Language-guided inverse design of multiclass metamaterials using generative artificial intelligence

Inverse design of metamaterials is commonly conditioned on numerical targets and restricted to a single structural family, limiting accessibility and cross-family exploration. Here, MetaGen-LLM is introduced as a language-guided framework that maps free-form design requests to high-fidelity geometries across eight metamaterial classes. A fine-tuned large language model converts user prompts into structured morphological and property conditions. A condition encoder, generator classifier, and diffusion model then predict a geometry-generator class and modeling parameters, while a surrogate model refines candidates toward target relative density, directional Young’s moduli, and thermal conductivities. Procedural geometry generators render smooth mesh representations that can be directly exported for additive manufacturing without an additional voxel-to-mesh reconstruction step. Across in-distribution and controlled out-of-distribution tests, the generated designs closely matched the requested properties. The trained models are further integrated into an agentic interface that supports iterative language-based design and three-dimensional visualization. MetaGen-LLM provides a unified route from natural language to continuous mesh representations of multiclass metamaterials.