Inspecting design rules of metal-nitrogen-carbon catalysts for electrochemical CO_(2)reduction reaction:From a data science perspective
作者机构：National Laboratory of Solid State MicrostructuresCollege of Engineering and Applied SciencesNanjing University22 Hankou RoadNanjing 210093China State Key Laboratory of CatalysisDalian National Laboratory for Clean EnergyDalian Institute of Chemical PhysicsChinese Academy of SciencesDalian 116023China
出 版 物：《Nano Research》 (纳米研究（英文版）)
年 卷 期：2023年第16卷第1期
页 面：264-280页
核心收录：
学科分类：081704[工学-应用化学] 07[理学] 070304[理学-物理化学(含∶化学物理)] 08[工学] 0817[工学-化学工程与技术] 0703[理学-化学]
主 题：electrochemical CO_(2)reduction reaction metal-nitrogen-carbon catalyst machine learning artificial intelligence data science
摘 要：The development of inexpensive metal-nitrogen-carbon(M-N-C)catalysts for electrochemical CO_(2)reduction reaction(CO_(2)RR)on an industrial scale has come to a *** the number of related studies and reviews has grown fast,the complexity of the M-N-C composite has limited researchers to focus on only a few variables and carry out sluggish trial-and-error optimizations in their *** a result,the conclusions are drawn only by artificial analysis based on a few orthogonal experimental *** obtain more general design strategies,we have innovatively introduced machine learning(ML)into this field to address this bottleneck.A standard workflow that comprehensively utilizes different ML algorithms and black-box interpretation methods is proposed for this *** predicting CO_(2)RR performance metrics for M-N-C catalysts,such as maximum faradaic efficiency with great accuracy,the ML models have also indicated simple and clear design strategies that would guide future exploration from a data science ***,we have also demonstrated the potential of the models in guiding the development of new material *** thereby believe that the new research paradigm proposed may accelerate the development of this field soon.
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