路易斯酸
化学
密度泛函理论
计算化学
价(化学)
原子轨道
有机化学
催化作用
电子
物理
量子力学
作者
Lian Zhuo,Yaqin Zheng,Lei Zeng,Yilin Zhao,Meng Li,Chunying Rong,Shubin Liu
摘要
Lewis acidity and basicity are among the most widely applied concepts across chemistry, biology, and related disciplines. Yet, their accurate calculation and prediction remain challenging. In this study, we employ descriptors derived from density-based reactivity theory to offer a new and quantitative perspective. To this end, we analyzed four series of Lewis acids and bases across two types of reactions. Our results demonstrate that Lewis acidity and basicity can be effectively quantified using a range of global and local descriptors from conceptual density functional theory and an information-theoretic approach in density functional theory. Additionally, various electronic properties, including frontier molecular orbitals, molecular electrostatic potential, natural valence atomic orbital energies, and several types of atomic charges, were identified as robust descriptors. Leveraging these features, we constructed machine-learning models capable of accurately predicting Lewis acidity and basicity. We also uncovered a strong correlation between Lewis acidity/basicity and electrophilicity/nucleophilicity, further bridging these conceptual frameworks. The consistent high correlations obtained across descriptors, coupled with the performance of our machine learning models, confirm that Lewis acidity and Lewis basicity can be quantitatively characterized with high fidelity. This work suggests that density-based frameworks could provide a powerful and novel foundation for understanding the hard and soft acids and bases principle.
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