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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]
+ Show Author Affiliations
  1. Johns Hopkins Univ., Baltimore, MD (United States). Dept. of Chemistry
  2. Chinese Academy of Sciences, Dalian (China). Dalian Institute of Chemical Physics, State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical Computational Chemistry
  3. Univ. of New Mexico, Albuquerque, NM (United States). Dept. of Chemistry and Chemical Biology
Publication Date:
Research Org.:
Johns Hopkins Univ., Baltimore, MD (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
OSTI Identifier:
1594971
Alternate Identifier(s):
OSTI ID: 1484918
Grant/Contract Number:  
SC0015997
Resource Type:
Accepted Manuscript
Journal Name:
Physical Chemistry Chemical Physics. PCCP
Additional Journal Information:
Journal Volume: 21; Journal Issue: 26; Journal ID: ISSN 1463-9076
Publisher:
Royal Society of Chemistry
Country of Publication:
United States
Language:
English
Subject:
74 ATOMIC AND MOLECULAR PHYSICS

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 States: N. p., 2018. Web. doi:10.1039/C8CP06598E.
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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 States. https://doi.org/10.1039/C8CP06598E
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Guan, Yafu, Zhang, Dong H., Guo, Hua, and Yarkony, David R. Sat . "Representation of coupled adiabatic potential energy surfaces using neural network based quasi-diabatic Hamiltonians: 1,2 2 A' states of LiFH". United States. https://doi.org/10.1039/C8CP06598E. https://www.osti.gov/servlets/purl/1594971.
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@article{osti_1594971,
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. PCCP},
number = 26,
volume = 21,
place = {United States},
year = {Sat Nov 17 00:00:00 EST 2018},
month = {Sat Nov 17 00:00:00 EST 2018}
}
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Journal Article:
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Publisher's Version of Record
https://doi.org/10.1039/C8CP06598E
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    • Westermayr, Julia; Gastegger, Michael; Menger, Maximilian F. S. J.
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    Neural network based quasi-diabatic Hamiltonians with symmetry adaptation and a correct description of conical intersections
    journal, June 2019

    • Guan, Yafu; Guo, Hua; Yarkony, David R.
    • The Journal of Chemical Physics, Vol. 150, Issue 21
    • DOI: 10.1063/1.5099106

    Neural-network potential energy surface with small database and high precision: A benchmark of the H + H 2 system
    journal, September 2019

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    • The Journal of Chemical Physics, Vol. 151, Issue 11
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    Machine learning enables long time scale molecular photodynamics simulations
    text, January 2018

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