Takano F, Hiratsuka M, Aoyagi T, Takahashi K Z
Kogakuin University, 1-24-2 Nishi-Shinjuku, Tokyo 163-8677, Japan.
Research Center for Computational Design of Advanced Functional Materials, National Institute of Advanced Industrial Science and Technology (AIST), Central 2, 1-1-1 Umezono, Tsukuba, Ibaraki 305-8568, Japan.
J Chem Phys. 2022 Nov 7;157(17):174507. doi: 10.1063/5.0121669.
The degradation of microplastics in relation to marine pollution has been receiving increasing attention. Because the spherulites that comprise microplastics have a highly ordered lamellar structure, their decomposition is thought to involve a lamellar structure collapse process. However, even in the simplest case of an order-disorder transition between lamellae and melt upon heating, the microscopic details of the transition have yet to be elucidated. In particular, it is unclear whether nucleation occurs at defects in the crystalline portion or at the interface between the crystalline and amorphous portions. To observe the transition in molecular simulations, an approach that distinguishes between the crystalline and amorphous structures that make up the lamella is needed. Local order parameters (LOPs) are an attempt to define the degree of order on a particle-by-particle basis and have demonstrated the ability to precisely render complex order structure transitions during phase transitions. In this study, 274 LOPs were considered to classify the crystalline and amorphous structures of polymers. Supervised machine learning was used to automatically and systematically search for the parameters. The identified optimal LOP does not require macroscopic information such as the overall orientation direction of the lamella layers but can precisely distinguish the crystalline and amorphous portions of the lamella layers using only a small amount of neighboring particle information.
微塑料降解与海洋污染的关系日益受到关注。由于构成微塑料的球晶具有高度有序的层状结构，其分解被认为涉及层状结构坍塌过程。然而，即使在加热时片层与熔体之间最简单的有序-无序转变情况下，该转变的微观细节仍有待阐明。特别是，尚不清楚成核是发生在晶体部分的缺陷处还是晶体与非晶部分的界面处。为了在分子模拟中观察这种转变，需要一种能够区分构成片层的晶体和非晶结构的方法。局部序参量（LOP）试图逐粒子定义有序程度，并已证明能够精确呈现相变过程中复杂的有序结构转变。在本研究中，考虑了274个LOP来对聚合物的晶体和非晶结构进行分类。使用监督机器学习自动系统地搜索这些参数。所确定的最佳LOP不需要诸如片层的整体取向方向等宏观信息，仅使用少量相邻粒子信息就能精确区分片层的晶体和非晶部分。
