Machine learning accelerated calculation and design of electrocatalysts for CO2 reduction. Issue 1 (30th March 2022)

• Record Type: Journal Article
• Authors: Sun, Zhehao; Yin, Hang; Liu, Kaili; Cheng, Shuwen; Li, Gang Kevin; Kawi, Sibudjing; Zhao, Haitao; Jia, Guohua; Yin, Zongyou
• Is Part Of: SmartMat. Volume 3:Issue 1(2022)
• Page Start: 68
• Page End: 83
• Publication Date: 2022-03-30
• Subjects: CO2 reduction reaction -- DFT calculation -- electrocatalyst -- machine learning -- rational design; Smart materials -- Periodicals; Materials science -- Periodicals; 620.11
• DOI: 10.1002/smm2.1107
• Languages: English
• ISSNs: 2688-819X

Abstract

Abstract: In the past decades, machine learning (ML) has impacted the field of electrocatalysis. Modern researchers have begun to take advantage of ML‐based data‐driven techniques to overcome the computational and experimental limitations to accelerate rational catalyst design. Hence, significant efforts have been made to perform ML to accelerate calculation and aid electrocatalyst design for CO2 reduction. This review discusses recent applications of ML to discover, design, and optimize novel electrocatalysts. First, insights into ML aided in accelerating calculation are presented. Then, ML aided in the rational design of the electrocatalyst is introduced, including establishing a data set/data source selection and validation of descriptor selection of ML algorithms validation and predictions of the model. Finally, the opportunities and future challenges are summarized for the future design of electrocatalyst for CO2 reduction with the assistance of ML. Abstract : Remarkable recent advances in applications of machine learning (ML) to discover, design, and optimize novel electrocatalysts for CO2 reduction inspire the data‐driven materials design. A review of ML aided in the rational design of the electrocatalyst is introduced, including accelerating calculation, establishing a data set/datasource selection, and validation of descriptor selection of ML algorithms validation and predictions of the model.