Accelerated photonic design of coolhouse film for photosynthesis via machine learning

Controlling the suitable light, temperature, and water is essential for plant photosynthesis. While greenhouses/warm-houses are effective in cold or dry climates by creating warm, humid environments, a cool-house that provides a cool local environment with minimal energy and water consumption is highly desirable but has yet to be realized in hot, water-scarce regions. Here, using a synergistic genetic algorithm and machine learning, we propose and demonstrate a coolhouse film that regulates temperature and water for photosynthesis without requiring additional energy or water. This scalable film, selected from hundreds of potential designs, selectively and precisely transmits sunlight needed for photosynthesis while reflecting excess heat, thereby reducing thermal load and evapotranspiration. Its optical properties also exhibit weak angle dependence. In demonstrations in subtropical and arid regions, the film reduces temperatures by 5-17 °C and cuts water loss by half, resulting in more than doubled biomass yield and survival rates. It also improves crop resistance to heat and drought in greenhouse cultivation. The integration of machine learning and photonics provides a powerful toolkit for designing photonic structures and devices aimed at sustainability.

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Publication Details

Journal
Nature Communications
Published
2025-02-06
DOI
https://doi.org/10.1038/s41467-024-54983-8
Citations
16
Primary Topic
Thermal Radiation and Cooling Technologies
Type
article
Field-Weighted Citation Impact
15.25

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article

Accelerated photonic design of coolhouse film for photosynthesis via machine learning

Miao Zhong, Yongmin Liu, Kefeng Zhang, Sheng Shu et al.
16 citations
Nature Communications
Thermal Radiation and Cooling Technologies
15.25
article

Accelerated photonic design of coolhouse film for photosynthesis via machine learning

Miao Zhong, Yongmin Liu, Kefeng Zhang, Sheng Shu, Bin Zhu, Juyou Wu, Zhe Liu, Yi Jiang, Dayang Zhao, Huanzhi Song, Ning Xu, Yihao Xu, Wei Li, Peng Wang, Bo Li, Qiang Li, Shanhui Fan, Jinlei Li, Jia Zhu, Yongguang Zhang
article en
16 citations

Abstract

Controlling the suitable light, temperature, and water is essential for plant photosynthesis. While greenhouses/warm-houses are effective in cold or dry climates by creating warm, humid environments, a cool-house that provides a cool local environment with minimal energy and water consumption is highly desirable but has yet to be realized in hot, water-scarce regions. Here, using a synergistic genetic algorithm and machine learning, we propose and demonstrate a coolhouse film that regulates temperature and water for photosynthesis without requiring additional energy or water. This scalable film, selected from hundreds of potential designs, selectively and precisely transmits sunlight needed for photosynthesis while reflecting excess heat, thereby reducing thermal load and evapotranspiration. Its optical properties also exhibit weak angle dependence. In demonstrations in subtropical and arid regions, the film reduces temperatures by 5-17 °C and cuts water loss by half, resulting in more than doubled biomass yield and survival rates. It also improves crop resistance to heat and drought in greenhouse cultivation. The integration of machine learning and photonics provides a powerful toolkit for designing photonic structures and devices aimed at sustainability.

Nature CommunicationsVol. 16(1)
Nanjing Agricultural University (CN), Northeastern University (US), Nanjing Tech University (CN), China Pharmaceutical University (CN), Chinese Academy of Sciences (CN), UNSW Sydney (AU), Soochow University (CN), Changchun Institute of Optics, Fine Mechanics and Physics (CN), Collaborative Innovation Center of Advanced Microstructures (CN), Zhejiang University (CN), Nanjing University (CN), Stanford University (US)
U.S. Department of Energy, National Natural Science Foundation of China, Government of Jiangsu Province, Natural Science Foundation of Jiangsu Province, Nanjing University, National Laboratory of Solid State Microstructures, Nanjing University, Wuhan Textile University, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 1%
Thermal Radiation and Cooling Technologies
15.25
DOIOpen Access PDFOpenAlex
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