Machine learning glass transition temperature of styrenic random copolymers

Abstract

For styrenic random copolymers, the glass transition temperature, Tg, is an important thermophysical parameter, which is sometimes difficult to measure and determine by experiments. Approaches based on data-driven modeling provide alternative methods to predict Tg in a fast and robust way. The Gaussian process regression (GPR) model is investigated to present the statistical relationship between important quantum chemical descriptors and glass transition temperature for styrenic random copolymers. 48 samples with Tg that have been measured experimentally are explored, which range from 246 K to 426 K. The modeling approach demonstrates high accuracy and stability, and provides a novel and promising tool for efficient and low-cost estimations of copolymer Tg values.