The role of machine learning in carbon neutrality:Catalyst property prediction,design,and synthesis for carbon dioxide reduction
作者机构：Department of Chemical and Environmental EngineeringThe University of Nottingham Ningbo ChinaNingbo315100PR China Research School of ChemistryThe Australian National UniversityACT2601Australia Curtin Institute of Functional Molecules and InterfacesSchool of Molecular and Life SciencesCurtin UniversityPerthWA6845Australia Materials Interfaces CenterShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055GuangdongPR China Key Laboratory for Carbonaceous Wastes Processing and Process Intensification Research of Zhejiang ProvinceUniversity of Nottingham Ningbo ChinaNingbo315100PR China Municipal Key Laboratory of Clean Energy Conversion TechnologiesUniversity of Nottingham Ningbo ChinaNingbo315100PR China New Materials InstituteUniversity of Nottingham Ningbo ChinaNingbo315042PR China
出 版 物：《eScience》 (电化学与能源科学（英文）)
年 卷 期：2023年第3卷第4期
页 面：1-11页
学科分类：0830[工学-环境科学与工程（可授工学、理学、农学学位）] 081704[工学-应用化学] 07[理学] 0817[工学-化学工程与技术] 070304[理学-物理化学(含∶化学物理)] 08[工学] 0807[工学-动力工程及工程热物理] 0805[工学-材料科学与工程（可授工学、理学学位）] 0703[理学-化学] 0702[理学-物理学]
基 金：gratefully express gratitude to all parties who have contributed toward the success of this project,both financially and technically,especially the S&T Innovation 2025 Major Special Programme(Grant No.2018B10022) the Ningbo Commonweal Programme(Grant No.2022S122)funded by the Ningbo Science and Technology Bureau,China,as well as the UNNC FoSE Faculty Inspiration Grant,China the support from the Ningbo Municipal Key Laboratory on Clean Energy Conversion Technologies(2014A22010)as well as the Zhejiang Provincial Key Laboratory for Carbonaceous Wastes Processing and Process Intensification Research funded by the Zhejiang Provincial Department of Science and Technology(2020E10018) support from the ANU Futures Scheme(Q4601024)
主 题：Carbon neutrality Carbon dioxide reduction reaction Machine learning Catalyst Rational design
摘 要：Achieving carbon neutrality is an essential part of responding to climate change caused by the deforestation and over-exploitation of natural resources that have accompanied the development of human *** carbon dioxide reduction reaction(CO_(2)RR)is a promising strategy to capture and convert carbon dioxide(CO_(2))into value-added chemical ***,the traditional trial-and-error method makes it expensive and time-consuming to understand the deeper mechanism behind the reaction,discover novel catalysts with superior performance and lower cost,and determine optimal support structures and electrolytes for the CO_(2)*** machine learning(ML)techniques provide an opportunity to integrate material science and artificial intelligence,which would enable chemists to extract the implicit knowledge behind data,be guided by the insights thereby gained,and be freed from performing repetitive *** this perspective article,we focus on recent ad-vancements in ML-participated CO_(2)RR *** a brief introduction to ML techniques and the CO_(2)RR,we first focus on ML-accelerated property prediction for potential CO_(2)RR *** we explore ML-aided prediction of catalytic activity and *** is followed by a discussion about ML-guided catalyst and electrode ***,the potential application of ML-assisted experimental synthesis for the CO_(2)RR is discussed.
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