Representation of coupled adiabatic potential energy surfaces using neural network based quasi-diabatic Hamiltonians: 1,2 2A′ states of LiFH-Reference-Cited by-同舟云学术

Representation of coupled adiabatic potential energy surfaces using neural network based quasi-diabatic Hamiltonians: 1,2 2A′ states of LiFH

Published:2019 Issue:26 Volume:21 Page:14205-14213
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Guan Yafu¹²³⁴^ORCID,Zhang Dong H.⁵⁶⁷⁸⁹,Guo Hua¹⁰¹¹¹²⁴^ORCID,Yarkony David R.¹²³⁴^ORCID

Affiliation:

1. Department of Chemistry

2. Johns Hopkins University

3. Baltimore

4. USA

5. State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical Computational Chemistry

6. Dalian Institute of Chemical Physics

7. Chinese Academy of Sciences

8. Dalian 116023

9. People's Republic of China

10. Department of Chemistry and Chemical Biology

11. University of New Mexico

12. Albuquerque

Abstract

A general algorithm for determining diabatic representations from adiabatic energies, energy gradients and derivative couplings using neural networks is introduced.

Funder

U.S. Department of Energy

National Natural Science Foundation of China

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://pubs.rsc.org/en/content/articlepdf/2019/CP/C8CP06598E

Reference58 articles.

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2. Recent Advances in Quantum Dynamics of Bimolecular Reactions

3. F. Gatti , Molecular Quantum Dynamics: from Theory to Applications , Springer , Berlin , 2014

4. Accurate nonadiabatic dynamics

5. Ab Initio Nonadiabatic Quantum Molecular Dynamics

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