Harnessing the Potential of Machine Learning to Optimize the Activity of Cu-Based Dual Atom Catalysts for CO₂ Reduction Reaction

The electrochemical CO2 reduction reaction (CO2RR) paved the way to carbon neutrality while producing value-added chemicals and fuels. While Cu-based catalysts show potential, they suffer from inadequate faradaic efficiency. In this study, we explore Cu(100) surface-based dual atom alloy (DAA) catalysts for the CO2RR to produce C1 and C2 products. Three distinct doping patterns involve two identical or different transition metals across 27 candidates. Machine learning (ML) based models were developed with high accuracy to predict the catalytic activity of unknown catalysts. The scaling relation between the adsorption energies of *CO and *CHO is circumvented by regulating the local environment with preferential dual atom doping. The integrated DFT+ML approach identifies 14 and 8 most suitable DAAs for C1 and C2 product formation, respectively. Feature importance analysis underscores the significance of valence d-orbital electrons in *CO adsorption. Additionally, PDOS analysis reveals atom-like electronic states in doped metals, characterized by highly localized d-states.