Exploration of the Application of Data-Driven and Generation Models in the Design of Thermoplastic Vulcanizate Rubbers

The rapid advancement of big data and artificial intelligence has highlighted the substantial potential of data-driven approaches in polymer material research and development. In the present study, data-driven predictive models were developed to accurately forecast the density, tensile strength, flexural strength and melt mass flow rate of thermoplastic vulcanizate (TPV) rubber. Furthermore, a generation model was used to produce new material formula data for TPV rubber, and predictions were made for the aforementioned properties. The results indicated that the predicted values are in good agreement with experimental data. This study introduces innovative strategies and methodologies for the intelligent design of polymer materials, which could potentially lower research and development costs and accelerate the emergence of novel materials.