Machine learning glass transition temperature of polyacrylamides using quantum chemical descriptors
Glass transition temperature, Tg, 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 Tg 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.