{"title":"A prospective on machine learning challenges, progress, and potential in polymer science","authors":"Daniel C. Struble, Bradley G. Lamb, Boran Ma","doi":"10.1557/s43579-024-00587-8","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>Artificial intelligence and machine learning (ML) continue to see increasing interest in science and engineering every year. Polymer science is no different, though implementation of data-driven algorithms in this subfield has unique challenges barring widespread application of these techniques to the study of polymer systems. In this Prospective, we discuss several critical challenges to implementation of ML in polymer science, including polymer structure and representation, high-throughput techniques and limitations, and limited data availability. Promising studies targeting resolution of these issues are explored, and contemporary research demonstrating the potential of ML in polymer science despite existing obstacles are discussed. Finally, we present an outlook for ML in polymer science moving forward.</p><h3 data-test=\"abstract-sub-heading\">Graphical Abstract</h3>","PeriodicalId":19016,"journal":{"name":"MRS Communications","volume":"6 1","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"MRS Communications","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1557/s43579-024-00587-8","RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Artificial intelligence and machine learning (ML) continue to see increasing interest in science and engineering every year. Polymer science is no different, though implementation of data-driven algorithms in this subfield has unique challenges barring widespread application of these techniques to the study of polymer systems. In this Prospective, we discuss several critical challenges to implementation of ML in polymer science, including polymer structure and representation, high-throughput techniques and limitations, and limited data availability. Promising studies targeting resolution of these issues are explored, and contemporary research demonstrating the potential of ML in polymer science despite existing obstacles are discussed. Finally, we present an outlook for ML in polymer science moving forward.
摘要 人工智能和机器学习（ML）在科学和工程领域的应用每年都在不断增加。高分子科学也不例外，不过在这一子领域实施数据驱动算法面临着独特的挑战，阻碍了这些技术在高分子系统研究中的广泛应用。在本前瞻中，我们将讨论在聚合物科学中实施 ML 所面临的几个关键挑战，包括聚合物结构和表征、高通量技术和局限性以及有限的数据可用性。我们探讨了以解决这些问题为目标的有前途的研究，并讨论了尽管存在现有障碍，但仍能证明 ML 在聚合物科学中的潜力的当代研究。最后，我们展望了 ML 在聚合物科学中的应用前景。
期刊介绍：
MRS Communications is a full-color, high-impact journal focused on rapid publication of completed research with broad appeal to the materials community. MRS Communications offers a rapid but rigorous peer-review process and time to publication. Leveraging its access to the far-reaching technical expertise of MRS members and leading materials researchers from around the world, the journal boasts an experienced and highly respected board of principal editors and reviewers.
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