Machine learning links material composition and performance in catalysts

From left to right , diagrams show an oxygen atom bonding with a metal, a metal oxide, and a perovskite. The new model could help chemical engineers design these three types of catalysts to improve the sustainability of fuel and fertilizer production as well as the manufacturing of household chemicals. Credit: Jacques Esterhuizen, Linic Lab, University of Michigan. In a finding that could help pave the way toward cleaner fuels and a more sustainable chemical industry, researchers at the University of Michigan have used machine learning to predict how the compositions of metal alloys and metal oxides affect their electronic structures. The electronic structure is key to understanding how the material will perform as a mediator, or catalyst, of chemical reactions. "We're learning to identify the fingerprints of materials and connect them with the material's performance,” said Bryan Goldsmith , the Dow Corning Assistant Professor of Chemical Engineering. A better ability to predict which metal and metal oxide compositions are best for guiding which reactions could improve large-scale chemical processes such as hydrogen production, production of other fuels and fertilizers, and manufacturing of household chemicals such as dish soap.