The glass transition temperature(T_(g))of styrene-butadiene rubber(SBR)is a key parameter determining its low-temperature flexibility and processing performance.Accurate prediction of T_(g)is crucial formaterial desig...The glass transition temperature(T_(g))of styrene-butadiene rubber(SBR)is a key parameter determining its low-temperature flexibility and processing performance.Accurate prediction of T_(g)is crucial formaterial 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,ninemachine learning models were employed to predict the T_(g)of SBR and compare their prediction performance.Ultimately,aGPR-XGBoost mixed model was constructed through model ensemble,achieving high-precision prediction with R^(2)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 T_(g),providing a theoretical basis for SBR formulation design and T_(g)regulation.The machine learning prediction framework established in this study combines high-precision prediction with interpretability,significantly enhancing the prediction performance of the T_(g)of SBR.It offers an efficient tool for SBR molecular design and holds great potential for promotion and application.展开更多
基金supported by the National Natural Science Foundation of China(grant numbers 52250357 and 52203003).
文摘The glass transition temperature(T_(g))of styrene-butadiene rubber(SBR)is a key parameter determining its low-temperature flexibility and processing performance.Accurate prediction of T_(g)is crucial formaterial 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,ninemachine learning models were employed to predict the T_(g)of SBR and compare their prediction performance.Ultimately,aGPR-XGBoost mixed model was constructed through model ensemble,achieving high-precision prediction with R^(2)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 T_(g),providing a theoretical basis for SBR formulation design and T_(g)regulation.The machine learning prediction framework established in this study combines high-precision prediction with interpretability,significantly enhancing the prediction performance of the T_(g)of SBR.It offers an efficient tool for SBR molecular design and holds great potential for promotion and application.
