.1039/d1cp00044f] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
We review the state-of-the-art in the theory of dissociative chemisorption (DC) of small gas phase molecules on metal surfaces, which is important to modeling heterogeneous catalysis for practical reasons, and for achieving an understanding of the wealth of experimental information that exists for this topic, for fundamental reasons. We first give a quick overview of the experimental state of the field. Turning to the theory, we address the challenge that barrier heights (Eb, which are not observables) for DC on metals cannot yet be calculated with chemical accuracy, although embedded correlated wave function theory and diffusion Monte-Carlo are moving in this direction. For benchmarking, at present chemically accurate Eb can only be derived from dynamics calculations based on a semi-empirically derived density functional (DF), by computing a sticking curve and demonstrating that it is shifted from the curve measured in a supersonic beam experiment by no more than 1 kcal mol-1. The approach capable of delivering this accuracy is called the specific reaction parameter (SRP) approach to density functional theory (DFT). SRP-DFT relies on DFT and on dynamics calculations, which are most efficiently performed if a potential energy surface (PES) is available. We therefore present a brief review of the DFs that now exist, also considering their performance on databases for Eb for gas phase reactions and DC on metals, and for adsorption to metals. We also consider expressions for SRP-DFs and briefly discuss other electronic structure methods that have addressed the interaction of molecules with metal surfaces. An overview is presented of dynamical models, which make a distinction as to whether or not, and which dissipative channels are modeled, the dissipative channels being surface phonons and electronically non-adiabatic channels such as electron-hole pair excitation. We also discuss the dynamical methods that have been used, such as the quasi-classical trajectory method and quantum dynamical methods like the time-dependent wave packet method and the reaction path Hamiltonian method. Limits on the accuracy of these methods are discussed for DC of diatomic and polyatomic molecules on metal surfaces, paying particular attention to reduced dimensionality approximations that still have to be invoked in wave packet calculations on polyatomic molecules like CH4. We also address the accuracy of fitting methods, such as recent machine learning methods (like neural network methods) and the corrugation reducing procedure. In discussing the calculation of observables we emphasize the importance of modeling the properties of the supersonic beams in simulating the sticking probability curves measured in the associated experiments. We show that chemically accurate barrier heights have now been extracted for DC in 11 molecule-metal surface systems, some of which form the most accurate core of the only existing database of Eb for DC reactions on metal surfaces (SBH10). The SRP-DFs (or candidate SRP-DFs) that have been derived show transferability in many cases, i.e., they have been shown also to yield chemically accurate Eb for chemically related systems. This can in principle be exploited in simulating rates of catalyzed reactions on nano-particles containing facets and edges, as SRP-DFs may be transferable among systems in which a molecule dissociates on low index and stepped surfaces of the same metal. In many instances SRP-DFs have allowed important conclusions regarding the mechanisms underlying observed experimental trends. An important recent observation is that SRP-DFT based on semi-local exchange DFs has so far only been successful for systems for which the difference of the metal work function and the molecule's electron affinity exceeds 7 eV. A main challenge to SRP-DFT is to extend its applicability to the other systems, which involve a range of important DC reactions of e.g. O2, H2O, NH3, CO2, and CH3OH. Recent calculations employing a PES based on a screened hybrid exchange functional suggest that the road to success may be based on using exchange functionals of this category.
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Affiliation(s)
- Geert-Jan Kroes
- Leiden Institute of Chemistry, Gorlaeus Laboratories, Leiden University, P.O. Box 9502, 2300 RA Leiden, The Netherlands.
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31
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Dutta J, Mandal S, Adhikari S, Spiering P, Meyer J, Somers MF. Effect of surface temperature on quantum dynamics of H 2 on Cu(111) using a chemically accurate potential energy surface. J Chem Phys 2021; 154:104103. [PMID: 33722025 DOI: 10.1063/5.0035830] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
The effect of surface atom vibrations on H2 scattering from a Cu(111) surface at different temperatures is being investigated for hydrogen molecules in their rovibrational ground state (v = 0, j = 0). We assume weakly correlated interactions between molecular degrees of freedom and surface modes through a Hartree product type wavefunction. While constructing the six-dimensional effective Hamiltonian, we employ (a) a chemically accurate potential energy surface according to the static corrugation model [M. Wijzenbroek and M. F. Somers, J. Chem. Phys. 137, 054703 (2012)]; (b) normal mode frequencies and displacement vectors calculated with different surface atom interaction potentials within a cluster approximation; and (c) initial state distributions for the vibrational modes according to Bose-Einstein probability factors. We carry out 6D quantum dynamics with the so-constructed effective Hamiltonian and analyze sticking and state-to-state scattering probabilities. The surface atom vibrations affect the chemisorption dynamics. The results show physically meaningful trends for both reaction and scattering probabilities compared to experimental and other theoretical results.
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Affiliation(s)
- Joy Dutta
- School of Chemical Sciences, Indian Association for the Cultivation of Science, Jadavpur, Kolkata 700 032, India
| | - Souvik Mandal
- School of Chemical Sciences, Indian Association for the Cultivation of Science, Jadavpur, Kolkata 700 032, India
| | - Satrajit Adhikari
- School of Chemical Sciences, Indian Association for the Cultivation of Science, Jadavpur, Kolkata 700 032, India
| | - Paul Spiering
- Leiden Institute of Chemistry, Gorlaeus Laboratories, Leiden University, P.O. Box 9502, 2300 RA Leiden, The Netherlands
| | - Jörg Meyer
- Leiden Institute of Chemistry, Gorlaeus Laboratories, Leiden University, P.O. Box 9502, 2300 RA Leiden, The Netherlands
| | - Mark F Somers
- Leiden Institute of Chemistry, Gorlaeus Laboratories, Leiden University, P.O. Box 9502, 2300 RA Leiden, The Netherlands
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32
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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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Panadés-Barrueta RL, Peláez D. Low-rank sum-of-products finite-basis-representation (SOP-FBR) of potential energy surfaces. J Chem Phys 2020; 153:234110. [PMID: 33353311 DOI: 10.1063/5.0027143] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
The sum-of-products finite-basis-representation (SOP-FBR) approach for the automated multidimensional fit of potential energy surfaces (PESs) is presented. In its current implementation, the method yields a PES in the so-called Tucker sum-of-products form, but it is not restricted to this specific ansatz. The novelty of our algorithm lies in the fact that the fit is performed in terms of a direct product of a Schmidt basis, also known as natural potentials. These encode in a non-trivial way all the physics of the problem and, hence, circumvent the usual extra ad hoc and a posteriori adjustments (e.g., damping functions) of the fitted PES. Moreover, we avoid the intermediate refitting stage common to other tensor-decomposition methods, typically used in the context of nuclear quantum dynamics. The resulting SOP-FBR PES is analytical and differentiable ad infinitum. Our ansatz is fully general and can be used in combination with most (molecular) dynamics codes. In particular, it has been interfaced and extensively tested with the Heidelberg implementation of the multiconfiguration time-dependent Hartree quantum dynamical software package.
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Affiliation(s)
- Ramón L Panadés-Barrueta
- Laboratoire de Physique des Lasers, Atomes et Molécules (PhLAM), Université Lille 1, Villeneuve d'Ascq Cedex, France
| | - Daniel Peláez
- Institut des Sciences Moléculaires d'Orsay (ISMO) - UMR 8214, Bât. 520, Université Paris-Saclay, 91405 Orsay Cedex, France
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34
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Smeets EWF, Kroes GJ. Designing new SRP density functionals including non-local vdW-DF2 correlation for H 2 + Cu(111) and their transferability to H 2 + Ag(111), Au(111) and Pt(111). Phys Chem Chem Phys 2020; 23:7875-7901. [PMID: 33291129 DOI: 10.1039/d0cp05173j] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Specific reaction parameter density functionals (SRP-DFs) that can describe dissociative chemisorption molecular beam experiments of hydrogen (H2) on cold transition metal surfaces with chemical accuracy have so far been shown to be only transferable among different facets of the same metal, but not among different metals. We design new SRP-DFs that include non-local vdW-DF2 correlation for the H2 + Cu(111) system, and evaluate their transferability to the highly activated H2 + Ag(111) and H2 + Au(111) systems and the non-activated H2 + Pt(111) system. We design our functionals for the H2 + Cu(111) system since it is the best studied system both theoretically and experimentally. Here we demonstrate that a SRP-DF fitted to reproduce molecular beam sticking experiments for H2 + Cu(111) with chemical accuracy can also describe such experiments for H2 + Pt(111) with chemical accuracy, and vice versa. Chemically accurate functionals have been obtained that perform very well with respect to reported van der Waals well geometries, and which improve the description of the metal over current generalized gradient approximation (GGA) based SRP-DFs. From a systematic comparison of our new SRP-DFs that include non-local correlation to previously developed SRP-DFs, for both activated and non-activated systems, we identify non-local correlation as a key ingredient in the construction of transferable SRP-DFs for H2 interacting with transition metals. Our results are in excellent agreement with experiment when accurately measured observables are available. It is however clear from our analysis that, except for the H2 + Cu(111) system, there is a need for more, more varied, and more accurately described experiments in order to further improve the design of SRP-DFs. Additionally, we confirm that, when including non-local correlation, the sticking of H2 on Cu(111) is still well described quasi-classically.
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Affiliation(s)
- Egidius W F Smeets
- Univeristeit Leiden, Leiden Institute of Chemistry, Einsteinweg 55, 2333 CC, Leiden, The Netherlands.
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35
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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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36
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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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37
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Roy S, K. J. N, Tiwari N, Tiwari AK. Energetics and dynamics of CH4 and H2O dissociation on metal surfaces. INT REV PHYS CHEM 2020. [DOI: 10.1080/0144235x.2020.1765598] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Sudipta Roy
- Department of Chemical Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur, India
| | - Nayanthara K. J.
- Department of Chemical Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur, India
| | - Nidhi Tiwari
- Department of Chemical Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur, India
| | - Ashwani K. Tiwari
- Department of Chemical Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur, India
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38
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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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39
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Chen R, Shao K, Fu B, Zhang DH. Fitting potential energy surfaces with fundamental invariant neural network. II. Generating fundamental invariants for molecular systems with up to ten atoms. J Chem Phys 2020; 152:204307. [PMID: 32486688 DOI: 10.1063/5.0010104] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Symmetry adaptation is crucial in representing a permutationally invariant potential energy surface (PES). Due to the rapid increase in computational time with respect to the molecular size, as well as the reliance on the algebra software, the previous neural network (NN) fitting with inputs of fundamental invariants (FIs) has practical limits. Here, we report an improved and efficient generation scheme of FIs based on the computational invariant theory and parallel program, which can be readily used as the input vector of NNs in fitting high-dimensional PESs with permutation symmetry. The newly developed method significantly reduces the evaluation time of FIs, thereby extending the FI-NN method for constructing highly accurate PESs to larger systems beyond five atoms. Because of the minimum size of invariants used in the inputs of the NN, the NN structure can be very flexible for FI-NN, which leads to small fitting errors. The resulting FI-NN PES is much faster on evaluating than the corresponding permutationally invariant polynomial-NN PES.
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Affiliation(s)
- Rongjun Chen
- 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, People's Republic of China
| | - Kejie Shao
- 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, People's Republic of 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 116023, People's Republic of 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, People's Republic of China
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40
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Song Q, Zhang Q, Meng Q. Revisiting the Gaussian process regression for fitting high-dimensional potential energy surface and its application to the OH + HO2→O2+ H2O reaction. J Chem Phys 2020; 152:134309. [PMID: 32268765 DOI: 10.1063/1.5143544] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Qingfei Song
- School of Chemistry and Chemical Engineering, Northwestern Polytechnical University, West Youyi Road 127, 710072 Xi’an, China
- Key Laboratory of Special Functional and Smart Polymer Materials of Ministry of Industry and Information Technology, Northwestern Polytechnical University, West Youyi Road 127, 710072 Xi’an, China
| | - Qiuyu Zhang
- School of Chemistry and Chemical Engineering, Northwestern Polytechnical University, West Youyi Road 127, 710072 Xi’an, China
- Key Laboratory of Special Functional and Smart Polymer Materials of Ministry of Industry and Information Technology, Northwestern Polytechnical University, West Youyi Road 127, 710072 Xi’an, China
| | - Qingyong Meng
- School of Chemistry and Chemical Engineering, Northwestern Polytechnical University, West Youyi Road 127, 710072 Xi’an, China
- Key Laboratory of Special Functional and Smart Polymer Materials of Ministry of Industry and Information Technology, Northwestern Polytechnical University, West Youyi Road 127, 710072 Xi’an, China
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41
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Serwatka T, Füchsel G, Tremblay JC. Scattering of NO(ν = 3) from Au(111): a stochastic dissipative quantum dynamical perspective. Phys Chem Chem Phys 2020; 22:6584-6594. [PMID: 32159168 DOI: 10.1039/c9cp06084g] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
In this work, we present a theoretical study of the scattering dynamics of NO(ν = 3) from an ideal unreconstructed Au(111) surface. The simulations are performed in reduced dimensions at the three high-symmetry sites employing our recent modification to the stochastic wave packet approach for diatomic-metal scattering [J. Chem. Phys., 2019, 150, 184105]. Energy exchange between molecular vibrational degrees of freedom and the electron-hole pairs (EHP) of the metal is accounted for by quantized stochastic jump operators, with associated rates obtained from a microscopic model based on Fermi's golden rule. The simulations are found to reproduce the experimentally observed trend of enhanced vibrational relaxation probabilities with increasing initial translational energy. Molecular dynamics simulations with electronic friction (MDEF) in the independent atom approximation were performed to compare classical and quantum dynamical descriptions of that system. Significant differences between these two descriptions were found indicating that intermode coupling must be described accurately by using a good potential energy surface, and pointing out at the potentially important influence of a quantized description of energy relaxation in describing the scattering of NO from Au(111).
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Affiliation(s)
- Tobias Serwatka
- Institut für Chemie und Biochemie, Freie Universität Berlin, D-14195, Germany.
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42
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Song Q, Zhang Q, Meng Q. Neural-network potential energy surface with small database and high precision: A benchmark of the H + H2 system. J Chem Phys 2019; 151:114302. [PMID: 31542037 DOI: 10.1063/1.5118692] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Qingfei Song
- Department of Applied Chemistry, Northwestern Polytechnical University, West Youyi Road 127, 710072 Xi’an, China
- Ministry-of-Education Key Laboratory of Materials Physics and Chemistry under Extraordinary Conditions, Northwestern Polytechnical University, West Youyi Road 127, 710072 Xi’an, China
| | - Qiuyu Zhang
- Department of Applied Chemistry, Northwestern Polytechnical University, West Youyi Road 127, 710072 Xi’an, China
- Ministry-of-Education Key Laboratory of Materials Physics and Chemistry under Extraordinary Conditions, Northwestern Polytechnical University, West Youyi Road 127, 710072 Xi’an, China
| | - Qingyong Meng
- Department of Applied Chemistry, Northwestern Polytechnical University, West Youyi Road 127, 710072 Xi’an, China
- Ministry-of-Education Key Laboratory of Materials Physics and Chemistry under Extraordinary Conditions, Northwestern Polytechnical University, West Youyi Road 127, 710072 Xi’an, China
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43
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Panadés-Barrueta RL, Martínez-Núñez E, Peláez D. Specific Reaction Parameter Multigrid POTFIT (SRP-MGPF): Automatic Generation of Sum-of-Products Form Potential Energy Surfaces for Quantum Dynamical Calculations. Front Chem 2019; 7:576. [PMID: 31475138 PMCID: PMC6702682 DOI: 10.3389/fchem.2019.00576] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Accepted: 07/30/2019] [Indexed: 11/13/2022] Open
Abstract
We present Specific Reaction Parameter Multigrid POTFIT (SRP-MGPF), an automated methodology for the generation of global potential energy surfaces (PES), molecular properties surfaces, e.g., dipole, polarizabilities, etc. using a single random geometry as input. The SRP-MGPF workflow integrates: (i) a fully automated procedure for the global topographical characterization of a (intermolecular) PES based on the Transition State Search Using Chemical Dynamical Simulations (TSSCDS) family of methods;i (ii) the global optimization of the parameters of a semiempirical Hamiltonian in order to reproduce a given level of electronic structure theory; and (iii) a tensor decomposition algorithm which turns the resulting SRP-PES into sum of products (Tucker) form with the Multigrid POTFIT algorithm. The latter is necessary for quantum dynamical studies within the Multiconfiguration Time-Dependent Hartree (MCTDH) quantum dynamics method. To demonstrate our approach, we have applied our methodology to the cis-trans isomerization reaction in HONO in full dimensionality (6D). The resulting SRP-PES has been validated through the computation of classical on-the-fly dynamical calculations as well as calculations of the lowest vibrational eigenstates of HONO as well as high-energy wavepacket propagations.
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Affiliation(s)
- Ramón L. Panadés-Barrueta
- Laboratoire de Physique des Lasers, Atomes et Molécules (PhLAM), Université de Lille, Villeneuve-d'Ascq, France
| | - Emilio Martínez-Núñez
- Departamento de Química Física, Facultade de Química, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Daniel Peláez
- Laboratoire de Physique des Lasers, Atomes et Molécules (PhLAM), Université de Lille, Villeneuve-d'Ascq, France
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44
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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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45
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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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46
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Jiang B, Guo H. Dynamics in reactions on metal surfaces: A theoretical perspective. J Chem Phys 2019; 150:180901. [PMID: 31091904 DOI: 10.1063/1.5096869] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Recent advances in theoretical characterization of reaction dynamics on metal surfaces are reviewed. It is shown that the widely available density functional theory of metals and their interactions with molecules have enabled first principles theoretical models for treating surface reaction dynamics. The new theoretical tools include methods to construct high-dimensional adiabatic potential energy surfaces, to characterize nonadiabatic processes within the electronic friction models, and to describe dynamics both quantum mechanically and classically. Three prototypical surface reactions, namely, dissociative chemisorption, Eley-Rideal reactions, and recombinative desorption, are surveyed with a focus on some representative examples. While principles governing gas phase reaction dynamics may still be applicable, the presence of the surface introduces a higher level of complexity due to strong interaction between the molecular species and metal substrate. Furthermore, most of these reactive processes are impacted by energy exchange with surface phonons and/or electron-hole pair excitations. These theoretical studies help to interpret and rationalize experimental observations and, in some cases, guide experimental explorations. Knowledge acquired in these fundamental studies is expected to impact many practical problems in a wide range of interfacial processes.
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Affiliation(s)
- Bin Jiang
- Hefei National Laboratory for Physical Science at the Microscale, Department of Chemical Physics, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei 230026, China
| | - Hua Guo
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, USA
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47
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Willatt MJ, Musil F, Ceriotti M. Atom-density representations for machine learning. J Chem Phys 2019; 150:154110. [DOI: 10.1063/1.5090481] [Citation(s) in RCA: 91] [Impact Index Per Article: 18.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Affiliation(s)
- Michael J. Willatt
- Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Félix Musil
- Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
- National Center for Computational Design and Discovery of Novel Materials (MARVEL), Lausanne, Switzerland
| | - Michele Ceriotti
- Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
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48
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Okamoto Y. Data sampling scheme for reproducing energies along reaction coordinates in high-dimensional neural network potentials. J Chem Phys 2019; 150:134103. [PMID: 30954039 DOI: 10.1063/1.5078394] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
We propose a data sampling scheme for high-dimensional neural network potentials that can predict energies along the reaction pathway calculated using the hybrid density functional theory. We observed that a data sampling scheme that combined partial geometry optimization of intermediate structures with random displacement of atoms successfully predicted the energies along the reaction path with respect to five chemical reactions: Claisen rearrangement, Diels-Alder reaction, [1,5]-sigmatropic hydrogen shift, concerted hydrogen transfer in the water hexamer, and Cornforth rearrangement.
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Affiliation(s)
- Yasuharu Okamoto
- Data Platform Center, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, Ibaraki 305-0047, Japan
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49
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Ghosh S, Ray D, Tiwari AK. Effects of alloying on mode-selectivity in H2O dissociation on Cu/Ni bimetallic surfaces. J Chem Phys 2019; 150:114702. [DOI: 10.1063/1.5085696] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Smita Ghosh
- Department of Chemical Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur 741246, India
| | - Dhiman Ray
- Department of Chemistry, University of California Irvine, Irvine, California 92617, USA
| | - Ashwani K. Tiwari
- Department of Chemical Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur 741246, India
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50
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Zhang Y, Maurer RJ, Guo H, Jiang B. Hot-electron effects during reactive scattering of H 2 from Ag(111): the interplay between mode-specific electronic friction and the potential energy landscape. Chem Sci 2019; 10:1089-1097. [PMID: 30774906 PMCID: PMC6346630 DOI: 10.1039/c8sc03955k] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Accepted: 11/07/2018] [Indexed: 01/29/2023] Open
Abstract
The breakdown of the Born-Oppenheimer approximation gives rise to nonadiabatic effects in gas-surface reactions at metal surfaces. However, for a given reaction, it remains unclear which factors quantitatively determine whether these effects measurably contribute to surface reactivity in catalysis and photo/electrochemistry. Here, we systematically investigate hot electron effects during H2 scattering from Ag(111) using electronic friction theory. We combine first-principles calculations of tensorial friction by time-dependent perturbation theory based on density functional theory and an analytical neural network representation, to overcome the limitations of existing approximations and explicitly simulate mode-specific nonadiabatic energy loss during molecular dynamics. Despite sizable hot-electron-induced energy loss, no measurable nonadiabatic effects can be found for H2 scattering on Ag(111). This is in stark contrast to previous reports for vibrationally excited H2 scattering on Cu(111). By detailed analysis of the two systems, we attribute this discrepancy to a subtle interplay between the magnitude of electronic friction along intramolecular vibration and the shape of the potential energy landscape that controls the molecular velocity at impact. On the basis of this characterization, we offer guidance for the search of highly nonadiabatic surface reactions.
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Affiliation(s)
- Yaolong Zhang
- Hefei National Laboratory for Physical Science at the Microscale , Department of Chemical Physics , University of Science and Technology of China , Hefei , Anhui 230026 , China .
| | - Reinhard J Maurer
- Department of Chemistry and Centre for Scientific Computing , University of Warwick , Gibbet Hill Road , Coventry , CV4 7AL , UK .
- Department of Chemistry , Yale University , New Haven , Connecticut 06520 , USA
| | - Hua Guo
- Department of Chemistry and Chemical Biology , University of New Mexico , Albuquerque , New Mexico 87131 , USA
| | - Bin Jiang
- Hefei National Laboratory for Physical Science at the Microscale , Department of Chemical Physics , University of Science and Technology of China , Hefei , Anhui 230026 , China .
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