Quantitative structure-property relations for polyester materials via statistical learning

We employ statistical learning to present a principled framework for the establishment of quantitative structure-property relationships (QSPR). We focus on property predictions of industrial polymers formed by multiple reagents and at varying molecular weights. We develop a theoretical description of QSPR as well as a rigorous mathematical method for the assimilation of experimental data. Results show that our methods can perform exceptionally well at establishing QSPR for glass transition temperature and intrinsic viscosity of polyesters.