Machine Learning-assisted Prediction of Polymer Glass Transition Temperature: A Structural Feature Approach

Bardia Afsordeh; Hadi Shirali

doi:10.1007/s10118-025-3361-3

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Machine Learning-assisted Prediction of Polymer Glass Transition Temperature: A Structural Feature Approach

RESEARCH ARTICLE | Updated：2025-08-25

- Machine Learning-assisted Prediction of Polymer Glass Transition Temperature: A Structural Feature Approach
- Machine Learning-assisted Prediction of Polymer Glass Transition Temperature: A Structural Feature Approach
- 高分子科学（英文） 2025年43卷第9期页码：1661-1670
- Affiliations：
  
  a.Department of Polymer Engineering and Color Technology, Amirkabir University of Technology, Tehran, Iran
  b.Department of Polymer Engineering and Color Technology, Amirkabir University of Technology, PO Box 15875-441, Tehran, Iran
- Author bio：
  
  hadishirali@aut.ac.ir
- Funds：
- DOI：10.1007/s10118-025-3361-3
  中图分类号：
- 收稿：2025-03-11，
  
  修回：2025-04-17，
  
  录用：2025-04-17，
  
  网络首发：2025-07-01，
  
  纸质出版：2025-09-05
- Accepted：
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Afsordeh, B.; Shirali, H. Machine learning-assisted prediction of polymer glass transition temperature: a structural feature approach. Chinese J. Polym. Sci. 2025, 43, 1661–1670

Bardia Afsordeh, Hadi Shirali. Machine Learning-assisted Prediction of Polymer Glass Transition Temperature: A Structural Feature Approach[J]. Chinese Journal of Polymer Science, 2025, 43(9): 1661-1670.
Afsordeh, B.; Shirali, H. Machine learning-assisted prediction of polymer glass transition temperature: a structural feature approach. Chinese J. Polym. Sci. 2025, 43, 1661–1670 DOI： 10.1007/s10118-025-3361-3.

Bardia Afsordeh, Hadi Shirali. Machine Learning-assisted Prediction of Polymer Glass Transition Temperature: A Structural Feature Approach[J]. Chinese Journal of Polymer Science, 2025, 43(9): 1661-1670. DOI： 10.1007/s10118-025-3361-3.

摘要

This study utilizes machine learning to predict the glass transition temperature (

) of polymers using a dataset of polymer structures. Key descriptors like flexibility and side chain occupancy were analyzed

with Extra Trees and Gaussian Process Regression achieving the best accuracy

highlighting the potential of data-driven appro

aches in polymer science.

Abstract

Machine learning (ML) has emerged as a powerful tool for predicting polymer properties

including glass transition temperature (

)

which is a critical factor influencing polymer applications. In this study

a dataset of polymer structures and their

values were created and represented as adjacency matrices based on molecular graph theory. Four key structural descriptors

flexibility

side chain occupancy length

polarity

and hydrogen bonding capacity

were extracted and used as inputs for ML models: Extra Trees (ET)

Random Forest (RF)

Gaussian Process Regression (GPR)

and Gradient Boosting (GB). Among these

ET and GPR achieved the highest predictive performance

with

values of 0.97

and mean absolute errors (MAE) of approximately 7–7.5 K. The use of these extracted features significantly improved the prediction accuracy compared to previous studies. Feature importance analysis revealed that flexibility had the strongest influence on

followed by side-chain occupancy length

hydrogen bonding

and polarity. This work demonstrates the potential of data-driven approaches in polymer science

providing a fast and reliable method for

prediction that does not require experimental inputs.

关键词

Keywords

references

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