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Issue 6, 2021
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Machine learning glass transition temperature of polyacrylamides using quantum chemical descriptors

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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

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Article information


Submitted
15 Nov 2020
Accepted
06 Jan 2021
First published
18 Jan 2021

Polym. Chem., 2021,12, 843-851
Article type
Paper

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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