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<p>The unsatisfactory stability of hydrogel coatings hinders their functional and service performance. Until now, the development of high-performance hydrogel coatings largely relies on the intuition and prior experience of researchers. Machine learning, as a powerful engine for material design, was demonstrated to accelerate the development of hydrogels with desired properties. However, the scarcity of labeled data of the target property is a fundamental challenge. Herein, we develop a miniaturized high-throughput evaluation method of hydrogel coatings. This method achieved a rapid and parallel investigation of the stability of a large number of unique acrylamide-based hydrogel coatings. Moreover, a list of main feature descriptors was screened and their quantitative contributions to coating stability were analyzed via interpretable machine learning technology. A new ternary hydrogel coating was prepared to validate the accuracy of the machine learning strategy. This advanced methodology facilitated the rational design of high-performance hydrogel coatings.</p>

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Rapid screening of the stability of polyacrylamide-based hydrogel coatings via droplet microarray analysis and interpretable machine learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月4日

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  • Jingzhi Yang,
  • Yami Ran,
  • Yuting Jin,
  • Annan Kong,
  • Mingyue Zhang,
  • Lingwei Ma,
  • Dawei Zhang

摘要

The unsatisfactory stability of hydrogel coatings hinders their functional and service performance. Until now, the development of high-performance hydrogel coatings largely relies on the intuition and prior experience of researchers. Machine learning, as a powerful engine for material design, was demonstrated to accelerate the development of hydrogels with desired properties. However, the scarcity of labeled data of the target property is a fundamental challenge. Herein, we develop a miniaturized high-throughput evaluation method of hydrogel coatings. This method achieved a rapid and parallel investigation of the stability of a large number of unique acrylamide-based hydrogel coatings. Moreover, a list of main feature descriptors was screened and their quantitative contributions to coating stability were analyzed via interpretable machine learning technology. A new ternary hydrogel coating was prepared to validate the accuracy of the machine learning strategy. This advanced methodology facilitated the rational design of high-performance hydrogel coatings.