OPNet: Optimized multi-head graph attention network for polymer properties prediction

Abstract

The optimized multi-head graph attention network (OPNet) uses the multi-head graph attention network to predict both the thermal decomposition temperature with a 5% mass loss (Td5%) and the glass transition temperature (Tg) of polymers as a machine learning model. The OPNet model provides reliable performance predictions for the Td5% and Tg datasets. The OPNet regression evaluation metrics R2 of Td5% is 0.76, which is the best, and the regression evaluation metrics R2 of Tg is 0.91, which is better than the current existing method models (the best model R2 ≈ 0.90). The OPNet model is an end-to-end network model, eliminating the need for manual data filtering or feature extraction. By analyzing the feature weights of the OPNet model, it is found that structures such as benzene rings play a more important role. At the same time, we have confirmed through other literature that such structures do have better stability and a higher thermal decomposition temperature and glass transition temperature. Therefore, the OPNet model exhibits interpretability and holds significant reference value for the field of materials science.