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通过机器学习模型探索生物基聚氨弹性体的热和机械性能

Rui Li^1,2, Yongjun Lv², Chunhui Xie¹

¹Department of Polymer Materials and Engineering, College of Materials and Metallurgy, Guizhou University, Guiyang, P. R. China.

Macromolecular rapid communications

|March 5, 2026

概括

这项研究开发了生物基聚氨弹性体 (BPUE) 的预测模型,准确预测关键的机械和热性能. 配方和单体结构显著影响这些特性,使得量身定制的材料设计.

科学领域:

材料科学材料科学材料科学
聚合物化学聚合物化学
数据科学数据科学数据科学

背景情况:

生物基聚氨弹性体 (BPUE) 对可持续应用至关重要.
了解它们的机械和热性能对于确定它们的适用性至关重要.
预测模型可以加速BPUE的设计和优化.

研究的目的:

开发精确的预测模型,用于BPUE的六个核心机械和热性能.
为了确定影响这些属性的关键特征,用于合理的材料设计.
为创建定制的BPUE提供数据驱动的洞察力.

主要方法:

编制了超过1500个BPUE样本的数据集,其中包含详细的成分,过程,结构和属性信息.
采用域知识增强功能工程来选择26个预测功能.
利用多目标回归模型来预测扬模量 (YM),拉力强度 (TS),破裂时延伸 (EB),玻璃过渡温度 (Tg),分解温度 (Td5) 和能量消散因子 (tanδ).

主要成果:

多目标回归模型在验证和盲测试中实现了YM,TS和EB的R2>0.70,Tg,Td5和tanδ的R2>0.80.
与化学结构和配方相关的特征占主导地位,解释了超过70%的特性变化.
确定了生物质原料,聚分子量和硬片段含量作为关键可调节的变量.

结论:

数据驱动的洞察力使BPUE的合理设计具有特定的机械和热性能.
配方参数和加工条件 (拉伸/加热率) 对于实现所需和可重复的BPUE性能至关重要.
开发的模型和识别的特征为有效开发适合目的的BPUE提供了途径.

关键词:

生物基聚氨弹性体消散因子的消散因子机器学习是机器学习.机械性能机械性能机械性能热性质热性质热性质

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