3 papers
stat.ML2025
Estimating Committor Functions via Deep Adaptive Sampling on Rare Transition Paths
Yueyang Wang, Kejun Tang, Xili Wang +3
The committor functions are central to investigating rare but important events in molecular simulations. It is known that computing the committor function suffers from the curse of…
physics.comp-ph2024★ 1 cited
Deep Learning Method for Computing Committor Functions with Adaptive Sampling
Bo Lin, Weiqing Ren
The committor function is a central object for quantifying the transitions between metastable states of dynamical systems. Recently, a number of computational methods based on deep…
physics.comp-ph2024
Computing Transition Pathways for the Study of Rare Events Using Deep Reinforcement Learning
Bo Lin, Yangzheng Zhong, Weiqing Ren
Understanding the transition events between metastable states in complex systems is an important subject in the fields of computational physics, chemistry and biology. The transiti…