MetaGen: A DSL, Database, and Benchmark for VLM-Assisted Metamaterial Generation
This work addresses the problem of metamaterial design for researchers and engineers, providing tools and data to advance the field, though it is incremental in building on existing vision-language models.
The paper tackles the difficulty of designing metamaterials due to geometric complexity and non-trivial architecture-behavior mapping by introducing MetaGen, which includes a domain-specific language (MetaDSL), a database (MetaDB) with over 150,000 parameterized programs and simulated properties, and a benchmark (MetaBench) for vision-language model capabilities, showing it enables integrated design and understanding of structure-property relationships.
Metamaterials are micro-architected structures whose geometry imparts highly tunable-often counter-intuitive-bulk properties. Yet their design is difficult because of geometric complexity and a non-trivial mapping from architecture to behaviour. We address these challenges with three complementary contributions. (i) MetaDSL: a compact, semantically rich domain-specific language that captures diverse metamaterial designs in a form that is both human-readable and machine-parsable. (ii) MetaDB: a curated repository of more than 150,000 parameterized MetaDSL programs together with their derivatives-three-dimensional geometry, multi-view renderings, and simulated elastic properties. (iii) MetaBench: benchmark suites that test three core capabilities of vision-language metamaterial assistants-structure reconstruction, property-driven inverse design, and performance prediction. We establish baselines by fine-tuning state-of-the-art vision-language models and deploy an omni-model within an interactive, CAD-like interface. Case studies show that our framework provides a strong first step toward integrated design and understanding of structure-representation-property relationships.