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Journal Articles Journal of Physical Chemistry Letters Year : 2018

Nonadiabatic Excited-State Dynamics with Machine Learning

Pavlo O Dral
  • Function : Author
Mario Barbatti


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.
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Dates and versions

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




  • HAL Id : hal-01965451 , version 1


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⟩
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