Meta-GPT: Decoding the Metasurface Genome with Generative Artificial Intelligence

• URL: http://arxiv.org/abs/2512.12888v1
• Date: Mon, 15 Dec 2025 00:09:14 GMT
• Title: Meta-GPT: Decoding the Metasurface Genome with Generative Artificial Intelligence
• Abstract summary: We introduce METASTRINGS, a symbolic language for photonics that expresses nanostructures as textual sequences.<n>We build a foundation transformer model trained on METASTRINGS and finetuned with physics-informed supervised, reinforcement, and chain-of-thought learning.<n>Results demonstrate that Meta-GPT can learn the compositional rules of light-matter interactions through METASTRINGS.
• Score: 0.4964239559434301
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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