Discovering Descriptors for CO ₂ ‐to‐Methanol Electrosynthesis on Cu‐Based Alloy Catalysts by Machine Learning

Abstract The electrochemical CO ₂ reduction reaction to methanol (CH ₃ OH) suggests an attractive approach toward carbon neutrality, while the activity and selectivity of CO ₂ ‐to‐CH ₃ OH are substantially low compared to most of the other C ₁ products like CH ₄ , largely due to the much less preferred thermodynamic CH ₃ OH formation pathway and the limited capability of controlling the adsorption of key intermediates toward CH ₃ OH. Herein, a machine learning approach with first‐principles calculations is demonstrated to identify key descriptors that govern the protonation of ^* CO intermediates and prevent C─C coupling, which are critical factors in determining product selectivity toward the CH ₃ OH formation. Datasets linking the intrinsic structural properties of heteroatom‐doped Cu catalysts to their catalytic performances are developed, among which the Bader charge, bond length, and d ‐band center are determined to be the key descriptors. The electronic structure analysis reveals that RhCu alloys demonstrate both enhanced selectivity and catalytic activity for the CO ₂ ‐to‐CH ₃ OH synthesis, with the d ‐band center shifting toward the Fermi level by 0.18 eV. This shift mitigates the competing hydrogen evolution reaction and simultaneously enhances the adsorption of key intermediates ( ^* CHO and ^* CO). This work presents a cost‐effective data‐driven approach for designing high‐performance Cu‐based catalysts toward selective CO ₂ ‐to‐CH ₃ OH electrosynthesis.