# Computational microstructure design for mechanical property optimization: a review

https://mdr.nims.go.jp/datasets/0fd78d5b-8a08-4ffb-8d01-07b6d004a992

## File

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## Id

0fd78d5b-8a08-4ffb-8d01-07b6d004a992

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2025-11-20T09:46:23.795974Z

## Updated at

2025-11-26T23:30:04.050951Z

## Published at

2025-11-26T23:24:00.184576Z

## Doi



## First published url

https://doi.org/10.1080/27660400.2025.2581359

## Date published

2025-12-31

## Recorded date published

2025-12-31

## Resource type

journal_article

## Manuscript type

vor

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## Title

- title: 'Computational microstructure design for mechanical property optimization:
    a review'
  title_type: original
  lang: en

## Description

- description: "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.\r\nThis 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.\r\nAs 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. "
  description_type: abstract
  lang: und

## Creator

- name: Keita Kambayashi
  role: author
  orcid: https://orcid.org/0009-0008-6439-1721
- name: Ikumu Watanabe
  role: author
  orcid: https://orcid.org/0000-0002-7693-1675

## Contact agent



## Publisher

organization: Informa UK Limited

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## Keyword

- subject: microstructures
  schema: not_defined
- subject: mechanical metamaterials
  schema: not_defined
- subject: computational design
  schema: not_defined
- subject: topology optimization
  schema: not_defined
- subject: deep learning-based design
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/

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## Journal

- title: 'Science and Technology of Advanced Materials: Methods'
  issn: '27660400'
  volume: '5'
  issue: '1'
  article_number: '2581359'

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  filename: 2025stam-m_kambayashi.pdf
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  size: 10504623
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## Thumbnail

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filename: 2025stam-m_kambayashi.pdf