ABSTRACT

Strain localization is a ubiquitous phenomenon of soft matters subjected to strain. Polymeric materials are a very important class of soft materials and widely used nowadays. Polymers have unique strain localization behavior such as crazing during tensile deformation. How to understand the mechanism at the molecular level of strain localization in polymeric materials has become an important topic in material science. In this work, tensile deformation process of polymers both under a melt state and a glassy state are investigated in MD simulations using a generic coarse-grained model. We use a machine learning technique, i.e., support vector machine (SVM) algorithm, to understand the local molecular structure and the dynamical properties during tensile deformation. By defining “softness” from the SVM model, we investigate the stress–strain behavior of both ductile polymer above glass transition temperature and brittle polymer glass during tensile deformation. We demonstrated that the softness can be used to predict physical properties efficiently; the softness provides deep physical insights into the non-equilibrium stress–strain process. We also find that the Hookean behavior of polymer glasses is mostly contributed by the hard regions of the system, and the elastic limit is quantitatively discussed as well.

摘要

应变局部化是软物质普遍存在的应变现象。聚合材料是一类非常重要的软材料，如今已广泛使用。聚合物具有独特的应变局部化行为，例如拉伸变形时出现龟裂。如何在高分子材料的应变水平的分子水平上理解机理已成为材料科学中的重要课题。在这项工作中，使用通用的粗粒度模型在MD模拟中研究了聚合物在熔融态和玻璃态下的拉伸变形过程。我们使用一种机器学习技术，即支持向量机（SVM）算法，来了解拉伸变形过程中的局部分子结构和动力学特性。通过从SVM模型定义“柔软度”，我们研究了拉伸变形过程中高于玻璃化转变温度的韧性聚合物和脆性聚合物玻璃的应力-应变行为。我们证明了柔软度可以有效地预测物理性能。柔软性为非平衡应力-应变过程提供了深刻的物理见解。我们还发现，聚合物玻璃的Hookean行为主要是由系统的硬区域引起的，并且对弹性极限也进行了定量讨论。