Zhang Yun, Xu Xiaojie
North Carolina State University, Raleigh, NC, 27695, USA.
J Mol Graph Model. 2021 Mar;103:107796. doi: 10.1016/j.jmgm.2020.107796. Epub 2020 Nov 10.
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而言，玻璃化转变温度Tg是一个重要的热物理参数，有时通过实验测量和确定该参数颇具难度。基于数据驱动建模的方法提供了以快速且稳健的方式预测Tg的替代方法。研究了高斯过程回归（GPR）模型，以呈现苯乙烯无规共聚物的重要量子化学描述符与玻璃化转变温度之间的统计关系。研究了48个已通过实验测量Tg的样品，其范围为246 K至426 K。该建模方法展示出了高精度和稳定性，并为高效且低成本地估算共聚物Tg值提供了一种新颖且有前景的工具。
