DMS Applied and Computational Mathematics Seminar

Speaker: Bao Wang, University of Utah

Title: Equivariant Generative Models for Molecular Modeling

Abstract: Molecular modeling tasks exhibit different symmetries, e.g. roto-translation equivariance and periodicity. A grand challenge in machine learning-assisted molecular modeling – e.g. molecule generation – is to account for different inherent symmetries. In this talk, I will discuss a few issues on building and training stable and expressive equivariant generative models, including normalizing flows and diffusion models, for molecule generations. Furthermore, I will discuss the role of steerable features of different types for equivariant machine learning.