Toward a Robust and Generalizable Metamaterial Foundation Model
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 12 of its 50 references that are in the map. First posted on arXiv 3 Jul 2025.
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12 of 50 references.
References · 12
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- 2021Transformers Can Do Bayesian Inferencecited by 360
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- 2023Mechanical Characterization and Inverse Design of Stochastic Architected Metamaterials Using Neural Operatorscited by 2
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Citing papers per year: 2026: 1.
1 paper.