# Generating 3D voxelized architected materials using 3D conditional generative adversarial network

https://mdr.nims.go.jp/datasets/0172ab31-42a0-4ff6-980e-4340b4a6c9fc

## File

- [3D-CGAN.zip](https://mdr.nims.go.jp/filesets/a2174a43-8720-436d-b7c4-a665b640b6f1/download) ([Detail](https://mdr.nims.go.jp/filesets/a2174a43-8720-436d-b7c4-a665b640b6f1.md))

## Id

0172ab31-42a0-4ff6-980e-4340b4a6c9fc

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2023-09-12T06:11:09.007469Z

## Updated at

2024-01-05T13:11:09.969296Z

## Published at

2023-09-14T04:30:06.132405Z

## Doi

https://doi.org/10.48505/nims.4230

## First published url

https://doi.org/10.1080/14686996.2022.2157682

## Date published

2023-12-31

## Recorded date published

2023-12-31

## Resource type

software

## Manuscript type

na

## Collection



## Title

- title: Generating 3D voxelized architected materials using 3D conditional generative
    adversarial network
  title_type: original
  lang: en

## Description

- description: 'This tutorial aims to give an introduction of how to use a deep generative
    model, 3D conditional generative adversarial network (3D-CGAN). The 3D-CGAN can
    be used for the inverse design of 3D voxelized microstructures with target properties.
    The 3D-CGAN is trained with supervised learning using a labeled dataset. The dataset
    consists of a large number of geometries (3D arrays) and their corresponding properties
    (e.g., elastic moduli). After training, the 3D-CGAN can generate a batch of geometries
    using target properties at inputs. In our previous tutorial, we have demonstrated
    how to use CGAN for the inverse design of 2D microstructures. This work is based
    on our previous publication for the inverse design of 3D architected materials.
    We hope this tutorial can be useful for those who are interested in the inverse
    design problems of microstructures. '
  description_type: abstract
  lang: en

## Creator

- name: Xiaoyang Zheng
  role: author
  orcid: https://orcid.org/0000-0003-1452-5855
  organization: National Institute for Materials Science
  department: Center for Basic Research on Materials
  ror: https://ror.org/026v1ze26
- name: Ikumu Watanabe
  role: author
  orcid: https://orcid.org/0000-0002-7693-1675
  organization: National Institute for Materials Science
  department: Center for Basic Research on Materials
  ror: https://ror.org/026v1ze26

## Contact agent

- name: Xiaoyang Zheng
  email: ZHENG.Xiaoyang@nims.go.jp
  orcid: https://orcid.org/0000-0003-1452-5855
  organization: National Institute for Materials Science
  department: Center for Basic Research on Materials
  ror: https://ror.org/

## Publisher

organization: Taylor & Francis

## Managing organization



## Keyword

- subject: Deep learning; Generative adversarial network; Inverse design; 3D shape;
    Microstructure; Mechanical metamaterial
  schema: not_defined

## Rights

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

## Other identifier(s)



## Data origin

- data_origin_type: informatics_and_data_science

## Embargo



## Journal

- title: Science and Technology of Advanced Materials
  issn: '14686996'

## Conference



## Related item



## Funding

- identifier: 22J11202
  funder_name: Japan Society for the Promotion of Science
  description: Grant-in-Aid for JSPS Fellows DC2

## Instrument



## Instrument operator



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## Measurement method



## Specimen



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## Specific property for specimen



## Process for specimen treatment



## Computational method



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## Fileset

- id: a2174a43-8720-436d-b7c4-a665b640b6f1
  filename: 3D-CGAN.zip
  content_type: application/zip
  size: 496230
  md5: dd64f23d05002158df9bf0ed6feb73f5

## Thumbnail

fileset_id: a2174a43-8720-436d-b7c4-a665b640b6f1
filename: 3D-CGAN.zip