Machine learning glass transition temperature of polyacrylamides using quantum chemical descriptors

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.

Automated summary

Glass transition temperature, T-g, is an important thermophysical property of polyacrylamides that can be difficult and resource-intensive to determine. A Gaussian process regression model based on quantum chemical descriptors is developed to predict T-g, showing high stability and accuracy for fast and low-cost estimations.