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首页> 外文期刊>Computational Chemistry >Development of Predictive QSPR Model of the First Reduction Potential from a Series of Tetracyanoquinodimethane (TCNQ) Molecules by the DFT (Density Functional Theory) Method
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Development of Predictive QSPR Model of the First Reduction Potential from a Series of Tetracyanoquinodimethane (TCNQ) Molecules by the DFT (Density Functional Theory) Method

机译：用DFT（密度泛函理论）方法开发一系列季戊四氮合二甲烷（TCNQ）分子的第一还原势的预测QSPR模型

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In this work, which consisted to develop a predictive QSPR (Quantitative Structure-Property Relationship) model of the first reduction potential, we were particularly interested in a series of forty molecules. These molecules have constituted our database. Here, thirty molecules were used for the tra ining set and ten molecules were used for the test set. For the calculation of the descriptors, all molecules have been firstly optimized with a frequency calculation at B3LYP/6-31G(d,p) theory level. Using statistical analysis methods, a predictive QSPR (Quantitative Structure-Property Relationship) model of the first reduction potential dependent on electronic affinity (EA) only have been developed. The statistical and validation parameters derived from this model have been determined and found interesting. These different parameters and the realized statistical tests have revealed that this model is suitable for predicting the first reduction potential of future TCNQ (tetracyanoquinodimethane) of this same family belonging to its applicability domain with a 95% confidence level.
机译：在这项工作中，该工作包括建立第一个还原电位的预测QSPR（定量结构-性质关系）模型，我们对一系列40个分子特别感兴趣。这些分子构成了我们的数据库。这里，交易集使用了30个分子，测试集使用了10个分子。为了计算描述符，所有分子都首先在B3LYP / 6-31G（d，p）理论水平上通过频率计算进行了优化。使用统计分析方法，已经开发出仅依赖于电子亲和力（EA）的第一还原电位的预测QSPR（定量结构-性质关系）模型。从该模型导出的统计和验证参数已经确定，并且很有趣。这些不同的参数和已实现的统计测试表明，该模型适用于以95％的置信度预测属于该适用范围的同一家族的未来TCNQ（四氰基对二甲烷）的首次还原潜力。

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