Understanding Local Conformation in Cyclic and Linear Polymers Using Molecular Dynamics and Point Cloud Neural Network

Wan-Chen Zhao; Hai-Yang Huo; Zhong-Yuan Lu; Zhao-Yan Sun

doi:10.1007/s10118-025-3293-y

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Understanding Local Conformation in Cyclic and Linear Polymers Using Molecular Dynamics and Point Cloud Neural Network

RESEARCH ARTICLE | Updated：2025-07-14

- Understanding Local Conformation in Cyclic and Linear Polymers Using Molecular Dynamics and Point Cloud Neural Network
- Chinese Journal of Polymer Science Vol. 43, Issue 5, Pages: 695-710(2025)
- Affiliations：
  
  a.State Key Laboratory of Supramolecular Structure and Materials, College of Chemistry, Jilin University, Changchun 130012, China
  b.State Key Laboratory of Polymer Physics and Chemistry & Key Laboratory of Polymer Science and Technology, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China
  c.School of Applied Chemistry and Engineering, University of Science and Technology of China, Hefei 230026, China
  d.Xinjiang Laboratory of Phase Transitions and Microstructures in Condensed Matters, College of Physical Science and Technology, Yili Normal University, Yining 835000, China
- Author bio：
  
  luzhy@jlu.edu.cn (Z.Y.L.)
  zysun@ciac.ac.cn (Z.Y.S.)
- Funds：
- DOI：10.1007/s10118-025-3293-y
  CLC：
- Received：02 December 2024，
  
  Revised：2025-01-01，
  
  Accepted：10 January 2025，
  
  Online First：03 March 2025，
  
  Published：30 April 2025
- Accepted：
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Zhao, W. C.; Huo, H. Y.; Lu, Z. Y.; Sun, Z. Y. Understanding local conformation in cyclic and linear polymers using molecular dynamics and point cloud neural network. Chinese J. Polym. Sci. 2025, 43, 695–710

Wan-Chen Zhao, Hai-Yang Huo, Zhong-Yuan Lu, et al. Understanding Local Conformation in Cyclic and Linear Polymers Using Molecular Dynamics and Point Cloud Neural Network[J]. Chinese Journal of Polymer Science, 2025, 43(5): 695-710.
Zhao, W. C.; Huo, H. Y.; Lu, Z. Y.; Sun, Z. Y. Understanding local conformation in cyclic and linear polymers using molecular dynamics and point cloud neural network. Chinese J. Polym. Sci. 2025, 43, 695–710 DOI： 10.1007/s10118-025-3293-y.

Wan-Chen Zhao, Hai-Yang Huo, Zhong-Yuan Lu, et al. Understanding Local Conformation in Cyclic and Linear Polymers Using Molecular Dynamics and Point Cloud Neural Network[J]. Chinese Journal of Polymer Science, 2025, 43(5): 695-710. DOI： 10.1007/s10118-025-3293-y.

摘要

Abstract

Understanding the conformational characteristics of polymers is key to elucidating their physical properties. Cyclic polymers

defined by their closed-loop structures

inherently differ from linear polymers possessing distinct chain ends. Despite these structural differences

both types of polymers exhibit locally random-walk-like conformations

making it challenging to detect subtle spatial variations using conventional methods. In this study

we address this challenge by integrating molecular dynamics simulations with point cloud neural networks to analyze the spatial conformations of cyclic and linear polymers. By utilizing the Dynamic Graph CNN (DGCNN) model

we classify polymer conformations based on the 3D coordinates of monomers

capturing local and global topological differences without considering chain connectivity sequentiality. Our findings reveal that the optimal local structural feature unit size scales linearly with molecular weight

aligning with theoretical predictions. Additionally

interpretability techniques such as Grad-CAM and SHAP identify significant conformational differences: cyclic polymers tend to form prolate ellipsoid shapes with pronounced elongation along the major axis

while linear polymers show elongated ends with more spherical centers. These findings reveal subtle yet critical differences in local conformations between cyclic and linear polymers that were previously difficult to discern

providing deeper insights into polymer structure-property relationships and offering guidance for future polymer science advancements.

关键词

Keywords

references

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