A Literature Data-driven Machine Learning Study of Structure-Property Relationship in Diels-Alder Polyurethane Elastomers Based on Hierarchical Domain-knowledge Features

Jun-jie Yuanli; Xiao-tian Chen; Zheng-wei You

doi:10.11777/j.issn1000-3304.2026.26122

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A Literature Data-driven Machine Learning Study of Structure-Property Relationship in Diels-Alder Polyurethane Elastomers Based on Hierarchical Domain-knowledge Features

更新时间：2026-07-10

- A Literature Data-driven Machine Learning Study of Structure-Property Relationship in Diels-Alder Polyurethane Elastomers Based on Hierarchical Domain-knowledge Features
- Acta Polymerica Sinica Pages: 1-13(2026)
- 作者机构：
  
  东华大学材料科学与工程学院先进纤维材料全国重点实验室上海 201620
- 作者简介：
  
  Zheng-wei You, E-mail: youzw@dhu.edu.cn
- 基金信息：
- DOI：10.11777/j.issn1000-3304.2026.26122
  CLC：
- CSTR：32057.14.GFZXB.2026.7632
- Received：25 April 2026，
  
  Accepted：27 May 2026，
  
  Online First：10 July 2026，
- 稿件说明：
移动端阅览
袁李隽桀, 陈晓天, 游正伟. 基于数据挖掘与领域知识分层特征构建的 Diels-Alder聚氨酯性能预测与构效解析. 高分子学报, doi: 10.11777/j.issn1000-3304.2026.26122.

Yuanli, J. J.; Chen, X. T.; You, Z. W. A literature data-driven machine learning study of structure-property relationship in Diels-Alder polyurethane elastomers based on hierarchical domain-knowledge features. Acta Polymerica Sinica (in Chinese), doi: 10.11777/j.issn1000-3304.2026.26122.
袁李隽桀, 陈晓天, 游正伟. 基于数据挖掘与领域知识分层特征构建的 Diels-Alder聚氨酯性能预测与构效解析. 高分子学报, doi: 10.11777/j.issn1000-3304.2026.26122. DOI： CSTR： 32057.14.GFZXB.2026.7632.

Yuanli, J. J.; Chen, X. T.; You, Z. W. A literature data-driven machine learning study of structure-property relationship in Diels-Alder polyurethane elastomers based on hierarchical domain-knowledge features. Acta Polymerica Sinica (in Chinese), doi: 10.11777/j.issn1000-3304.2026.26122. DOI： CSTR： 32057.14.GFZXB.2026.7632.

摘要

含有Diels-Alder (DA)动态共价键的聚氨酯弹性体兼具优异的力学性能与热可逆自愈合能力，是自修复弹性体领域的重要研究方向. 本工作基于文献数据驱动方法，围绕DA聚氨酯弹性体(DAPU)拉伸强度、断裂伸长率和愈合效率这3项性能的构效关系开展研究. 通过对相关文献中配方与性能数据的系统筛选，构建了涵盖组成、化学特性和结构机理3个层次的参数体系. 进一步选取支持向量回归(SVR)、极限梯度提升(XGBoost)和高斯过程回归(GPR)分别建立了3项性能的定量解析模型，其决定系数(

)分别达到0.76、0.66和0.81. 结果表明，所构建的特征体系能够较好表征DAPU弹性体主要结构变量与性能之间的关系. 夏普利加性解释(SHapley Additive exPlanations，SHAP)分析进一步表明，软段结晶性和软段分子量是影响力学强度与愈合效率的最关键参数，DA功能单体含量是调控延展性的核心变量，3项性能之间呈现出复杂的非单调相关关系. 上述结果可为深入理解DAPU弹性体的构效关系，指导配方设计提供参考.

Abstract

Diels-Alder (DA) dynamic covalent polyurethane (DAPU) elastomers are highly regarded in the field of self-healing materials due to their excellent mechanical properties and thermo-reversible healing capabilities. In this study

we employed a literature data-driven approach to investigate the structure-property relationships governing the tensile strength

elongation at break

and healing efficiency of DAPU elastomers. By systematically screening the fo

rmulation and performance data from the relevant literature

we constructed a comprehensive feature space spanning the compositional content

chemical characteristics

and structural mechanisms. Three machine learning models

namely Support Vector Regression (SVR)

XGBoost

and Gaussian Process Regression (GPR)

were established to quantitatively analyze these properties

yielding

values of 0.76

0.66

and 0.81

respectively. The results demonstrate that the proposed feature system effectively captures the relationships between the key structural variables and properties of DAPU elastomers. Subsequent SHapley Additive exPlanations (SHAP) analysis revealed that soft-segment crystallinity and soft-segment molecular weight were the most critical parameters governing mechanical strength and healing efficiency

whereas DA functional monomer content was the primary determinant of ductility. Furthermore

complex non-monotonic correlations were identified among the three properties. This work provides a reference for understanding DAPU structure-property relationships and guiding rational-formulation design.

关键词

Keywords

references

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