  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.

Chemical Physics;Machine Learning