Information-theoretic quantities as effective descriptors of electrophilicity and nucleophilicity in density functional theory

IF 2.1 4区 化学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY Journal of Molecular Modeling Pub Date : 2024-09-18 DOI:10.1007/s00894-024-06116-7
Jia Fu, Meng Li, Chunying Rong, Dongbo Zhao, Shubin Liu
{"title":"Information-theoretic quantities as effective descriptors of electrophilicity and nucleophilicity in density functional theory","authors":"Jia Fu,&nbsp;Meng Li,&nbsp;Chunying Rong,&nbsp;Dongbo Zhao,&nbsp;Shubin Liu","doi":"10.1007/s00894-024-06116-7","DOIUrl":null,"url":null,"abstract":"<div><h3>Context</h3><p>Electrophilicity and nucleophilicity are two vastly important chemical concepts gauging the capability of atoms in molecules to accept and donate the maximal number of electrons. In our earlier studies, we proposed to simultaneously quantify them using the Kullback–Leibler divergence from the information-theoretic approach in density functional theory. However, several issues with this scheme remain to be clarified such as its general validity, predictability, and relationship with other information-theoretic quantities. In this work, we revisit the matter with bigger datasets and deeper theoretical insights. Five information-theoretic quantities including Kullback–Leibler divergence, Hirshfeld charge, Ghost-Berkowitz-Parr entropy, and second and third orders of relative Onicescu information energy are found to be reliable and robust descriptors of electrophilicity and nucleophilicity propensities. Employing these five descriptors, we design a list of new compounds and predict their electrophilicity and nucleophilicity scales. This work should markedly improve our confidence and capability in applying information-theoretic quantities to evaluate electrophilicity and nucleophilicity propensities and henceforth pave the route for more applications of these quantities from information-theoretic approach in density functional theory in the future.</p><h3>Methods</h3><p>All structures were fully optimized at the M06-2X/6–311 + G(d) level of DFT functional using the Gaussian 16 package (version C01) with integration grids and tight self-consistent-field convergence. The solvent effect was taken into account by using the implicit solvent model (CPCM) in the CH<sub>2</sub>Cl<sub>2</sub> solvent, and all 3D contour surfaces of Fukui function, local temperature, and ITA (information-theoretic approach) quantities were generated by GaussView. The Multiwfn 3.8 program was used to calculate the ITA indexes and atomic charges.</p><h3>Graphical Abstract</h3>\n<div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":651,"journal":{"name":"Journal of Molecular Modeling","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Molecular Modeling","FirstCategoryId":"92","ListUrlMain":"https://link.springer.com/article/10.1007/s00894-024-06116-7","RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Context

Electrophilicity and nucleophilicity are two vastly important chemical concepts gauging the capability of atoms in molecules to accept and donate the maximal number of electrons. In our earlier studies, we proposed to simultaneously quantify them using the Kullback–Leibler divergence from the information-theoretic approach in density functional theory. However, several issues with this scheme remain to be clarified such as its general validity, predictability, and relationship with other information-theoretic quantities. In this work, we revisit the matter with bigger datasets and deeper theoretical insights. Five information-theoretic quantities including Kullback–Leibler divergence, Hirshfeld charge, Ghost-Berkowitz-Parr entropy, and second and third orders of relative Onicescu information energy are found to be reliable and robust descriptors of electrophilicity and nucleophilicity propensities. Employing these five descriptors, we design a list of new compounds and predict their electrophilicity and nucleophilicity scales. This work should markedly improve our confidence and capability in applying information-theoretic quantities to evaluate electrophilicity and nucleophilicity propensities and henceforth pave the route for more applications of these quantities from information-theoretic approach in density functional theory in the future.

Methods

All structures were fully optimized at the M06-2X/6–311 + G(d) level of DFT functional using the Gaussian 16 package (version C01) with integration grids and tight self-consistent-field convergence. The solvent effect was taken into account by using the implicit solvent model (CPCM) in the CH2Cl2 solvent, and all 3D contour surfaces of Fukui function, local temperature, and ITA (information-theoretic approach) quantities were generated by GaussView. The Multiwfn 3.8 program was used to calculate the ITA indexes and atomic charges.

Graphical Abstract

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
信息理论量作为密度泛函理论中亲电性和亲核性的有效描述符
背景亲电性和亲核性是两个非常重要的化学概念,衡量分子中原子接受和提供最大数量电子的能力。在早期的研究中,我们曾提出利用密度泛函理论中信息论方法的库尔贝克-莱布勒发散同时量化这两个概念。然而,这一方案的几个问题仍有待澄清,如它的普遍有效性、可预测性以及与其他信息论量的关系。在这项工作中,我们用更大的数据集和更深入的理论见解重新审视了这一问题。研究发现,包括库尔巴克-莱伯勒发散、希尔施菲尔德电荷、Ghost-Berkowitz-Parr 熵以及相对奥尼克斯库信息能的二阶和三阶在内的五个信息理论量是亲电性和亲核性倾向性可靠而稳健的描述因子。利用这五种描述符,我们设计了一份新化合物清单,并预测了它们的亲电性和亲核性尺度。这项工作将显著提高我们应用信息理论量评估亲电性和亲核性倾向性的信心和能力,从而为今后在密度泛函理论中更多地应用这些信息理论量铺平道路。方法所有结构都在 M06-2X/6-311 + G(d) DFT 函数水平上使用高斯 16 软件包(C01 版)进行了完全优化,并使用了积分网格和紧密自洽场收敛。在 CH2Cl2 溶剂中使用隐式溶剂模型(CPCM)考虑了溶剂效应,并通过 GaussView 生成了 Fukui 函数、局部温度和 ITA(信息理论方法)量的所有三维等高线表面。利用 Multiwfn 3.8 程序计算了 ITA 指数和原子电荷。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Journal of Molecular Modeling
Journal of Molecular Modeling 化学-化学综合
CiteScore
3.50
自引率
4.50%
发文量
362
审稿时长
2.9 months
期刊介绍: The Journal of Molecular Modeling focuses on "hardcore" modeling, publishing high-quality research and reports. Founded in 1995 as a purely electronic journal, it has adapted its format to include a full-color print edition, and adjusted its aims and scope fit the fast-changing field of molecular modeling, with a particular focus on three-dimensional modeling. Today, the journal covers all aspects of molecular modeling including life science modeling; materials modeling; new methods; and computational chemistry. Topics include computer-aided molecular design; rational drug design, de novo ligand design, receptor modeling and docking; cheminformatics, data analysis, visualization and mining; computational medicinal chemistry; homology modeling; simulation of peptides, DNA and other biopolymers; quantitative structure-activity relationships (QSAR) and ADME-modeling; modeling of biological reaction mechanisms; and combined experimental and computational studies in which calculations play a major role.
期刊最新文献
Exploring structural variances in monatomic metallic glasses using machine learning and molecular dynamics simulation Interactions involved in the adsorption of ethylene glycol and 2-hydroxyethoxide on the Au(111) surface: a Density Functional Theory study Advances in machine learning methods in copper alloys: a review The molecular structure, electronic properties, and decomposition mechanism of FOX-7 under external electric field were calculated based on density functional theory A kinetic and mechanistic study of the self-reaction between two propargyl radicals
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1