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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];  [2];  [1]
  1. Johns Hopkins Univ., Baltimore, MD (United States)
  2. Univ. of New Mexico, Albuquerque, NM (United States)
+ Show Author Affiliations
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:
Johns Hopkins Univ., Baltimore, MD (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Univ. of New Mexico, Albuquerque, NM (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities Division; USDOE Office of Science Education and Technical Information (ET)
Grant/Contract Number:
SC0015997
OSTI ID:
1577596
Alternate ID(s):
OSTI ID: 1524125
OSTI ID: 1595109
Journal Information:
Journal of Chemical Physics, Journal Name: Journal of Chemical Physics Journal Issue: 21 Vol. 150; ISSN 0021-9606
Publisher:
American Institute of Physics (AIP)Copyright Statement
Country of Publication:
United States
Language:
English

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

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Related Subjects

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
74 ATOMIC AND MOLECULAR PHYSICS
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