# Controllable inverse design of auxetic metamaterials using deep learning

https://mdr.nims.go.jp/datasets/5a9fea9b-7d0b-47d4-a30d-41f91dd5b053

## File

- [zheng_MD2021b.pdf](https://mdr.nims.go.jp/filesets/09341b48-090f-4004-abc9-6267008028ec/download) ([Detail](https://mdr.nims.go.jp/filesets/09341b48-090f-4004-abc9-6267008028ec.md))

## Id

5a9fea9b-7d0b-47d4-a30d-41f91dd5b053

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2023-02-22T06:17:50.733442Z

## Updated at

2024-01-05T13:12:12.621675Z

## Published at

2023-02-28T02:37:18.987935Z

## Doi



## First published url

https://doi.org/10.1016/j.matdes.2021.110178

## Date published

2021-10-18

## Recorded date published

2021-12

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Controllable inverse design of auxetic metamaterials using deep learning
  title_type: original
  lang: en

## Description

- description: As typical mechanical metamaterials with negative Poisson's  ratios,
    auxetic metamaterials exhibit counterintuitive auxetic behaviors that are highly
    dependent on their geometric arrangements. The realization of the geometric arrangement
    required to achieve a negative Poisson's  ratio relies considerably on the experience
    of designers and trial-and-error approaches. This report proposes an inverse design
    method for auxetic metamaterials using deep learning, in which a batch of auxetic
    metamaterials with a user-defined Poisson's  ratio and Young's  modulus can be
    generated by a conditional generative adversarial network without prior knowledge.
    The network was trained based on supervised learning using a large number of geometrical
    patterns generated by Voronoi tessellation. The performance of the network was
    demonstrated by verifying the mechanical properties of the generated patterns
    using finite element method simulations and uniaxial compression tests. The successful
    realization of user-desired properties can potentially accelerate the inverse
    design and development of mechanical metamaterials.
  description_type: abstract
  lang: eng

## Creator

- name: Xiaoyang Zheng
  role: author
- name: Ta-Te Chen
  role: author
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Xiaofeng Guo
  role: author
- name: Sadaki Samitsu
  role: author
  orcid: https://orcid.org/0000-0002-4139-1656
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Ikumu Watanabe
  role: author
  orcid: https://orcid.org/0000-0002-7693-1675
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26

## Contact agent



## Publisher

organization: Elsevier BV

## Managing organization



## Keyword

- subject: Negative Poisson’s ratio
  schema: not_defined
- subject: Metamaterial
  schema: not_defined
- subject: Generative adversarial network
  schema: not_defined
- subject: Additive manufacturing
  schema: not_defined
- subject: Voronoi tessellation
  schema: not_defined

## Rights

- description: Creative Commons BY Attribution 4.0 International
  identifier: https://creativecommons.org/licenses/by/4.0/

## Other identifier(s)



## Data origin



## Embargo



## Journal

- title: MATERIALS & DESIGN
  issn: '02641275'
  volume: '211'
  start_page: 110178
  end_page: 110178

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## Instrument



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## Specimen



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## Fileset

- id: '09341b48-090f-4004-abc9-6267008028ec'
  filename: zheng_MD2021b.pdf
  content_type: application/pdf
  size: 3887231
  md5: e07ee87f60a47c3a5916d8fd30677d32

## Thumbnail

fileset_id: '09341b48-090f-4004-abc9-6267008028ec'
filename: zheng_MD2021b.pdf