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

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Identifying Conformation States of Polymer through Unsupervised Machine Learning

ARTICLE | Updated：2020-06-30

- Identifying Conformation States of Polymer through Unsupervised Machine Learning
- Chinese Journal of Polymer Science Vol. 38, Issue 12, Pages: 1403-1408(2020)
- 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：
- DOI：10.1007/s10118-020-2442-6
  CLC：
- Received：09 March 2020，
  
  Revised：2020-4-1，
  
  Accepted：10 April 2020，
  
  Online First：30 June 2020，
  
  Published：10 September 2020
- Accepted：
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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.

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references

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