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Accelerating the computational discovery of catalyst design rules and exceptions with machine learning

Heather Kulik

Over the past decade, first-principles computation has emerged as a powerful complement to experiment in the discovery of new catalysts and materials. In many cases, computation has excelled most in distilling rules for catalyst structure-property relationships in well defined spaces such as bulk metals into descriptors or linear free energy relationships. More development is needed of computational tools for them to show the same promise in emerging catalytic materials such as single-site metal-organic framework catalysts or single atom catalysts that have increased promise of atom economy and selectivity. In this talk, I will outline our efforts to accelerate first-principles (i.e., with density functional theory, or DFT) screening of open-shell transition metal catalysts with a focus on challenging reactions (e.g., selective partial hydrocarbon oxidation).

Speaker: Heather Kulik, Massachusetts Institute of Technology

Wednesday, 01/29/20

Contact:

Website: Click to Visit

Cost:

Free

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Tan Hall

UC Berkeley
Room 180
Berkeley, CA 94720