首页|Data on Machine Learning Described by Researchers at McGill University (Adsorbat e-dependent Electronic Structure Descriptors for Machine Learning-driven Binding Energy Predictions In Diverse Single Atom Alloys: a Reductionist Approach)
Data on Machine Learning Described by Researchers at McGill University (Adsorbat e-dependent Electronic Structure Descriptors for Machine Learning-driven Binding Energy Predictions In Diverse Single Atom Alloys: a Reductionist Approach)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Researchers detail new data in Machine Learning. According to news reporting originatingin Montreal, Canada, by NewsR x journalists, research stated, “A long-standing challenge in thedesign of sing le atom alloys (SAAs), for catalytic applications, is the determination of a fea ture space thatmaximally correlates to molecular binding energies per the Sabat ier principle. The more representativea feature space is of the underlying bind ing properties, the greater the predictive capability of a givenmachine learnin g (ML) algorithm.”
MontrealCanadaNorth and Central Amer icaAlloysCyborgsEmerging TechnologiesMachine LearningMcGill University
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