Generative Discovery of Alloy Catalysts for Radical-Mediated Methane-to-Methanol Conversion

Selective oxidation of methane to methanol under mild conditions remains a “holy grail” in chemical manufacturing, constrained not only by the inertness of the C–H bond but also by the difficulty of controlling highly reactive intermediates once methane is activated. Plasma activation provides a nonequilibrium reaction environment in which methyl radicals are generated independently of the catalytic surface, effectively bypassing surface-mediated C–H activation. However, this shifts the bottleneck to subsequent interfacial chemistry, where radical interception, product selectivity, and catalyst stability impose competing constraints. Here, we introduce a plasma-electrochemical reaction framework coupled with a generative catalyst discovery strategy to navigate this multiobjective landscape. By integrating graph-neural-network-accelerated evaluation with generative exploration across a multimetallic alloy space spanning ∼10 29 possible configurations, the framework identifies transferable catalytic motifs rather than individual compositions. This process reveals an emergent catalytic architecture consisting of an Ag-rich matrix with isolated Pd/Pt sites, which provide localized reactivity for radical interception and C–O bond formation while maintaining weak oxygen affinity that suppresses overoxidation. Guided by this motif-level principle, we identify experimentally realizable Ag 3 Pd and Ag 3 Pt catalysts. In a hybrid plasma-electrocatalytic system, Ag 3 Pd achieves a methanol faradaic efficiency of up to 80.1% at ∼1.7 mA cm –2 with a productivity of 64.5 μmol·cm –2 ·h –1, outperforming previously reported plasma-assisted and electrochemical methane conversion systems. More broadly, this work demonstrates how generative algorithms can uncover emergent catalytic architectures across vast chemical spaces.