Machine learning for electronically excited states of molecules

Abstract

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.

Machine Learning of Free Energies in Chemical Compound Space Using Ensemble Representations: Reaching Experimental Uncertainty for Solvation

“…The recent success of machine learning (ML) in the domain of theoretical and computational chemistry due to unprecedented availability of calculated single-point geometry quantum data, has been manifested for challenging molecular problems, such as accurate prediction of molecular electronic properties such as atomization energies 19,20 , application to elpasolites 21 , carbenes 22 , excited states 23 or fragment based learning with AMONS 24 .…”