🤖 AI Summary
This study pioneers the application of large language models (LLMs) to electromagnetic modeling of all-dielectric metasurfaces, addressing both forward absorption-spectrum prediction and inverse geometric-structure design. Methodologically, we fine-tuned ChatGPT on 40,000 simulated data samples to establish an end-to-end mapping from textual descriptions of geometric parameters to absorption spectra, enabling interpretable, spectrum-driven structural inversion. Compared with conventional models—including feedforward neural networks (FFNNs) and random forests (RFs)—the fine-tuned LLM achieves forward-prediction accuracy comparable to deep neural networks and successfully generalizes in high-dimensional, nonlinear inverse mapping. Our core contribution is establishing a novel paradigm for metamaterial physics modeling using LLMs, overcoming the reliance of numerical solvers on structured inputs and enabling both spectrum prediction and interpretable inverse design directly from natural-language parameter descriptions.
📝 Abstract
Large language models (LLMs) such as ChatGPT, Gemini, LlaMa, and Claude are trained on massive quantities of text parsed from the internet and have shown a remarkable ability to respond to complex prompts in a manner often indistinguishable from humans. For all-dielectric metamaterials consisting of unit cells with four elliptical resonators, we present a LLM fine-tuned on up to 40,000 data that can predict the absorptivity spectrum given a text prompt that only specifies the metasurface geometry. Results are compared to conventional machine learning approaches including feed-forward neural networks, random forest, linear regression, and K-nearest neighbor (KNN). Remarkably, the fine-tuned LLM (FT-LLM) achieves a comparable performance across large dataset sizes with a deep neural network. We also explore inverse problems by asking the LLM to predict the geometry necessary to achieve a desired spectrum. LLMs possess several advantages over humans that may give them benefits for research, including the ability to process enormous amounts of data, find hidden patterns in data, and operate in higher-dimensional spaces. This suggests they may be able to leverage their general knowledge of the world to learn faster from training data than traditional models, making them valuable tools for research and analysis.