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Nano Research 2023, 16(8): 11511-11520 https://doi.org/10.1007/s12274-023-5773-0
Research Article | Issue | Published: 15 June 2023

Applying machine-learning screening of single transition metal atoms anchored on N-doped γ-graphyne for carbon monoxide electroreduction toward C1 products

Show Author's Information Dongxu Jiao1,§,  Dantong Zhang3,§,  Dewen Wang1,  Jinchang Fan1,  Xingcheng Ma1,  Jingxiang Zhao2( ),  Weitao Zheng1( ),  Xiaoqiang Cui1( )
State Key Laboratory of Automotive Simulation and Control, School of Materials Science and Engineering, and Key Laboratory of Automobile Materials of MOE, Jilin University, Changchun 130012, China
College of Chemistry and Chemical Engineering, and Key Laboratory of Photonic and Electronic Bandgap Materials, Ministry of Education, Harbin Normal University, Harbin 150025, China
Multiscale Crystal Materials Research Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

§ Dongxu Jiao and Dantong Zhang contributed equally to this work.

Keywords:
machine learning, density functional theory, single atom catalysts, γ-graphyne, CO electroreduction
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Jiao D, Zhang D, Wang D, et al. Applying machine-learning screening of single transition metal atoms anchored on N-doped γ-graphyne for carbon monoxide electroreduction toward C1 products. Nano Research, 2023, 16(8): 11511-11520. https://doi.org/10.1007/s12274-023-5773-0
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Abstract Full text Electronic supplementary material About this article

Carbon monoxide electroreduction (COER) has been a key part of tandem electrolysis of carbon dioxide (CO2), in which searching for high catalytic performance COER electrocatalysts remains a great challenge. Herein, by means of density functional theory (DFT) computations, we explored the potential of a series of transition metal atoms anchored on N-doped γ-graphyne (TM@N-GY, TM from Ti to Au) as the COER electrocatalysts. We found that the final product selectivity of these single-atom catalysts depended on the position of the metal atom in the periodic table, with metals in the front and middle of each periodic period exhibiting high selectivity for CH4, while metals in the back producing CH3OH. Machine learning (ML) found that metal atomic number was intrinsic to the difference in COER performance of these single-atom catalysts (SACs). The free energy changes showed that Mn@N-GY and Ni@N-GY exhibited outstanding COER catalytic performance for producing CH4 and CH3OH, respectively. Our results provide theoretical and experimental guidance for designing efficient COER catalysts to generate C1 products.


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About this article

Applying machine-learning screening of single transition metal atoms anchored on N-doped γ-graphyne for carbon monoxide electroreduction toward C1 products

Show Author's information Dongxu Jiao1,§, Dantong Zhang3,§, Dewen Wang1, Jinchang Fan1, Xingcheng Ma1, Jingxiang Zhao2( ), Weitao Zheng1( ), Xiaoqiang Cui1( )
State Key Laboratory of Automotive Simulation and Control, School of Materials Science and Engineering, and Key Laboratory of Automobile Materials of MOE, Jilin University, Changchun 130012, China
College of Chemistry and Chemical Engineering, and Key Laboratory of Photonic and Electronic Bandgap Materials, Ministry of Education, Harbin Normal University, Harbin 150025, China
Multiscale Crystal Materials Research Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

§ Dongxu Jiao and Dantong Zhang contributed equally to this work.

Abstract

Carbon monoxide electroreduction (COER) has been a key part of tandem electrolysis of carbon dioxide (CO2), in which searching for high catalytic performance COER electrocatalysts remains a great challenge. Herein, by means of density functional theory (DFT) computations, we explored the potential of a series of transition metal atoms anchored on N-doped γ-graphyne (TM@N-GY, TM from Ti to Au) as the COER electrocatalysts. We found that the final product selectivity of these single-atom catalysts depended on the position of the metal atom in the periodic table, with metals in the front and middle of each periodic period exhibiting high selectivity for CH4, while metals in the back producing CH3OH. Machine learning (ML) found that metal atomic number was intrinsic to the difference in COER performance of these single-atom catalysts (SACs). The free energy changes showed that Mn@N-GY and Ni@N-GY exhibited outstanding COER catalytic performance for producing CH4 and CH3OH, respectively. Our results provide theoretical and experimental guidance for designing efficient COER catalysts to generate C1 products.

Keywords: machine learning, density functional theory, single atom catalysts, γ-graphyne, CO electroreduction

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Publication history
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Acknowledgements

Publication history

Received: 15 March 2023
Revised: 23 April 2023
Accepted: 23 April 2023
Published: 15 June 2023
Issue date: August 2023

Copyright

© Tsinghua University Press 2023

Acknowledgements

Acknowledgement

This work was financially supported by the National Natural Science Foundation of China (Nos. 12034002, 22279044 and 22202080), and the Jilin Province Science and Technology Development Program (No. 20210301009GX).

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