机器学习驱动的冷却窗口设计超越了超模的元材料
Seok-Beom Seo1, Ye-Rin Choi1, Jong-Goog Lee1
1Department of Applied Physics Kyung Hee University Yongin South Korea.
Nanophotonics (Berlin, Germany)
|March 9, 2026
概括
机器学习 (ML) 反向设计创造了优越的超薄冷却窗口涂层. 优化了ML的多层超越了传统设计,提供了高可见透射率和近红外反射率以节省能源.
科学领域:
- 材料科学 材料科学 材料科学
- 光学是什么?光学是什么?
- 计算科学 计算科学
背景情况:
- 分析的多层设计仅限于狭窄的光谱带.
- 机器学习 (ML) 提供了在多个频段优化多层的潜力.
- 在相同的约束条件下,ML优化的多层与分析设计的性能基本上没有得到证实.
研究的目的:
- 实验验证ML驱动的反向设计对多层涂层的优越性.
- 开发具有高平均可见透射率 (AVT) 和高平均近红外反射率 (ANR) 的冷却窗口涂层.
- 为了比较 ML 优化的无周期设计与周期性的超模态元材料 (HMM) 对应物.
主要方法:
- 使用了一个因子化机器,集成与模拟化,用于ML驱动的反向设计.
- 设计和制造的ZnS/Ag多层.
- 基准测试的ML设计与周期性超模态元材料 (HMM) 结构相比较.
主要成果:
- 在156纳米厚度约束下,ML设计的涂层与HMM (0.49 AVT,0.83 ANR) 相比,实现了更高的性能 (0.57 AVT,0.98 ANR).
- 一个扩展的ML设计 (300 nm) 通过抑制法布里-佩罗共振达到0.79 AVT和0.97 ANR.
- 与HMM不同,ML驱动的多层显示器在可见光谱中展示了可调的传输颜色,而不是HMM.
结论:
- ML驱动的反向设计是一种强大的方法,用于创建高性能,超薄和可调色的冷却窗口涂层.
- 这些先进的涂层为城市节能提供了巨大的潜力.
- 该研究通过实验证实了ML优化对多层光学涂层传统分析方法的优势.
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