We present a rare event sampling scheme applicable to coupled electronic excited states. In particular, we extend the forward flux sampling (FFS) method for rare event sampling to a nonadiabatic version (NAFFS) that uses the trajectory surface hopping (TSH) method for nonadiabatic dynamics. NAFFS is applied to two dynamically relevant excited-state models that feature an avoided crossing and a conical intersection with tunable parameters. We investigate how nonadiabatic couplings, temperature, and reaction barriers affect transition rate constants in regimes that cannot be otherwise obtained with plain, traditional TSH. The comparison with reference brute-force TSH simulations for limiting cases of rareness shows that NAFFS can be several orders of magnitude cheaper than conventional TSH and thus represents a conceptually novel tool to extend excited-state dynamics to time scales that are able to capture rare nonadiabatic events.

Machine learning has proven useful in countless different areas over the past years, including theoretical and computational chemistry, where various issues can be addressed by means of machine learning methods. Some of these involve electronic excited-state calculations, such as those performed in nonadiabatic molecular dynamics simulations. Here, we review the current literature highlighting recent developments and advances regarding the application of machine learning to computer simulations of molecular dynamics involving electronically excited states.