Microphase Separation of Semiflexible Ring Diblock Copolymers  

作　　者：Dan-Yan Qin Sheng-Da Zhao Zhi-Xin Liu Jing Zhang Xing-Hua Zhang 

机构地区：[1]School of Physical Science and Engineering,Beijing Jiaotong University,Beijing,100044,China

出　　处：《Chinese Journal of Polymer Science》2024年第2期267-276,I0011,共11页高分子科学（英文版）

基　　金：financially supported by the National Natural Science Foundation of China (No. 22173004)。

摘　　要：Aiming at the difficult problem of solving the conformation statistics of complex polymers, this study presents a novel and concise conformation statistics theoretical approach based on Monte Carlo and Neural Network method. This method offers a new research idea for investigating the conformation statistics of complex polymers, characterized by its simplicity and practicality. It can be applied to more complex topological structure, more higher degree of freedom polymer systems with higher dimensions, theory research on dynamic self-consistent field theory and polymer field theory, as well as the analysis of scattering experimental data. The conformation statistics of complex polymers determine the structure and response properties of the system. Using the new method proposed in this study, taking the semiflexible ring diblock copolymer as an example, Monte Carlo simulation is used to sample this ring conformation to construct the dataset of polymer. The structure factor describing conformation statistics are expressed as continuous functions of structure parameters by neural network supervised learning. This is the innovation of this work. As an application, the structure factors represented by neural networks were introduced into the random phase approximation theory to study the microphase separation of semiflexible ring diblock copolymers. The influence of the ring's topological properties on the phase transition behavior was pointed out.

关 键 词：Ring copolymer Diblock copolymers Semiflexible The order-disorder transition 

分 类 号：TQ317[化学工程—高聚物工业]