摘要
由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-机器学习的新研究是一篇报道的主题。据新闻报道来自中华人民共和国郑州的报道，由NewsRx Research记者报道声明：“推进阴极催化剂的设计，以显著最大限度地提高铂利用率和延长寿命已成为燃料电池领域的一个巨大挑战。在此，我们理性地催化氧还原的高熵金属间化合物(HEIC,pt(feconicu))的设计反应(ORR)的一种有效机器学习策略采用RE来加快多组分设计。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - New research on Machine Learning is th e subject of a report. According to newsreporting originating from Zhengzhou, P eople’s Republic of China, by NewsRx correspondents, researchstated, “Advancing the design of cathode catalysts to significantly maximize platinum utilization andaugment the longevity has emerged as a formidable challenge in the field of fuel cells. Herein, we rationallydesign a high entropy intermetallic compound ( HEIC, Pt(FeCoNiCu)) for catalyzing oxygen reductionreaction (ORR) by an efficie nt machine learning stategy, where crystal graph convolutional neural networksa re employed to expedite the multicomponent design.”
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