A QSPR Study of the Glass Transition Temperatures of Linear Polymers and Cross-linked Epoxy Resins

Abstract

This work proposes a unified approach to predict glass transition temperatures (Tgs) of polymers by machine-learning approaches based QSPR (Quantitative Structure–Property Relationships) study. Our approach encompass all the three senarios: linear homo- and heteropolymers, plus reticulated heteropolymers by generating descriptors of reagents undergoing polymerization. Three predictive SVR (Support Vector Regression) models are discussed here generated from ISIDA (In Silico design and Data Analysis) descriptors. In 12 times repeated 3-fold cross-validation challenges, it displayed the highest accuracy of Q2 = 0.920, RMSE = 34.3 K over the training set of 270 polymers, and R2 = 0.779, RMSE 35.9 K for an external test set of 119 polymers. GTM (Generative Topographic Mapping) analysis produced a 2D map of “polymer chemical space”, highlighting the various classes of polymers included in the study and their relationship with respect to Tg values.