Synergistic Effects and Activity Descriptors in Double-Atom Catalysts for Electrochemical CO ₂ Reduction: A Computational Insight

The electrochemical CO₂ reduction reaction (CO₂RR) using double-atom catalysts (DACs) represents a transformative approach to sustainable energy conversion and carbon mitigation. In this study, we employ density functional theory (DFT) to systematically investigate 55 homogeneous (M-M) and heterogeneous (M₁-M₂) diatomic catalysts supported on N-doped graphene (M₁M₂-NC), comprising 10 transition metals. Our screening identifies Cu-Cr, Ni-Pd, and Pd-Pd as top-performing DACs for CO and CH₃OH production, while Co-Co, Fe-Fe, Cu-Cr, and Co-Cu excel in HCOOH generation via the *COOH pathway. Alternatively, the *OCHO pathway is most efficient on the Co-Ni, Cu-Cu, and Co-Pd systems. For CH₄ formation, Cu-V, Cr-Cr, and V-Pd show superior activity via the *CO route, whereas Fe-Pd, Mn-Cu, Cu-Cr, and V-Pd dominate the *HCOOH pathway. These catalysts exhibit strong synergistic effects, optimal intermediate adsorption, and low energy barriers, as rationalized by key descriptors: the d-band center (ε_d), Bader charge (<i>q</i>_B), adsorption energy, Gibbs free energy (Δ<i>G</i>_max), and crystal orbital Hamilton population (COHP). Our findings not only highlight structure-activity relationships but also provide a design framework for high-performance DACs in the CO₂RR, advancing the development of graphene-based electrocatalysts.