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Nonadiabatic Excited-State Dynamics with Machine Learning

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.
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Submitted on : Wednesday, December 26, 2018 - 9:33:57 AM
Last modification on : Friday, April 22, 2022 - 9:22:05 AM
Long-term archiving on: : Wednesday, March 27, 2019 - 12:30:55 PM

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

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