Data and Machine Learning in Polymer Science

Yun-Qi Li; Ying Jiang; Li-Quan Wang; Jian-Feng Li

doi:10.1007/s10118-022-2868-0

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Data and Machine Learning in Polymer Science

REVIEW | Updated：2023-08-24

- Data and Machine Learning in Polymer Science
- Chinese Journal of Polymer Science Vol. 41, Issue 9, Pages: 1371-1376(2023)
- Affiliations：
  
  a.Department of Polymer Materials and Engineering, College of Materials and Metallurgy, Guizhou University, Guiyang 550025, China
  b.School of Chemistry, Beihang University, Beijing 100191, China
  c.Shanghai Key Laboratory of Advanced Polymeric Materials, School of Materials Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
  d.The State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Fudan University, Shanghai 200433, China
- Author bio：
  
  liyq@gzu.edu.cn (Y.Q.L.)
  yjiang@buaa.edu.cn (Y.J)
  lq_wang@ecust.edu.cn (L.Q.W.)
  lijf@fudan.edu.cn (J.F.L.)
- Funds：
- DOI：10.1007/s10118-022-2868-0
  CLC：
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Yun-Qi Li, Ying Jiang, Li-Quan Wang, et al. Data and Machine Learning in Polymer Science. [J]. Chinese Journal of Polymer Science 41(9):1371-1376(2023)
DOI：

Yun-Qi Li, Ying Jiang, Li-Quan Wang, et al. Data and Machine Learning in Polymer Science. [J]. Chinese Journal of Polymer Science 41(9):1371-1376(2023) DOI： 10.1007/s10118-022-2868-0.

摘要

Data is the cornerstone and machine learning supports a new paradigm, their combination is promoting leading-edge innovations in polymer materials. It provides a unique way to interpretate, predict and infer various targests in polymer research.

Abstract

Data-driven innovation has shown great power in solving problems in multifactor correlation, convergence and optimization, synergistic and antagonistic effects, pattern and boundary identification, critical behavior and phase transition, which are ubiquitous in polymer science. Either for the in-depth understanding of physical problems or in the discovery of new polymer materials, integrating data and machine learning into conventional experimental, theoritical, modeling and simulation approaches becomes blooming. Here we present a perspective based on our research interests, highlight some key issues and provide a prospection in this emerging direction. We focus on a number of typical advances in the description and identification of polymer conformation and structures, and the interpretation and prediction of structure-property correlations, that have applied data and machine learning in polymer science.

关键词

Keywords

Machine learningBig dataStructure-property relationshipOptimizationPrediction

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