Daytime radiative cooling multilayer films designed by a machine learning method and genetic algorithm
Siyuan Li, Meng An, Zhiheng Zheng, Yuchun Gou, Wenlei Lian, Wei Yu, Ping Zhang
Abstract
Recently, there has been growing interest and attention towards daytime radiative cooling. This cooling technology is considered a potentially significant alternative to traditional cooling methods because of its neither energy consumption nor harmful gas emission during operation. In this paper, a daytime radiative cooling emitter (DRCE) consisting of polydimethylsiloxane, silicon dioxide, and aluminum nitride from top to bottom on a silver-silicon substrate was designed by a machine learning method (MLM) and genetic algorithm to achieve daytime radiative cooling. The optimal DRCE had 94.43% average total hemispherical emissivity in the atmospheric window wavelength band and 98.25% average total hemispherical reflectivity in the solar radiation wavelength band. When the ambient temperature was 30°C, and the power of solar radiation was about 900W/m 2 , the net cooling power of the optimal DRCE could achieve 140.38W/m 2 . The steady-state temperature of that could be approximately 9.08°C lower than the ambient temperature. This paper provides a general research strategy for MLM-driven design of DRCE.