统一模型同时生成结构、预测性能、验证条件，提升机械超材料设计效率。
UniMate: A Unified Model for Mechanical Metamaterial Generation, Property Prediction, and Condition Confirmation
- 通过模态对齐与协同扩散机制，统一处理结构、密度和性能三者关系。
- 在结构生成、性能预测和条件验证任务上分别提升80.2%、5.1%、50.2%。
- 适合超材料设计、智能制造及多目标优化研究者使用。
超材料是人工设计以实现自然界罕见特性的材料，如超高刚度和负泊松比。机械超材料设计通常涉及三个关键方面：三维拓扑结构、密度条件和力学性能。现实复杂应用场景要求机器学习模型能同时考虑这三个方面。然而，现有研究大多仅关注其中两个方面，例如给定拓扑预测性能，或给定性能生成拓扑。因此，当前机器学习模型仍难以全面捕捉三者关联。为此，我们提出统一模型 UniMate，包含模态对齐模块和协同扩散生成模块。实验表明，UniMate 在拓扑生成、性能预测和条件确认任务中分别优于基线模型最多80.2%、5.1%和50.2%。相关代码与结果已开源：https://github.com/wzhan24/UniMate。
原文摘要 · Abstract (English)
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.
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