Nonadiabatic Excited-State Dynamics with Machine Learning - Aix-Marseille Université Access content directly
Journal Articles Journal of Physical Chemistry Letters Year : 2018

Nonadiabatic Excited-State Dynamics with Machine Learning

Pavlo O Dral
  • Function : Author
Mario Barbatti

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 one-dimensional 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.
Fichier principal
Vignette du fichier
p132_dral_ml_jpcl_2018-preprint.pdf (529.05 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01965451 , version 1 (26-12-2018)

Licence

Attribution

Identifiers

  • HAL Id : hal-01965451 , version 1

Cite

Pavlo O Dral, Mario Barbatti, Walter Thiel. Nonadiabatic Excited-State Dynamics with Machine Learning. Journal of Physical Chemistry Letters, 2018, 9 (19), pp.5660-5663. ⟨hal-01965451⟩
43 View
206 Download

Share

Gmail Facebook Twitter LinkedIn More