首页|Studies from University of Toronto Update Current Data on Machine Learning (Mach ine Learning-predicted Ternary Molybdenum Chalcogenophosphides for High-efficien cy Hydrogen Evolution Catalysis)
Studies from University of Toronto Update Current Data on Machine Learning (Mach ine Learning-predicted Ternary Molybdenum Chalcogenophosphides for High-efficien cy Hydrogen Evolution Catalysis)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators discuss new findings in Machine Learning. According to news reportingoriginating from Toronto, Canada, by NewsRx correspondents, research stated, “The search for efficient andcost-ef fective alternatives to platinum-based catalysts for the alkaline hydrogen evolu tion reaction (HER)remains a formidable challenge, driving the need for innovat ive materials. In this study, we employedmachine learning-driven moment tensor potentials in conjunction with particle swarm optimization topredict a new fami ly of ternary molybdenum chalcogenophosphides, specifically Mo2SP and Mo3SP.”
TorontoCanadaNorth and Central Ameri caChalcogensCyborgsElementsEmerging TechnologiesGasesHydrogenInorg anic ChemicalsMachine LearningMolybdenumTransition ElementsUniversity of Toronto
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