  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.
