Machine learning and excited-state molecular dynamics
This addresses the challenge of modeling electronically excited states in quantum chemistry, which is crucial for understanding light-induced molecular processes, but it is incremental as it is a survey paper.
The paper surveys recent advances in applying machine learning to excited-state molecular dynamics, highlighting successes, pitfalls, challenges, and future avenues for light-induced processes in quantum chemistry.
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.