Data-Driven Design for Metamaterials and Multiscale Systems: A Review
Abstract
Metamaterials are artificial materials designed to exhibit effective material parameters that go beyond those found in nature. Composed of unit cells with rich designability that are assembled into multiscale systems, they hold great promise for realizing next-generation devices with exceptional, often exotic, functionalities. However, the vast design space and intricate structure-property relationships pose significant challenges in their design. A compelling paradigm that could bring the full potential of metamaterials to fruition is emerging: data-driven design. In this review, we provide a holistic overview of this rapidly evolving field, emphasizing the general methodology instead of specific domains and deployment contexts. We organize existing research into data-driven modules, encompassing data acquisition, machine learning-based unit cell design, and data-driven multiscale optimization. We further categorize the approaches within each module based on shared principles, analyze and compare strengths and applicability, explore connections between different modules, and identify open research questions and opportunities.
Cite
@article{arxiv.2307.05506,
title = {Data-Driven Design for Metamaterials and Multiscale Systems: A Review},
author = {Doksoo Lee and Wei Wayne Chen and Liwei Wang and Yu-Chin Chan and Wei Chen},
journal= {arXiv preprint arXiv:2307.05506},
year = {2023}
}