Zhanglei Wang1,2, Shuo Yan1,2, Jingyu Gao1,2, Haoyu Wu1,2, Baili Wang1,2, Xiuying Zhao1,2,*, Shikai Hu1,2,*
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.075667
- 10 February 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… More >
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