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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

Journal Article · · Physical Chemistry Chemical Physics. PCCP
DOI: https://doi.org/10.1039/C8CP06598E · OSTI ID:1594971
ORCiD logo [1];  [2]; ORCiD logo [3]; ORCiD logo [4]
  1. Johns Hopkins Univ., Baltimore, MD (United States). Dept. of Chemistry; Johns Hopkins University
  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
  4. Johns Hopkins Univ., Baltimore, MD (United States). Dept. of Chemistry
+ Show Author Affiliations

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

Research Organization:
Johns Hopkins Univ., Baltimore, MD (United States)
Sponsoring Organization:
USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
Grant/Contract Number:
SC0015997
OSTI ID:
1594971
Journal Information:
Physical Chemistry Chemical Physics. PCCP, Journal Name: Physical Chemistry Chemical Physics. PCCP Journal Issue: 26 Vol. 21; ISSN 1463-9076; ISSN PPCPFQ
Publisher:
Royal Society of ChemistryCopyright Statement
Country of Publication:
United States
Language:
English

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Cited By (5)

Two-state diabatic potential energy surfaces of ClH 2 based on nonadiabatic couplings with neural networks journal January 2019
Machine learning enables long time scale molecular photodynamics simulations journal January 2019
Neural network based quasi-diabatic Hamiltonians with symmetry adaptation and a correct description of conical intersections journal June 2019
Neural-network potential energy surface with small database and high precision: A benchmark of the H + H 2 system journal September 2019
Machine learning enables long time scale molecular photodynamics simulations text January 2018

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