Dynamic Polaronic Control of Metal Cluster Adaptability on Reducible Oxides

Abstract

Metal–oxide interactions are ubiquitous in many technological applications and involve a complex interplay between the oxide support and the metal nanoparticle. Particularly, it has been proposed that in strong metal–support interaction, the defect chemistry affects the metal cluster morphology. Here we develop a physics-guided machine learning framework to decode these interactions using Pt7 and Pt13 representative of planar and tridimensional clusters, analyzing the impact of across oxygen vacancy concentrations of CeO2–x = 0–12.5% (528 configurations). Our models (R2 > 0.97) reveal that polaron swarms, rather than defect concentrations, predominantly control cluster shape and charge through size-dependent pathways. The framework yields quantitative design principles for defect-driven catalyst optimization and provides a general methodology for systematic mechanisms of metal–support interactions across diverse catalyst systems