Improved Quantitative Structure Property Relationships for the Prediction of Dielectric Constants for a Set of Diverse Compounds by Subsetting of the Data Set

Abstract

In a recent publication we explored the development of quantitative structure property relationships for the calculation of dielectric constants, which resulted in a general model for a wide range of compounds. Our current work explores the division of the set of compounds into eight more homogeneous subsets for which local models are developed. The full data set consists of 454 compounds with dielectric constants ranging from 1 to 40. A pool of up to 16 molecular descriptors is calculated for each of the eight data sets. The descriptors include dipole moment, polarizability, counts of elemental types or functional groups, charged partial surface area, and molecular connectivity. All possible 4-16 descriptor models are calculated for each of the eight data sets, and the best models are selected and compared to the results obtained from the best general model for all 454 compounds. Neural networks using the Broyden-Fletcher-Goldfarb-Shanno training algorithm are employed to build the models. The resulting combined mean test set error for the eight local models of 1.31 is significantly better than the mean test set error of 1.85 for the general model.

Quantitative Correlation of Physical and Chemical Properties with Chemical Structure: Utility for Prediction

“…The molecular connectivity and CPSA descriptors were found to make important contributions to the model. The authors developed improved QSPR models for the prediction of dielectric constants for various subsets of compounds . Their full data set consisted of 454 compounds with dielectric constants ranging from 1 to 40.…”

“…The authors developed improved QSPR models for the prediction of dielectric constants for various subsets of compounds. 263 Their full data set consisted of 454 compounds with dielectric constants ranging from 1 to 40. The authors divided the full data set into eight subsets, and for each subset, molecular descriptors were calculated as in their previous paper and nonlinear models were constructed.…”

A general quantitative structure–property relationship treatment for dielectric constants of polymers

“…The dielectric constant is an important fundamental molecular property, which can be a useful predictor of other electrical properties of polymers [1–3]. Nevertheless, accurate experimental ε values of polymers are often unavailable.…”

“…The QSPR approach has been successfully used to predict many properties of polymers, such as refractive index [1, 9–14], glass transition temperature [10, 15–23], cohesive energy [24], thermal decomposition temperature [25], and solubility parameter [26]. Meanwhile, several QSPR models for the dielectric constants of small organic molecules have also been reported in the literature [3, 27–30]. However, there have been relatively few attempts to correlate and predict the dielectric constants of polymers [1, 31].…”

Electric Multipole Moments and (Hyper)Polarizability of X–C≡C–X, X = F, Cl, Br and I

“…The systematic use of electric polarizabilities in modeling the pharmacological activity of molecular substances [28] and in quantitative structure-activity relationship (QSAR) studies [29] has also been noted.…”