Committor Function

The committor function quantifies the probability of a dynamical system transitioning between two defined states, providing crucial insights into rare events and metastable behavior. Current research focuses on developing efficient and accurate algorithms for computing high-dimensional committor functions, employing methods such as kernel-based machines and deep learning models with adaptive sampling strategies to improve both speed and accuracy. These advancements are significantly impacting the study of complex systems in various fields, enabling more precise analysis of transitions between metastable states in areas like molecular dynamics and materials science. Improved accuracy and efficiency in committor function calculation facilitates a deeper understanding of rare events in these systems.