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  Nonadiabatic Excited-State Dynamics with Machine Learning

Dral, P. O., Barbatti, M., & Thiel, W. (2018). Nonadiabatic Excited-State Dynamics with Machine Learning. The Journal of Physical Chemistry Letters, 9(19), 5660-5663. doi:10.1021/acs.jpclett.8b02469.

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jz8b02469_si_001.pdf (Supplementary material), 3MB
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 Creators:
Dral, Pavlo O.1, Author              
Barbatti, Mario2, Author
Thiel, Walter1, Author              
Affiliations:
1Research Department Thiel, Max-Planck-Institut für Kohlenforschung, Max Planck Society, ou_1445590              
2Aix Marseille Univ, CNRS, ICR, Marseille, France, ou_persistent22              

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 Abstract: We show that machine learning (ML) can be used to accurately reproduce nonadiabatic excited-state dynamics with decoherence-corrected fewest switches surface hopping in a 1-D model system. We propose to use ML to significantly reduce the simulation time of realistic, high-dimensional systems with good reproduction of observables obtained from reference simulations. Our approach is based on creating approximate ML potentials for each adiabatic state using a small number of training points. We investigate the feasibility of this approach by using adiabatic spin-boson Hamiltonian models of various dimensions as reference methods.

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Language(s): eng - English
 Dates: 2018-08-112018-09-102018-09-102018-10-04
 Publication Status: Published in print
 Pages: 4
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1021/acs.jpclett.8b02469
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Title: The Journal of Physical Chemistry Letters
  Abbreviation : J. Phys. Chem. Lett.
Source Genre: Journal
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Publ. Info: Washington, DC : American Chemical Society
Pages: - Volume / Issue: 9 (19) Sequence Number: - Start / End Page: 5660 - 5663 Identifier: CoNE: https://pure.mpg.de/cone/journals/resource/1948-7185