摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-调查人员发布了关于马学习的新报告。据新华社赣州市新闻报道，NewsRx记者报道，“氧电还原反应(Orgen reduction reaction，ORR和放氧反应，OER)是金属-空气电池等应用领域的两个关键反应，而SLO-W动力学对B元件的整体反应效率有重要影响，强调了催化剂开发的深远意义。采用密度泛函理论(DFT)和机器学习(ML)相结合的方法，系统地研究了稀土掺杂石墨烯(RENxC4-x)作为电催化剂的催化活性。
Abstract
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).”
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