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 (MO₂) catalysts. Our findings reveal a strong dependence of adsorption energy (<i>E</i> _ad) on two primary descriptors: the effective charge (<i>e</i> _eff) of oxygen atoms in oxygenates and the electron affinity (EA) of the surface metal atoms in MO₂. We observed that oxygenates with more negative <i>e</i> _eff exhibit stronger adsorption, while MO₂ with lower EA offer greater adsorption stability. Using these two descriptors, a predictive <i>E</i> _ad scaling relationship was developed and validated across different MO₂ surfaces. This descriptor-based model establishes an efficient framework for accurately predicting adsorption strength and offers valuable theoretical insights for designing and screening MO₂ catalysts with optimized adsorption properties.