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

IF 4.8 3区 化学 Q2 CHEMISTRY, MULTIDISCIPLINARY
Lian Zhuo, Yaqin Zheng, Lei Zeng, Yilin Zhao, Meng Li, Chunying Rong, Shubin Liu
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引用次数: 0

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.

路易斯酸度和路易斯碱度的定量:基于密度的反应性理论研究
刘易斯酸度和碱度是化学、生物学和相关学科中应用最广泛的概念之一。然而,他们的精确计算和预测仍然具有挑战性。在这项研究中,我们采用了从基于密度的反应性理论衍生出来的描述符来提供一个新的定量视角。为此,我们在两种类型的反应中分析了四个系列的路易斯酸和碱。我们的研究结果表明,利用密度泛函概念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Lewis酸度和碱度。此外,各种电子性质,包括前沿分子轨道、分子静电势、自然价原子轨道能和几种类型的原子电荷,被确定为鲁棒描述符。利用这些特征,我们构建了能够准确预测Lewis酸度和碱度的机器学习模型。我们还发现刘易斯酸度/碱度与亲电性/亲核性之间存在很强的相关性,进一步弥合了这些概念框架。在描述符之间获得一致的高相关性,再加上我们的机器学习模型的性能,证实了Lewis酸度和Lewis碱度可以以高保真度定量表征。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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来源期刊
CiteScore
6.60
自引率
3.30%
发文量
247
审稿时长
1.7 months
期刊介绍: This distinguished journal publishes articles concerned with all aspects of computational chemistry: analytical, biological, inorganic, organic, physical, and materials. The Journal of Computational Chemistry presents original research, contemporary developments in theory and methodology, and state-of-the-art applications. Computational areas that are featured in the journal include ab initio and semiempirical quantum mechanics, density functional theory, molecular mechanics, molecular dynamics, statistical mechanics, cheminformatics, biomolecular structure prediction, molecular design, and bioinformatics.
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