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：2025-04-29

- Understanding Local Conformation in Cyclic and Linear Polymers Using Molecular Dynamics and Point Cloud Neural Network
- Understanding Local Conformation in Cyclic and Linear Polymers Using Molecular Dynamics and Point Cloud Neural Network
- 高分子科学（英文） 2025年43卷第5期页码：695-710
- 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：
  
  We would like to thank the National Key R&D Program of China (No. 2022YFB3707303) and National Natural Science Foundation of China (No. 52293471). We are also grateful to the Network and Computing Center at the Changchun Institute of Applied Chemistry for hardware support.;Electronic supplementary information (ESI) is available free of charge in the online version of this article at http://doi.org/10.1007/s10118-025-3293-y.
- DOI：10.1007/s10118-025-3293-y
  中图分类号：
- 收稿日期：2024-12-02，
  
  修回日期：2025-01-01，
  
  录用日期：2025-01-10，
  
  网络出版日期：2025-03-03，
  
  纸质出版日期：2025-04-30
- 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

Kricheldorf, H. R. Cyclic polymers: synthetic strategies and physical properties. J. Polym. Sci. Part A: Polym. Chem. 2010 , 48 , 251−284..

Ungar, G.; Zeng, X. B. Learning polymer crystallization with the aid of linear, branched and cyclic model compounds. Chem. Rev. 2001 , 101 , 4157−4188..

Su, H. H.; Chen, H. L.; Díaz, A.; Casas, M. T.; Puiggalí, J.; Hoskins, J. N.; Grayson, S. M.; Pérez, R. A.; Müller, A. J. New insights on the crystallization and melting of cyclic PCL chains on the basis of a modified Thomson–Gibbs equation. Polymer 2013 , 54 , 846−859..

Clarson, S.; Dodgson, K.; Semlyen, J. Studies of cyclic and linear poly(dimethylsiloxanes): 19. Glass transition temperatures and crystallization behaviour. Polymer 1985 , 26 , 930−934..

Huang, D.; Simon, S. L.; McKenna, G. B. Chain length dependence of the thermodynamic properties of linear and cyclic alkanes and polymers. J. Chem. Phys. 2005 , 122 , 084907..

Di Marzio, E. A.; Guttman, C. M. The glass temperature of polymer rings. Macromolecules 1987 , 20 , 1403−1407..

Liu, X.; Chen, D.; He, Z.; Zhang, H.; Hu, H. Molecular weight dependence of the glass transition of cyclic polystyrene. Polym. Commun. 1991 , 32 , 123−125..

Gan, Y.; Dong, D.; Hogen-Esch, T. E. Effects of lithium bromide on the glass transition temperatures of linear and macrocyclic poly(2-vinylpyridine) and polystyrene. Macromolecules 1995 , 28 , 383−385..

Santangelo, P.; Roland, C.; Chang, T.; Cho, D.; Roovers, J. Dynamics near the glass temperature of low molecular weight cyclic polystyrene. Macromolecules 2001 , 34 , 9002−9005..

Dodgson, K.; Bannister, D.; Semlyen, J. Studies of cyclic and linear poly (dimethylsiloxanes). 4. Bulk viscosities. Polymer 1980 , 21 , 663−667..

Orrah, D.; Semlyen, J.; Ross-Murphy, S. Studies of cyclic and linear poly(dimethylsiloxanes): 27. Bulk viscosities above the critical molar mass for entanglement. Polymer 1988 , 29 , 1452−1454..

Orrah, D.; Semlyen, J.; Ross-Murphy, S. Studies of cyclic and linear poly(dimethylsiloxanes): 28. Viscosities and densities of ring and chain poly(dimethylsiloxane) blends. Polymer 1988 , 29 , 1455−1458..

Mills, P.; Mayer, J.; Kramer, E.; Hadziioannou, G.; Lutz, P.; Strazielle, C.; Rempp, P.; Kovacs, A. Diffusion of polymer rings in linear polymer matrices. Macromolecules 1987 , 20 , 513−518..

Kruteva, M.; Allgaier, J.; Richter, D. Topology matters: conformation and microscopic dynamics of ring polymers. Macromolecules 2023 , 56 , 7203−7229..

Zimm, B. H.; Stockmayer, W. H. The dimensions of chain molecules containing branches and rings. J. Chem. Phys. 1949 , 17 , 1301−1314..

Casassa, E. F. Some statistical properties of flexible ring polymers. J. Polym. Sci., Part A. 1965 , 3 , 605−614..

Burchard, W.; Schmidt, M. Static and dynamic structure factors calculated for flexible ring macromolecules. Polymer 1980 , 21 , 745−749..

Arrighi, V.; Gagliardi, S.; Dagger, A. C.; Semlyen, J. A.; Higgins, J. S.; Shenton, M. J. Conformation of cyclics and linear chain polymers in bulk by SANS. Macromolecules 2004 , 37 , 8057−8065..

Brown, S.; Szamel, G. Computer simulation study of the structure and dynamics of ring polymers. J. Chem. Phys. 1998 , 109 , 6184−6192..

Brown, S.; Szamel, G. Structure and dynamics of ring polymers. J. Chem. Phys. 1998 , 108 , 4705−4708..

Deutsch, J. Equilibrium size of large ring molecules. Phys. Rev. E 1999 , 59 , R2539..

Hur, K.; Jeong, C.; Winkler, R. G.; Lacevic, N.; Gee, R. H.; Yoon, D. Y. Chain dynamics of ring and linear polyethylene melts from molecular dynamics simulations. Macromolecules 2011 , 44 , 2311−2315..

Hur, K.; Winkler, R. G.; Yoon, D. Y. Comparison of ring and linear polyethylene from molecular dynamics simulations. Macromolecules 2006 , 39 , 3975−3977..

Suzuki, J.; Takano, A.; Matsushita, Y. Topological effect in ring polymers invest igated with Monte Carlo simulation. J. Chem. Phys. 2008 , 129 , 034903..

Suzuki, J.; Takano, A.; Deguchi, T.; Matsushita, Y. Dimension of ring polymers in bulk studied by Monte-Carlo simulation and self-consistent theory. J. Chem. Phys. 2009 , 131 , 144902..

Müller, M.; Wittmer, J.; Cates, M. Topological effects in ring polymers: a computer simulation study. Phys. Rev. E. 1996 , 53 , 5063..

Papadopoulos, G. D.; Tsalikis, D. G.; Mavrantzas, V. G. Microscopic dynamics and topology of polymer rings immersed in a host matrix of longer linear polymers: results from a detailed molecular dynamics simulation study and comparison with experimental data. Polymers 2016 , 8 , 283..

Halverson, J. D.; Kremer, K.; Grosberg, A. Y. Comparing the results of lattice and off-lattice simulations for the melt of nonconcatenated rings. J. Phys. A: Math. Theor. 2013 , 46 , 065002..

Halverson, J. D.; Lee, W. B.; Grest, G. S.; Grosberg, A. Y.; Kremer, K. Molecular dynamics simulation study of nonconcaten ated ring polymers in a melt. I. Statics. J. Chem. Phys. 2011 , 134 , 204904..

Halverson, J. D.; Lee, W. B.; Grest, G. S.; Grosberg, A. Y.; Kremer, K. Molecular dynamics simulation study of nonconcatenated ring polymers in a melt. II. Dynamics. J. Chem. Phys. 2011 , 134 , 204905..

Tsalikis, D. G.; Mavrantzas, V. G. Size and diffusivity of polymer rings in linear polymer matrices: the key role of threading events. Macromolecules 2020 , 53 , 803−820..

De Gennes, P. Kinetics of collapse for a flexible coil. J. Phys. Lett. 1985 , 46 , 639−642..

Roovers, J.; Toporowski, P. Synthesis of high molecular weight ring polystyrenes. Macromolecules 1983 , 16 , 843−849..

Cates, M.; Deutsch, J. Conjectures on the statistics of ring polymers. J. Phys. (Paris) 1986 , 47 , 2121−2128..

Gros berg, A. Y.; Nechaev, S. K.;Shakhnovich, E. I. The role of topological constraints in the kinetics of collapse of macromolecules. J. Phys. (Paris) 1988 , 49 , 2095−2100..

Vettorel, T.; Grosberg, A. Y.; Kremer, K. Statistics of polymer rings in the melt: a numerical simulation study. Phys. Biol. 2009 , 6 , 025013..

Müller, M.; Wittmer, J.; Cates, M. Topological effects in ring polymers. II. Influence of persistence length. Phys. Rev. E 2000 , 61 , 4078..

Müller, M.; Wittmer, J.; Barrat, J.-L. On two intrinsic length scales in polymer physics: topological constraints vs. entanglement length. Europhys. Lett. 2000 , 52 , 406..

Kruteva, M.; Allgaier, J.; Monkenbusch, M.; Porcar, L.; Richter, D. Self-similar polymer ring conformations based on elementary loops: A direct observation by SANS. ACS Macro Lett. 2020 , 9 , 507−511..

Arrighi, V.; Higgins, J. S. Local effects of ring topology observed in polymer conformation and dynamics by neutron scattering A review. Polymers 2020 , 12 , 1884..

Kröger, M.; Hess, S. Rheological evidence for a dynamical crossover in polymer melts via nonequilibrium molecular dynamics. Phys. Rev. Lett. 2000 , 85 , 1128..

Halverson, J. D.; Smrek, J.; Kremer, K.; Grosberg, A. Y. From a melt of rings to chromosome territories: the role of topological constraints in genome folding. Rep. Prog. Phys. 2014 , 77 , 022601..

Smrek, J.; Grosberg, A. Y. A novel family of space-filling curves in their relation to chromosome conformation in eukaryotes. Physica A 2013 , 392 , 6375−6388..

Smrek, J.; Grosberg, A. Y. Minimal surfaces on unconcatenated polymer rings in melt. ACS Macro Lett. 2016 , 5 , 750−754..

Khanal, P.; Peddireddy, K. R.; Marfai, J.; McGorty, R.; Robertson-Anderson, R. M. DNA topology dictates emergent bulk elasticity and hindered macromolecular diffusion in DNA-dextran composites. J. Rheol. 2022 , 66 , 699−715..

Tuckerman, M. E.; Mundy, C. J.; Balasubramanian, S.; Klein, M. L. Modified nonequilibrium molecular dynamics for fluid flows with energy conservation. J. Chem. Phys. 1997 , 106 , 5615−5621..

Hoogerbrugge, P.; Koelman, J. Simulating microscopic hydrodynamic phenomena with dissipative particle dynamics. Europhys. Lett. 1992 , 19 , 155..

Espanol, P.; Warren, P. Statistical mechanics of dissipative particle dynamics. Europhys. Lett. 1995 , 30 , 191..

Soddemann, T.; Dünweg, B.; Kremer, K. Dissipative particle dynamics: a useful thermostat for equilibrium and nonequilibrium molecular dynamics simulations. Phys. Rev. E 2003 , 68 , 046702..

Grosberg, A.; Rabin, Y.; Havlin, S.; Neer, A. Crumpled globule model of the three-dimensional structure of DNA. Europhys. Lett. 1993 , 23 , 373..

Obukhov, S. P.; Rubinstein, M.; Duke, T. Dynamics of a ring polymer in a gel. Phys. Rev. Lett. 1994 , 73 , 1263..

Grosberg, A. Y. Annealed lattice animal model and Flory theory for the melt of nonconcatenated rings: towards the physics of crumpling. Soft Matter 2014 , 10 , 560−565..

Tsalikis, D. G.; Koukoulas, T.; Mavrantzas, V. G. Dynamic, conformational and topological properties of ring–linear poly(ethylene oxide) blends from molecular dynamics simulations. React. Funct. Polym. 2014 , 80 , 61−70..

Smrek, J.; Grosberg, A. Y. Understanding the dynamics of rings in the melt in terms of the annealed tree model. J. Phys.: Condens. Matter 2015 , 27 , 064117..

Zhang, H.; Wang, C.; Tian, S.; Lu, B.; Zhang, L.; Ning, X.; Bai, X. Deep learning based 3D point cloud classification: A systematic survey and outlook. Displays 2023 , 79 , 102456..

Kumar, A.; Anders, K.; Winiwarter, L.; Höfle, B. Feature relevance analysis for 3D point cloud classification using deep learning. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2019 , 4 , 373−380..

Lu, H.; Shi, H. Deep learning for 3d point cloud understanding: a survey. arXiv Prepr. 2020 , 2009 , 08920..

Bello, S. A.; Yu, S.; Wang, C.; Adam, J. M.; Li, J. Deep learning on 3D point clouds. Remote Sens. 2020 , 12 , 1729..

Ding, Z.; Sun, Y.; Xu, S.; Pan, Y.; Peng, Y.; Mao, Z. Recent advances and perspectives in deep learning techniques for 3D point cloud data processing. Robotics 2023 , 12 , 100..

Sarker, S.; Sarker, P.; Stone, G.; Gorman, R.; Tavakkoli, A.; Bebis, G.; Sattarvand, J. A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation. Mach. Vision Appl. 2024 , 35 , 67..

Elharrouss, O.; Hassine, K.; Zayyan, A.; Chatri, Z.; Al-Maadeed, S.; Abualsaud, K. 3D point cloud for objects and scenes classification, recognition, segmentation, and reconstruction: a Review. Cloud Computing and Data Science 2023 , 4 , 134−160..

Matrone, F.; Paolanti, M.; Felicetti, A.; Martini, M.; Pierdicca, R. Bubblex: An explainable deep learning framework for pointcloud classification. IEEE J. Sel. Top Appl. Earth Observ. Rem. Sens. 2022 , 15 , 6571−6587..

Panati, C.; Wagner, S.; Brüggenwirth, S. Feature relevance evaluation using grad-CAM, LIME and SHAP for deep learning SAR data classification. 23rd International Radar Symposium (IRS) . 2022 , 457–462..

Selvaraju, R. R.; Cogswell, M.; Das, A.; Vedantam, R.; Parikh, D.; Batra, D. Grad-CAM: visual explanations from deep networks via gradient-based localization. Int. J. Comput. Vision 2020 , 128 , 336−359..

Kremer, K.; Grest, G. S. Dynamics of entangled linear polymer melts: a molecular dynamics simulation. J. Chem. Phys. 1990 , 92 , 5057−5086..

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