# Data driven approaches in nanophotonics: A review of AI-enabled metadevices

arXiv: https://arxiv.org/abs/2510.00283 · HTML: https://mlsanity.com/paper/2510.00283/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 151 references that are in the map.
- First posted on arXiv 30 Sep 2025.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [A Survey on Visual Transformer](https://mlsanity.com/paper/2012.12556/) (2020)
- [Efficient Training of Visual Transformers with Small Datasets](https://mlsanity.com/paper/2106.03746/) (2021)
- [Deep neural networks for the evaluation and design of photonic devices](https://mlsanity.com/paper/2007.00084/) (2020)
- [Data-Driven Design for Metamaterials and Multiscale Systems: A Review](https://mlsanity.com/paper/2307.05506/) (2023)
- [Multifunctional Metasurface Design with a Generative Adversarial Network](https://mlsanity.com/paper/1908.04851/) (2019)
- [OptoGPT: A Foundation Model for Inverse Design in Optical Multilayer Thin Film Structures](https://mlsanity.com/paper/2304.10294/) (2023)
- [Diffusion Probabilistic Model Based Accurate and High-Degree-of-Freedom Metasurface Inverse Design](https://mlsanity.com/paper/2304.13038/) (2023)
- [Transformers for Low-Resource Languages: Is F'eidir Linn!](https://mlsanity.com/paper/2403.01985/) (2024)
- [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)
- [Chat to Chip: Large Language Model Based Design of Arbitrarily Shaped Metasurfaces](https://mlsanity.com/paper/2509.24196/) (2025)
- [Artificial Intelligence-Generated Terahertz Multi-Resonant Metasurfaces via Improved Transformer and CGAN Neural Networks](https://mlsanity.com/paper/2307.11794/) (2023)

## Built on by (most cited first, 2 of 2)

- [The Physics Constraint Paradox: When Removing Explicit Constraints Improves Physics-Informed Data for Machine Learning](https://mlsanity.com/paper/2512.22261/) (2025)
- [Towards a universal meta-optics solver via large language models](https://mlsanity.com/paper/2608.26417/) (2026)

## Abstract (arXiv)

Data-driven approaches have revolutionized the design and optimization of photonic metadevices by harnessing advanced artificial intelligence methodologies. This review takes a model-centric perspective that synthesizes emerging design strategies and delineates how traditional trial-and-error and computationally intensive electromagnetic simulations are being supplanted by deep learning frameworks that efficiently navigate expansive design spaces. We discuss artificial intelligence implementation in several metamaterial design aspects from high-degree-of-freedom design to large language model-assisted design. By addressing challenges such as transformer model implementation, fabrication limitations, and intricate mutual coupling effects, these AI-enabled strategies not only streamline the forward modeling process but also offer robust pathways for the realization of multifunctional and fabrication-friendly nanophotonic devices. This review further highlights emerging opportunities and persistent challenges, setting the stage for next-generation strategies in nanophotonic engineering.