首页|Carnegie Mellon University Reports Findings in Machine Learning (Pourbaix Machin e Learning Framework Identifies Acidic Water Oxidation Catalysts Exhibiting Supp ressed Ruthenium Dissolution)
Carnegie Mellon University Reports Findings in Machine Learning (Pourbaix Machin e Learning Framework Identifies Acidic Water Oxidation Catalysts Exhibiting Supp ressed Ruthenium Dissolution)
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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 originating from Pittsburgh, Pennsylvania, by NewsRx correspondents, research stated, "The demand for green h ydrogen has raised concerns over the availability of iridium used in oxygen evol ution reaction catalysts. We identify catalysts with the aid of a machine learni ng-aided computational pipeline trained on more than 36,000 mixed metal oxides." Our news editors obtained a quote from the research from Carnegie Mellon Univers ity, "The pipeline accurately predicts Pourbaix decomposition energy () from unr elaxed structures with a mean absolute error of 77 meV per atom, enabling us to screen 2070 new metallic oxides with respect to their prospective stability unde r acidic conditions. The search identifies RuCrTiO as a candidate having the pro mise of increased durability: experimentally, we find that it provides an overpo tential of 267 mV at 100 mA cm and that it operates at this current density for over 200 h and exhibits a rate of overpotential increase of 25 mV h. Surface den sity functional theory calculations reveal that Ti increases metal-oxygen covale ncy, a potential route to increased stability, while Cr lowers the energy barrie r of the HOO* formation rate-determining step, increasing activity compared to R uO and reducing overpotential by 40 mV at 100 mA cm while maintaining stability. "
PittsburghPennsylvaniaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine Learning
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