A Multi-Agent Framework Integrating Large Language Models and Generative AI for Accelerated Metamaterial Design (2503.19889v2)

Abstract: Metamaterials, renowned for their exceptional mechanical, electromagnetic, and thermal properties, hold transformative potential across diverse applications, yet their design remains constrained by labor-intensive trial-and-error methods and limited data interoperability. Here, we introduce CrossMatAgent -- a novel multi-agent framework that synergistically integrates LLMs with state-of-the-art generative AI to revolutionize metamaterial design. By orchestrating a hierarchical team of agents -- each specializing in tasks such as pattern analysis, architectural synthesis, prompt engineering, and supervisory feedback -- our system leverages the multimodal reasoning of GPT-4o alongside the generative precision of DALL-E 3 and a fine-tuned Stable Diffusion XL model. This integrated approach automates data augmentation, enhances design fidelity, and produces simulation- and 3D printing-ready metamaterial patterns. Comprehensive evaluations, including CLIP-based alignment, SHAP interpretability analyses, and mechanical simulations under varied load conditions, demonstrate the framework's ability to generate diverse, reproducible, and application-ready designs. CrossMatAgent thus establishes a scalable, AI-driven paradigm that bridges the gap between conceptual innovation and practical realization, paving the way for accelerated metamaterial development.