Machine Learning for Nonadiabatic Molecular Dynamics: Best Practices and Recent Progress
Understanding molecular excited states is vital for progress in organic chemistry, chemical biology, and materials science. This knowledge is particularly important for designing functional molecules inspired by nature. The photophysical properties of chromophores dictate their behavior and applications. Exploring these excited states enables the creation of novel materials and bio-inspired systems.