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 …

Theoretical simulations of electronic excitations and associated processes in molecules are
indispensable for fundamental research and technological innovations. However, such …

Quantum-chemistry simulations based on potential energy surfaces of molecules provide
invaluable insight into the physicochemical processes at the atomistic level and yield such …

Machine learning (ML) methods are being used in almost every conceivable area of
electronic structure theory and molecular simulation. In particular, ML has become firmly …

Chemical compound space (CCS), the set of all theoretically conceivable combinations of
chemical elements and (meta-) stable geometries that make up matter, is colossal. The first …

SchNetPack is a versatile neural network toolbox that addresses both the requirements of
method development and the application of atomistic machine learning. Version 2.0 comes …

Computational study of molecules and materials from first principles is a cornerstone of
physics, chemistry, and materials science, but limited by the cost of accurate and precise …

Time-resolved photoelectron spectroscopy (TRPES) has become one of the most
widespread techniques for probing nonadiabatic dynamics in the excited electronic states of …

This roadmap article highlights recent advances, challenges and future prospects in studies
of the dynamics of molecules and clusters in the gas phase. It comprises nineteen …

Machine learning (ML) has shown to advance the research field of quantum chemistry in
almost any possible direction and has also recently been applied to investigate the …