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

Abstract

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

Authors:
ORCiD logo [1];  [2]; ORCiD logo [3]; ORCiD logo [1]
  1. Department of Chemistry, Johns Hopkins University, Baltimore, USA
  2. 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
  3. Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, USA
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1484918
Grant/Contract Number:  
SC0015997
Resource Type:
Publisher's Accepted Manuscript
Journal Name:
Physical Chemistry Chemical Physics
Additional Journal Information:
Journal Name: Physical Chemistry Chemical Physics Journal Volume: 21 Journal Issue: 26; Journal ID: ISSN 1463-9076
Publisher:
Royal Society of Chemistry (RSC)
Country of Publication:
United Kingdom
Language:
English

Citation Formats

Guan, Yafu, Zhang, Dong H., Guo, Hua, and Yarkony, David R. Representation of coupled adiabatic potential energy surfaces using neural network based quasi-diabatic Hamiltonians: 1,2 2 A′ states of LiFH. United Kingdom: N. p., 2019. Web. doi:10.1039/C8CP06598E.
Guan, Yafu, Zhang, Dong H., Guo, Hua, & Yarkony, David R. Representation of coupled adiabatic potential energy surfaces using neural network based quasi-diabatic Hamiltonians: 1,2 2 A′ states of LiFH. United Kingdom. doi:10.1039/C8CP06598E.
Guan, Yafu, Zhang, Dong H., Guo, Hua, and Yarkony, David R. Wed . "Representation of coupled adiabatic potential energy surfaces using neural network based quasi-diabatic Hamiltonians: 1,2 2 A′ states of LiFH". United Kingdom. doi:10.1039/C8CP06598E.
@article{osti_1484918,
title = {Representation of coupled adiabatic potential energy surfaces using neural network based quasi-diabatic Hamiltonians: 1,2 2 A′ states of LiFH},
author = {Guan, Yafu and Zhang, Dong H. and Guo, Hua and Yarkony, David R.},
abstractNote = {A general algorithm for determining diabatic representations from adiabatic energies, energy gradients and derivative couplings using neural networks is introduced.},
doi = {10.1039/C8CP06598E},
journal = {Physical Chemistry Chemical Physics},
number = 26,
volume = 21,
place = {United Kingdom},
year = {2019},
month = {7}
}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on November 17, 2019
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