MetaDesigner: a deep learning enabled integrated tool for accelerated design of metamaterials

Abstract

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.