Machine learning-guided discovery of -Al2O3 as defect-rich structural ensemble governs single-atom Pt catalysis
Ka Lok Cliff Choong , Ran Luo , Zhi-Jian Zhao , Jinlong Gong
ENG. Chem. Eng. ›› 2026, Vol. 20 ›› Issue (10) : 77
-Al2O3 is one of the most widely used catalyst supports in heterogeneous catalysis, yet its catalytic role remains unclear due to intrinsic structural disorder and complex surface species. This paper describes the development of a high-accuracy Al–O–H machine learning interatomic potential combined with global optimization to explore the structural landscape of -Al2O3 and its influence on propane dehydrogenation over single-atom Pt catalysts. Two energetically favorable structures, -no aluminum vacancy (NAV) and -aluminum vacancy (AV), are identified with energies lower than the conventional model by up to 34.44 meV∙(f.u.)–1. These optimized structures expose abundant penta-coordinated Al3+ sites on the (100) surface, serving as preferred anchoring sites for Pt atoms. Simulated X-ray diffraction patterns indicate that -AV shows better qualitative agreement with experimental data. Surface phase diagrams further reveal that defect-rich surfaces are thermodynamically stabilized under realistic reaction conditions. Catalytic calculations demonstrate that -NAV and -AV significantly reduce propane dehydrogenation activation barriers through enhanced metal-support interactions associated with penta-coordinated Al3+ sites and defect-modulated local environments. These results suggest that -Al2O3 is better described as an ensemble of defect-rich surface configurations rather than a single crystal structure. These findings establish a direct relationship between atomic structure, defect chemistry, and catalytic performance in -Al2O3.
γ-Al2O3 / machine learning interatomic potential / propane dehydrogenation / single-atom catalysis / defect chemistry
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