基于康威模式的光谱选择性红外超表面的机器学习设计
Chengyu Xiao1,2, Jing Li1,2, Wansu Hua1,2
1State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, P. R. China.
Physical chemistry chemical physics : PCCP
|July 30, 2025
概括
本研究介绍了一种机器学习平台,使用康威模式来设计先进的红外超表面. 这种新的方法通过优化复杂的几何形状和材料选择来增强辐射冷却和隐形应用.
科学领域:
- 光学和光子学 在光学和光子学.
- 材料科学 材料科学 材料科学
- 计算物理 计算物理
背景情况:
- 超表面为热管理和隐形等应用提供了对红外辐射的精确控制.
- 当前的计算方法受到简单的设计和材料选择的限制,阻碍了性能.
- 需要先进的设计策略来释放红外超表面的全部潜力.
研究的目的:
- 开发一种基于机器学习的平台,用于设计带选择性的红外超表面.
- 通过结合复杂的几何形状和各种材料来克服现有方法的局限性.
- 为了能够发现具有增强光学和热性质的新型地表结构.
主要方法:
- 集成一个自动化优化框架与康威灵感的模式和基于规则的形态.
- 利用深度神经网络进行前预测和粒子群集优化进行反向设计.
- 采用71种材料的广泛数据库,并结合图像过和对称处理以优化图案.
主要成果:
- 成功生成具有带选择性特性的多样化,高性能红外超表面.
- 在5-8微米大气透明度窗口内展示了卓越的辐射冷却和隐身能力.
- 优化的马赛克式图案提高了制造可行性,并减少了偏振依赖.
结论:
- 开发的机器学习平台显著扩大了红外超表面的设计空间.
- 这种方法有助于发现具有光学和热管理多功能能力的新型结构.
- 这些发现为下一代红外设备铺平了道路,提高了性能和多功能性.
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