Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learning

-azobenzene photoisomerization. Our active-learning protocol includes sampling based on physics-informed uncertainty quantification, ensuring the quality of each adiabatic surface, low error in energy gaps, and precise calculation of the hopping probability.