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Text-to-Microstructure Generation Using Generative Deep Learning
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AI
DOI：10.1002/smll.202402685.png)
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Abstract
En 中文
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, a pioneering text-to-microstructure deep generative network (Txt2Microstruct-Net) is proposed that enables the generation of 3D material microstructures directly from text prompts without additional optimization procedures. The Txt2Microstruct-Net model is trained on a large microstructure-caption paired dataset that is extensible using the algorithms provided. Moreover, the model is sufficiently flexible to generate different geometric representations, such as voxels and point clouds. The model's performance is 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. What if an AI system can generate material microstructures via chatting? With the text-to-microstructure deep generative network (Txt2Microstruct-Net), users can get diverse and realistic 3D microstructures directly from text prompts without additional optimization procedures. It is also capable of material inverse design when fed with target geometric and mechanical properties. image
Keywords:
architected material
artificial intelligence
deep generative model
deep learning
inverse design
metamaterial
microstructure
Journal
IF:
12.1
Papers:
3.0W
Citations:
16.4W
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