体系统作为物理信息机器学习的实验平台
Namhee Kang1, Yeonseo Joo1, Hyosung An2
1Department of Chemical Engineering and Materials Science, Ewha Womans University, Seoul 03760, South Korea. hyerimhwang@ewha.ac.kr.
Nanoscale horizons
|September 25, 2025
概括
体系统通过将粒子行为与材料属性联系起来,为凝聚物质物理学提供了独特的见解. 本综述强调了用于先进材料设计的体建模和机器学习.
科学领域:
- 凝聚物质物理学 凝聚物质物理学
- 材料科学 材料科学 材料科学
- 统计力学 统计力学
背景情况:
- 体系统为研究凝聚物质现象提供了独特的实验优势,因为它们的粒子大小,热运动和可调的相互作用.
- 它们可以同时观察微观动力学和宏观反应,在原子或分子系统中无法达到的桥梁尺度.
- 直接将本地结构变化和动态重组与系统级行为联系起来是一个关键的能力.
研究的目的:
- 将体建模作为材料研究挑战的预测框架,如相位分类,动态停止和缺陷介导力学.
- 描述从实验成像数据中提取结构,动态和机械描述符的方法.
- 为了说明这些描述符在机器学习中的应用,用于相位识别,动态预测和反向设计.
主要方法:
- 使用实时,实时空间,单颗粒分辨率成像的合体系统.
- 开发和应用方法来从成像数据中提取结构,动态和机械描述符.
- 将这些描述符与用于数据驱动材料设计的机器学习算法集成.
主要成果:
- 证明提取的描述符有效地捕捉了物质行为的支配变量.
- 展示了机器学习的成功应用,用于相位识别,动态预测和使用体数据的反向设计.
- 通过将微观机制与宏观可观测物联系起来,突出了结构化和可泛化的数据集的生成.
结论:
- 体系统是开发可解释和基于物理的机器学习模型的宝贵培训基地.
- 将体数据与数据驱动方法集成,为预测性和可转移材料设计策略提供了一个有前途的途径.
- 这种方法提高了我们设计具有理想性质的新材料的能力.
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