  Electronically excited states of molecules are at the heart of
photochemistry, photophysics, as well as photobiology and also play a role in
material science. Their theoretical description requires highly accurate
quantum chemical calculations, which are computationally expensive. In this
review, we focus on how machine learning is employed not only to speed up such
excited-state simulations but also how this branch of artificial intelligence
can be used to advance this exciting research field in all its aspects.
Discussed applications of machine learning for excited states include
excited-state dynamics simulations, static calculations of absorption spectra,
as well as many others. In order to put these studies into context, we discuss
the promises and pitfalls of the involved machine learning techniques. Since
the latter are mostly based on quantum chemistry calculations, we also provide
a short introduction into excited-state electronic structure methods,
approaches for nonadiabatic dynamics simulations and describe tricks and
problems when using them in machine learning for excited states of molecules.
