Radiative cooling is an energy-efficient technology without consuming power. Depending on their use, radiative coolers (RCs) can be designed to be either solar-transparent or solar-opaque, which requires complex spectral characteristics. Our research introduces a novel deep learning-based inverse design methodology for creating thin-film type RCs. Our deep learning algorithm determines the optimal optical constants, material volume ratios, and particle size distributions for oxide/nitride nanoparticle-embedded polyethylene films. It achieves the desired optical properties for both types of RCs through Mie Scattering and effective medium theory. We also assess the optical and thermal performance of each RCs.

中文翻译：

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深度学习辅助纳米颗粒嵌入式辐射冷却器的逆向设计

辐射冷却是一种不消耗电力的节能技术。根据其用途，辐射冷却器 (RC) 可以设计为太阳能透明或太阳能不透明，这需要复杂的光谱特性。我们的研究引入了一种新颖的基于深度学习的逆向设计方法，用于创建薄膜型 RC。我们的深度学习算法确定了嵌入氧化物/氮化物纳米粒子的聚乙烯薄膜的最佳光学常数、材料体积比和粒度分布。它通过米氏散射和有效介质理论实现了两种类型 RC 所需的光学特性。我们还评估了每个 RC 的光学和热性能。