Recent advances in machine learning for electronic excited state molecular dynamics simulations Check Access
-
Published:19 Dec 2022
-
Special Collection: 2022 ebook collection
B. Bachmair, M. M. Reiner, M. X. Tiefenbacher, and P. Marquetand, in Chemical Modelling
Download citation file:
Machine learning has proven useful in countless different areas over the past years, including theoretical and computational chemistry, where various issues can be addressed by means of machine learning methods. Some of these involve electronic excited-state calculations, such as those performed in nonadiabatic molecular dynamics simulations. Here, we review the current literature highlighting recent developments and advances regarding the application of machine learning to computer simulations of molecular dynamics involving electronically excited states.
