Deep Learning‐Assisted Active Metamaterials with Heat‐Enhanced Thermal Transport

Heat management is crucial for state‐of‐the‐art applications such as passive radiative cooling, thermally adjustable wearables, and camouflage systems. Their adaptive versions, to cater to varied requirements, lean on the potential of adaptive metamaterials. Existing efforts, however, feature with h...

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Vydáno v:	Advanced materials (Weinheim) Ročník 36; číslo 5; s. e2305791 - n/a
Hlavní autoři:	Jin, Peng, Xu, Liujun, Xu, Guoqiang, Li, Jiaxin, Qiu, Cheng‐Wei, Huang, Jiping
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Germany Wiley Subscription Services, Inc 01.02.2024
Témata:	active metamaterials Adaptive systems Ambient temperature Deep learning Diffusion barriers enhanced thermal conduction Heat Isotropic material Metamaterials self‐adaptability Thermal diffusion deep learning enhanced thermal conduction active metamaterials self-adaptability
ISSN:	0935-9648, 1521-4095, 1521-4095
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Abstract	Heat management is crucial for state‐of‐the‐art applications such as passive radiative cooling, thermally adjustable wearables, and camouflage systems. Their adaptive versions, to cater to varied requirements, lean on the potential of adaptive metamaterials. Existing efforts, however, feature with highly anisotropic parameters, narrow working‐temperature ranges, and the need for manual intervention, which remain long‐term and tricky obstacles for the most advanced self‐adaptive metamaterials. To surmount these barriers, heat‐enhanced thermal diffusion metamaterials powered by deep learning is introduced. Such active metamaterials can automatically sense ambient temperatures and swiftly, as well as continuously, adjust their thermal functions with a high degree of tunability. They maintain robust thermal performance even when external thermal fields change direction, and both simulations and experiments demonstrate exceptional results. Furthermore, two metadevices with on‐demand adaptability, performing distinctive features with isotropic materials, wide working temperatures, and spontaneous response are designed. This work offers a framework for the design of intelligent thermal diffusion metamaterials and can be expanded to other diffusion fields, adapting to increasingly complex and dynamic environments. Drawing parallels from nonlinear optics, artificial intelligence assists a configurable nonlinear thermal material whose effective thermal conductivity being responsive to its temperature gradient. Such deep learning‐assisted nonlinear thermal material promotes two typical self‐adaptive devices, which can perceive their environment deeply. One maintains stable function, and another switches its function, in a changeable environment.
AbstractList	Heat management is crucial for state‐of‐the‐art applications such as passive radiative cooling, thermally adjustable wearables, and camouflage systems. Their adaptive versions, to cater to varied requirements, lean on the potential of adaptive metamaterials. Existing efforts, however, feature with highly anisotropic parameters, narrow working‐temperature ranges, and the need for manual intervention, which remain long‐term and tricky obstacles for the most advanced self‐adaptive metamaterials. To surmount these barriers, heat‐enhanced thermal diffusion metamaterials powered by deep learning is introduced. Such active metamaterials can automatically sense ambient temperatures and swiftly, as well as continuously, adjust their thermal functions with a high degree of tunability. They maintain robust thermal performance even when external thermal fields change direction, and both simulations and experiments demonstrate exceptional results. Furthermore, two metadevices with on‐demand adaptability, performing distinctive features with isotropic materials, wide working temperatures, and spontaneous response are designed. This work offers a framework for the design of intelligent thermal diffusion metamaterials and can be expanded to other diffusion fields, adapting to increasingly complex and dynamic environments. Drawing parallels from nonlinear optics, artificial intelligence assists a configurable nonlinear thermal material whose effective thermal conductivity being responsive to its temperature gradient. Such deep learning‐assisted nonlinear thermal material promotes two typical self‐adaptive devices, which can perceive their environment deeply. One maintains stable function, and another switches its function, in a changeable environment. Heat management is crucial for state‐of‐the‐art applications such as passive radiative cooling, thermally adjustable wearables, and camouflage systems. Their adaptive versions, to cater to varied requirements, lean on the potential of adaptive metamaterials. Existing efforts, however, feature with highly anisotropic parameters, narrow working‐temperature ranges, and the need for manual intervention, which remain long‐term and tricky obstacles for the most advanced self‐adaptive metamaterials. To surmount these barriers, heat‐enhanced thermal diffusion metamaterials powered by deep learning is introduced. Such active metamaterials can automatically sense ambient temperatures and swiftly, as well as continuously, adjust their thermal functions with a high degree of tunability. They maintain robust thermal performance even when external thermal fields change direction, and both simulations and experiments demonstrate exceptional results. Furthermore, two metadevices with on‐demand adaptability, performing distinctive features with isotropic materials, wide working temperatures, and spontaneous response are designed. This work offers a framework for the design of intelligent thermal diffusion metamaterials and can be expanded to other diffusion fields, adapting to increasingly complex and dynamic environments. Heat management is crucial for state-of-the-art applications such as passive radiative cooling, thermally adjustable wearables, and camouflage systems. Their adaptive versions, to cater to varied requirements, lean on the potential of adaptive metamaterials. Existing efforts, however, feature with highly anisotropic parameters, narrow working-temperature ranges, and the need for manual intervention, which remain long-term and tricky obstacles for the most advanced self-adaptive metamaterials. To surmount these barriers, heat-enhanced thermal diffusion metamaterials powered by deep learning is introduced. Such active metamaterials can automatically sense ambient temperatures and swiftly, as well as continuously, adjust their thermal functions with a high degree of tunability. They maintain robust thermal performance even when external thermal fields change direction, and both simulations and experiments demonstrate exceptional results. Furthermore, two metadevices with on-demand adaptability, performing distinctive features with isotropic materials, wide working temperatures, and spontaneous response are designed. This work offers a framework for the design of intelligent thermal diffusion metamaterials and can be expanded to other diffusion fields, adapting to increasingly complex and dynamic environments.Heat management is crucial for state-of-the-art applications such as passive radiative cooling, thermally adjustable wearables, and camouflage systems. Their adaptive versions, to cater to varied requirements, lean on the potential of adaptive metamaterials. Existing efforts, however, feature with highly anisotropic parameters, narrow working-temperature ranges, and the need for manual intervention, which remain long-term and tricky obstacles for the most advanced self-adaptive metamaterials. To surmount these barriers, heat-enhanced thermal diffusion metamaterials powered by deep learning is introduced. Such active metamaterials can automatically sense ambient temperatures and swiftly, as well as continuously, adjust their thermal functions with a high degree of tunability. They maintain robust thermal performance even when external thermal fields change direction, and both simulations and experiments demonstrate exceptional results. Furthermore, two metadevices with on-demand adaptability, performing distinctive features with isotropic materials, wide working temperatures, and spontaneous response are designed. This work offers a framework for the design of intelligent thermal diffusion metamaterials and can be expanded to other diffusion fields, adapting to increasingly complex and dynamic environments.
Author	Xu, Guoqiang Li, Jiaxin Xu, Liujun Huang, Jiping Qiu, Cheng‐Wei Jin, Peng
Author_xml	– sequence: 1 givenname: Peng orcidid: 0000-0002-4440-5240 surname: Jin fullname: Jin, Peng organization: Fudan University – sequence: 2 givenname: Liujun surname: Xu fullname: Xu, Liujun organization: Graduate School of China Academy of Engineering Physics – sequence: 3 givenname: Guoqiang surname: Xu fullname: Xu, Guoqiang organization: National University of Singapore – sequence: 4 givenname: Jiaxin surname: Li fullname: Li, Jiaxin organization: National University of Singapore – sequence: 5 givenname: Cheng‐Wei surname: Qiu fullname: Qiu, Cheng‐Wei email: chengwei.qiu@nus.edu.sg organization: National University of Singapore – sequence: 6 givenname: Jiping orcidid: 0000-0002-3617-3275 surname: Huang fullname: Huang, Jiping email: jphuang@fudan.edu.cn organization: Fudan University
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Snippet	Heat management is crucial for state‐of‐the‐art applications such as passive radiative cooling, thermally adjustable wearables, and camouflage systems. Their... Heat management is crucial for state-of-the-art applications such as passive radiative cooling, thermally adjustable wearables, and camouflage systems. Their...
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SubjectTerms	active metamaterials Adaptive systems Ambient temperature Deep learning Diffusion barriers enhanced thermal conduction Heat Isotropic material Metamaterials self‐adaptability Thermal diffusion
Title	Deep Learning‐Assisted Active Metamaterials with Heat‐Enhanced Thermal Transport
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