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Author: search.filters.author.Praserthdam, PiyasanSubject: search.filters.subject.Alloys

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    Machine-learning-accelerated density functional theory screening of Cu-based high-entropy alloys for carbon dioxide reduction to ethylene
    (2025-03-01)
    Rittiruam, Meena
    ;
    Khamloet, Pisit
    ;
    Tiwtusthada, Sirapat
    ;
    Ektarawong, Annop
    ;
    Saelee, Tinnakorn
    Computational screening of high-entropy alloy (HEA) catalysts as alternatives to the typical Cu electrocatalyst for CO<inf>2</inf> reduction reaction (CO<inf>2</inf>RR) has been extensively focused on C<inf>1</inf> products, but C<inf>2+</inf> products have received significantly less attention. This work optimized CuZnPdAgAu HEA catalyst composition for CO<inf>2</inf>RR to ethylene via density functional theory and supervised machine learning regression techniques. Candidates were identified from 106,045 HEA data for enthalpy of adsorption of *CO<inf>2</inf>, *H, *HOCCOH, and *C<inf>2</inf>H<inf>4</inf> species, and the Gibbs free energy of *H. The electrocatalytic properties during the reaction were examined on the surface of the optimized HEA candidate – Cu<inf>0.36</inf>Zn<inf>0.18</inf>Pd<inf>0.10</inf>Ag<inf>0.18</inf>Au<inf>0.18</inf> benchmarked to Cu (111). The Pd site of such a candidate functions as the active site for the CO<inf>2</inf> activation step. In terms of catalytic activity, it showed lower Gibbs free energy for the potential determining step – the *OCCOH formation step compared to that on the Cu (111). Insight into electronic properties demonstrated that the candidate reduces the uphill reaction energy for *HOCCOH production pathway due to increased electron density in the C–C bond, donated from two Cu sites. It is shown that the HEA catalyst candidate has the potential for CO<inf>2</inf>RR targeting ethylene, an alternative to a common Cu catalyst.