Automatic discovery of photoisomerization mechanisms with nanosecond machine learning photodynamics simulations.

Photochemical reactions are widely used by academic and industrial researchers to construct complex molecular architectures mechanisms that often require harsh reaction conditions. Photodynamics simulations provide time-resolved snapshots of molecular excited-state structures required to understand and predict reactivities and chemoselectivities. Molecular excited-states are often nearly degenerate and require computationally intensive multiconfigurational quantum mechanical methods, especially at conical intersections.