Prediction of the Specific Heat of Polymers from Experimental Data and Machine Learning Methods

Abstract Specific heat at constant pressure (Cp) of polymers is an important physical quantity that varies with temperature and is an essential parameter in characterizing thermal diffusivity of materials. Despite the need to predict both thermal evolution in structural composites and the resulting in situ and ex situ impacts on performance, there remain significant challenges in predicting Cp. In this work, we took an initial step toward predicting Cp at room temperature by applying machine learning (ML) methods. Decision Tree and Principal Component Analysis methods were employed in a comprehensive ML investigation. Despite the relatively small data set, the results indicate our approach is useful in designing novel polymers with desired Cp values. Our investigation about the underlying impact of polymer descriptors on Cp could pave the way for experimental syntheses of novel materials with tailored properties.