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 large language models 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.
View on arXiv@article{tian2025_2503.19889,
title={ A Multi-Agent Framework Integrating Large Language Models and Generative AI for Accelerated Metamaterial Design },
author={ Jie Tian and Martin Taylor Sobczak and Dhanush Patil and Jixin Hou and Lin Pang and Arunachalam Ramanathan and Libin Yang and Xianyan Chen and Yuval Golan and Xiaoming Zhai and Hongyue Sun and Kenan Song and Xianqiao Wang },
journal={arXiv preprint arXiv:2503.19889},
year={ 2025 }
}