Illuminating Photodynamics with Machine Learning Techniques - Müller, Carolin (2023)
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 simulation