Issue 3, 2023

Machine learning assisted binary alloy catalyst design for the electroreduction of CO2 to C2 products

Abstract

The carbon dioxide reduction reaction (CO2RR) has become one of the most important catalytic reactions due to its potential impact on global emissions. Among the many products this reaction yields, C2 products are the most valuable due to their potential use as hydrocarbon fuels. For the efficient conversion of CO2 into C2 products, however, further work needs to be done on understanding the reaction pathway mechanisms and ideal catalytic surfaces. Herein, we gain insight into the C2 pathway through a combination of Density Functional Theory (DFT) and machine learning (ML) by studying the adsorption of *COCOH on eight different types of Cu-based binary alloy catalysts (BAC) and subsequently discover the ideal BAC surfaces through configurational space exploration. 8 different ML models were evaluated with descriptors for elemental period, group, electronegativity, and the number of unpaired d orbital electrons. The top performing models could successfully predict the adsorption energy of *COCOH on Cu-based BACs to within 0.095 eV mean absolute error (MAE). The most accurate models found Cu/Ag and Cu/Au BACs with 2–3 atom nanoislands on the surface and high Ag/Au density subsurfaces had the most favorable reaction energy pathway which corresponds with the weakest *COCOH adsorption energies.

Graphical abstract: Machine learning assisted binary alloy catalyst design for the electroreduction of CO2 to C2 products

Supplementary files

Article information

Article type
Paper
Submitted
16 11月 2022
Accepted
09 2月 2023
First published
13 2月 2023
This article is Open Access
Creative Commons BY-NC license

Energy Adv., 2023,2, 410-419

Machine learning assisted binary alloy catalyst design for the electroreduction of CO2 to C2 products

Z. Gariepy, G. Chen, A. Xu, Z. Lu, Z. W. Chen and C. V. Singh, Energy Adv., 2023, 2, 410 DOI: 10.1039/D2YA00316C

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