Publications
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
- No. of pages
- 19
- ISSN
- 2632-2153
- DOI
- https://doi.org/10.1088/2632-2153/ab9c3e
- Publication date
- 09-2020
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 103036 Theoretical physics, 104017 Physical chemistry
- Keywords
- ASJC Scopus subject areas
- Software, Artificial Intelligence, Human-Computer Interaction
- Portal url
- https://ucrisportal.univie.ac.at/en/publications/b0297506-d00d-49ce-8bf7-8fb8a9e91740