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Title: Neural network based quasi-diabatic Hamiltonians with symmetry adaptation and a correct description of conical intersections

Abstract

Previously, we demonstrated in a report that artificial neural networks (NNs) can be used to generate quasidiabatic Hamiltonians (H d) that are capable of representing adiabatic energies, energy gradients, and derivative couplings. In this work, two additional issues are addressed. First, symmetry-adapted functions such as permutation invariant polynomials are introduced to account for complete nuclear permutation inversion symmetry. Second, a partially diagonalized representation is introduced to facilitate a better description of near degeneracy points. The diabatization of 1, 2 1A states of NH 3 is used as an example. The NN fitting findings are compared to that of a previous fitting with symmetry adapted polynomials.

Authors:
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); Univ. of New Mexico, Albuquerque, NM (United States); Johns Hopkins Univ., Baltimore, MD (United States)
Sponsoring Org.:
USDOE Office of Science Education and Technical Information (ET); USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1577596
Alternate Identifier(s):
OSTI ID: 1524125; OSTI ID: 1595109
Grant/Contract Number:  
SC0015997
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Chemical Physics
Additional Journal Information:
Journal Volume: 150; Journal Issue: 21; Journal ID: ISSN 0021-9606
Publisher:
American Institute of Physics (AIP)
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY

Citation Formats

None, None. Neural network based quasi-diabatic Hamiltonians with symmetry adaptation and a correct description of conical intersections. United States: N. p., 2019. Web. doi:10.1063/1.5099106.
None, None. Neural network based quasi-diabatic Hamiltonians with symmetry adaptation and a correct description of conical intersections. United States. doi:10.1063/1.5099106.
None, None. Mon . "Neural network based quasi-diabatic Hamiltonians with symmetry adaptation and a correct description of conical intersections". United States. doi:10.1063/1.5099106.
@article{osti_1577596,
title = {Neural network based quasi-diabatic Hamiltonians with symmetry adaptation and a correct description of conical intersections},
author = {None, None},
abstractNote = {Previously, we demonstrated in a report that artificial neural networks (NNs) can be used to generate quasidiabatic Hamiltonians (Hd) that are capable of representing adiabatic energies, energy gradients, and derivative couplings. In this work, two additional issues are addressed. First, symmetry-adapted functions such as permutation invariant polynomials are introduced to account for complete nuclear permutation inversion symmetry. Second, a partially diagonalized representation is introduced to facilitate a better description of near degeneracy points. The diabatization of 1, 21A states of NH3 is used as an example. The NN fitting findings are compared to that of a previous fitting with symmetry adapted polynomials.},
doi = {10.1063/1.5099106},
journal = {Journal of Chemical Physics},
number = 21,
volume = 150,
place = {United States},
year = {2019},
month = {6}
}

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