Recent advances and challenges in experiment-oriented polymer informatics

Kan Hatakeyama-Sato*

*この研究の対応する著者

研究成果査読

抄録

This review summarizes recent advances in experimental polymer chemistry supported by data science. The area of polymer informatics is rapidly growing based on cheminformatics, materials informatics, and data science platforms. Data-driven analyses, predictions, and suggestions for experimental polymer research are becoming more practical, and machine learning models can now predict various macromolecular properties with reasonable accuracy. At the same time, the limitations of current polymer informatics are being revealed. Developing appropriate treatments for higher-order structures and experimental procedures is critical to adequately process the hierarchical relationships of polymer systems. Recent attempts to treat this advanced information and future challenges in polymer informatics are discussed.

本文言語English
ページ(範囲)117-131
ページ数15
ジャーナルPolymer Journal
55
2
DOI
出版ステータスPublished - 2023 2月

ASJC Scopus subject areas

  • ポリマーおよびプラスチック
  • 材料化学

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