Glass transition temperature, T_g, is an important thermophysical property of polyacrylamides, which can be difficult to determine experimentally and resource-intensive to calculate. Data-driven modeling approaches provide alternative methods to predict T_g in a rapid and robust way. We develop the Gaussian process regression model to predict the glass transition temperature of polyacrylamides based on quantum chemical descriptors. The modeling approach shows a high degree of stability and accuracy, which contributes to fast and low-cost glass transition temperature estimations.

中文翻译：

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使用量子化学描述符对聚丙烯酰胺的机器学习玻璃化转变温度

玻璃化转变温度T _g是聚丙烯酰胺的重要热物理性质，这可能很难通过实验确定并且难以计算。数据驱动的建模方法提供了以快速而可靠的方式预测T _g的替代方法。我们开发了基于量子化学描述子的高斯过程回归模型，以预测聚丙烯酰胺的玻璃化转变温度。该建模方法显示出高度的稳定性和准确性，这有助于快速，低成本地估计玻璃化转变温度。