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[Display omitted] • Double transition metal atoms on GDY break the scaling relationship. • Machine learning speeds up the screening catalysts and reveals the descriptors. • Relations of intermediate adsorption energies aid catalyst selection in deep CO 2 RR. • GDY-based catalysts with strong CO binding exhibit high selectivity towards CH 4. Electrochemical CO 2 reduction reaction (CO 2 RR) has become a promising application in addressing energy challenges and environmental crises. However, the scaling relationship between the reaction intermediates constrains the successful deep reduction of CO 2. Dual-metal-site catalysts (DMSCs) have emerged as potential electrocatalysts for CO 2 RR by breaking the scaling relationship due to their more adaptable active sites. Herein, this study aims to investigate the correlation between the adsorption energies of essential intermediates in CO 2 RR catalysis with double transition metal atoms anchored on graphdiyne monolayer (TM 1 -TM 2 @GDY) through machine-learning (ML) assisted density functional theory (DFT) calculations. The results reveal the important descriptors of CO 2 RR catalyzed by TM 1 -TM 2 @GDY, and demonstrate that the heteronuclear TM 1 -TM 2 @GDY have great potential for deep CO 2 reduction. Especially, Co-Mo@GDY and Co-W@GDY show low limiting potential (-0.60 V and −0.39 V, respectively) and high selectivity on the reaction from CO 2 to CH 4 based on the free energy diagrams. This study indicates that the two TM atoms on GDY act cooperatively for the catalysis of CO 2 RR. Notably, utilizing ML eliminates the need to calculate all transition metal combinations by DFT, which is a great boost in quickly investigating catalytic performance and high screening for excellent catalysts. [ABSTRACT FROM AUTHOR]