Links

• Research Group Website
• Google Scholar

Biography

Chas McGill is an Assistant Professor in Chemical and Life Science Engineering at Virginia Commonwealth University. Chas obtained his PhD from North Carolina State University, studying under Professor Phil Westmoreland to develop elementary kinetic mechanisms using computational chemistry. He continued his study in his postdoc appointment at MIT working in the Bill Green group, developing methods for Machine Learning chemical property prediction. As part of this work, he was a lead developer for the Chemprop software package, which supports the training of ML chemical property prediction models. Prior to his graduate study, he worked at Corning Incorporated as a senior development engineer in Corning's optical fiber division.

His interests are centered on using computational tools to investigate problems in the chemical world. His group uses machine learning tools, quantum chemistry, and mathematical modeling. They apply these tools to describe chemical phenomena and make predictions about their behavior in different contexts. His current research interests include using machine learning property models to predict chemical separation processes, incorporating uncertainty quantification into model predictions, and using machine learning to efficiently guide experiment selection.