Designing Heat-Resistant and Moldable Polyester Resin by the Integration of Machine Learning Models with Expert Knowledge

Polyester resin has advantages in transparency and chemical resistance and is widely used in films and containers. In industrial applications, multiple conflicting properties of polyester resin must be optimized. Nevertheless, few reports have been found dealing with the design of polyester resins with machine learning (ML) models. Herein, we report a multiobjective design strategy of heat tolerant and moldable polyester resin, represented by the glass-transition temperature (Tg) and the softening point (SP). Our proposed workflow is an interplay between ML models and expert knowledge. Highly accurate interpretable linear regression models using chemical structural features were constructed for Tg and SP, which were utilized for evaluating previously uninvestigated monomers. Insights into substructures with which highly correlated properties (Tg and SP) were compromised were obtained by analyzing the regression coefficients of a linear model for SP/Tg. Based on the insight from the SP/Tg model, four dicarboxylic monomers consisting of untested molecular scaffolds were proposed and with which polyester resins were actually synthesized. The synthesized resins exhibited desired properties, consistent with prediction results by ML models. The reported workflow successfully proposed the dicarboxylic monomers with which polyester resins had desirable multiple properties.