Abstract
Multi-band spectral filters that can transmit visible light but block UV and infrared light in the solar spectrum are applicable to energy-saving windows. However, such filters are usually designed to consider normal incident light only. Here, we report photonic structures allowing selective solar spectrum transmission in wide angles using a quantum-computing-enhanced active learning scheme, which includes machine learning, quantum annealing, and wave-optics simulation in an iterative loop. We experimentally demonstrate the optical characteristics of the photonic structure and its capability to reduce the temperature rise in an enclosure when combined with a thermal radiation layer (temperature reduction of 5.4°C–7.2°C and annual energy saving of ∼97.5 MJ/m2). This structure can be incorporated into existing windows in buildings or automobiles to reduce cooling energy consumption, and the active learning scheme can be applied to design materials with complex properties in general.
| Original language | English |
|---|---|
| Article number | 101847 |
| Journal | Cell Reports Physical Science |
| Volume | 5 |
| Issue number | 3 |
| DOIs | |
| State | Published - Mar 20 2024 |
Funding
This research was supported by the Quantum Computing Based on Quantum Advantage Challenge Research (grant RS-2023-00255442 ) through the National Research Foundation of Korea (NRF) funded by the Korean government ( Ministry of Science and ICT ). This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory , which is supported by the Office of Science of the U.S. Department of Energy under contract no. DE-AC05-00OR22725 . This manuscript has in part been authored by UT-Battelle , LLC, under contract no. DE-AC05-00OR22725 with the U.S. Department of Energy . The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan . This research was supported by the Quantum Computing Based on Quantum Advantage Challenge Research (grant RS-2023-00255442) through the National Research Foundation of Korea (NRF) funded by the Korean government (Ministry of Science and ICT). This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under contract no. DE-AC05-00OR22725. This manuscript has in part been authored by UT-Battelle, LLC, under contract no. DE-AC05-00OR22725 with the U.S. Department of Energy. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan. The U.S. government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. government retains a non-exclusive, paid up, irrevocable, worldwide license to publish or reproduce the published form of the manuscript, or allow others to do so, for U.S. government purposes. S.K. E.L. and T.L. conceived the idea and initiated this project. S.K. and E.L. constructed the QA-enhanced active learning scheme. S.K. performed optimization. S.K. performed all experiments. S.K. and A.B. conducted energy energy-saving simulation. S.K. S.J. E.L. and T.L. discussed the results. S.K. E.L. and T.L. wrote the manuscript. The authors declare no competing interests.
Keywords
- active learning
- multi-band spectral selectivity
- quantum computing
- radiative cooling
- wide-angle spectral filter