CBE Seminar: John Kitchin (Carnegie Mellon University)
April 29 @ 11:30 am - 12:30 pm
Abstract
Data Science & Machine Learning Approaches to Catalysis and Chemical Engineering
Data science and machine learning (DS/ML) are changing the way many people approach catalysis research, ranging from new design of experiment approaches, new methods in simulation, even new ways of interacting with the scientific literature. It is challenging today to even keep up with new work as it changes so quickly. In this talk, I will provide an overview of several ways we have incorporated DS/ML into our catalysis research, beginning with a state of the art large catalysis machine-learned model from the Open Catalyst project. I will then show how we have used concepts from DS/ML to develop new solutions to problems in chemical engineering using automatic differentiation. The takeaway messages from this talk will be that DS/ML is here to stay, and worth learning about. It is not a panacea solution though, with remaining challenges to address including educational, technical and communication challenges.
Biography
John Kitchin completed his B.S. in Chemistry at North Carolina State University. He completed a M.S. in Materials Science and a PhD 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 1 ½ years studying alloy segregation with Karsten Reuter and Matthias Scheffler in the Theory Department at the Fritz Haber Institut. Professor Kitchin began a tenure-track faculty position in the Chemical Engineering Department at Carnegie Mellon University in January of 2006. He is currently a Full Professor. At CMU, Professor Kitchin works in the areas of alloy catalysis and molecular simulation. He was awarded a DOE Early Career award in 2010 to investigate multifunctional oxide electrocatalysts for the oxygen evolution reaction in water splitting using experimental and computational methods. 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.
