DFT and machine learning guided investigation into the design of new dual-atom catalysts based on α-2 graphyne.

The realm of atomic catalysts has witnessed notable advancements; yet, the predominant focus remains on single atomic catalysts (SACs). The exploration and successful implementation of dual atomic catalysts (DACs) pose intricate challenges, primarily concerning thermodynamic stability and optimal metallic composition. To address these issues, we present a comprehensive theoretical investigation of α-2 graphyne (GPY)-based DACs, synthesized in-house with a keen focus on formation stability. Density functional theory (DFT) simulations were leveraged to ascertain each DAC structure's stability, considering numerous transition metal permutations totalling about 823 DACs. Furthermore, we developed a machine learning (ML) model that predicts stability based solely on the physical characteristics of the constituent elements in the DACs, thus eliminating the need for extensive DFT calculations. Our findings not only offer detailed insights into atomic interactions but also highlight promising candidates for DACs, pushing beyond traditional trial-and-error synthesis approaches. This study fosters a deeper understanding of DACs and paves new pathways for exploring atomic catalysts for practical applications.