A physical organic strategy to predict and interpret stabilities of chemical bonds in energetic compounds for the discovery of thermal-resistant properties

IF 2.1 4区 化学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY
Haitao Liu, Peng Chen, Xin Huang, Xianfeng Wei
{"title":"A physical organic strategy to predict and interpret stabilities of chemical bonds in energetic compounds for the discovery of thermal-resistant properties","authors":"Haitao Liu,&nbsp;Peng Chen,&nbsp;Xin Huang,&nbsp;Xianfeng Wei","doi":"10.1007/s00894-024-05877-5","DOIUrl":null,"url":null,"abstract":"<div><h3>Context</h3><p>The in-depth understanding about the stability of chemical bonds in energetic compounds plays a central role for molecular design and safety-related evaluations. Most energetic compounds contain nitro as explosophores, and nitro cleavage is fundamental for thermal and mechanical stability. However, the quantum chemistry approach to accurately predict energy and temperature properties related to bond stability is challenging, due to the tradeoff between computational costs and deviations. Herein, the bond orders are proposed as accurate and computational-cost efficient descriptors for predicting the chemical bond stability and thermal-resistant properties. The intrinsic bond strength index (IBSI) demonstrates the best prediction for experimental homolytic bond dissociation energies (<i>R</i><sup>2</sup> &gt; 0.996), which is on par with the results from high-precision quantum chemistry methods. The effects from bond connectivity and steric hindrance hierarchy were analyzed to reveal underlying mechanisms. Additionally, the IBSI descriptors are successfully applied to predict the thermal decomposition temperatures of 24 heat-resistant energetic compounds (<i>R</i><sup>2</sup> = 0.995), thus validating the effectiveness for the prediction and interpretation of chemical bond stability in energetic compounds via a physical organic approach.</p><h3>Methods</h3><p>All DFT calculations were performed with Gaussian 09 software. To investigate the dependence of the method on functionals and basis sets, 9 DFT methods were considered (B3LYP/6-31G(d,p), B3LYP/6-311G(d,p), B3LYP/def2-TZVP, M062X/6-31G(d,p), M062X/6-311G(d,p), M062X/def2-TZVP, ωB97XD/6-31G(d,p), ωB97XD/6-311G(d,p), and ωB97XD/def2-TZVP). The bond order descriptors LBO and IBSI are obtained through the bond order analysis module in the Multiwfn software.</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":"30 3","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2024-02-26","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/s00894-024-05877-5","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

The in-depth understanding about the stability of chemical bonds in energetic compounds plays a central role for molecular design and safety-related evaluations. Most energetic compounds contain nitro as explosophores, and nitro cleavage is fundamental for thermal and mechanical stability. However, the quantum chemistry approach to accurately predict energy and temperature properties related to bond stability is challenging, due to the tradeoff between computational costs and deviations. Herein, the bond orders are proposed as accurate and computational-cost efficient descriptors for predicting the chemical bond stability and thermal-resistant properties. The intrinsic bond strength index (IBSI) demonstrates the best prediction for experimental homolytic bond dissociation energies (R2 > 0.996), which is on par with the results from high-precision quantum chemistry methods. The effects from bond connectivity and steric hindrance hierarchy were analyzed to reveal underlying mechanisms. Additionally, the IBSI descriptors are successfully applied to predict the thermal decomposition temperatures of 24 heat-resistant energetic compounds (R2 = 0.995), thus validating the effectiveness for the prediction and interpretation of chemical bond stability in energetic compounds via a physical organic approach.

Methods

All DFT calculations were performed with Gaussian 09 software. To investigate the dependence of the method on functionals and basis sets, 9 DFT methods were considered (B3LYP/6-31G(d,p), B3LYP/6-311G(d,p), B3LYP/def2-TZVP, M062X/6-31G(d,p), M062X/6-311G(d,p), M062X/def2-TZVP, ωB97XD/6-31G(d,p), ωB97XD/6-311G(d,p), and ωB97XD/def2-TZVP). The bond order descriptors LBO and IBSI are obtained through the bond order analysis module in the Multiwfn software.

Graphical abstract

Abstract Image

预测和解释高能化合物化学键稳定性的物理有机策略,以发现耐热特性
背景深入了解高能化合物中化学键的稳定性对于分子设计和安全相关评估至关重要。大多数高能化合物都含有硝基作为爆炸物,而硝基裂解是热稳定性和机械稳定性的基础。然而,由于计算成本和偏差之间的权衡,用量子化学方法准确预测与键稳定性相关的能量和温度特性具有挑战性。在此,我们提出了键序,作为预测化学键稳定性和耐热性能的精确且计算成本低廉的描述符。本征键强度指数(IBSI)对实验同解键解离能的预测效果最佳(R2 > 0.996),与高精度量子化学方法的结果相当。分析了键连通性和立体阻碍层次的影响,以揭示其潜在机制。此外,IBSI 描述因子还成功地应用于预测 24 种耐热高能化合物的热分解温度(R2 = 0.995),从而验证了通过物理有机方法预测和解释高能化合物化学键稳定性的有效性。为了研究该方法对函数和基集的依赖性,考虑了 9 种 DFT 方法(B3LYP/6-31G(d,p)、B3LYP/6-311G(d、p)、B3LYP/def2-TZVP、M062X/6-31G(d,p)、M062X/6-311G(d,p)、M062X/def2-TZVP、ωB97XD/6-31G(d,p)、ωB97XD/6-311G(d,p)和 ωB97XD/def2-TZVP)。键序描述符 LBO 和 IBSI 是通过 Multiwfn 软件中的键序分析模块获得的。
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 求助全文
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.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信