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

TL;DR

Meta-GPT introduces a generative AI model trained on a symbolic language for photonics, enabling accurate and valid design of metasurfaces with optical responses matching targets, advancing AI-driven photonics.

Contribution

The paper presents METASTRINGS, a symbolic language for photonics, and Meta-GPT, a transformer model trained on this language with physics-informed learning, for interpretable metasurface design.

Findings

Achieves <3% spectral error in design tasks

Maintains >98% syntactic validity in generated prototypes

Successfully matches experimental optical responses to targets

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.