Robotics & Machine Learning Daily News2024,Issue(Dec.30) :103-104.

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)

Robotics & Machine Learning Daily News2024,Issue(Dec.30) :103-104.

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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Abstract

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.”

Key words

Toronto/Canada/North and Central Ameri ca/Chalcogens/Cyborgs/Elements/Emerging Technologies/Gases/Hydrogen/Inorg anic Chemicals/Machine Learning/Molybdenum/Transition Elements/University of Toronto

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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