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Li et al. J. Mater. Inf. 2025, 5, 29 Journal of
DOI: 10.20517/jmi.2024.103
Materials Informatics

Research Article Open Access

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

1,*
1
2
1,#
2
Wentao Li 1,# , Zemeng Wang , Min Zhao , Jiangfeng Pei , Yiwen Hu , Rui Yang , Xiaonan Wang 1,*
1
Department of Chemical Engineering, Tsinghua University, Beijing 100084, China.
2
Xi’an Modern Chemistry Research Institute, Xi’an 710018, Shannxi, China.
#
Authors contributed equally.
* Correspondence to: Prof. Rui Yang, Prof. Xiaonan Wang, Department of Chemical Engineering, Tsinghua University, Beijing
100084, China. E-mail: yangr@tsinghua.edu.cn; wangxiaonan@tsinghua.edu.cn
How to cite this article: Li, W.; Wang, Z.; Zhao, M.; Pei, J.; Hu, Y.; Yang, R.; Wang, X. Symbolic regression accelerates the
discovery of quantitative relationships in rubber material aging. J. Mater. Inf. 2025, 5, 29. https://dx.doi.org/10.20517/jmi.2024.
103
Received: 30 Dec 2024 First Decision: 25 Jan 2025 Revised: 28 Feb 2025 Accepted: 6 Mar 2025 Published: 28 Mar 2025

Academic Editors: Runhai Ouyang, Bohayra Mortazavi Copy Editor: Pei-Yun Wang Production Editor: Pei-Yun Wang

Abstract
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.

Keywords: Symbolic regression algorithm, microscopic and macroscopic properties, rubber, materials aging,
knowledge discovery

© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing,
adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as
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