Prediction of glass transition temperatures of aromatic heterocyclic polyimides using an ANN model

Aromatic heterocyclic polyimides are used extensively in industries for their excellent mechanical properties, high glass transition temperatures ( T g ), and so on. A quantitative structure–property relationship (QSPR) model was developed to predict the T g values with 54 aromatic heterocyclic polyimides by using an artificial neural network (ANN) back‐propagation algorithm. Fifty‐four aromatic heterocyclic polyimides were randomly divided into a training set (36) and a test set (18). Three molecular descriptors (the connectivity index X1A , the topological descriptor PW3 , and the 3D‐MoRSE descriptor Mor09e ) were selected to produce the mode. Simulated with the final optimum ANN model with 3‐3‐1 structure, the results show that the predicted T g values are in good agreement with the experimental ones, with the root mean square errors (RMSEs) of 12.4 K ( R = 0.935) and 16.4 K ( R = 0.937) for the training set and the test set, respectively. POLYM. ENG. SCI., 50:1547–1557, 2010.