# Spring-based mechanical metamaterials with deep-learning-accelerated design

https://mdr.nims.go.jp/datasets/1743045a-60d0-4f49-bae7-a7a9d7914a15

## File

- [guo_matdes2025.pdf](https://mdr.nims.go.jp/filesets/f18ddecb-a864-4e8a-81d7-0d06396a481b/download) ([Detail](https://mdr.nims.go.jp/filesets/f18ddecb-a864-4e8a-81d7-0d06396a481b.md))

## Id

1743045a-60d0-4f49-bae7-a7a9d7914a15

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2025-04-03T01:26:38.916127Z

## Updated at

2025-04-22T23:30:21.753711Z

## Published at

2025-04-22T23:17:18.380164Z

## Doi



## First published url

https://doi.org/10.1016/j.matdes.2025.113800

## Date published

2025-03-06

## Recorded date published

2025-4

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Spring-based mechanical metamaterials with deep-learning-accelerated design
  title_type: original
  lang: en

## Description

- description: "Mechanical metamaterials exhibit unique properties that depend on
    their microstructure and surpass those of their constituent materials. Flexible
    mechanical metamaterials, in particular, hold significant potential for applications
    requiring substantial deformations, such as soft robotics and energy absorption.
    In this study, we proposed a collection of flexible mechanical metamaterials discretely
    assembled using structural spring elements. These spring elements enhance both
    flexibility and reversibility, allowing the materials to withstand large deformations.
    The geometric regularity of the metamaterials enables zero-shot learning, allowing
    deep learning frameworks to address property prediction and inverse design problems
    beyond the training dataset. \r\nUsing a property-prediction model, the effective
    mechanical properties of these metamaterials can be accurately predicted based
    on specified design parameters. Furthermore, an inverse-design model enables the
    direct generation of mechanical metamaterials with desired target properties,
    even outside the training dataspace, in the range of Young's modulus in (0, 350)
    kPa and Poisson's ratio in (-0.12, 0.12). The properties of these inversely designed
    metamaterials are analyzed through finite element method simulations and mechanical
    testing. The deep learning-accelerated design approach not only streamlines the
    development process but also provides a framework for advancing metamaterial design,
    encompassing property prediction and inverse design."
  description_type: abstract
  lang: und

## Creator

- name: Xiaofeng Guo
  role: author
  orcid: https://orcid.org/0000-0003-1971-7442
- name: Xiaoyang Zheng
  role: author
  orcid: https://orcid.org/0000-0003-1452-5855
- name: Jiaxin Zhou
  role: author
  orcid: https://orcid.org/0000-0001-7681-1668
- name: Takayuki Yamada
  role: author
  orcid: https://orcid.org/0000-0002-5349-6690
- name: Yong Yi
  role: author
  orcid: https://orcid.org/0000-0003-0627-5446
- name: Ikumu Watanabe
  role: author
  orcid: https://orcid.org/0000-0002-7693-1675

## Contact agent



## Publisher

organization: Elsevier BV

## Managing organization



## Keyword

- subject: Mechanical metamaterial
  schema: not_defined

## Rights

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

## Other identifier(s)



## Data origin



## Embargo



## Journal

- title: Materials & Design
  issn: '02641275'
  volume: '252'
  article_number: '113800'

## Conference



## Related item



## Funding

- identifier: JPMJSP2124
  funder_name: Japan Science and Technology Agency
  description: Supporting Program for Innovative Research on Cutting Edge Science
    and Technology

## Instrument



## Instrument operator



## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



## Specific property for specimen



## Process for specimen treatment



## Computational method



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## Fileset

- id: f18ddecb-a864-4e8a-81d7-0d06396a481b
  filename: guo_matdes2025.pdf
  content_type: application/pdf
  size: 3961922
  md5: 9bc611ddeb5ac1329589a9c757de20fa

## Thumbnail

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filename: guo_matdes2025.pdf