Application of Supervised Machine Learning Regression Algorithm to Prediction of Dielectric Properties of PPy/Kufeki Stone Composites for Energy Implementations

Abstract

Highlights Supervised machine learning models predicted the dielectric parameters of polypyrrole composites.  Limited experimental data of the samples were used as training sets for machine learning models.  The accuracy of the estimated parameters was evaluated by R 2 , RMSE, and MAE errors.  The potential of these composites in energy storage applications was revealed by machine learning.

Machine learning models for estimating the AC conductivity mechanism of Edirne Kufeki stone reinforced Polypyrrole composites

“…In References 16 and 17, the ML models are used to investigate dielectric properties of PANI composites. Supervised ML regression algorithm are applied to predict the dielectric properties of polymer composites for energy implementations by Reference 18. Also there are many studies in the literature that use the ML algorithms for the prediction of the dielectric properties of polymer composites and nanomaterials 19–21 …”