Year: 2026
Type: article
Abstract: High Resolution Image Download MS PowerPoint Slide 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 Pt 7 and Pt 13 representative of planar and tridimensional clusters, analyzing the impact of across oxygen vacancy concentrations of CeO 2– x = 0–12.5% (528 configurations). Our models ( R 2 > 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.
DOI: 10.1021/jacs.5c13140
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,500
OpenAlex ID: W7127266163