Design Transparent Radiative Cooler using Quantum Computing-Assisted Active Learning

Passive radiative coolers, which allow emitting radiation through an atmospheric window (8 < λ < 13 μm), have attracted much attention as a solution to climate change issues, owing to their potential in energy- and refrigerant-free cooling capability. In particular, transparent radiative cooler (TRC) that has high transmission in visible light and high emission in the atmospheric window has been developed for energy-saving windows. However, it is difficult to design a high-performance TRC with high visible transparency along with high ultraviolet and near-infrared reflection and high cooling capability. In this work, we design a visibly transparent radiative cooler using a quantum computing-assisted active learning design scheme that combines machine learning, quantum annealing, optical simulation, and on-the-fly dataset acquisition in one iteration. The optimally designed cooler has high visible light transmission and low ultraviolet and near-infrared light transmission to maximize cooling performance, and high emission in the atmospheric window range. We experimentally demonstrate the optical properties of the designed cooler and demonstrate its passive cooling ability under dynamic environments by field experiments. The field testing results show a temperature reduction of up to 6.1°C when using our fabricated TRC compared with common glass. In addition, we calculate the energy saving for cooling when a standard room uses our designed cooler instead of normal windows, and the results show that the cooler can lead to a potential energy saving of ~86.3 MJ/m² annually in hot regions. We believe that the designed TRC can be applied for energy-saving windows and the quantum computing-assisted active learning scheme can be efficiently used for functional material design in general.