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Register a new userLocal constitutive parameters of metamaterials
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Authors
Constantin R. Simovski
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The reason of the non-locality of constitutive (material) parameters extracted in a usual way from the reflection-transmission coefficients of composite slab at moderately low frequencies is explained. The physical meaning of these parameters is clarified. Local constitutive parameters of metamaterial lattices are discussed and their existence at moderate frequencies is demonstrated. It is shown how to extract local material parameters from the dispersion characteristics of an infinite lattice and from reflection and transmission coefficients of metamaterial layers.
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The parameter retrieval is a procedure in which effective material properties are assigned to a given metamaterial. A widely used technique bases on the inversion of reflection and transmission from a metamaterial slab. Thus far, local constitutive relations have been frequently considered in this retrieval procedure to describe the metamaterial at the effective level. This, however, is insufficient. The retrieved local material properties frequently fail to predict reliably the optical response from the slab in situations that deviate from those that have been considered in the retrieval, e.g. when illuminating the slab at a different incidence angle. To significantly improve the situation, we describe here a parameter retrieval, also based on the inversion of reflection and transmission from a slab, that describes the metamaterial at the effective level with nonlocal constitutive relations. We retrieve the effective material parameters at the example of a fishnet metamaterial. We demonstrate that the nonlocal constitutive relation can describe the optical response much better than local constitutive relation would do. Our approach is widely applicable to a large class of metamaterials.
We demonstrate that there is a strong diamagnetic response of metamaterials, consisting of open or closed split ring resonators (SRRs). Detailed numerical work shows that for densely packed SRRs the magnetic permeability, $mu(omega)$, does not approach unity, as expected for frequencies lower and higher than the resonance frequency, $omega_0$. Below $omega_0$, $mu(omega)$ gives values ranging from 0.9 to 0.6 depending of the width of the metallic ring, while above $omega_0$, $mu(omega)$ is close to 0.5. Closed rings have $muapprox 0.5$ over a wide frequency range independently of the width of the ring. A simple model that uses the inner and outer current loop of the SRRs can easily explain theoretically this strong diamagnetic response, which can be used in magnetic levitation.
We have obtained spectra of second-harmonic generation, third harmonic generation, and four-wave mixing from a fishnet metamaterial around its magnetic resonance. The resonant behaviors are distinctly different from those for ordinary materials. They result from the fact that the resonance is plasmonic, and its enhancement appears through the local field in the nanostructure.
The elastic properties of materials derive from their electronic and atomic nature. However, simulating bulk materials fully at these scales is not feasible, so that typically homogenized continuum descriptions are used instead. A seamless and lossless transition of the constitutive description of the elastic response of materials between these two scales has been so far elusive. Here we show how this problem can be overcome by using Artificial Intelligence (AI). A Convolutional Neural Network (CNN) model is trained, by taking the structure image of a nanoporous material as input and the corresponding elasticity tensor, calculated from Molecular Statics (MS), as output. Trained with the atomistic data, the CNN model captures the size- and pore-dependency of the materials elastic properties which, on the physics side, can stem from surfaces and non-local effects. Such effects are often ignored in upscaling from atomistic to classical continuum theory. To demonstrate the accuracy and the efficiency of the trained CNN model, a Finite Element Method (FEM) based result of an elastically deformed nanoporous beam equipped with the CNN as constitutive law is compared with that by a full atomistic simulation. The good agreement between the atomistic simulations and the FEM-AI combination for a system with size and surface effects establishes a new lossless scale bridging approach to such problems. The trained CNN model deviates from the atomistic result by 9.6% for porosity scenarios of up to 90% but it is about 230 times faster than the MS calculation and does not require to change simulation methods between different scales. The efficiency of the CNN evaluation together with the preservation of important atomistic effects makes the trained model an effective atomistically-informed constitutive model for macroscopic simulations of nanoporous materials and solving of inverse problems.
Magnetoelectric susceptibility of a metamaterial built from split ring resonators have been investigated both experimentally and within an equivalent circuit model. The absolute values have been shown to exceed by two orders of magnitude that of classical magnetoelectric materials. The metamaterial investigated reaches the theoretically predicted value of the magnetoelectric susceptibility which is equal to the geometric average of the electric and magnetic susceptibilities.
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