• glass transition temperature;
  • modeling and predicting;
  • polymethacrylates;
  • support vector regression;
  • regression analysis


Thumbnail image of graphical abstract

Based on six quantum chemical descriptors (|L-1.356|, Etotal, qC6, α, q, and Etherm), the hybrid PSO-SVR is proposed to establish a model for predicting the glass transition temperature (Tg) of 37 polymethacrylates. The prediction performance of SVR was compared with those of reported MLR and ANN models. The results show that the RMSE, MAPE, and R2 calculated by SVR are superior to those achieved by MLR or ANN model for the identical training set and test set. This investigation reveals that the SVR model is more suitable to be used for prediction of the Tg values for unknown polymethacrylates possessing similar structure than the conventional MLR or ANN model, and provides a clue that the method proposed in this study may be useful in computer-aided design of new polymethacrylates with desired Tg.