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[Display omitted] • SW defect is the most stable defect structure, with the formation energy of 2.067 eV. • The stability of TMN x -Si structures gradually enhances with the increase of N atoms. • CuN 2 -o-Si structure shows the highest quantum capacitance of 145.499 μF/cm2. • Both defects and doping can enhance the quantum capacitance of silicene-based electrode materials. The design of electrode materials with high energy storage properties plays a key role in developing electric double-layer supercapacitor technology. In recent years, silicene has aroused great interest in the field of energy storage. In this paper, the electronic structure and quantum capacitance of single vacancy defects, topological defects and four kinds of double vacancy defects were systematically investigated, based on density functional theory. It is found that silicene with the topological defect is the most stable defect structure, with the formation energy of 2.067 eV. In the systems of Ag, Au, Cu, Ti, Mn and N co-doping on silicene, the structural stability enhances with the increase of N atoms. CuN 2 -o-Si structure shows the highest quantum capacitance of 145.499 μF/cm2, which could be an ideal anode material for supercapacitors. The increased quantum capacitance is due to the introduction of localized states near the Fermi level. Both defects and doping can enhance the quantum capacitance of silicene-based electrode materials, and then improve the total interfacial capacitance. [ABSTRACT FROM AUTHOR]

[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]