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 Kas 2020
Accepted
06 Oca 2021
First published
18 Oca 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

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements