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.

对于苯乙烯无规共聚物，玻璃化转变温度Tg是重要的热物理参数，有时难以通过实验测量和确定。基于数据驱动的建模的方法提供了另一种方法来以快速，可靠的方式预测Tg。研究了高斯过程回归（GPR）模型，以给出苯乙烯无规共聚物重要的量子化学描述子与玻璃化转变温度之间的统计关系。探索了实验测量的48个Tg样品，范围从246 K到426K。该建模方法证明了其高准确性和稳定性，并为有效且低成本地估算共聚物Tg值提供了一种新颖而有前途的工具。