Issue 6, 2021

Machine learning glass transition temperature of polyacrylamides using quantum chemical descriptors

Abstract

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.

Graphical abstract: Machine learning glass transition temperature of polyacrylamides using quantum chemical descriptors

Article information

Article type
Paper
Submitted
15 11 2020
Accepted
06 1 2021
First published
18 1 2021

Polym. Chem., 2021,12, 843-851

Machine learning glass transition temperature of polyacrylamides using quantum chemical descriptors

Y. Zhang and X. Xu, Polym. Chem., 2021, 12, 843 DOI: 10.1039/D0PY01581D

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