High‐Throughput Screening of Dual‐Atom Catalysts for Methane Combustion: A Combined Density Functional Theory and Machine‐Learning Study

Abstract Ceria‐supported precious metal catalysts have undergone extensive investigation for the catalytic methane combustion. However, it remains a significant challenge to achieve both highly synergistic oxidation activity and efficient atom utilization remains a challenge for commonly used supported nanoparticles and single‐atom catalysts. Dual‐atom catalysts (DACs) emerges as a frontier of advanced catalysts, presenting unique catalytic properties that benefit from the synergy of neighboring metal sites. In this study, 361 ceria‐supported DACs (M₁M₂/CeO₂) encompassing combinations of 19 transition metals are systematically explored. Using high‐throughput density functional theory calculations, the structures, stability as well as activity of M₁M₂/CeO₂ are assessed. Notably, Au₁Ga₁/CeO₂ is identified as a promising DAC exhibiting high activity for methane total oxidation, substantiated by comprehensive DFT‐calculated reaction pathways. Furthermore, employing six machine‐learning algorithms, the structure‐properties relationship is explored within ceria‐based DACs and highlight the importance of oxidation states and atomic radii of doped metals as the descriptors. The trained model by computational dataset exhibits high accuracy and predict a more active Mn₁Au₁/CeO₂ than those screened using only DFT datasets. The high‐throughput strategy demonstrated in this work not only provides insights into the rational design of methane oxidation catalysts, but also paves the way for exploring DACs for diverse applications.