Identifying Conformation States of Polymer through Unsupervised Machine Learning

Li-Wang Sun; Hong Li; Xiao-Qin Zhang; He-Bei Gao; Meng-Bo Luo

doi:10.1007/s10118-020-2442-6

Your Location：

Home >

Browse articles >

Identifying Conformation States of Polymer through Unsupervised Machine Learning

ARTICLE | Updated：2020-06-30

- Identifying Conformation States of Polymer through Unsupervised Machine Learning
- Identifying Conformation States of Polymer through Unsupervised Machine Learning
- Chinese Journal of Polymer Science 2020年38卷第12期页码：1403-1408
- Affiliations：
  
  a.College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China
  b.Department of Information, Wenzhou Polytechnic, Wenzhou 325035, China
  c.Department of Physics, Zhejiang University, Hangzhou 310027, China
- Author bio：
  
  lihong@wzu.edu.cn (H.L.)
  xqzhang@wzu.edu.cn (X.Q.Z.)
  bogolyx@126.com (H.B.G)
- Funds：
  
  This work was financially supported by the National Natural Science Foundation of China (No. 11775161).
- DOI：10.1007/s10118-020-2442-6
  中图分类号：
- 收稿：2020-03-09，
  
  修回：2020-4-1，
  
  录用：2020-4-10，
  
  网络首发：2020-06-30，
  
  纸质出版：2020-09-10
- Accepted：
Scan QR Code
Sun, L. W.; Li, H.; Zhang, X. Q.; Gao, H. B.; Luo, M. B. Identifying conformation states of polymer through unsupervised machine learning. Chinese J. Polym. Sci. 2020, 38, 1403–1408

Li-Wang Sun, Hong Li, Xiao-Qin Zhang, et al. Identifying Conformation States of Polymer through Unsupervised Machine Learning[J]. Chinese Journal of Polymer Science, 2020, 38(12): 1403-1408.
Sun, L. W.; Li, H.; Zhang, X. Q.; Gao, H. B.; Luo, M. B. Identifying conformation states of polymer through unsupervised machine learning. Chinese J. Polym. Sci. 2020, 38, 1403–1408 DOI： 10.1007/s10118-020-2442-6.

Li-Wang Sun, Hong Li, Xiao-Qin Zhang, et al. Identifying Conformation States of Polymer through Unsupervised Machine Learning[J]. Chinese Journal of Polymer Science, 2020, 38(12): 1403-1408. DOI： 10.1007/s10118-020-2442-6.

摘要

Abstract

The study of the critical behavior is important for classifying different configuration states. Recently

machine learning is capable of discriminating polymer states in the presence of human supervision. Here

we introduce an unsupervised approach based on the self-organizing map (SOM) and the autoencoder network to locate critical phase transitions from raw configuration without the necessity for manual feature engineering. High-dimensional configuration data can be encoded to low-dimensional codes by employing neural network of multilayer restrictive Boltzmann machines and the intermediate code can also be reconstructed to high-dimensional input vector. And then the codes are used to cluster different configuration states for polymers adsorbed on the homogeneous and the stripe-patterned surface by the SOM network and K-Means method. This work presents an unusual tool to identify polymer configuration.

关键词

Keywords

references

LeCun, Y.; Bengio, Y.; Hinton, G . Deep learning . Nature , 2015 . 521 436 - 444 . DOI: 10.1038/nature14539 http://doi.org/10.1038/nature14539 .

Zhang, X. Q; Liu, Q. Q.; Wang, D.; Zhao, L.; Gu, N.; Maybank, S . Self-taught semisupervised dictionary learning with nonnegative constraint . IEEE T. Ind. Inform. , 2020 . 16 532 - 543 . DOI: 10.1109/TII.2019.2926778 http://doi.org/10.1109/TII.2019.2926778 .

Zhang, X. Q.; Wang, T.; Wang, J. X.; Tang, G. Y.; Zhao, L . Pyramid channel-based feature attention network for image dehazing . Computer Comput. Vis. Image Underst. , 2020 . 197−198 103003 DOI: 10.1016/j.cviu.2020.103003 http://doi.org/10.1016/j.cviu.2020.103003 .

Zhang, X. Q.; Wang, D.; Zhou, Z. Y.; Ma, Y . Robust low-rank tensor recovery with rectification and alignment . IEEE T. Pattern Anal. , 2020 . DOI: 10.1109/TPAMI.2019.2929043 DOI: 10.1109/TPAMI.2019.2929043 http://doi.org/10.1109/TPAMI.2019.2929043 .

Zhang, X. Q.; Zheng, J. J.; Wang, D.; Zhao, L . Exemplar-based denoising: a unified low-rank recovery framework . IEEE T. Circ. Syst. Vid. , 2020 . DOI: 10.1109/TCSVT.2019.2927603 DOI: 10.1109/TCSVT.2019.2927603 http://doi.org/10.1109/TCSVT.2019.2927603 .

Zhang, X. Q.; Hu, W. M.; Xie, N. H.; Bao, H. J.; Maybank, S . A robust tracking system for low frame rate video . Int. J. Computer Vision. , 2015 . 115 279 - 304 . DOI: 10.1007/s11263-015-0819-8 http://doi.org/10.1007/s11263-015-0819-8 .

Carrasquilla, J.; Melko, R. G . Machine learning phases of matter . Nat. Phys. , 2017 . 13 431 - 434 . DOI: 10.1038/nphys4035 http://doi.org/10.1038/nphys4035 .

Wei, Q.; Melko, R. G.; Chen, J. Z. Y . Identifying polymer states by machine learning . Phys. Rev. E , 2017 . 95 032504 .

Rodriguez-Nieva, J. F.; Scheurer, M. S . Identifying topological order through unsupervised machine learning . Nat. Phys. , 2019 . 15 790 - 795 . DOI: 10.1038/s41567-019-0512-x http://doi.org/10.1038/s41567-019-0512-x .

Ch’ng, K.; Carrasquilla, J.; Melko, R. G . Machine learning phases of strongly correlated fermions . Phys. Rev. X , 2017 . 7 031038 .

Zhang, Y.; Kim, E. A . Quantum loop topography for machine learning . Phys. Rev. Lett. , 2017 . 118 216401 DOI: 10.1103/PhysRevLett.118.216401 http://doi.org/10.1103/PhysRevLett.118.216401 .

Zhang, Y.; Melko, R. G.; Kim, E. A . Machine learning Z 2 quantum spin liquids with quasiparticle statistics . Phys. Rev. B , 2017 . 96 245119 DOI: 10.1103/PhysRevB.96.245119 http://doi.org/10.1103/PhysRevB.96.245119 .

van Nieuwenburg, E. P. L.; Liu, Y. H.; Huber, S. D . Learning phase transitions by confusion . Nat. Phys. , 2017 . 13 435 - 439 . DOI: 10.1038/nphys4037 http://doi.org/10.1038/nphys4037 .

Ohtsuki, T.; Ohtsuki, T . Deep learning the quantum phase transitions in random electron systems: applications to three dimensions . J. Phys. Soc. Jpn. , 2017 . 86 044708 DOI: 10.7566/JPSJ.86.044708 http://doi.org/10.7566/JPSJ.86.044708 .

Yoshioka, N.; Akagi, Y.; Katsura, H . Learning disordered topological phases by statistical recovery of symmetry . Phys. Rev. B , 2018 . 97 205110 DOI: 10.1103/PhysRevB.97.205110 http://doi.org/10.1103/PhysRevB.97.205110 .

Zhang, P.; Shen, H.; Zhai, H . Machine learning topological invariants with neural networks . Phys. Rev. Lett. , 2018 . 120 066401 DOI: 10.1103/PhysRevLett.120.066401 http://doi.org/10.1103/PhysRevLett.120.066401 .

Carvalho, D.; García-Martínez, N. A.; Lado, J. L . Real-space mapping of topological invariants using artificial neural networks . Phys. Rev. B , 2018 . 97 115453 DOI: 10.1103/PhysRevB.97.115453 http://doi.org/10.1103/PhysRevB.97.115453 .

Beach, M. J.; Golubeva, A.; Melko, R. G . Machine learning vortices at the Kosterlitz-Thouless transition . Phys. Rev. B , 2018 . 97 045207 DOI: 10.1103/PhysRevB.97.045207 http://doi.org/10.1103/PhysRevB.97.045207 .

Wang, C.; Zhai, H . Machine learning of frustrated classical spin models (II): kernel principal component analysis . Front. Phys. , 2018 . 13 130507 DOI: 10.1007/s11467-018-0798-7 http://doi.org/10.1007/s11467-018-0798-7 .

Hu, W.; Singh, R. R.; Scalettar, R. T . Discovering phases, phase transitions, and crossovers through unsupervised machine learning: a critical examination . Phys. Rev. E , 2017 . 95 062122 .

Xu, X.; Wei, Q.; Li, H . Recognition of polymer configurations by unsupervised learning . Phys. Rev. E , 2019 . 99 043307 DOI: 10.1103/PhysRevE.99.043307 http://doi.org/10.1103/PhysRevE.99.043307 .

Zhang, X. Q.; Li, W.; Ye, X. Z.; Maybank, S . Robust hand tracking via novel multi-cue integration . Neurocomputing , 2015 . 157 296 - 305 . DOI: 10.1016/j.neucom.2015.01.002 http://doi.org/10.1016/j.neucom.2015.01.002 .

Zhang, X. Q.; Hu, W. M.; Chen, S. Y.; Maybank, S . Graph-embedding-based learning for robust object tracking . IEEE T. Ind. Electron. , 2014 . 61 1072 - 1084 . DOI: 10.1109/TIE.2013.2258306 http://doi.org/10.1109/TIE.2013.2258306 .

Zhang, X. Q.; Li, C. C.; Tong, X. F.; Hu, W. M.; Maybank, S.; Zhang, Y . Human pose estimation and tracking via parsing a tree structure based human model . IEEE T. Syst. Man Cy-S. , 2014 . 44 580 - 592 . DOI: 10.1109/TSMC.2013.2280438 http://doi.org/10.1109/TSMC.2013.2280438 .

Hinton, G. E.; Salakhutdinov, R. R . Reducing the dimensionality of data with neural networks . Science , 2006 . 313 504 - 507 . DOI: 10.1126/science.1127647 http://doi.org/10.1126/science.1127647 .

Liou, C. Y.; Cheng, W. C.; Liou, J. W . Autoencoder for words . Neurocomputing , 2014 . 139 84 - 96 . DOI: 10.1016/j.neucom.2013.09.055 http://doi.org/10.1016/j.neucom.2013.09.055 .

Kohonen, T . The self-organizing map . Proc. IEEE. , 1990 . 78 1464 - 1480 . DOI: 10.1109/5.58325 http://doi.org/10.1109/5.58325 .

Vesanto, J.; Alhoniemi, E . Clustering of the self-organizing map . IEEE T. Neural Networks , 2000 . 11 586 - 600 . DOI: 10.1109/72.846731 http://doi.org/10.1109/72.846731 .

Li, H.; Qian, C. J.; Luo, M. B . Simulation of a flexible polymer tethered to a flat adsorbing surface . J. Appl. Polym. Sci. , 2012 . 124 282 - 287 . DOI: 10.1002/app.34576 http://doi.org/10.1002/app.34576 .

Li, H.; Qian, C. J.; Sun, L. Z . Conformational properties of a polymer tethered to an interacting flat surface . Polym. J. , 2010 . 42 383 - 385 . DOI: 10.1038/pj.2010.9 http://doi.org/10.1038/pj.2010.9 .

Li, H.; Qian, C. J.; Wang, C . Critical adsorption of a flexible polymer confined between two parallel interacting surfaces . Phys. Rev. E , 2013 . 87 012602 DOI: 10.1103/PhysRevE.87.012602 http://doi.org/10.1103/PhysRevE.87.012602 .

Li, H.; Qian, C. J.; Luo, M. B . Critical adsorption of copolymer tethered on selective surfaces . J. Chem. Phys. , 2016 . 144 164901 DOI: 10.1063/1.4947016 http://doi.org/10.1063/1.4947016 .

Luo, M. B . The critical adsorption point of self-avoiding walks: a finite-size scaling approach . J. Chem. Phys. , 2008 . 128 044912 DOI: 10.1063/1.2826372 http://doi.org/10.1063/1.2826372 .

Luo, M. B.; Huang, J. H . Monte Carlo simulation of polymer chain with ferromagnetic Ising interaction . J. Chem. Phys. , 2003 . 119 2439 - 2443 . DOI: 10.1063/1.1583672 http://doi.org/10.1063/1.1583672 .

Li, H.; Gong, B.; Qian, C. J.; Luo, M. B . Critical adsorption of a flexible polymer on a stripe-patterned surface . Soft Matter , 2015 . 11 3222 - 3231 . DOI: 10.1039/C5SM00426H http://doi.org/10.1039/C5SM00426H .

Zhang, X. Q.; Li, W.; Hu, W. M.; Ling, H. B.; Maybank, S . Block covariance based l 1 tracker with a subtle template dictionary . Pattern Recogn. , 2013 . 46 1750 - 1761 . DOI: 10.1016/j.patcog.2012.08.015 http://doi.org/10.1016/j.patcog.2012.08.015 .

Haario, H.; Saksman, E.; Tamminen, J . An adaptive Metropolis algorithm . Bernoulli. , 2001 . 7 223 - 242 . DOI: 10.2307/3318737 http://doi.org/10.2307/3318737 .

Szu, H.; Hartley, R . Fast simulated annealing . Phys. Rev. A , 1987 . 122 157 - 162 . .

Sun, L. W.; Li, H.; Wang, P. J . Recognition of adsorption phase transition of polymer on surface by neural network . Acta Phys. Sin. , 2019 . 68 60 - 66 . .

浏览量

139

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

Liquid Crystal-driven Superspreading of Polymer Droplets at the Air-Water Interface

Real-Time Monitoring of Component Conversion during the Foaming and Curing Process of Self-expanding Polyurethane Grouts

Self-powered Posture Recognition Based on Thermoelectric Hydrogel

Prediction of Stress-strain Behavior for Polyurethane Elastomers Based on Machine Learning

Enhanced Segment Mobility Promotes Stereocomplex Crystallization in Polymer Blends via Elevated Segmental Miscibility