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 …

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 …

Nonadiabatic dynamics simulations in the long timescale (much longer than 10 ps) are the
next challenge in computational photochemistry. This paper delimits the scope of what we …

Machine learning-based approaches for surface hop** (SH) offer the prospect of SH
simulations with ab initio accuracy, but with a computational cost more similar to classical …

Machine learning has proven useful in countless different areas over the past years,
including theoretical and computational chemistry, where various issues can be addressed …

Machine learning has proven useful in countless different areas over the past years,
including theoretical and computational chemistry, where various issues can be addressed …

Computer simulations are an invaluable tool elucidate processes occurring on the atomistic
scale. The more precise the description of a system, however, the more computationally …

This paper aims at introducing the formal foundations of the application of reduced density-
matrix theory and Green's function theory to the analysis of molecular electronic transitions …