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The intricate geometrical features of microstructures are key to achieving novel macroscopic structural properties. Recently, mechanical metamaterials, known for exhibiting mechanical properties that surpass those of natural materials, have drawn significant attention. Determining their optimal microstructural morphology to achieve desired mechanical properties is challenging, necessitating advanced computational design techniques. Concurrently, manufacturing technology must advance to produce these increasingly complex microstructures.
This review specifically examines the interrelationship between structure and property within the broader process-structure-property-performance reciprocity framework of material design. We comprehensively categorize and present computational methods for both forward and inverse design problems.
As computational design methods progress, mechanical metamaterials, already applied in fields like soft robotics, medical devices, and aerospace, are expected to evolve dramatically into more advanced functional materials. We also address challenges and future prospects in microstructure fabrication, explicitly incorporating process considerations. This paper aims to provide valuable insights for all researchers involved in materials design with a focus on microstructural heterogeneity, regardless of their primary engagement with computational methods.

Rights:

• Creative Commons BY Attribution 4.0 International
  

Keyword: microstructures, mechanical metamaterials, computational design, topology optimization, deep learning-based design

Date published: 2025-12-31

Publisher: Informa UK Limited

Journal:

• Science and Technology of Advanced Materials: Methods (ISSN: 27660400) vol. 5 issue. 1 2581359

Funding:

Manuscript type: Publisher's version (Version of record)

MDR DOI:

First published URL: https://doi.org/10.1080/27660400.2025.2581359

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Updated at: 2025-11-27 08:30:04 +0900

Published on MDR: 2025-11-27 08:24:00 +0900