Machine learning for electronically excited states of molecules

Julia Westermayr, Philipp Marquetand

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 not only how machine learning is employed 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 and approaches for nonadiabatic dynamics simulations and describe tricks and problems when using them in machine learning for excited states of molecules.

Department of Theoretical Chemistry, Research Platform Accelerating Photoreaction Discovery, Research Network Data Science
Chemical Reviews
Publication date
Peer reviewed
Austrian Fields of Science 2012
104016 Photochemistry, 106032 Photobiology, 104022 Theoretical chemistry
ASJC Scopus subject areas
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