Machine learning for electronically excited states of molecules
It addresses the computational bottleneck in modeling excited states for researchers in chemistry and materials science, but as a review, it is incremental in summarizing existing applications and challenges.
This review examines how machine learning can accelerate and enhance simulations of electronically excited states of molecules, which are crucial in photochemistry and material science but computationally expensive with traditional quantum chemical methods.
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.