Machine Learning-aided Discovery of Synthesizable, Active and Stable Heterogeneous Catalysts

Project Personnel

Suljo Linic

Principal Investigator

University of Michigan, Ann Arbor

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Bryan Goldsmith

University of Michigan, Ann Arbor

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Nirala Singh

University of Michigan, Ann Arbor

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Eranda Nikolla

Wayne State University

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Funding Divisions

Division of Chemical, Bioengineering, Environmental, and Transport Systems (CBET)

Catalytic materials have long been used to improve the efficiency and product selectivity of many processes of vital importance to chemical manufacturing, petroleum refining, and pollution control. Given the complexity of catalytic reactions, and the need for the catalyst to operate under harsh conditions in many cases, considerable development effort – particularly from industry - has gone into the design of catalyst materials that can be readily synthesized, and that maintain stable performance for long time-on-stream. Academic research efforts, in contrast, have largely focused on theoretical, computational, and experimental identification of more active and/or lower-cost catalytic materials, but with little attention to synthesizability and stability. 

The project creates a new catalytic materials research framework that combines the search for more active materials with screening for synthesizability and stability under reaction conditions. The added complexity is addressed through the addition of powerful machine learning (ML) approaches that augment theoretical and computational tools to yield a more complete set of properties, or “descriptors,” associated with synthesizable, highly active, and stable catalytic materials. Ultimately, the goal is to package the various discovery tools in the form of an intuitive approach that delivers optimal results for catalysis practitioners.