迈向鲁棒且具泛化性的超材料基础模型
Toward a Robust and Generalizable Metamaterial Foundation Model
AI总结：
针对AI驱动超材料设计存在的任务专属重训练、OOD泛化差及正反设计需独立模型等问题，提出基于贝叶斯Transformer的超材料基础模型MetaFO，实现零样本预测与OOD下的逆向设计，拓展设计空间并推动范式转变。
摘要：
材料功能的进步推动着各领域的创新，其中由结构而非成分定义的超材料走在前列。尽管人工智能（AI）驱动的设计策略不断涌现，但其应用受限于需针对特定任务重新训练、分布外（OOD）泛化能力差，以及正向设计和逆向设计需使用独立模型的问题。为解决这些局限，我们提出超材料基础模型（Metamaterial Foundation Model，MetaFO），这是受大语言模型启发、基于贝叶斯Transformer的基础模型。MetaFO学习超材料的内在力学机制，可针对多样的、未见过的材料属性与结构响应组合，实现概率性的零样本预测。它在非线性逆向设计中也表现出色，即使在分布外条件下依然如此。通过将超材料视为将材料属性映射为结构响应的算子，MetaFO揭示了复杂的结构-属性关系，并显著拓展了设计空间。这一可扩展、具泛化性的框架标志着AI驱动的超材料发现领域的范式转变，为下一代创新铺平了道路。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
