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

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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. This review provides a holistic overview of this rapidly evolving field, emphasizing the general methodology instead of specific domains and deployment contexts. Existing research is organized into data‐driven modules, encompassing data acquisition, machine learning‐based unit cell design, and data‐driven multiscale optimization. The approaches are further categorized 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.

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Bibliographic citation: Data‐Driven Design for Metamaterials and Multiscale Systems: A Review ; day:05 ; month:12 ; year:2023 ; extent:45
Advanced materials ; (05.12.2023) (gesamt 45)

Creator: Lee, Doksoo
Chen, Wei (Wayne)
Wang, Liwei
Chan, Yu‐Chin
Chen, Wei

DOI: 10.1002/adma.202305254

URN: urn:nbn:de:101:1-2023120614224184643421

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:37 AM CEST

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Lee, Doksoo
Chen, Wei (Wayne)
Wang, Liwei
Chan, Yu‐Chin
Chen, Wei

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

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