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.

翻译：元材料是人工设计的材料，旨在展现超越自然界存在的有效材料参数。它们由具有丰富设计性的单元胞组成，并组装成多尺度系统，在实现具有非凡（通常是奇异）功能的下一代器件方面展现出巨大潜力。然而，庞大的设计空间和复杂的结构-性能关系给其设计带来了巨大挑战。一种能够充分发挥元材料全部潜力的引人注目的范式正在兴起：数据驱动设计。本文对这一快速发展的领域进行了全面概述，重点强调通用方法论而非特定领域和部署背景。我们将现有研究组织为数据驱动模块，涵盖数据采集、基于机器学习的单元胞设计以及数据驱动的多尺度优化。我们进一步基于共同原则对每个模块中的方法进行分类，分析并比较其优势和适用性，探索不同模块之间的联系，并识别开放的研究问题与机遇。