Computational progress of designing single-atom alloy catalysts for methane activation

Nonoxidative coupling of methane to value-added hydrocarbons remains a major challenge in catalysis due to inert C-H bonds in methane and rapid catalyst deactivation caused by coke contamination. Single-atom alloys (SAAs), atomically dispersing isolated metal atoms into a host metal surface, have emerged as promising catalysts for activating light alkanes. Here, we present a review of recent progress on designing SAA catalysts for the efficient and selective conversion of methane and other alkanes using computational modeling tools, including quantum mechanical simulations and machine learning models. First, we describe various studies applying first-principles simulations to discern reaction mechanisms, understand the role of isolated dopant atoms in reducing C-H activation barriers, and rationalize the synergy between the dopant and host in improving selectivity towards desired products. Next, we present how the rapid development of machine learning models enables the high-throughput screening of different dopants, hosts, and facets of SAA catalysts for methane transformation. We then extend the discussion from methane to the chemistry of activating other light alkanes, demonstrating that the dopant-host synergy promoting methane activation can also be applied to multi-carbon hydrocarbons. Finally, we provide some perspectives on the future outlook for the computational SAA catalyst design for methane activation. The goal of this review article is to present a computational perspective that systematically bridges first-principles mechanistic insights with data-driven workflows to enable the discovery of active, selective, and coke-resistant SAA catalysts for methane and light-alkane activation.