Active Learning Accelerating to Screen Dual-Metal-Site Catalysts for Electrochemical Carbon Dioxide Reduction Reaction

Dual-metal-site catalysts (DMSCs) are increasingly important catalysts in the field of electrochemical carbon dioxide reduction reaction (CO2RR) in recent years. However, rapid screening of suitable metal combinations of DMSCs remains a huge challenge. Herein, we constructed an active learning (AL)...

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Bibliographic Details
Published in:	ACS applied materials & interfaces 2023-03, Vol.15 (10), p.12986-12997
Main Authors:	Ding, Hu, Shi, Yawen, Li, Zeyang, Wang, Si, Liang, Yujie, Feng, Haisong, Deng, Yuan, Song, Xin, Pu, Pengxin, Zhang, Xin
Format:	Article
Language:	eng
Subjects:	Energy, Environmental, and Catalysis Applications
Online Access:	Get full text
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Summary:	Dual-metal-site catalysts (DMSCs) are increasingly important catalysts in the field of electrochemical carbon dioxide reduction reaction (CO2RR) in recent years. However, rapid screening of suitable metal combinations of DMSCs remains a huge challenge. Herein, we constructed an active learning (AL) framework to study CO2RR to HCOOH. This AL framework turned out a success in the accurate prediction of 282 DMSCs for CO2RR through interactive learning between users and machine learning (ML) models. Among the 42 DMSCs calculated in three iteration loops of AL, 29 DMSCs were obtained, where the screening success rate was as high as 70%. Furthermore, we found five experimentally unexplored DMSCs that exhibited better CO2RR activity and selectivity than pure Bi. Low prediction errors on other DMSCs show that the AL model possessed outstanding universality. The results prove the excellent potential of the AL method and provide guidance on the design of high-performance electrocatalysts for CO2RR.
ISSN:	1944-8244 1944-8252