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PICS Colloquium: Complex Polymer Design in the Age of AI: Why, What, and How?
October 17, 2025 at 2:00 PM - 3:00 PM
Polymers are essential to a wide range of technologies, yet designing them with targeted structural and functional properties remains a grand challenge. A major opportunity lies in applying machine learning to help navigate the vast combinatorial design space—spanning sequence, composition, architecture, morphology, processing, and more—to discover new formulations or replace existing ones with more sustainable alternatives. However, this complexity, combined with data scarcity and characterization challenges, limits the effectiveness of purely rational design and/or high-throughput screening. In this talk, I will describe some of our recent efforts to integrate molecular simulation, machine learning, and theory to map and navigate structure–function relationships in chemically and topologically diverse polymeric materials. I will describe strategies that we have employed across a range of applications to overcome data limitations in polymer science by developing physics-informed (or guided) models and exploring other algorithmic innovations. A focal example will examine how we can design complex polymer additives that tune material rheology, with a particular focus on shear-thinning fluids. This example, along with other case studies, will showcase utility, limitations, and opportunities for data-driven approaches in modern-day science; and how coupling them with (or using them to develop) physical insight can accelerate innovation and deepen materials understanding.
Michael A. Webb
Assistant Professor of Chemical and Biological Engineering at Princeton University
Michael Webb is an Assistant Professor in the Chemical and Biological Engineering department at Princeton University and affiliated faculty with Andlinger Center for Energy and the Environment, Center for Statistics and Machine Learning, Princeton Institute for Computational Science and Engineering, and the Princeton Materials Institute. Prior to joining Princeton, he obtained his B.S. from UC Berkeley in 2011 and his Ph.D from Caltech in 2016, both in Chemical Engineering. He performed postdoctoral study at the University of Chicago and Argonne National Laboratory between 2016-2019. His current research emphasizes theory and computational approaches, including molecular simulation and machine learning, for understanding and designing materials, primarily polymer-based, for diverse applications. Specific interests relate to characterizing interfacial phenomena and physics in heterogeneous environments, simulating and controlling the behavior of stimuli-responsive systems, and formulating data-efficient strategies for machine learning of polymer properties. He is a recipient of the NSF CAREER award, a Howard B. Wentz Junior Faculty, a Doctoral New Investigator award from the ACS Petroleum Research Foundation, and an ACS OpenEye Outstanding Junior Faculty Award. He has also received multiple commendations for outstanding teaching and been featured as an `Emerging Investigator’ in the journal Molecular Systems, Design, & Engineering and as a `Rising Star’ in ACS Polymers Au.