# Chat to Chip: Large Language Model Based Design of Arbitrarily Shaped Metasurfaces

arXiv: https://arxiv.org/abs/2509.24196 · HTML: https://mlsanity.com/paper/2509.24196/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 39 references that are in the map.
- First posted on arXiv 29 Sep 2025.

## Builds on (most cited first, 15 of 15)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [A Survey on Multimodal Large Language Models](https://mlsanity.com/paper/2306.13549/) (2023)
- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [GraphGPT: Graph Instruction Tuning for Large Language Models](https://mlsanity.com/paper/2310.13023/) (2023)
- [When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method](https://mlsanity.com/paper/2402.17193/) (2024)
- [LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks](https://mlsanity.com/paper/2206.06565/) (2022)
- [Global optimization of dielectric metasurfaces using a physics-driven neural network](https://mlsanity.com/paper/1906.04157/) (2019)
- [Data-Driven Design for Metamaterials and Multiscale Systems: A Review](https://mlsanity.com/paper/2307.05506/) (2023)
- [A multi-agentic framework for real-time, autonomous freeform metasurface design](https://mlsanity.com/paper/2503.20479/) (2025)
- [Diffusion Probabilistic Model Based Accurate and High-Degree-of-Freedom Metasurface Inverse Design](https://mlsanity.com/paper/2304.13038/) (2023)
- [Learning Electromagnetic Metamaterial Physics With ChatGPT](https://mlsanity.com/paper/2404.15458/) (2024)
- [Deep Convolutional Neural Networks to Predict Mutual Coupling Effects in Metasurfaces](https://mlsanity.com/paper/2102.01761/) (2021)

## Built on by (most cited first, 4 of 4)

- [A Self-Evolving Agentic Framework for Metasurface Inverse Design](https://mlsanity.com/paper/2604.01480/) (2026)
- [Data driven approaches in nanophotonics: A review of AI-enabled metadevices](https://mlsanity.com/paper/2510.00283/) (2025)
- [Towards a universal meta-optics solver via large language models](https://mlsanity.com/paper/2608.26417/) (2026)
- [HALO: A Physics-Aware LLM Agent Framework for Nanophotonic Design](https://mlsanity.com/paper/2608.28877/) (2026)

## Abstract (arXiv)

Traditional metasurface design is limited by the computational cost of full-wave simulations, preventing thorough exploration of complex configurations. Data-driven approaches have emerged as a solution to this bottleneck, replacing costly simulations with rapid neural network evaluations and enabling near-instant design for meta-atoms. Despite advances, implementing a new optical function still requires building and training a task-specific network, along with exhaustive searches for suitable architectures and hyperparameters. Pre-trained large language models (LLMs), by contrast, sidestep this laborious process with a simple fine-tuning technique. However, applying LLMs to the design of nanophotonic devices, particularly for arbitrarily shaped metasurfaces, is still in its early stages; as such tasks often require graphical networks. Here, we show that an LLM, fed with descriptive inputs of arbitrarily shaped metasurface geometries, can learn the physical relationships needed for spectral prediction and inverse design. We further benchmarked a range of open-weight LLMs and identified relationships between accuracy and model size at the billion-parameter level. We demonstrated that 1-D token-wise LLMs provide a practical tool to designing 2-D arbitrarily shaped metasurfaces. Linking natural-language interaction to electromagnetic modelling, this "chat-to-chip" workflow represents a step toward more user-friendly data-driven nanophotonics.