Publications
Recent advances in machine learning for electronic excited state molecular dynamics simulations
- Author(s)
- Brigitta Bachmair, Madlen Maria Reiner, Maximilian Xaver Tiefenbacher, Philipp Marquetand
- Abstract
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.
- Organisation(s)
- Research Platform Accelerating Photoreaction Discovery, Department of Pharmaceutical Sciences, Department of Theoretical Chemistry
- Volume
- 17
- Pages
- 178-200
- DOI
- https://doi.org/10.1039/9781839169342
- Publication date
- 12-2022
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 104022 Theoretical chemistry
- Portal url
- https://ucris.univie.ac.at/portal/en/publications/recent-advances-in-machine-learning-for-electronic-excited-state-molecular-dynamics-simulations(b52c3423-9959-4917-9c62-2e954fef5a5e).html