Polymer Informatics Method for Fast and Accurate Prediction of the Glass Transition Temperature from Chemical...

We present a new polymer informatics framework that successfully predicts the glass transition temperature T _g of polymers based on their chemical structure. The framework combines ideas from group additive properties (GAP) and quantitative structure-property relationship (QSPR) methods, where GAP (or group contributions) assumes that submonomer motifs contribute additively to T _g, and QSPR links T _g to the physicochemical properties of the structure through a set of molecular descriptors. By integrating these methodologies, our combined QSPR-GAP framework overcomes limitations inherent in using either method independently. We demonstrate its application on a data set of 146 linear homo- and copolymers of the poly-(aryl ether ketone) (PAEK) family, achieving a median root mean square error of 8 K for T _g, representing a significant improvement over standalone QSPR or GAP models. Moreover, using a genetic algorithm, we identify two molecular descriptors that predominantly drive T _g predictions. The QSPR-GAP framework can be readily adapted to forecast other physical properties and activity (QSAR) or transferred to other polymer families, including conjugated and biopolymers.