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.