Prediction of Dielectric Constant in Series of Polymers by Quantitative Structure-Property Relationship (QSPR).

ABSTRACT: This work is devoted to the investigation of dielectric permittivity which is influenced by electronic, ionic, and dipolar polarization mechanisms, contributing to the material's capacity to store electrical energy. In this study, an extended dataset of 86 polymers was analyzed, and two quantitative structure-property relationship (QSPR) models were developed to predict dielectric permittivity. From an initial set of 1273 descriptors, the most relevant ones were selected using a genetic algorithm, and machine learning models were built using the Gradient Boosting Regressor (GBR). In contrast to Multiple Linear Regression (MLR)- and Partial Least Squares (PLS)-based models, the gradient boosting models excel in handling nonlinear relationships and multicollinearity, iteratively optimizing d

SUBMITTER: Ascencio-Medina E 

PROVIDER: S-EPMC11478900 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature