Glass Transition Temperature Prediction of Polymers via Graph Reinforcement Learning

An expansive array of graph-based models has been utilized for accurate prediction of the structure-property relation of polymers. However, these approaches notably underutilize unsupervised structural information. Concentrating on the domain of heterocyclic polymers, particularly polyimides, this study delves into the glass transition temperature (