Laboratoire d'Electrochimie, Chimie des Interfaces et Modélisation pour l'Energie, CNRS UMR-7575, Ecole Nationale Supérieure de Chimie de Paris, 11 rue P. et M. Curie, 75231, Paris Cedex 05, France.
J Mol Model. 2010 Apr;16(4):805-12. doi: 10.1007/s00894-009-0634-7. Epub 2010 Jan 5.
The quantitative structure-property relationship (QSPR) methodology was applied to predict the decomposition enthalpies of 22 nitroaromatic compounds, used as indicators of thermal stability. An extended series of descriptors (constitutional, topological, geometrical charge related and quantum chemical) was calculated at two different levels of theory: density functional theory (DFT) and semi-empirical AM1 approaches. Reliable models have been developed for each level, leading to similar correlations between calculated and experimental data (R(2) > 0.98). Hence, both of them can be employed as screening tools for the prediction of thermal stability of nitroaromatic compounds. If using the AM1 model presents the advantage to be less time consuming, DFT allows the calculation of more accurate molecular quantum properties, e.g., conceptual DFT descriptors. In this study, our best QSPR model is based on such descriptors, providing more chemical comprehensive relationships with decomposition reactivity, a particularly complex property for the specific class of nitroaromatic compounds.
定量构效关系(QSPR)方法被应用于预测 22 种用作热稳定性指示剂的硝基芳香族化合物的分解焓。在两种不同理论水平(密度泛函理论(DFT)和半经验 AM1 方法)下计算了扩展的描述符系列(结构、拓扑、几何电荷相关和量子化学)。为每个水平都建立了可靠的模型,导致计算数据与实验数据之间存在相似的相关性(R(2)>0.98)。因此,两者都可以用作预测硝基芳香族化合物热稳定性的筛选工具。如果使用 AM1 模型具有耗时更少的优点,那么 DFT 则可以计算更准确的分子量子性质,例如概念性 DFT 描述符。在这项研究中,我们最好的 QSPR 模型基于这些描述符,提供了与分解反应性更具化学综合性的关系,而分解反应性是硝基芳香族化合物这一特定类别中特别复杂的性质。