Data-driven Strategies to Navigate Sequence, Composition, and Architectural Complexity in Polymer Design

Image
Portrait of Michael Webb.
Event Speaker
Michael Webb
Assistant Professor in Chemical and Biological Engineering Department at Princeton University
Event Type
CBEE Seminar
Date
Event Location
Kelley 1001
Event Description

Understanding and designing polymers with target structural and/or functional properties are grand challenges in materials science. The field of polymer physics provides invaluable scaffolding to elucidate general phenomena of polymer-based materials, but contributions fall short of proffering chemically specific insights or usefully guiding design. Meanwhile, artificial intelligence and machine learning (ML) have greatly enhanced design efforts for many materials classes, including polymers, but success has been so far limited to simpler systems, like linear homopolymers, or restricted in chemical exploration.

In this talk, I will describe our recent efforts to combine simulation, machine learning, and concepts from polymer physics to navigate complex polymer design spaces and accurately construct structure-function relationships for chemically and topologically diverse polymers. Examples will include the design of tunable biomolecular condensates as well as copolymers that modulate enzymatic activity. Furthermore, in considering the rheology of architecturally diverse polymers, I will explain how essential concepts from polymer physics can be “taught” to natively naive algorithms to enhance data efficiency and ML model performance. These results establish a pathway to formulate chemically specific predictions as perturbations from baseline theories to enhance future polymer design. These vignettes will highlight both methodological advancements as well as intriguing application areas.

Speaker Biography

Michael Webb is an Assistant Professor in the Chemical and Biological Engineering department at Princeton University. He is also 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 then performed postdoctoral study at the University of Chicago and Argonne National Laboratory between 2016-2019. His current research emphasizes computational approaches and theory 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 award at Princeton University, a Doctoral New Investigator award from the ACS Petroleum Research Foundation. He has also been featured as an `Emerging Investigator’ in the journal Molecular Systems, Design, & Engineering and as a `Rising Star’ in ACS Polymers Au.