Development of Predictive QSPR Model of the First Reduction Potential from a Series of Tetracyanoquinodimethane (TCNQ) Molecules by the DFT (Density Functional Theory) Method
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 training 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.
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