A committor-based method to uniformly sample rare reactive events

Abstract

Enhanced sampling methods aim to simulate rare physical and chemical reactive processes involving transitions between long-lived states. Existing methods often disproportionally sample either metastable or transition states. A machine-learning approach combines the strengths of these two cases to characterize entire rare events with the same thoroughness in a single calculation.