Speaker
Abstract
Rare transition events in metastable systems under noisy fluctuations are crucial to processes ranging from molecular conformational changes to nucleation and phase transformations. This talk presents variational approaches for accelerating the identification of the most probable transition pathways in high-dimensional energy landscapes. StringNET represents a continuous pathway using a neural network and employs a temperature-dependent max-flux objective to pretrain the path and accelerate subsequent geometric-action minimization, providing an effective initialization strategy. To further address the problem of finding minimum-energy paths in the low-temperature limit, a Hessian-free iterative variational method is developed within a Riemannian framework that unifies minimum-energy and minimum-free-energy paths. The consistency and convergence of the method toward generalized minimum-energy paths are established. Numerical examples involving alanine dipeptide, chignolin folding, and Barnase–Barstar association demonstrate the accuracy and efficiency of the proposed approach.
Bio
Zhiyou WU is a fourth-year PhD student in the Department of Data Science at City University of Hong Kong, supervised by Professor Xiang Zhou. His research focuses on developing scientific machine learning methods for finding transition pathways in metastable systems.