Defect‐Enhanced Low‐Temperature CO Oxidation on Graphene‐Supported Pt‐Au Nanoclusters

ABSTRACT The catalytic properties of bimetallic nanoclusters are governed by a complex interplay between their size, composition, chemical ordering, and interaction with the support. Here, we perform a comprehensive data‐driven computational study using the USPEX evolutionary algorithm for global structure prediction of Pt–Au nanoclusters (up to 24 atoms), followed by high‐throughput screening for O and CO adsorption energy with MACE foundation model and simulations of chemical reactor. We combine about 12 000 density functional theory (DFT) calculations with a machine learning workflow that adds over 80 000 additional data points to enable extensive sampling of ground‐state and metastable isomers. Our analysis reveals that adsorption energies are primarily determined by local atomic composition of the active site rather than by the precise global‐minimum structure. Consequently, a representative set of low‐energy isomers provides the same adsorption information as the most stable clusters, which is crucial for experiments where monodisperse samples are difficult to obtain. We further demonstrate that a graphene substrate with a single carbon monovacancy strongly anchors cluster, enhances adsorption, and lowers CO oxidation barriers by activating traditionally inert Au atoms. Molecular dynamics simulations of CO saturation show that the supported cluster undergoes adsorption‐induced reconstruction, with Pt atoms segregating to the surface, and that the support significantly alters the saturation profile compared to the free‐standing cluster. This work establishes that rational design of bimetallic catalysts must take into account for local site chemistry, dynamic synergy with the support, and practical relevance of isomer ensembles.