Quantitative Structure─Permittivity Relationship Study of a Series of Polymers

Abstract

Dielectric constant is an important property which is widely utilized in many scientific fields and characterizes the degree of polarization of substances under the external electric field. In this work, a structure–property relationship of the dielectric constants (ε) for a diverse set of polymers was investigated. A transparent mechanistic model was developed with the application of a machine learning approach that combines genetic algorithm and multiple linear regression analysis, to obtain a mechanistically explainable and transparent model. Based on the evaluation conducted using various validation criteria, four- and eight-variable models were proposed. The best model showed a high predictive performance for training and test sets, with R 2 values of 0.905 and 0.812, respectively. Obtained statistical performance results and selected descriptors in the best models were analyzed and discussed. With the validation procedures applied, the models were proven to have a good predictive ability and robustness for further applications in polymer permittivity prediction.

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“…Previous QSPR modeling approaches with respect to polymers have focused on determining various properties important to the materials science community, including, among others, dielectric constants, refractive indices, , glass transition temperatures, , viscosity, or fouling release activity . Although the chemical nature of macromolecular structures usually prevents direct applications of classical molecular descriptor generation methods to whole polymeric systems, the referenced studies include many examples of classification and regression models with high predictive performance.…”