MetaGen: A DSL, Database, and Benchmark for VLM-Assisted Metamaterial Generation

Liane Makatura, Benjamin Tod Jones, Siyuan Bian, Wojciech Matusik

Keywords: metamaterials, architected materials, microstructures, domain specific language, benchmark, design assistant, coding assistant

TL;DR: To facilitate VLM-assisted metamaterial design, we introduce (1) a database based on our domain-specific language for metamaterials, (2) a benchmark targeting three major tasks in metamaterial design, and (3) inaugural VLM-assistants for this domain.

Abstract: 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.

Submission Track: Benchmarking in AI for Materials Design - Full Paper

Submission Category: All of the above

Institution Location: Cambridge, USA

AI4Mat Journal Track: Yes

AI4Mat RLSF: Yes

Submission Number: 79