首页|Accelerating the design of catalysts for CO2 electroreduction to HCOOH:A data-driven DFT-ML screening of dual atom catalysts

Accelerating the design of catalysts for CO2 electroreduction to HCOOH:A data-driven DFT-ML screening of dual atom catalysts

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Dual-atom catalysts(DACs)have emerged as potential catalysts for effective electroreduction of CO2 due to their high atom utilization efficiency and multiple active sites.However,the screening of DACs remains a challenge due to the large number of possible combinations,making exhaustive experimental or computational screening a daunting task.In this study,a density functional theory(DFT)-based machine learning(ML)-accelerated(DFT-ML)hybrid approach was developed to test a set of 406 dual transition metal catalysts on N-doped graphene(NG)for the electroreduction of CO2 to HCOOH.The results showed that the ML algorithms can successfully capture the relationship between the descriptors of the DACs(inputs)and the limiting potential for HCOOH generation(output).Of the four ML algorithms studied in this work,the feedforward neural network model achieved the highest prediction accuracy(the highest correlation coefficient(R2)of 0.960 and the lowest root mean square error(RMSE)of 0.319 eV on the test set)and the predicted results were verified by DFT calculations with an average abso-lute error of 0.14 eV.The DFT-ML approach identified Co-Co-NG and Ir-Fe-NG as the most active and stable electrocatalysts for the electrochemical reduction of CO2 to HCOOH.The DFT-ML hybrid approach exhibits exceptional prediction accuracy while enabling a significant reduction in screening time by an impressive 64％compared to conventional DFT-only calculations.These results demonstrate the immense potential of using ML methods to accelerate the screening and rational design of efficient catalysts for various energy and environmental applications.

CO2 electroreduction reactionDual atom catalystsRapid screeningDensity functional theoryMachine learning

Huiwen Zhu、Zeyu Guo、Dawei Lan、Shuai Liu、Min Liu、Jianwen Zhang、Xiang Luo、Jiahui Yu、Tao Wu

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College of Digital Technology and Engineering,Ningbo University of Finance & Economics,Ningbo 315175,Zhejiang,China

Department of Chemical and Environmental Engineering,University of Nottingham Ningbo China,Ningbo 315100,Zhejiang,China

Key Laboratory of Carbonaceous Wastes Processing and Process Intensification Research of Zhejiang Province,University of Nottingham Ningbo China,Ningbo 315100,Zhejiang,China

School of Mechatronics and Energy Engineering,NingboTech University,Ningbo 315104,Zhejiang,China

Medical Science and Technology Innovation Centre,Shandong First Medical University & Shandong Academy of Medical Sciences,Jinan 250117,Shandong,China

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2024

10.1016/j.jechem.2024.08.015

能源化学
中国科学院大连化学物理研究所 中国科学院成都有机化学研究所

能源化学

CSTPCDEI
影响因子：0.654
ISSN：2095-4956
年,卷(期)：2024.99(12)