Deep learning based analysis of microstructured materials for thermal radiation control

Deep learning-assisted inverse design of nanoparticle-embedded radiative coolers

Radiative cooling is an energy-efficient technology without consuming power. Depending on their use, radiative coolers (RCs) can be designed to be either solar-transparent or solar-opaque, which requires complex spectral characteristics. Our research introduces a novel deep learning-based inverse design methodology for creating thin-film type RCs. Our deep learning algorithm determines the optimal optical constants, material volume ratios, and particle size distributions for oxide/nitride nanoparticle-embedded polyethylene films. It achieves the desired optical properties for both types of RCs through Mie Scattering and effective medium theory. We also assess the optical and thermal performance of each RCs.

Machine Learning Aided Design and Optimization of Thermal Metamaterials

Artificial Intelligence (AI) has advanced material research that were previously intractable, for example, the machine learning (ML) has been able to predict some unprecedented thermal properties. In this review, we first elucidate the methodologies underpinning discriminative and generative models, as well as the paradigm of optimization approaches. Then, we present a series of case studies showcasing the application of machine learning in thermal metamaterial design. Finally, we give a brief discussion on the challenges and opportunities in this fast developing field. In particular, this review provides: (1) Optimization of thermal metamaterials using optimization algorithms to achieve specific target properties. (2) Integration of discriminative models with optimization algorithms to enhance computational efficiency. (3) Generative models for the structural design and optimization of thermal metamaterials.

Designing Metasurfaces for Efficient Solar Energy Conversion

Metasurfaces have recently emerged as a promising technological platform, offering unprecedented control over light by structuring materials at the nanoscale using two-dimensional arrays of subwavelength nanoresonators. These metasurfaces possess exceptional optical properties, enabling a wide variety of applications in imaging, sensing, telecommunication, and energy-related fields. One significant advantage of metasurfaces lies in their ability to manipulate the optical spectrum by precisely engineering the geometry and material composition of the nanoresonators' array. Consequently, they hold tremendous potential for efficient solar light harvesting and conversion. In this Review, we delve into the current state-of-the-art in solar energy conversion devices based on metasurfaces. First, we provide an overview of the fundamental processes involved in solar energy conversion, alongside an introduction to the primary classes of metasurfaces, namely, plasmonic and dielectric metasurfaces. Subsequently, we explore the numerical tools used that guide the design of metasurfaces, focusing particularly on inverse design methods that facilitate an optimized optical response. To showcase the practical applications of metasurfaces, we present selected examples across various domains such as photovoltaics, photoelectrochemistry, photocatalysis, solar-thermal and photothermal routes, and radiative cooling. These examples highlight the ways in which metasurfaces can be leveraged to harness solar energy effectively. By tailoring the optical properties of metasurfaces, significant advancements can be expected in solar energy harvesting technologies, offering new practical solutions to support an emerging sustainable society.

Photonic structures in radiative cooling

Radiative cooling is a passive cooling technology without any energy consumption, compared to conventional cooling technologies that require power sources and dump waste heat into the surroundings. For decades, many radiative cooling studies have been introduced but its applications are mostly restricted to nighttime use only. Recently, the emergence of photonic technologies to achieves daytime radiative cooling overcome the performance limitations. For example, broadband and selective emissions in mid-IR and high reflectance in the solar spectral range have already been demonstrated. This review article discusses the fundamentals of thermodynamic heat transfer that motivates radiative cooling. Several photonic structures such as multilayer, periodical, random; derived from nature, and associated design procedures were thoroughly discussed. Photonic integration with new functionality significantly enhances the efficiency of radiative cooling technologies such as colored, transparent, and switchable radiative cooling applications has been developed. The commercial applications such as reducing cooling loads in vehicles, increasing the power generation of solar cells, generating electricity, saving water, and personal thermal regulation are also summarized. Lastly, perspectives on radiative cooling and emerging issues with potential solution strategies are discussed.

Deep learning-based inverse design of microstructured materials for optical optimization and thermal radiation control

Microstructures with engineered properties are critical to thermal management in aerospace and space applications. Due to the overwhelming number of microstructure design variables, traditional approaches to material optimization can have time-consuming processes and limited use cases. Here, we combine a surrogate optical neural network with an inverse neural network and dynamic post-processing to form an aggregated neural network inverse design process. Our surrogate network emulates finite-difference time-domain simulations (FDTD) by developing a relationship between the microstructure's geometry, wavelength, discrete material properties, and the output optical properties. The surrogate optical solver works in tandem with an inverse neural network to predict a microstructure's design properties that will match an input optical spectrum. As opposed to conventional approaches that are constrained by material selection, our network can identify new material properties that best optimize the input spectrum and match the output to an existing material. The output is evaluated using critical design constraints, simulated in FDTD, and used to retrain the surrogate-forming a self-learning loop. The presented framework is applicable to the inverse design of various optical microstructures, and the deep learning-derived approach will allow complex and user-constrained optimization for thermal radiation control in future aerospace and space systems.

Recent Advances in Reconfigurable Metasurfaces: Principle and Applications

Metasurfaces have shown their great capability to manipulate electromagnetic waves. As a new concept, reconfigurable metasurfaces attract researchers' attention. There are many kinds of reconfigurable components, devices and materials that can be loaded on metasurfaces. When cooperating with reconfigurable structures, dynamic control of the responses of metasurfaces are realized under external excitations, offering new opportunities to manipulate electromagnetic waves dynamically. This review introduces some common methods to design reconfigurable metasurfaces classified by the techniques they use, such as special materials, semiconductor components and mechanical devices. Specifically, this review provides a comparison among all the methods mentioned and discusses their pros and cons. Finally, based on the unsolved problems in the designs and applications, the challenges and possible developments in the future are discussed.