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 …

The introduction of modern Machine Learning Potentials (MLPs) has led to a paradigm
change in the development of potential energy surfaces for atomistic simulations. By …

Recent advances in machine-learning interatomic potentials have enabled the efficient
modeling of complex atomistic systems with an accuracy that is comparable to that of …

The function of light-harvesting complexes is determined by a complex network of dynamic
interactions among all the different components: the aggregate of pigments, the protein, and …

Numerous linear and non-linear spectroscopic techniques have been developed to
elucidate structural and functional information of complex systems ranging from natural …

Including both environmental and vibronic effects is important for accurate simulation of
optical spectra, but combining these effects remains computationally challenging. We outline …

We propose a machine learning (ML)-based strategy for an inexpensive calculation of
excitonic properties of light-harvesting complexes (LHCs). The strategy uses classical …

We present a machine learning (ML) method to accelerate the nuclear ensemble approach
(NEA) for computing absorption cross sections. ML-NEA is used to calculate cross sections …

Time-resolved spectroscopy is an important tool for unraveling the minute details of
structural changes in molecules of biological and technological significance. The nonlinear …