Machine learning and excited-state molecular dynamics

Author(s)
Julia Westermayr, Philipp Marquetand
Abstract

Machine learning is employed at an increasing rate in the research field of quantum chemistry. While the majority of approaches target the investigation of chemical systems in their electronic ground state, the inclusion of light into the processes leads to electronically excited states and gives rise to several new challenges. Here, we survey recent advances for excited-state dynamics based on machine learning. In doing so, we highlight successes, pitfalls, challenges and future avenues for machine learning approaches for light-induced molecular processes.

Organisation(s)
Department of Theoretical Chemistry, Research Platform Accelerating Photoreaction Discovery, Research Network Data Science
Journal
Machine Learning: Science and Technology
Volume
1
ISSN
2632-2153
DOI
https://doi.org/10.1088/2632-2153/ab9c3e
Publication date
09-2020
Peer reviewed
Yes
Austrian Fields of Science 2012
104017 Physical chemistry, 103036 Theoretical physics
Keywords
Portal url
https://ucris.univie.ac.at/portal/en/publications/machine-learning-and-excitedstate-molecular-dynamics(b0297506-d00d-49ce-8bf7-8fb8a9e91740).html