UniMate: A Unified Model for Mechanical Metamaterial Generation, Property Prediction, and Condition Confirmation
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915350571909120 |
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| author | Zhan, Wangzhi Chen, Jianpeng Fu, Dongqi Zhou, Dawei |
| author_facet | Zhan, Wangzhi Chen, Jianpeng Fu, Dongqi Zhou, Dawei |
| contents | Metamaterials are artificial materials that are designed to meet unseen properties in nature, such as ultra-stiffness and negative materials indices. In mechanical metamaterial design, three key modalities are typically involved, i.e., 3D topology, density condition, and mechanical property. Real-world complex application scenarios place the demanding requirements on machine learning models to consider all three modalities together. However, a comprehensive literature review indicates that most existing works only consider two modalities, e.g., predicting mechanical properties given the 3D topology or generating 3D topology given the required properties. Therefore, there is still a significant gap for the state-of-the-art machine learning models capturing the whole. Hence, we propose a unified model named UNIMATE, which consists of a modality alignment module and a synergetic diffusion generation module. Experiments indicate that UNIMATE outperforms the other baseline models in topology generation task, property prediction task, and condition confirmation task by up to 80.2%, 5.1%, and 50.2%, respectively. We opensource our proposed UNIMATE model and corresponding results at https://github.com/wzhan24/UniMate. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_15722 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UniMate: A Unified Model for Mechanical Metamaterial Generation, Property Prediction, and Condition Confirmation Zhan, Wangzhi Chen, Jianpeng Fu, Dongqi Zhou, Dawei Machine Learning Artificial Intelligence Metamaterials are artificial materials that are designed to meet unseen properties in nature, such as ultra-stiffness and negative materials indices. In mechanical metamaterial design, three key modalities are typically involved, i.e., 3D topology, density condition, and mechanical property. Real-world complex application scenarios place the demanding requirements on machine learning models to consider all three modalities together. However, a comprehensive literature review indicates that most existing works only consider two modalities, e.g., predicting mechanical properties given the 3D topology or generating 3D topology given the required properties. Therefore, there is still a significant gap for the state-of-the-art machine learning models capturing the whole. Hence, we propose a unified model named UNIMATE, which consists of a modality alignment module and a synergetic diffusion generation module. Experiments indicate that UNIMATE outperforms the other baseline models in topology generation task, property prediction task, and condition confirmation task by up to 80.2%, 5.1%, and 50.2%, respectively. We opensource our proposed UNIMATE model and corresponding results at https://github.com/wzhan24/UniMate. |
| title | UniMate: A Unified Model for Mechanical Metamaterial Generation, Property Prediction, and Condition Confirmation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2506.15722 |