{"title":"Data and Machine Learning in Polymer Science","authors":"Yun-Qi Li, Ying Jiang, Li-Quan Wang, Jian-Feng Li","doi":"10.1007/s10118-022-2868-0","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":517,"journal":{"name":"Chinese Journal of Polymer Science","volume":"41 9","pages":"1371 - 1376"},"PeriodicalIF":4.1000,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10118-022-2868-0.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chinese Journal of Polymer Science","FirstCategoryId":"92","ListUrlMain":"https://link.springer.com/article/10.1007/s10118-022-2868-0","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"POLYMER SCIENCE","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍：
Chinese Journal of Polymer Science (CJPS) is a monthly journal published in English and sponsored by the Chinese Chemical Society and the Institute of Chemistry, Chinese Academy of Sciences. CJPS is edited by a distinguished Editorial Board headed by Professor Qi-Feng Zhou and supported by an International Advisory Board in which many famous active polymer scientists all over the world are included. The journal was first published in 1983 under the title Polymer Communications and has the current name since 1985.
CJPS is a peer-reviewed journal dedicated to the timely publication of original research ideas and results in the field of polymer science. The issues may carry regular papers, rapid communications and notes as well as feature articles. As a leading polymer journal in China published in English, CJPS reflects the new achievements obtained in various laboratories of China, CJPS also includes papers submitted by scientists of different countries and regions outside of China, reflecting the international nature of the journal.
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