Journal articles
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.
Cited literature [23 references]
https://hal-amu.archives-ouvertes.fr/hal-01965451
Contributor : Mario Barbatti Connect in order to contact the contributor
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
Contributor : Mario Barbatti Connect in order to contact the contributor
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
File
p132_dral_ml_jpcl_2018-preprin...
Files produced by the author(s)
Licence
Distributed under a Creative Commons Attribution 4.0 International License