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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 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.

SUBMITTER: Dral PO

PROVIDER: S-EPMC6174422 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Nonadiabatic Excited-State Dynamics with Machine Learning.

Dral Pavlo O PO Barbatti Mario M Thiel Walter W

The journal of physical chemistry letters 20180913 19

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 inv ...[more]

PMID: 30200766

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