Symbolic regression accelerates the discovery of quantitative relationships in rubber material aging

Polymer materials, especially rubber, play an indispensable role in modern life and manufacturing. However, their aging and deterioration pose serious challenges to their stability and service life. Unexpected aging can lead to the deterioration of the physical and chemical properties of materials, thereby triggering a series of safety hazards and environmental pollution issues. Exploring the correspondence between the microscopic characteristics and macroscopic properties of materials during the aging process helps researchers deeply understand and control the aging process of materials. Symbolic regression (SR) algorithm, as a machine learning method with strong interpretability, plays an important role in exploring the quantitative relationship of data in scientific fields. This method has a strong potential for discovering the intrinsic quantitative relationships within the experimental data of material aging. In this study, we propose a comprehensive evaluation framework for SR, aiming to identify SR algorithms that are truly suitable for aging experimental data. Furthermore, by integrating characterization data of aging experiments, we conduct further validation and knowledge discovery with the selected method. The results obtained from our experimental data demonstrate a strong consistency with those of the proposed evaluation framework. Notably, this research methodology exhibits extensibility and can serve as a guiding light for the discovery of knowledge and the elucidation of mechanisms within other realms of polymer materials and diverse material systems.