Meta-GPT: Decoding the Metasurface Genome with Generative Artificial Intelligence
This work addresses the challenge of interpretable and physics-compatible AI design for photonics researchers, representing an incremental step toward a metasurface genome project.
The researchers tackled the problem of designing photonic metasurfaces by introducing METASTRINGS, a symbolic language for nanostructures, and Meta-GPT, a transformer model trained on this representation, which achieved <3% mean-squared spectral error and >98% syntactic validity in generating metasurface prototypes that experimentally match target spectra.
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.