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.

Machine Learning Aided Design and Optimization of Thermal Metamaterials.

The major advantage of the soft magnetic composite (SMC) material is to be able to manufacture electrical machines by the highly matured powder metallurgical technology at low cost. This paper presents our extensive studies on the measurement and modeling of magnetic properties of SMC materials and novel machine topologies of 3D fluxes.

Low cost high performance SMC motors: From material to application

Soft Magnetic Composites (SMC) consist of pure iron particles with an inorganic phosphorous coating and an additional iron oxide layer at the boundaries between them It should be specified if the iron oxide is at the boundary of iron and the phosphorous coating or at the outer layer It is assumed that it's a layer between the iron and the phosphorous layer This combination of “ductile” and “brittle” constituents represents a challenge for preparing a specimen suitable for both microstructural and micromechanical studies In particular for micromechanical tests, such as microcompression, microcantilever beam bending, micro tensile tests, and nanoindentation, the quality of the surface is critical We demonstrate the effect of different surface preparation methods both on the microstructure and the mechanical properties Mechanical polishing with two different end-polishing steps and combinations of mechanical polishing and ion-beam polishing are compared Electron microscopy clearly showed the 

Preparing Soft Magnetic Composites for Structural and Micromechanical Investigations

Currently, heart disease stands as the most formidable threat to human life. The application of machine learning in scrutinizing data holds the promise of augmenting early detection and prevention strategies for this ailment. Within this study, a suite of six distinctive and classical machine learning models—Logistic Regression, Random Forest, Decision Tree, K-Nearest Neighbor, Support Vector Classifier, and Neural Network—are introduced and meticulously evaluated. These models leverage data gathered from heart patients across four distinct regions, contributing to a comprehensive assessment. The investigative process encompasses five pivotal stages: data collection, preprocessing, K-Means clustering, application of classification algorithms models for heart disease prediction, and rigorous evaluation. At the end of the study, a comprehensive summary of heart disease prediction was presented.

Exploring Heart Disease Prediction through Machine Learning Techniques

A full numerical study combining artificial intelligence (AI) methods and electromagnetic simulation software on a multilayered structure for radiative cooling (RC) is investigated. The original structure is made of SiO 2 /Si nanometer‐thick layers that make a Bragg mirror for wavelengths in the solar irradiance window (0.3–4 μm). The structures are then optimized in terms of the calculated net cooling power and characterized via the reflected and absorbed incident light as a function of their structural parameters. This investigation provides with optimal designs of beyond‐Bragg, all‐dielectric, ultra‐broadband mirrors that provide net cooling powers in the order of ≈100 W m −2 , similar to the best‐performing structures in literature. Furthermore, it explains AI's success in producing these structures and enables the analysis of resonant conditions in metal‐free multilayers with unconventional layer thickness distributions, offering innovative tools for designing highly efficient structures in RC. 

Artificial Intelligence‐Enhanced Metamaterial Bragg Multilayers for Radiative Cooling

Attractive features of soft magnetic composites (SMCs) are high resistivity, isotropic three-dimensional flux behavior, and easy compression into the complicated shapes required in electromagnetic devices. Comprehensive understanding of the materials will help optimize design of electromagnetic devices. This paper presents the magnetization processes in a SMC sample in micron scale by means of the magneto-optical imaging technique. The sample was magnetized by magnetic fields tangential or perpendicular to the observation surface. It is observed that the flux density is higher at the particle region but lower at the interparticle space. When a tangential field is applied, the stray fields change polarization at the particle boundaries. Both results suggest that the magnetized sample behaves as a collection of individual magnetized particles rather than as a uniform and continuous magnetic substance such as soft iron although there are some interactions between neighboring particles.

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Visualization of magnetization processes of soft magnetic composites by the magneto-optical imaging technique

Recently, there has been growing interest and attention towards daytime radiative cooling. This cooling technology is considered a potentially significant alternative to traditional cooling methods because of its neither energy consumption nor harmful gas emission during operation. In this paper, a daytime radiative cooling emitter (DRCE) consisting of polydimethylsiloxane, silicon dioxide, and aluminum nitride from top to bottom on a silver-silicon substrate was designed by a machine learning method (MLM) and genetic algorithm to achieve daytime radiative cooling. The optimal DRCE had 94.43% average total hemispherical emissivity in the atmospheric window wavelength band and 98.25% average total hemispherical reflectivity in the solar radiation wavelength band. When the ambient temperature was 30°C, and the power of solar radiation was about 900W/m 2, the net cooling power of the optimal DRCE could achieve 140.38W/m 2. The steady-state temperature of that could be approximately 9.08°C lower than the ambient temperature. This paper provides a general research strategy for MLM-driven design of DRCE. 

Daytime radiative cooling multilayer films designed by a machine learning method and genetic algorithm

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Visualization of magnetization processes of soft magnetic composites by the magneto-optical imaging technique

Daytime radiative cooling multilayer films designed by a machine learning method and genetic algorithm