机器学习辅助设计和开发聚合物材料的最新进展
Longyu Ma1,2, Wenjing Li1, Jian Yuan1
1State and Local Joint Engineering Laboratory for Novel Functional Polymeric Materials, Jiangsu Key Laboratory of Advanced Functional Polymer Design and Application, College of Chemistry, Chemical Engineering and Materials Science, Soochow University, Suzhou, China.
Macromolecular rapid communications
|July 7, 2025
概括
机器学习 (ML) 通过分析大数据加速了聚合物材料的发现,超越了缓慢的试错. 本综述涵盖了聚合物设计,性能预测和分类的ML技术,突出了未来的研究方向.
科学领域:
- 聚合物科学 聚合物科学
- 材料科学 材料科学 材料科学
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 传统的聚合物研究依赖于低效的试错方法.
- 现代研发需要更快,数据驱动的方法.
- 大数据和人工智能技术正在改变科学发现.
研究的目的:
- 提供在聚合物科学中的机器学习 (ML) 技术的概述.
- 总结材料开发中使用的常见ML算法.
- 审查 ML 辅助聚合物设计和应用的最新进展.
主要方法:
- 在聚合物科学中ML应用的文献综述.
- ML算法的分类与聚合物研究相关.
- 分析ML使用案例,包括序列设计,属性预测和分类.
主要成果:
- ML显著提高了聚合物材料设计和开发效率.
- 关键应用包括聚合物序列设计和材料性质预测.
- 计算机视觉技术在机器学习驱动的聚合物研究中越来越多地得到利用.
结论:
- 机器学习正在彻底改变聚合物材料的研发.
- 解决聚合物ML当前的挑战对于未来的进步至关重要.
- 机器学习提供了一个强大的范式从传统的实验方法转变.
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