Jiaoxuan Lin

Abstract Mechanical metamaterials derive their exceptional properties from architected microstructures, yet inverse design for tailored performance remains computationally intensive and unintuitive. Here, we introduce TrussGPT, a large language model (LLM)-enabled generative framework that integrates natural language understanding with structural generation for automated inverse design of truss metamaterials. The framework comprises three key components.