Quantitative Structure-Property Relations for Polyester Materials via Statistical Learning

Statistical learning is employed to present a principled framework for the establishment of quantitative structure-property relationships (QSPR). Property predictions of industrial polymers formed by multiple reagents and at varying molecular weights are focused. A theoretical description of QSPR as well as a rigorous mathematical method is developed for the assimilation of experimental data. Results show that these methods can perform exceptionally well at establishing QSPR for glass transition temperature and intrinsic viscosity of polyesters. Statistical Learning QSPR: A leading-edge statistical learning model is used to predict the properties of polyesters. The model is tested using industrial data for glass transition temperature and intrinsic viscosity and shows improved predictions and adds uncertainty quantification beyond point estimates. This procedure serves as a template for further integration of advanced statistical methods in the field of QSPR. image