Machine learning glass transition temperature of polymethacrylates

The glass transition temperature, Tg, is an important thermophysical property for polymethacrylates, which can be difficult to determine experimentally. Data-driven modeling approaches provide alternative methods to predict Tg in a rapid and robust way. Here, we develop the Gaussian process regressi...

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Bibliographic Details
Published inMolecular Crystals and Liquid Crystals Vol. 730; no. 1; pp. 9 - 22
Main Authors Zhang, Yun, Xu, Xiaojie
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 22.11.2021
Taylor & Francis Ltd
Subjects
Gaussian process
Gaussian process regression
Glass transition temperature
Machine learning
polymethacrylate
Polymethacrylates
Quantum chemistry
Regression models
Robustness (mathematics)
Temperature
Thermophysical properties
Transition temperatures
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Summary:The glass transition temperature, Tg, is an important thermophysical property for polymethacrylates, which can be difficult to determine experimentally. Data-driven modeling approaches provide alternative methods to predict Tg in a rapid and robust way. Here, we develop the Gaussian process regression model to shed light on the relationship between quantum chemical descriptors and the glass transition temperature for the polymethacrylate. A total of 37 samples with the glass transition temperature ranging from 203 K to 428 K are examined. The model is highly stable and accurate that contributes to fast and low-cost estimations of the glass transition temperature.
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content type line 14
ISSN:1542-1406
1563-5287
1527-1943
DOI:10.1080/15421406.2021.1946348