聚合物分子链微结构-力学性能关系的数据驱动模型

聚合物一般由随机分布的大分子链组成，分子链的分布、缠绕、交联等微结构状态显著影响聚合物的力学和物理性能。本文通过数据驱动方法，建立了聚合物分子链微结构-力学性能关系。使用有限元方法建立了两种分子链的随机微结构模型并得到了其力学性能。基于微结构-力学性能关系建立数据集，以聚合物的随机分子链微结构为输入，以聚合物的弹性刚度为响应输出，进行数据驱动模型的训练和验证。得到了精度满意的微结构-力学性能关系的分析结果。结果表明，通过数据驱动方法研究聚合物的弹性刚度问题是可靠的。

A data-driven model of the relationship between polymer molecular chain microstructure and mechanical properties

Polymers are generally composed of randomly distributed macromolecular chains. The distribution, entanglement, and cross-linking of molecular chains significantly affect the mechanical and physical properties of the polymer. In this paper, a data-driven method was used to establish the relationship between the microstructure and mechanical properties of polymer molecular chains. Using the finite element method, the random microstructure models of two molecular chains were established and their mechanical properties were obtained. A data set was established based on the relationship between microstructure and mechanical properties, with the random molecular chain microstructure of the polymer as input, and the elastic stiffness of the polymer as the response output, to train and verify the data-driven model. We have obtained the analysis results of the microstructure-mechanical properties relationship with satisfactory accuracy. The result shows that it is reliable to study the elastic stiffness of polymers through data-driven methods.