Toward accurate machine learning-driven prediction of polymeric composites properties based on experimental data

Abstract

In response to climate change, there has been a focus on developing lightweight and environmentally friendly materials, with active research aimed at enhancing the energy efficiency of electric and hybrid vehicles. In this context, the development of polymer composites with superior thermal conductivity (TC) has been recognized as critical to meeting mechanical property requirements. This paper presents a machine learning model that utilized 1774 experimental data points to predict various properties of polymer composites, such as density, heat deflection temperature, flexural modulus, flexural strength, tensile yield strength, impact strength, and TC. Various data representation methods for composition data are employed, and the XGBoost model is trained, achieving high accuracy with an average R² score of 0.95. This machine learning model, informed by experimental data, is a useful tool for predicting and optimizing the properties of polymer composites.