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

arXiv: https://arxiv.org/abs/2512.12888 · HTML: https://mlsanity.com/paper/2512.12888/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 3 references that are in the map.
- First posted on arXiv 15 Dec 2025.

## Built on by (most cited first, 1 of 1)

- [A Comprehensive Review of Large Language Models for Nanophotonics: From Surrogate Modeling to Autonomous Design](https://mlsanity.com/paper/2608.18279/) (2026)

## Abstract (arXiv)

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.