用文字描述纳米结构，让AI设计出能精准调控光的超表面。
Meta-GPT: Decoding the Metasurface Genome with Generative Artificial Intelligence
- 将超表面结构转为可读文本，实现人类可理解的设计表示。
- 生成结果误差低于3%，且语法正确率超98%，实验验证效果匹配目标光谱。
- 适合对光学设计、AI生成物理结构感兴趣的科研人员。
推进人工智能在物理科学中的应用需要既可解释又符合自然规律的表示方式。我们提出METASTRINGS，一种用于光子学的符号语言，将纳米结构表示为编码材料、几何形状和晶格构型的文本序列，类似于化学中的分子文本表示。该表示框架通过捕捉光子超表面的结构层次，连接了人类可解释性与计算设计。基于此表示，我们开发了Meta-GPT，一个在METASTRINGS上训练的基础Transformer模型，并通过物理引导的监督学习、强化学习及思维链学习进行微调。在多种设计任务中，模型实现了小于3%的均方光谱误差，保持超过98%的语法有效性，生成的多样化超表面原型其实验测得的光学响应与目标光谱高度一致。这些结果表明，Meta-GPT可通过METASTRINGS学习光-物质相互作用的组合规则，为人工智能驱动的光子学奠定严谨基础，并标志着迈向超表面基因组项目的重要一步。
原文摘要 · Abstract (English)
Advancing artificial intelligence for physical sciences requires representations that are both interpretable and compatible with the underlying laws of nature. We introduce METASTRINGS, a symbolic language for photonics that expresses nanostructures as textual sequences encoding materials, geometries, and lattice configurations. Analogous to molecular textual representations in chemistry, METASTRINGS provides a framework connecting human interpretability with computational design by capturing the structural hierarchy of photonic metasurfaces. Building on this representation, we develop Meta-GPT, a foundation transformer model trained on METASTRINGS and finetuned with physics-informed supervised, reinforcement, and chain-of-thought learning. Across various design tasks, the model achieves <3% mean-squared spectral error and maintains >98% syntactic validity, generating diverse metasurface prototypes whose experimentally measured optical responses match their target spectra. These results demonstrate that Meta-GPT can learn the compositional rules of light-matter interactions through METASTRINGS, laying a rigorous foundation for AI-driven photonics and representing an important step toward a metasurface genome project.
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