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Open Access

ARTICLE

Machine Learning-Driven Prediction of the Glass Transition Temperature of Styrene-Butadiene Rubber

Zhanglei Wang1,2, Shuo Yan1,2, Jingyu Gao1,2, Haoyu Wu1,2, Baili Wang1,2, Xiuying Zhao1,2,*, Shikai Hu1,2,*
1 State Key Laboratory of Organic-Inorganic Composites, Beijing University of Chemical Technology, Beijing, 100029, China
2 Beijing Engineering Research Center of Advanced Elastomers, Beijing University of Chemical Technology, Beijing, 100029, China
* Corresponding Author: Xiuying Zhao. Email: email; Shikai Hu. Email: email
(This article belongs to the Special Issue: Machine Learning Methods in Materials Science)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.075667

Received 05 November 2025; Accepted 29 December 2025; Published online 20 January 2026

Abstract

The glass transition temperature (Tg) of styrene-butadiene rubber (SBR) is a key parameter determining its low-temperature flexibility and processing performance. Accurate prediction of Tg is crucial for material design and application optimisation. Addressing the limitations of traditional experimental measurements and theoretical models in terms of efficiency, cost, and accuracy, this study proposes a machine learning prediction framework that integrates multi-model ensemble and Bayesian optimization by constructing a multi-component feature dataset and algorithm optimization strategy. Based on the constructed high-quality dataset containing 96 SBR samples, nine machine learning models were employed to predict the Tg of SBR and compare their prediction performance. Ultimately, a GPR-XGBoost mixed model was constructed through model ensemble, achieving high-precision prediction with R2 values greater than 0.9 on both the training and test sets. Further feature attribution and local effect analysis were conducted using feature analysis methods such as SHAP and ALE, revealing the nonlinear influence patterns of various components on Tg, providing a theoretical basis for SBR formulation design and Tg regulation. The machine learning prediction framework established in this study combines high-precision prediction with interpretability, significantly enhancing the prediction performance of the Tg of SBR. It offers an efficient tool for SBR molecular design and holds great potential for promotion and application.

Keywords

Machine learning; styrene-butadiene rubber; glass transition temperature

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