Microphase Separation of Semiflexible Ring Diblock Copolymers

Dan-Yan Qin; Sheng-Da Zhao; Zhi-Xin Liu; Jing Zhang; Xing-Hua Zhang

doi:10.1007/s10118-023-3024-1

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Microphase Separation of Semiflexible Ring Diblock Copolymers

RESEARCH ARTICLE | Updated：2024-01-12

- Microphase Separation of Semiflexible Ring Diblock Copolymers
- Microphase Separation of Semiflexible Ring Diblock Copolymers
- 高分子科学（英文版） 2024年42卷第2期页码：267-276
- Affiliations：
  
  School of Physical Science and Engineering, Beijing Jiaotong University, Beijing 100044, China
- Author bio：
  
  zhangxh@bjtu.edu.cn
- Funds：
  
  This work was financially supported by the National Natural Science Foundation of China (No. 22173004).;Electronic supplementary information (ESI) is available free of charge in the online version of this article at http://doi.org/10.1007/s10118-023-3024-1.
- DOI：10.1007/s10118-023-3024-1
  中图分类号：
- 收稿：2023-05-29，
  
  修回：2023-6-20，
  
  录用：2023-6-29，
  
  网络首发：2023-08-07，
  
  纸质出版：2024-02-01
- Accepted：
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Qin, D. Y.; Zhao, S. D.; Liu, Z. X.; Zhang, J.; Zhang, X. H. Microphase separation of semiflexible ring diblock copolymers. Chinese J. Polym. Sci. 2024, 42, 267–276

Dan-Yan Qin, Sheng-Da Zhao, Zhi-Xin Liu, et al. Microphase Separation of Semiflexible Ring Diblock Copolymers[J]. Chinese Journal of Polymer Science, 2024, 42(2): 267-276.
Qin, D. Y.; Zhao, S. D.; Liu, Z. X.; Zhang, J.; Zhang, X. H. Microphase separation of semiflexible ring diblock copolymers. Chinese J. Polym. Sci. 2024, 42, 267–276 DOI： 10.1007/s10118-023-3024-1.

Dan-Yan Qin, Sheng-Da Zhao, Zhi-Xin Liu, et al. Microphase Separation of Semiflexible Ring Diblock Copolymers[J]. Chinese Journal of Polymer Science, 2024, 42(2): 267-276. DOI： 10.1007/s10118-023-3024-1.

摘要

This paper presents a computational method for calculating the structure factors of complex polymers using three-dimensional Monte Carlo simulations and neural network techniques. The effectiveness of the algorithm is demonstrated by using a semiflexible ring diblock copolymer as an example in the study.

Abstract

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.

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