用AI模型统一设计多种超材料，零样本预测新结构性能。
Toward a Robust and Generalizable Metamaterial Foundation Model
- 基于贝叶斯Transformer构建通用超材料模型，学习结构与性能关系。
- 零样本预测新组合材料性能，跨分布泛化能力显著提升。
- 适合超材料设计、智能制造等领域研究人员快速探索新结构。
材料功能的进展推动了多领域创新，其中超材料（由结构而非成分定义）尤为突出。尽管人工智能驱动的设计方法兴起，但受限于任务特定重训练、分布外泛化差，以及正向与逆向设计需分开建模。为此，我们提出超材料基础模型（MetaFO），一种受大语言模型启发的贝叶斯变压器模型。MetaFO学习超材料内在力学机制，实现对未见材料属性与结构响应组合的概率性零样本预测，并在非线性逆向设计中表现优异，即使在分布外条件下亦然。通过将超材料视为从材料属性映射到结构响应的算子，MetaFO揭示复杂结构-性能关系，大幅拓展设计空间。该可扩展且通用的框架标志着人工智能驱动超材料发现的范式转变，为下一代创新铺平道路。
原文摘要 · Abstract (English)
Advances in material functionalities drive innovations across various fields, where metamaterials-defined by structure rather than composition-are leading the way. Despite the rise of artificial intelligence (AI)-driven design strategies, their impact is limited by task-specific retraining, poor out-of-distribution(OOD) generalization, and the need for separate models for forward and inverse design. To address these limitations, we introduce the Metamaterial Foundation Model (MetaFO), a Bayesian transformer-based foundation model inspired by large language models. MetaFO learns the underlying mechanics of metamaterials, enabling probabilistic, zero-shot predictions across diverse, unseen combinations of material properties and structural responses. It also excels in nonlinear inverse design, even under OOD conditions. By treating metamaterials as an operator that maps material properties to structural responses, MetaFO uncovers intricate structure-property relationships and significantly expands the design space. This scalable and generalizable framework marks a paradigm shift in AI-driven metamaterial discovery, paving the way for next-generation innovations.
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