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.

翻译：超材料以其卓越的机械、电磁和热学特性而闻名，在众多应用中具有变革性潜力，但其设计仍受限于劳动密集型的试错方法和有限的数据互操作性。本文提出CrossMatAgent——一种新颖的多智能体框架，它协同整合大型语言模型与最先进的生成式人工智能，以彻底改变超材料设计。通过协调一个分层协作的智能体团队——每个智能体专精于模式分析、架构合成、提示工程和监督反馈等任务——我们的系统利用了GPT-4o的多模态推理能力，并结合了DALL-E 3和微调后的Stable Diffusion XL模型的生成精度。这种集成方法实现了数据增强自动化，提高了设计保真度，并生成了可直接用于仿真和3D打印的超材料图案。包括基于CLIP的对齐度评估、SHAP可解释性分析以及不同载荷条件下的力学仿真在内的综合评估表明，该框架能够生成多样化、可复现且可直接应用的设计。因此，CrossMatAgent建立了一个可扩展的、人工智能驱动的范式，弥合了概念创新与实际实现之间的鸿沟，为加速超材料发展铺平了道路。