# Modular metamaterials with deep learning–enabled customizable stress–strain responses

https://mdr.nims.go.jp/datasets/78eb7e36-d0dd-4bb0-8977-03d55c9bc818

## File

- [2026MaterDes_Guo.pdf](https://mdr.nims.go.jp/filesets/c3e5b2f0-f8e1-4ef7-833d-146df0ffdd5b/download) ([Detail](https://mdr.nims.go.jp/filesets/c3e5b2f0-f8e1-4ef7-833d-146df0ffdd5b.md))

## Id

78eb7e36-d0dd-4bb0-8977-03d55c9bc818

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2026-02-26T09:05:07.939347Z

## Updated at

2026-02-27T07:30:39.941679Z

## Published at

2026-02-27T04:49:39.675066Z

## Doi



## First published url

https://doi.org/10.1016/j.matdes.2026.115584

## Date published

2026-01-30

## Recorded date published

2026-3

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Modular metamaterials with deep learning–enabled customizable stress–strain
    responses
  title_type: original
  lang: en

## Description

- description: Modular mechanical metamaterials offer unique opportunities for programmable
    and reconfigurable functionality through simple geometric rearrangements. Inspired
    by the modularity of Lego blocks, we propose a new class of modular metamaterials
    composed of three standardized modules—linear, yielding-like, and snap-through
    buckling elements—that can be assembled into two- and three-dimensional grids
    to realize diverse nonlinear stress–strain responses. To accelerate design and
    optimization, we integrate deep learning (DL) with the metamaterial design process.
    A convolutional neural network-based predictor rapidly estimates the stress–strain
    curves of given modular configurations, achieving a prediction accuracy of . Furthermore,
    a conditional variational autoencoder-based inverse designer enables the automatic
    generation of modular configurations that match target stress–strain curves, demonstrating
    high fidelity (). The proposed DL framework allows rapid, scalable, and reprogrammable
    design of nonlinear mechanical responses without exhaustive simulations or manual
    tuning. This study establishes a universal, data-driven strategy for the inverse
    design of modular metamaterials, paving the way toward intelligent, reconfigurable
    material systems for applications in soft robotics and adaptive structures.
  description_type: abstract
  lang: und

## Creator

- name: Xiaofeng Guo
  role: author
  orcid: https://orcid.org/0000-0003-1971-7442
- name: Miaomiao He
  role: author
- name: Ikumu Watanabe
  role: author
  orcid: https://orcid.org/0000-0002-7693-1675
- 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
- name: Xiaoyang Zheng
  role: author
  orcid: https://orcid.org/0000-0003-1452-5855

## Contact agent



## Publisher

organization: Elsevier BV

## Managing organization



## Keyword

- subject: Mechanical metamaterials
  schema: not_defined
- subject: Deep learning
  schema: not_defined
- subject: Inverse design
  schema: not_defined
- subject: Nonlinear mechanical property
  schema: not_defined
- subject: Modular material
  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: '263'
  article_number: '115584'

## Conference



## Related item



## Funding

- funder_name: Hirose Foundation Grant
- funder_name: Google Research Grant

## 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



## Energy level/transition state



## Software



## Custom property



## Fileset

- id: c3e5b2f0-f8e1-4ef7-833d-146df0ffdd5b
  filename: 2026MaterDes_Guo.pdf
  content_type: application/pdf
  size: 5495089
  md5: 78c297e8165e1ac7bf7277a57a2e68cb

## Thumbnail

fileset_id: c3e5b2f0-f8e1-4ef7-833d-146df0ffdd5b
filename: 2026MaterDes_Guo.pdf