Descriptor-Driven Prediction of Adsorption Energy of Oxygenates on Metal Dioxide Surfaces.

Adsorption is a critical factor in heterogeneous catalysis, as the interaction between adsorbate and adsorbent significantly impacts catalytic efficiency and selectivity. In this study, we utilized density functional theory (DFT) to comprehensively analyze the adsorption behavior of various oxygenates on the surfaces of metal dioxide (MO) catalysts. Our findings reveal a strong dependence of adsorption energy ( ) on two primary descriptors: the effective charge ( ) of oxygen atoms in oxygenates and the electron affinity (EA) of the surface metal atoms in MO. We observed that oxygenates with more negative exhibit stronger adsorption, while MO with lower EA offer greater adsorption stability. Using these two descriptors, a predictive scaling relationship was developed and validated across different MO surfaces. This descriptor-based model establishes an efficient framework for accurately predicting adsorption strength and offers valuable theoretical insights for designing and screening MO catalysts with optimized adsorption properties.