Accelerated photonic design of coolhouse film for photosynthesis via machine learning

Jinlei Li, Yi Jiang, Bo Li, Yihao Xu, Huanzhi Song, Ning Xu, Peng Wang, Dayang Zhao, Zhe Liu, Sheng Shu, Juyou Wu, Miao Zhong, Yongguang Zhang, Kefeng Zhang, Bin Zhu (), Qiang Li, Wei Li (), Yongmin Liu (), Shanhui Fan and Jia Zhu ()
Additional contact information
Jinlei Li: Nanjing University
Yi Jiang: Nanjing University
Bo Li: Chinese Academy of Sciences
Yihao Xu: Northeastern University
Huanzhi Song: University of New South Wales
Ning Xu: Nanjing University
Peng Wang: Nanjing Agricultural University
Dayang Zhao: Nanjing University
Zhe Liu: Nanjing Agricultural University
Sheng Shu: Nanjing Agricultural University
Juyou Wu: Nanjing Agricultural University
Miao Zhong: Nanjing University
Yongguang Zhang: Nanjing University
Kefeng Zhang: University of New South Wales
Bin Zhu: Nanjing University
Qiang Li: Zhejiang University
Wei Li: Chinese Academy of Sciences
Yongmin Liu: Northeastern University
Shanhui Fan: Stanford University
Jia Zhu: Nanjing University

Nature Communications, 2025, vol. 16, issue 1, 1-11

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-024-54983-8

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