The ever-evolving field of materials design and discovery has been revolutionized by the emergence of data-driven algorithms for generative designs of materials and explorations of structure-property relationships. In particular, AIguided design frameworks have been successfully applied to the field of artificially structured electromagnetic composites known as metamaterials where their use has not only alleviated the computational burden associated with simulations based on first principles but also facilitated faster, more efficient sampling of vast parameter spaces to converge on a solution. MetaDesigner is a user-friendly web application which simplifies and automates the inverse design of metamaterials, i.e., it is a tool powered by generative and discriminative deep learning models for enabling ‘design-by-specification’. The practical application of this framework is exemplified by the successful end-to end design of a metamaterial broadband absorber as well as the demonstration of plasmonic metasurface for generating structural color ‘at will’. We envision that MetaDesigner's user-friendly interface will accommodate users with varying levels of expertise by providing access to multiple inverse algorithms and play a pivotal role in expediting the design and exploration of metamaterial-based devices. As this work is still under development and the technologies underpinning its development are expected to change over time, this abstract is aimed primarily at explaining the overall philosophy and design goals of this project.
Physics informed neural networks (PINNs) solve supervised learning tasks by incorporating partial differential equations describing the governing physics. We use a PINN based on Maxwell’s equations in the frequency domain to predict the electrical permittivity parameters, and hence the electric fields, of circular split-ring resonator-based metamaterials thereby bypassing full-wave solutions based on finite-element methods. We demonstrate the use of a PINN for the inverse prediction of the electrical permittivity of a circular split ring resonator metamaterial given the spatial e-field distributions at the resonant frequency. Our results validate the PINN framework for the inverse retrieval of permittivities from field distributions.
The COVID-19 pandemic attributed to the SARs-Cov-2 virus has disrupted the lives of individuals in every corner of the world, causing millions of infections and numerous deaths worldwide. Identifying and isolating infected people is very crucial to slow down the spread of the disease. In this paper, we report a design of highly sensitive, graphene-metasurface based biosensor for detecting the S1 spike protein expressed on the surface of the SARSCoV-2 virus in the terahertz band. Our structure consists of a silicon dioxide substrate sandwiched between a complete gold layer at the bottom, and a graphene monolayer on top etched with a phi-shaped slot tilted at 45 degree, which performs a wideband reflective-type cross-polarization conversion of the incident electromagnetic (EM) wave. The optimized polarization conversion ratio (PCR) has been achieved at 0.75eV chemical potential value of the graphene layer. When samples of Sars-CoV-2 virus contained in a phosphate buffer saline (PBS) solvent is put on top of proposed design of the sensing surface, the spike proteins of the virus interact with the spike antibody grown on the sensing surface; and it changes the refractive index of the overall system (Biosensor + Analyte), which in turn changes the PCR and the corresponding frequency of the reflected wave. The biosensor response has been computed using the Finite Integration Technique (FIT) in the terahertz region. The sensitivity of the biosensor is found to be 354 GHz/RIU at the PCR of 0.9.
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