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

Hu Ding; Yawen Shi; Zeyang Li; Si Wang; Yujie Liang; Haisong Feng; Yuan Deng; Xin Song; Pengxin Pu; Xin Zhang

doi:10.1021/acsami.2c21332

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

ACS Appl Mater Interfaces. 2023 Mar 15;15(10):12986-12997. doi: 10.1021/acsami.2c21332. Epub 2023 Feb 28.

Authors

Hu Ding¹, Yawen Shi¹, Zeyang Li¹, Si Wang¹, Yujie Liang¹, Haisong Feng¹, Yuan Deng¹, Xin Song¹, Pengxin Pu¹, Xin Zhang¹

Affiliation

¹ State Key Laboratory of Chemical Resource Engineering, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, P. R. China.

PMID: 36853996
DOI: 10.1021/acsami.2c21332

Abstract

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.

Keywords: active learning; carbon dioxide reduction reaction; density functional theory; dual-metal-site catalysts; performance prediction.