Machine learning and excited-state molecular dynamics

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. Keywords: machine learning, photodynamics, photochemistry, excited states, quantum chemistry, spin-orbit couplings, nonadiabatic couplings.