Deep‐Learning‐Enabled Intelligent Design of Thermal Metamaterials

Thermal metamaterials are mixture-based materials that are engineered to manipulate, control, and process the flow of heat, enabling numerous advanced thermal metadevices. Conventional thermal metamaterials are predominantly designed with tractable regular geometries owing to the delicate analytical solution and easy-to-implement effective structures. Nevertheless, it is challenging to achieve the design of thermal metamaterials with arbitrary geometry, letting alone intelligent (automatic, real-time, and customizable) design of thermal metamaterials. Here, an intelligent design framework of thermal metamaterials is presented via a pre-trained deep learning model, which gracefully achieves the desired functional structures of thermal metamaterials with exceptional speed and efficiency, regardless of arbitrary geometry. It possesses incomparable versatility and is of great flexibility to achieve the corresponding design of thermal metamaterials with different background materials, anisotropic geometries, and thermal functionalities. The transformation thermotics-induced, freeform, background-independent, and omnidirectional thermal cloaks, whose structural configurations are automatically designed in real-time according to shape and background, are numerically and experimentally demonstrated. This study sets up a novel paradigm for an automatic and real-time design of thermal metamaterials in a new design scenario. More generally, it may open a door to the realization of an intelligent design of metamaterials in also other physical domains.

[1]  CMTO: Configurable-design-element multiscale topology optimization , 2023, Additive Manufacturing.

[2]  S. Guenneau,et al.  Deep learning based design of thermal metadevices , 2022, International Journal of Heat and Mass Transfer.

[3]  M. Kadic,et al.  Design of thermal cloaks with isotropic materials based on machine learning , 2022, International Journal of Heat and Mass Transfer.

[4]  Wei Ma,et al.  Deep learning modeling strategy for material science: from natural materials to metamaterials , 2022, Journal of Physics: Materials.

[5]  R. Valentí,et al.  Phase diagram of a distorted kagome antiferromagnet and application to Y-kapellasite , 2021, npj Computational Materials.

[6]  Chiara Daraio,et al.  Mechanical cloak via data-driven aperiodic metamaterial design , 2021, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Yongkeun Park,et al.  Non-resonant power-efficient directional Nd:YAG ceramic laser using a scattering cavity , 2021, Nature communications.

[8]  Ranjit T. Koodali,et al.  Machine learning in experimental materials chemistry , 2020, Catalysis Today.

[9]  Liang Gao,et al.  Illusion thermotics with topology optimization , 2020 .

[10]  Jiaqi Jiang,et al.  Deep neural networks for the evaluation and design of photonic devices , 2020, Nature Reviews Materials.

[11]  Wei Chen,et al.  Deep Generative Modeling for Mechanistic-based Learning and Design of Metamaterial Systems , 2020, Computer Methods in Applied Mechanics and Engineering.

[12]  Jiaqi Jiang,et al.  Robust Freeform Metasurface Design Based on Progressively Growing Generative Networks , 2020, ACS Photonics.

[13]  Youhei Akimoto,et al.  Topology-optimized thermal carpet cloak expressed by an immersed-boundary level-set method via a covariance matrix adaptation evolution strategy , 2019, International Journal of Heat and Mass Transfer.

[14]  Jonathan A. Fan,et al.  Global optimization of dielectric metasurfaces using a physics-driven neural network , 2019, Nano letters.

[15]  Feng Cheng,et al.  Probabilistic Representation and Inverse Design of Metamaterials Based on a Deep Generative Model with Semi‐Supervised Learning Strategy , 2019, Advanced materials.

[16]  Jason Hickey,et al.  Data-driven metasurface discovery , 2018, ArXiv.

[17]  Itzik Malkiel,et al.  Plasmonic nanostructure design and characterization via Deep Learning , 2018, Light, science & applications.

[18]  Kyu-Tae Lee,et al.  A Generative Model for Inverse Design of Metamaterials , 2018, Nano letters.

[19]  Youhei Akimoto,et al.  Exploring optimal topology of thermal cloaks by CMA-ES , 2018 .

[20]  Zongfu Yu,et al.  Training Deep Neural Networks for the Inverse Design of Nanophotonic Structures , 2017, 2019 Conference on Lasers and Electro-Optics (CLEO).

[21]  X. Tong Functional Metamaterials and Metadevices , 2017, MRS Bulletin.

[22]  Xiaopeng Zhao,et al.  Illusion thermal device based on material with constant anisotropic thermal conductivity for location camouflage , 2016 .

[23]  Mark D. Huntington,et al.  Subwavelength Lattice Optics by Evolutionary Design , 2014, Nano letters.

[24]  Jiping Huang,et al.  Shaped graded materials with an apparent negative thermal conductivity , 2008 .

[25]  Xiuli Yue,et al.  Monolayer thermal meta-device with switching functions , 2022, International Journal of Heat and Mass Transfer.