首页|University of Michigan Reports Findings in Machine Learning (High-Performance Ir idium-Molybdenum Oxide Electrocatalysts for Water Oxidation in Acid: Bayesian Op timization Discovery and Experimental Testing)
University of Michigan Reports Findings in Machine Learning (High-Performance Ir idium-Molybdenum Oxide Electrocatalysts for Water Oxidation in Acid: Bayesian Op timization Discovery and Experimental Testing)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting from Ann Arbor, Michigan, by NewsRx journalists, research stated, "Ir oxides are costly and scarce catalysts for oxygen evolution reaction (OER) in acid. There has been extensive interest i n developing alternatives that are either Ir-free or require smaller amounts of Ir to drive the reactions at acceptable rates." The news correspondents obtained a quote from the research from the University o f Michigan, "One design strategy is to identify Ir-based mixed oxides that achie ve similar performance while requiring smaller amounts of Ir. The obstacle to th is strategy has been a very large phase space of the Ir-based mixed metal oxides , in terms of the metals combined with Ir and the different crystallographic str uctures of the mixed oxides, which prevents a thorough exploration of possible m aterials. In this work, we developed a workflow that uses machine-learning-aided Bayesian optimization in combination with density functional theory to make the exploration of this phase space plausible. This screening identified Mo as a pr omising dopant for forming acid-tolerant Ir-based oxides for the OER. We synthes ized and characterized the Ir-Mo mixed oxides in the form of thin-film electroca talysts with a known surface area. We show that these mixed oxides exhibited ove rpotentials 30 mV lower than a pure Ir control while maintaining 24% lower Ir dissolution rates than the Ir control."
Ann ArborMichiganUnited StatesNort h and Central AmericaAnionsCyborgsEmerging TechnologiesMachine LearningOxidesOxygen Compounds
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