Data-Driven Design for Metamaterials and Multiscale Systems: A Review

TL;DR

This review discusses how data-driven methods, including machine learning and optimization, are transforming the design of complex metamaterials and multiscale systems, addressing challenges in their intricate structure-property relationships.

Contribution

It provides a comprehensive overview of data-driven design methodologies for metamaterials, categorizing approaches and analyzing their strengths and applications.

Findings

Organized existing research into data acquisition, design, and optimization modules.

Compared different data-driven approaches based on shared principles.

Identified open research questions and future opportunities.

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.