Application of machine-learning techniques for characteristic analysis of refractory materials

Abstract

Flat optics have become capable of achieving unprecedented functionalities through electromagnetic (EM) wave manipulation by employing the metasurfaces. The most crucial part in the design of metasurface is the selection the constitutive component i.e. the meta-atom's material and structure so that it exhibits the precise operation as per the desired application. The unit-cell design calls for an iterative loop of simulations in order to explore the EM responses for intended operation. In this work, we have studied the absorption response of refractory materials under visible light radiations for their utilization in energy harvesting applications. The absorption response estimation using machine-learning techniques for the materials having very high melting-points, mechanical stabilities and inertness to the atmosphere has been carried out to investigate their performance in the broadband range. The presented regression models incorporate hybrid data format i.e. they simultaneously contend with 3-D and 1-D properties of various shapes of nano-resonators. The images' feature extraction is carried out by employing Singular Value Decomposition. The trained models are potent enough to bypass the repetitive sequence of optimization involved in conventional EM solvers. Additionally, the models are capable of predicting the optimum shape along with structural dimensions of unit-cell. For forward model, the MSEs for training and testing are 1.302×10 -2 and 3.269×10 -2 while R 2 scores are 0.9804 and 0.8764, respectively. The approach applied is so robust that, irrespective of complexity of unit-cell structure is, it serves the purpose of predicting the distinct structure with highest performance while bypassing the time-and computationally-intensive EM simulations.

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