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

Author:

Guan Yafu1234ORCID,Zhang Dong H.56789,Guo Hua1011124ORCID,Yarkony David R.1234ORCID

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

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