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)

Robotics & Machine Learning Daily News2024，Issue(Apr.17) ：102-103.

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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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Abstract

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.”

Key words

Montreal/Canada/North and Central Amer ica/Alloys/Cyborgs/Emerging Technologies/Machine Learning/McGill University

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出版年

2024

Robotics & Machine Learning Daily News

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