Machine learning accelerated photodynamics simulations

Machine learning (ML) continues to revolutionize computational chemistry by accelerating predictions and simulations by training on experimental or accurate but expensive quantum chemical (QC) calculations. Photodynamics simulations require hundreds of trajectories coupled with multiconfigurational QC calculations of energies, forces, and non-adiabatic couplings that contribute to the prohibitive computational cost at long timescales and complex organic molecules. ML accelerates photodynamics simulations by combining nonadiabatic photodynamics simulations with an ML model trained with high-fidelity QC calculations of energies, forces, and non-adiabatic couplings. This approach has provided time-dependent molecular structural information for understanding photochemical reaction mechanisms of organic reactions in vacuum and complex environments (i.e., explicit solvation). This review focuses on the fundamentals of QC calculations and machine learning techniques. We then discuss the strategies to balance adequate training data and the computational cost of generating these training data. Finally, we demonstrate the power of applying these ML-photodynamics simulations to understand the origin of reactivities and selectivities of organic photochemical reactions, such as cis-trans isomerization, [2+2]-cycloaddition, 4π-electrostatic ring-closing, and hydrogen roaming mechanism.