Chris Paolucci

Chris Paolucci is an Assistant Professor in the Department of Chemical Engineering at the University of Virginia. His research focuses on computational catalysis, quantum chemical calculations, and modeling material synthesis/deactivation. He holds a B.S. and Ph.D. from the University of Notre Dame (2012, 2017), followed by a postdoc at Stanford University (2017-2018). His group develops hybrid simulation methods bridging microscopic and macroscopic scales, leveraging machine learning and Monte Carlo techniques. Collaborations with experimentalists ensure model validation. Recent work emphasizes copper ion dynamics in zeolites, sulfur poisoning effects in catalysts, and novel material discovery through computational approaches.

Education:

• B.S. Chemical Engineering, University of Notre Dame (2012)
• Ph.D. Chemical Engineering, University of Notre Dame (2017)
• Postdoctoral Research, Stanford University (2017-2018)

Research interests span computational catalysis (e.g., NOx reduction, ethylene epoxidation), quantum chemical methods, and material design for energy applications. His work bridges theory and experiment to optimize catalyst performance and stability. Recent studies include dynamic Cu species behavior under reaction conditions and machine learning-driven force fields for catalyst reconstruction analysis.

Key awards include the 2017 Eli J. and Helen Shaheen Graduate School Award, 2016 Gordon Research Conference poster award, and multiple AIChE honors (2015). His lab actively explores catalyst deactivation mechanisms and novel computational tools for accelerated material discovery.

Advising and grants: While no students are listed, his lab’s focus on interdisciplinary computational/experimental work suggests active mentorship. Grants are not explicitly detailed but implied through award recognition and research scope.

Labs/teams: The Paolucci Group operates within the Department of Chemical Engineering, emphasizing collaborative projects with experimental groups to advance catalytic materials and computational methodologies.