Prediction of high weight polymers glass transition temperature using RBF neural networks

A novel approach to the prediction of the glass transition temperature (Tg) for high molecular polymers is presented. A new quantitative structure-property relationship (QSPR) model is obtained using Radial Basis Function (RBF) neural networks and a set of four-parameter descriptors, ∑MV(ter)(Rter), LF, ΔXSB and ∑PEI. The produced QSPR model (R2=0.9269) proved to be considerably more accurate compared to a multiple linear regression model (R2=0.8227).