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Description:

Designing novel materials is greatly dependent on understanding the design principles, physical mechanisms, and modeling methods of material microstructures, requiring experienced designers with expertise and several rounds of trial and error. Although recent advances in deep generative networks have enabled the inverse design of material microstructures, most studies involve property-conditional generation and focus on a specific type of structure, resulting in limited generation diversity and poor human--computer interaction. In this study, we proposed a pioneering text-to-microstructure deep generative network (Txt2Microstruct-Net) that enabled the generation of three-dimensional material microstructures directly from text prompts without additional optimization procedures. The Txt2Microstruct-Net model was trained on a large microstructure-caption paired dataset that was extensible using the algorithms provided. Moreover, the model was sufficiently flexible to generate different geometric representations, such as voxels and point clouds. The model's performance was also demonstrated in the inverse design of material microstructures and metamaterials. It has promising potential for interactive microstructure design when associated with large language models and could be a user-friendly tool for material design and discovery.

Rights:

• Creative Commons BY Attribution 4.0 International
  

Keyword: microstructure generation, deep learning, natural language processing

Date published: 2024-05-21

Publisher: Wiley

Journal:

• Small (ISSN: 16136810) vol. 20 2402685

Funding:

• Japan Society for the Promotion of Science 22KJ0407
• Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung JP EG special 032023 11

Manuscript type: Publisher's version (Version of record)

MDR DOI:

First published URL: https://doi.org/10.1002/smll.202402685

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Updated at: 2024-09-20 08:30:21 +0900

Published on MDR: 2024-09-20 08:30:21 +0900