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

Journal Article · · Journal of Chemical Physics
DOI: https://doi.org/10.1063/1.5099106 · OSTI ID:1577596
 [1]; ORCiD logo [2]; ORCiD logo [1]
  1. Johns Hopkins Univ., Baltimore, MD (United States)
  2. Univ. of New Mexico, Albuquerque, NM (United States)
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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.

Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Univ. of New Mexico, Albuquerque, NM (United States); Johns Hopkins Univ., Baltimore, MD (United States)
Sponsoring Organization:
USDOE Office of Science Education and Technical Information (ET); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities Division
Grant/Contract Number:
SC0015997
OSTI ID:
1577596
Alternate ID(s):
OSTI ID: 1524125; OSTI ID: 1595109
Journal Information:
Journal of Chemical Physics, Vol. 150, Issue 21; ISSN 0021-9606
Publisher:
American Institute of Physics (AIP)Copyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 37 works
Citation information provided by
Web of Science

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Cited By (3)

Statistical distributions of the tuning and coupling collective modes at a conical intersection using the hierarchical equations of motion journal December 2019
Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamics journal April 2020
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