Skip to Main content Skip to Navigation
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.
Document type :
Journal articles
Complete list of metadatas

Cited literature [23 references]  Display  Hide  Download

https://hal-amu.archives-ouvertes.fr/hal-01965451
Contributor : Mario Barbatti <>
Submitted on : Wednesday, December 26, 2018 - 9:33:57 AM
Last modification on : Wednesday, November 6, 2019 - 9:18:30 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

Identifiers

  • HAL Id : hal-01965451, version 1

Collections

Citation

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⟩

Share

Metrics

Record views

97

Files downloads

266