Meta-GPT: Decoding the Metasurface Genome with Generative Artificial Intelligence
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arXiv
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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917146626359296 |
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| author | Dang, David Love, Stuart Salib, Meena Dang, Quynh Rothfarb, Samuel Alnatour, Mysk Salij, Andrew Chen, Hou-Tong Wai, Ho Lee Kort-Kamp, Wilton J. M. |
| author_facet | Dang, David Love, Stuart Salib, Meena Dang, Quynh Rothfarb, Samuel Alnatour, Mysk Salij, Andrew Chen, Hou-Tong Wai, Ho Lee Kort-Kamp, Wilton J. M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_12888 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Meta-GPT: Decoding the Metasurface Genome with Generative Artificial Intelligence Dang, David Love, Stuart Salib, Meena Dang, Quynh Rothfarb, Samuel Alnatour, Mysk Salij, Andrew Chen, Hou-Tong Wai, Ho Lee Kort-Kamp, Wilton J. M. Optics Artificial Intelligence Computation and Language Machine Learning 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. |
| title | Meta-GPT: Decoding the Metasurface Genome with Generative Artificial Intelligence |
| topic | Optics Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2512.12888 |