首页|Findings from Jiangxi University of Science and Technology Update Knowledge of Machine Learning (Rare Earth Modified Carbonbased Catalysts for Oxygen Electrode Reactions: a Machine Learning Assisted Density Functional Theory Investigation)
Findings from Jiangxi University of Science and Technology Update Knowledge of Machine Learning (Rare Earth Modified Carbonbased Catalysts for Oxygen Electrode Reactions: a Machine Learning Assisted Density Functional Theory Investigation)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ma chine Learning. According to news reporting originating from Ganzhou, People’s R epublic of China, by NewsRx correspondents, research stated, “The oxygen electro de reactions (oxygen reduction reaction, ORR and oxygen evolution reaction, OER) are two key reactions in applications such as metal-air batteries, however, slo w kinetics have a significant impact on the overall reaction efficiency of the b atteries, thus emphasizing the profound significance of catalyst development. In this study, we systematically investigated the catalytic activity of rare-earth - doped graphene (RENxC4-x) as electrocatalysts using a combination of density fu nctional theory (DFT) and machine learning (ML).”
GanzhouPeople's Republic of ChinaAsiaChalcogensCyborgsEmerging TechnologiesMachine LearningJiangxi University of Science and Technology
NETL
NSTL
