Michael A. Webb

Michael A. Webb is an Assistant Professor in the Department of Chemical and Biological Engineering at Princeton University, where he leads the Webb Research Group focused on computational materials design. His research integrates theory, simulation, and machine learning to develop soft materials for sustainability and health applications, including batteries, water treatment, and drug delivery systems.

Education

• Ph.D., Chemical Engineering, California Institute of Technology, 2016
• M.S., Chemical Engineering, California Institute of Technology, 2015
• B.S., Chemical and Biomolecular Engineering, University of California – Berkeley, 2011

Research Focus

The Webb Group develops predictive computational frameworks to study charge transport in polymers, stimuli-responsive biopolymers, and interfacial phenomena. Key methodologies include coarse-grained molecular dynamics, machine learning, and multiscale modeling, with applications spanning energy storage, environmental remediation, and biomedical engineering. Current efforts emphasize data-driven design of architecturally complex polymers and biomimetic systems.

Recent publication trends highlight three core themes: (1) Physics-guided machine learning for polymer property prediction, (2) Phase behavior of biomolecular condensates and disordered proteins, and (3) Sustainable material design through computational optimization. Methodological innovations in coarse-graining and neural networks underpin much of this work.

Awards and Honors

• ACS COMP OpenEye Cadence Molecular Sciences Outstanding Junior Faculty Award (2025)
• NSF CAREER Award (2023)
• Howard B. Wentz, Jr. SEAS Junior Faculty Award (2022)
• Herbert Newby McCoy Award, Caltech (2016)
• Chemical Computing Group Excellence Award (2016)
• Resnick Sustainability Institute Fellowship (2012–2014)

Research Group and Advising

Professor Webb advises 10 graduate students working on polymer informatics, biomolecular simulations, and energy materials. The Webb Research Group collaborates extensively with experimentalists and employs high-throughput computational screening. Recent group achievements include machine-learning frameworks for enzyme-polymer hybrids and awards for poster presentations at major conferences.