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 (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 95000 virtual polyesters were designed, largely expanding the available space for polyester materials. External assessments highlight the good extrapolation ability of the QSPR model. Furthermore, we experimentally synthesized diverse virtual polyesters with Tgs covering a sufficient large temperature range. It is believed that this data-driven approach can drive future product development of polymer industry.

The thermodynamic properties at variable temperature and pressure, such as density (ρ) and viscosity (η) are necessary in chemical process design. The quantitative structure-property relationship (QSPR) is a quick and accurate method to obtain the properties from a large number of potential ionic liquids (ILs). The QSPR models for ρ and η may have “pseudo-high” robustness validated by leave-one-out cross-validation (LOO-CV) and weakened stability with the unbalanced data point distribution. A rigorous model evaluation method named the leave-one-ion-out cross-validation (LOIO-CV) was proposed to evaluate robustness of ILs QSPR models. Balancing the distribution of data points in ILs, two f(T,P,I)-QSPR models were developed with norm index (I) to predict ρ and η of ILs at variable temperature and pressure. LOIO-CV method can enhance the stability QSPR model in predicting the properties of ILs with new cations and anions, which is essential for data driven design of ILs.