Active Learning for Materials Design with Kevin Tran – TWiML Talk #238
Today we’re joined by Kevin Tran, PhD student in the department of chemical engineering at Carnegie Mellon University.
Kevin’s research focuses on creating and using automated, active learning workflows to perform density functional theory, or DFT, simulations, which are used to screen for new catalysts for a myriad of materials applications. In our conversation, we explore the challenges surrounding one such application—the creation of renewable energy fuel cells, which is discussed in his recent Nature paper “Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution.” We dig into the role and need for good catalysts in this application, the role that quantum mechanics plays in finding them, and how Kevin uses machine learning and optimization to predict electrocatalyst performance.
The complete show notes for this episode can be found at twimlai.com/talk/238.
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