John Kitchin (CMU): Schmidt AI in Science Speaker Series
Organized by the University of Chicago’s Eric and Wendy Schmidt AI in Science Fellowship Program.
Agenda
4:00pm – 4:45pm: Presentation
4:45pm – 5:00pm: Q&A
5:00pm – 5:30pm: Reception
Meeting location
William Eckhardt Research Center. Room 401
5640 S Ellis Avenue, Chicago, IL 60637
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Title: Large machine-learned potentials for materials design with catalysis and energy applications
Abstract: Designing multicomponent materials is challenging with density functional theory (DFT) because it computationally expensive to evaluate all the ways that elements may combine and react. It is also difficult to estimate free energy contributions to reactions and to locate reaction barriers with DFT. The Open Catalyst Project is developing machine learned potentials (MLP) to mitigate these challenges. These MLPs are trained on 100M+ DFT calculations spanning 55 different elements and 80+ adsorbates that are relevant in catalysis and energy applications. Nominally these models were trained to predict energy and forces, and from these one can derive reaction energies. We will show in this talk, however, that these models also show great utility in computing reaction barriers, and in estimating free energy contributions to reactions. This opens the door to a post-scaling era of computational catalysis where reaction barriers can be computed in complex reaction networks with near DFT accuracy rather than relying on less accurate linear scaling relations. We will show some case studies of results and discuss future research directions in this area. Finally, I will discuss the growing role of generative AI in scientific research with examples from our most recent work.
Bio: John Kitchin, Professor of Chemical Engineering, Carnegie Mellon University. John Kitchin works at the intersection of machine learning, data science, and scientific programming with science and engineering. He develops software for modeling materials, solving engineering problems, and writing scientific documents. He uses these tools to model catalysts with applications in energy, to solve inverse problems in engineering, and to find new approaches in developing surrogate models for engineering systems.
Kitchin completed his B.S. in chemistry at North Carolina State University. He completed an M.S. in materials science and a Ph.D. in chemical engineering at the University of Delaware in 2004 under the advisement of Dr. Jingguang Chen and Dr. Mark Barteau.
He received an Alexander von Humboldt postdoctoral fellowship and lived in Berlin, Germany for 1½ years studying alloy segregation with Karsten Reuter and Matthias Scheffler in the Theory Department at the Fritz Haber Institut. Kitchin began a tenure-track faculty position in the Chemical Engineering Department at Carnegie Mellon in January 2006. He was awarded a DOE Early Career award in 2010. He received a Presidential Early Career Award for Scientists and Engineers in 2011. He completed a sabbatical in the Accelerated Science group at Google learning to apply machine learning to scientific and engineering problems in 2018. In 2023, he was the recipient of the AIChE Award for Innovation in Chemical Engineering Education.
