首页|Tianjin University Reports Findings in Machine Learning (Machine learning-assist ed dual-atom sites design with interpretable descriptors unifying electrocatalyt ic reactions)
Tianjin University Reports Findings in Machine Learning (Machine learning-assist ed dual-atom sites design with interpretable descriptors unifying electrocatalyt ic reactions)
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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 originating from Tianjin, People’s Repu blic of China, by NewsRx correspondents, research stated, “Low-cost, efficient c atalyst high-throughput screening is crucial for future renewable energy technol ogy. Interpretable machine learning is a powerful method for accelerating cataly st design by extracting physical meaning but faces huge challenges.”Our news journalists obtained a quote from the research from Tianjin University, “This paper describes an interpretable descriptor model to unify activity and s electivity prediction for multiple electrocatalytic reactions (i.e., O/CO/N redu ction and O evolution reactions), utilizing only easily accessible intrinsic pro perties. This descriptor, named ARSC, successfully decouples the atomic property (A), reactant ®, synergistic (S), and coordination effects (C) on the d-band sh ape of dual-atom sites, which is built upon our developed physically meaningful feature engineering and feature selection/sparsification (PFESS) method. Driven by this descriptor, we can rapidly locate optimal catalysts for various products instead of over 50,000 density functional theory calculations. The model’s univ ersality has been validated by abundant reported works and subsequent experiment s, where Co-Co/Ir-Qv3 are identified as optimal bifunctional oxygen reduction an d evolution electrocatalysts.”
TianjinPeople’s Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine Learning
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