Zhang Yun, Xu Xiaojie
North Carolina State University, Raleigh, NC 27695, USA.
Heliyon. 2020 Oct 6;6(10):e05055. doi: 10.1016/j.heliyon.2020.e05055. eCollection 2020 Oct.
As an important thermophysical property, polymers' glass transition temperature, Tg, could sometimes be difficult to determine experimentally. Modeling methods, particularly data-driven approaches, are promising alternatives to predictions of Tg in a fast and robust way. The molecular traceless quadrupole moment and molecule average hexadecapole moment are closely correlated with polymers' Tg. In the current work, these two parameters are used as descriptors in the Gaussian process regression model to predict Tg. We investigate 60 samples with Tg values from 194 K to 440 K. The model provides rapid and low-cost Tg estimations with high accuracy and stability.
作为一种重要的热物理性质，聚合物的玻璃化转变温度Tg有时很难通过实验确定。建模方法，特别是数据驱动方法，有望以快速且稳健的方式预测Tg。分子无迹四极矩和分子平均十六极矩与聚合物的Tg密切相关。在当前工作中，这两个参数被用作高斯过程回归模型中的描述符来预测Tg。我们研究了60个Tg值在194 K至440 K之间的样品。该模型能够以高精度和稳定性提供快速且低成本的Tg估计。
