Prediction of glass transition temperatures of aromatic heterocyclic polymers

Abstract

Abstract A quantitative structure –property relationship model was constructed to correlate the glass transition temperatures (T g) of aromatic heterocyclic polymers with four molecular descriptors calculated from the repeating units. Multiple linear regression analysis was used to develop the model. The data set of 52 polymers was randomly divided into a training set, and a testing set. The training set was used to develop the model, which was evaluated externally using the test set. Even with the diversity of the data set, the optimal model exhibited some predictive power in estimating the T g, with the mean relative errors of 3.299% (R = 0.963, n = 28) for the training set and 3.064% (R = 0.959, n = 23) for the test set.