Machine learning exciton dynamics

By Florian Hase, Stéphanie Valleau, Edward O. Pyzer-Knapp, Al´an Aspuru-Guzik. 2015

86 citations recorded by the source corpus.

Machine learning ground state QM/MM for accelerated computation of exciton dynamics.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics, Materials Chemistry

Citations in the corpus

• Machine-learned potentials for next-generation matter simulations (2021, unknown): “Initial efforts in combining classical force fields and machine learning methods 144 and ML/MM simulations 145 , as well as approaches to use machine learning to parameterize coarse-grained representations 146 were shown in literature.”