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路易斯酸度和路易斯碱度的量化:基于密度的反应活性理论研究

Quantification of Lewis Acidity and Lewis Basicity: A Density-Based Reactivity Theory Study.

作者信息

Zhuo Lian, Zheng Yaqin, Zeng Lei, Zhao Yilin, Li Meng, Rong Chunying, Liu Shubin

机构信息

College of Chemistry and Chemical Engineering, Hunan Normal University, Changsha, Hunan, China.

Department of Chemistry and Chemical Biology, McMaster University, Hamilton, Ontario, Canada.

出版信息

J Comput Chem. 2025 Aug 15;46(22):e70212. doi: 10.1002/jcc.70212.

Abstract

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.

摘要

路易斯酸度和碱度是化学、生物学及相关学科中应用最为广泛的概念之一。然而,对它们进行准确的计算和预测仍然具有挑战性。在本研究中,我们采用基于密度的反应性理论导出的描述符,提供一种全新的定量视角。为此,我们分析了两类反应中的四系列路易斯酸和碱。我们的结果表明,利用概念密度泛函理论中的一系列全局和局部描述符以及密度泛函理论中的信息论方法,可以有效地量化路易斯酸度和碱度。此外,包括前线分子轨道、分子静电势、自然价原子轨道能量以及几种类型的原子电荷在内的各种电子性质,被确定为可靠的描述符。利用这些特征,我们构建了能够准确预测路易斯酸度和碱度的机器学习模型。我们还发现路易斯酸度/碱度与亲电性/亲核性之间存在很强的相关性,进一步架起了这些概念框架之间的桥梁。描述符之间始终保持的高相关性,以及我们机器学习模型的性能,证实了路易斯酸度和路易斯碱度可以被高保真地定量表征。这项工作表明,基于密度的框架可以为理解软硬酸碱原理提供一个强大而新颖的基础。

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