Published 2018
| Version v1
Journal article
Representation of coupled adiabatic potential energy surfaces using neural network based quasi-diabatic Hamiltonians: 1,2 2 A' states of LiFH
Creators
- 1. Johns Hopkins University, 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. University of New Mexico, Albuquerque, NM (United States). Dept. of Chemistry and Chemical Biology
Description
A general algorithm for determining diabatic representations from adiabatic energies, energy gradients and derivative couplings using neural networks is introduced.
Availability note (English)
Available from https://www.osti.gov/servlets/purl/1594971; https://www.osti.gov/biblio/1594971; DOE Accepted Manuscript full text, or the publishers Best Available Version will be available free of charge after the embargo periodAdditional details
Identifiers
Publishing Information
- Journal Title
- Physical Chemistry Chemical Physics. PCCP (Print)
- Journal Volume
- 21
- Journal Issue
- 26
- Journal Page Range
- p. 14205-14213
- ISSN
- 1463-9076
INIS
- Country of Publication
- United States
- Country of Input or Organization
- United States
- INIS RN
- 54043855
- Subject category
- S71: CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; S97: MATHEMATICAL METHODS AND COMPUTING;
- Descriptors DEI
- ALGORITHMS; COUPLINGS; HAMILTONIANS; NEURAL NETWORKS; POTENTIAL ENERGY; SURFACES
- Descriptors DEC
- ENERGY; MATHEMATICAL LOGIC; MATHEMATICAL OPERATORS; QUANTUM OPERATORS
Optional Information
- Contract/Grant/Project number
- SC0015997
- Funding organization
- USDOE Office of Science - SC, Basic Energy Sciences (BES) (United States)
- Secondary number(s)
- OSTIID--1594971