自上而下宏观测量的自下而上原子论描述:哈米特电子参数的计算基准

IF 3.7 Q2 CHEMISTRY, PHYSICAL
Guilian Luchini,  and , Robert S. Paton*, 
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引用次数: 0

摘要

将取代基的电子效应与化学反应性联系起来的能力是物理有机化学和线性自由能关系的基石。电子参数的计算越来越具有吸引力,因为它们可以在没有可用实验数据的情况下快速获得结构和取代基的参数,并且可以应用于芳香取代基以外的领域,例如过渡金属配合物以及脂肪族和自由基体系的研究。然而,使用 "自下而上 "的计算方法描述 "自上而下 "的宏观观测指标(如哈梅特参数),给实践者带来了一些挑战。我们对各种计算电荷方案的性能进行了研究和基准测试,其中包括分割电荷密度的量子力学方法、将电荷与物理观测值相匹配的方法,以及通过对 NMR 值进行半经验调整而增强的方法。我们研究了用于获取这些描述符的原子位置及其与经验哈米特参数和电子效应导致的速率差异的相关性。这些看似微小的选择所产生的影响远比以前想象的要大得多,甚至超过了所使用的理论或基础集的水平。我们观察到不同计算协议的性能差异很大,并观察到计算参数在捕捉副电子效应与元电子效应的能力上存在明显而令人惊讶的差异。一般来说,σm 的预测结果比 σp 差得多。因此,选择在何处计算这些描述符--环碳或相连的 H 原子或其他取代原子--会影响其捕捉实验电子差异的能力。基于密度的方案(如 Hirshfeld 电荷)对附近官能团产生的非物理电荷扰动更加稳定,性能优于所有其他计算描述符,包括几种常用的基于基集的方案(如自然群体分析)。使用附着原子还能改善统计相关性。我们从计算描述符中获得了全局预测实验哈梅特参数的一般线性关系,可用于统计建模研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Bottom-Up Atomistic Descriptions of Top-Down Macroscopic Measurements: Computational Benchmarks for Hammett Electronic Parameters

Bottom-Up Atomistic Descriptions of Top-Down Macroscopic Measurements: Computational Benchmarks for Hammett Electronic Parameters

Bottom-Up Atomistic Descriptions of Top-Down Macroscopic Measurements: Computational Benchmarks for Hammett Electronic Parameters

The ability to relate substituent electronic effects to chemical reactivity is a cornerstone of physical organic chemistry and Linear Free Energy Relationships. The computation of electronic parameters is increasingly attractive since they can be obtained rapidly for structures and substituents without available experimental data and can be applied beyond aromatic substituents, for example, in studies of transition metal complexes and aliphatic and radical systems. Nevertheless, the description of “top-down” macroscopic observables, such as Hammett parameters using a “bottom-up” computational approach, poses several challenges for the practitioner. We have examined and benchmarked the performance of various computational charge schemes encompassing quantum mechanical methods that partition charge density, methods that fit charge to physical observables, and methods enhanced by semiempirical adjustments alongside NMR values. We study the locations of the atoms used to obtain these descriptors and their correlation with empirical Hammett parameters and rate differences resulting from electronic effects. These seemingly small choices have a much more significant impact than previously imagined, which outweighs the level of theory or basis set used. We observe a wide range of performance across the different computational protocols and observe stark and surprising differences in the ability of computational parameters to capture para- vs meta-electronic effects. In general, σm predictions fare much worse than σp. As a result, the choice of where to compute these descriptors─for the ring carbons or the attached H or other substituent atoms─affects their ability to capture experimental electronic differences. Density-based schemes, such as Hirshfeld charges, are more stable toward unphysical charge perturbations that result from nearby functional groups and outperform all other computational descriptors, including several commonly used basis set based schemes such as Natural Population Analysis. Using attached atoms also improves the statistical correlations. We obtained general linear relationships for the global prediction of experimental Hammett parameters from computed descriptors for use in statistical modeling studies.

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来源期刊
CiteScore
3.70
自引率
0.00%
发文量
0
期刊介绍: ACS Physical Chemistry Au is an open access journal which publishes original fundamental and applied research on all aspects of physical chemistry. The journal publishes new and original experimental computational and theoretical research of interest to physical chemists biophysical chemists chemical physicists physicists material scientists and engineers. An essential criterion for acceptance is that the manuscript provides new physical insight or develops new tools and methods of general interest. Some major topical areas include:Molecules Clusters and Aerosols; Biophysics Biomaterials Liquids and Soft Matter; Energy Materials and Catalysis
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