Abstract: Model reduction is one of the most important problems for analysis of complex molecular kinetics. The general aim of model reduction is to find a small number of observations of the system state, usually called reaction coordinate (RC), and project the original kinetics to the space of the observations so that the essential part of the full kinetics can be accurately characterized. In this talk, we introduce a flow based machine learning approach, called RC flow, for discovery of low-dimensional kinetic models of molecular systems. The RC flow utilizes a normalizing flow to design the coordinate transformation and a Brownian dynamics model to approximate the kinetics of RC, where all model parameters can be estimated in a data-driven manner. In contrast to existing model reduction methods for molecular kinetics, RC flow offers a trainable and tractable model of reduced kinetics in continuous time and space due to the invertibility of the normalizing flow. Furthermore, the Brownian dynamics-based reduced kinetic model investigated in this work yields a readily discernible representation of metastable states within the phase space of the molecular system. Numerical experiments demonstrate how effectively the proposed method discovers interpretable and accurate low-dimensional representations of given full-state kinetics from simulations.

Bio: Hao Wu is an associate professor in the Institute of Natural Sciences at Shanghai Jiao Tong University. He earned his Bachelor's and Ph.D. degrees in Computer Science and Technology from Tsinghua University in 2002 and 2007, respectively. Dr. Wu conducted postdoctoral research at the Institute of Mathematics, Free University of Berlin, from 2007 to 2018, and later served as a faculty member in the School of Mathematical Sciences at Tongji University until 2023. During his time in Berlin, he also served as the PI of the research group on “Machine Learning for Time Series” at the Zuse Institute Berlin from 2017 to 2018. His primary research focus lies in the development of machine learning methodologies for modeling and analysis of simulation data of molecular systems.

Contact: Lei Wang 9853