Learning mechanisms of rare events from short-trajectory data

Understanding mechanisms of rare events requires estimating statistics such as expected hitting times, rates, and committors. In systems with well-defined metastable states and free energy barriers, these quantities can be estimated using enhanced sampling methods combined with classical rate theories. However, calculating such statistics for more complex processes with rugged landscapes and/or multiple pathways requires more general numerical methods. In this lecture, I will describe my group's recent efforts to develop both linear (Galerkin) and nonlinear (neural-network) methods for estimating transition-path statistics by combining information from many short molecular dynamics trajectories.