Author(s): Carolin Müller
Publication: Bunsen-Magazin 2023, 6, 191-193
Publisher: Deutsche Bunsen-Gesellschaft für physikalische Chemie e.V., Frankfurt
Language: English
DOI: 10.26125/0ehr-vk47
Abstract: When molecules absorb light, they enter non-equilibrium states, triggering a cascade of nonadiabatic processes. Theoretical modeling of such photoinduced dynamics is pivotal for advancing research and innovation. Nevertheless, these simulations are constrained due to the resource-intensive aspects of quantum chemical methods. Machine learning (ML) offers a solution to this challenge. This article outlines how ML can accelerate and facilitate excited-state simulations.
Cite this: C. Müller, Bunsen-Magazin 2023, 6, 191-193, DOI: 10.26125/0ehr-vk47
Get the full article (pdf)