QSPR analysis of copolymers by recursive neural networks: Prediction of the glass transition temperature of (meth)acrylic random copolymers

The glass transition temperature (Tg) of acrylic and methacrylic random copolymers was investigated by means of Quantitative Structure-Property Relationship (QSPR) methodology based on Recursive Neural Networks (RNN). This method can directly take molecular structures as input, in the form of labelled trees, without needing predefined descriptors. It was applied to three data sets containing up to 615 polymers (340 homopolymers and 275 copolymers). The adopted representation was able to account for the structure of the repeating unit as well as average macromolecular characteristics, such as stereoregularity and molar composition. The best result, obtained on a data set focused on copolymers, showed a Mean Average Residual (MAR) of 4.9 K, a standard error of prediction (S) of 6.1 K and a squared correlation coefficient (R2) of 0.98 for the test set, with an optimal rate with respect to the training error. Through the treatment of homopolymers and copolymers both as separated and merged data sets, we also showed that the proposed approach is particularly suited for generalizing prediction of polymer properties to various types of chemical structures in a uniform setting.