Efficient mapping of CO₂RR intermediates adsorption energies on Cu_1-xM_x bimetallic alloys via Machine Learning

Electrochemical CO₂ reduction (CO₂RR) may decrease greenhouse effect while producing valuable C₂ chemicals like ethylene and ethanol, nevertheless achieving high selectivity remains challenging due to complex competing reactions occurring at the catalyst surface. Crucial to the formation of these molecules are the adsorption energies of CO and other reaction intermediates like H, O and OH. In this work, we used Machine Learning algorithms trained with Density Functional Theory data to predict the CO adsorption energies on Cu-based surfaces containing metal atom impurities. Classification algorithms were used to evaluate binding site stability, while regression models were employed to predict CO adsorption energies. Accuracy of the predictions was confirmed by F1 scores exceeding 98% in classification and MSE below 0.05 eV² in regression. This two-step analysis appeared to be robust also when employed to predict CO binding energies on Cu surfaces containing impurity concentrations up to six times higher than that used during the training phase. We are currently applying a similar approach to develop ML models to predict the binding of other adsorbates relevant to electrocatalysis such as H, O and OH. In this way, we aim at obtaining an in-depth understanding of the CO₂RR, enabling rapid evaluation of promising candidates for effective CO₂ reduction.