Accelerating Polyester Intelligence: Machine-Learning-Assisted Prediction of Glass Transition Temperature and Virtual Molecules Screening

Rapid development of the economy and society has resulted in a need for polyesters that are tailored to diverse performance requirements. Unfortunately, the innovation of polyester materials is mainly dependent on experience and intuitive guidance. Herein, we propose various interpretable quantitative-structure-property relationship (QSPR) models based on machine-learning-assisted approaches, which can accurately predict polyesters' glass transition temperatures (<i>T</i>_g) and facilitate the exploration of novel polyesters. Initially, 695 polyesters with <i>T</i>_g values are collected to establish multiple QSPR models using three different algorithms, which undergo both internal and external validation. The relative coefficient (<i>R</i>²) values of the best deep neural network (DNN) model on the training set and testing set reach 0.9588 and 0.9314, respectively, which is among the better levels in related studies. The use of Morgan fingerprint with frequency (MFF) descriptors and associated Shapley Additive Explanations analysis does reveal a couple of interesting physical trends associated with variation of <i>T</i>_g with the substructure beyond what was reported before. To better widen the chemical space of the existing polyester material family, a virtual polyester library is constructed using a retrosynthetic strategy. Furthermore, this workflow identifies 20 novel polyesters with low synthetic complexity by high-throughput screening and validates these polyesters through molecular dynamics simulations, which show an average absolute error of 9.42 °C between the model-predicted and MD-simulated values. Machine-learning-assisted approach not only improves the efficiency of polyester material discovery but also provides a promising perspective for understanding the thermal properties of polyesters from a microscopic chemical structural viewpoint.