Leveraging data-driven strategy for accelerating the discovery of polyesters with targeted glass transition temperatures

To overcome the limitations of empirical synthesis and expedite the discovery of new polymers, this work aims to develop a data-driven strategy for profoundly aiding in the design and screening of novel polyester materials. Initially, we collected 695 polyesters with their associated glass transition temperatures (Tgs) to develop a quantitative structure-property relationship (QSPR) model. The model underwent rigorous validation (i.e., external validation, internal validation, Y-random, and application domain analysis) to demonstrate its robust predictive capabilities and high stability. Subsequently, by employing an in-silico retrosynthesis strategy, over 95,000 virtual polyesters were designed, largely expanding the available space for polyester material family. External assessments were performed, highlighting good extrapolation ability of the QSPR model. Furthermore, we experimentally synthesized 10 designed polyesters with predicted Tgs covering a large temperature range from -42.52 to 103.61 degrees C, and characterization results gave an average absolute error of 17.40 degrees C relative to the predicted ones. It is believed that such data-driven approach can drive future product development of polymer industry.