The role of machine learning in carbon neutrality:Catalyst property prediction,design,and synthesis for carbon dioxide reduction  

作　　者：Zhuo Wang Zhehao Sun Hang Yin Honghe Wei Zicong Peng Yoong Xin Pang Guohua Jia Haitao Zhao Cheng Heng Pang Zongyou Yin 

机构地区：[1]Department of Chemical and Environmental Engineering,The University of Nottingham Ningbo China,Ningbo,315100,PR China [2]Research School of Chemistry,The Australian National University,ACT,2601,Australia [3]Curtin Institute of Functional Molecules and Interfaces,School of Molecular and Life Sciences,Curtin University,Perth,WA,6845,Australia [4]Materials Interfaces Center,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen,518055,Guangdong,PR China [5]Key Laboratory for Carbonaceous Wastes Processing and Process Intensification Research of Zhejiang Province,University of Nottingham Ningbo China,Ningbo,315100,PR China [6]Municipal Key Laboratory of Clean Energy Conversion Technologies,University of Nottingham Ningbo China,Ningbo,315100,PR China [7]New Materials Institute,University of Nottingham Ningbo China,Ningbo,315042,PR China

出　　处：《eScience》2023年第4期1-11,共11页电化学与能源科学（英文）

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).

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 society.The carbon dioxide reduction reaction(CO_(2)RR)is a promising strategy to capture and convert carbon dioxide(CO_(2))into value-added chemical products.However,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)RR.Emerging 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 experiments.In this perspective article,we focus on recent ad-vancements in ML-participated CO_(2)RR applications.After a brief introduction to ML techniques and the CO_(2)RR,we first focus on ML-accelerated property prediction for potential CO_(2)RR catalysts.Then we explore ML-aided prediction of catalytic activity and selectivity.This is followed by a discussion about ML-guided catalyst and electrode design.Next,the potential application of ML-assisted experimental synthesis for the CO_(2)RR is discussed.

关 键 词：Carbon neutrality Carbon dioxide reduction reaction Machine learning CATALYST Rational design 

分 类 号：O64[理学—物理化学]