Local order parameter that distinguishes crystalline and amorphous portions in polymer crystal lamellae.

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. [ABSTRACT FROM AUTHOR]