Automated and high speed freeform metasurface design using an agentic large language model framework

We present MetaChat, an innovative text-based interface leveraging large language models for nanophotonics simulation and design. This platform integrates a decentralized ensemble of physics-augmented, high-speed surrogate full-wave solvers, enabling real-time interactive design across various incident source field angles and wavelengths. MetaChat introduces a novel method for conditioning machine learning surrogate solvers using light-weight conditional control networks, enhancing adaptability for specialized applications. We demonstrate its capabilities by designing large-area metasurfaces composed of scatterers co-optimized using the adjoint variables method, translating semantic design objectives into quantitative figures of merit. As a proof of concept, we showcase the rapid design of a multifunctional TiO2 metasurface operating across multiple visible wavelengths. This new design paradigm promises to streamline workflows in multi-physics computing, making advanced design tools more accessible to practitioners.

Authors

Institutions

Publication Details

Published
2025-03-19
DOI
https://doi.org/10.1117/12.3043991
Primary Topic
Modular Robots and Swarm Intelligence
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Automated and high speed freeform metasurface design using an agentic large language model framework

Chenkai Mao, Jonathan A. Fan, Yixuan Shao, Robert C. Lupoiu et al.
Modular Robots and Swarm Intelligence
article

Automated and high speed freeform metasurface design using an agentic large language model framework

Chenkai Mao, Jonathan A. Fan, Yixuan Shao, Robert C. Lupoiu, Kofi Edée
article en

Abstract

We present MetaChat, an innovative text-based interface leveraging large language models for nanophotonics simulation and design. This platform integrates a decentralized ensemble of physics-augmented, high-speed surrogate full-wave solvers, enabling real-time interactive design across various incident source field angles and wavelengths. MetaChat introduces a novel method for conditioning machine learning surrogate solvers using light-weight conditional control networks, enhancing adaptability for specialized applications. We demonstrate its capabilities by designing large-area metasurfaces composed of scatterers co-optimized using the adjoint variables method, translating semantic design objectives into quantitative figures of merit. As a proof of concept, we showcase the rapid design of a multifunctional TiO2 metasurface operating across multiple visible wavelengths. This new design paradigm promises to streamline workflows in multi-physics computing, making advanced design tools more accessible to practitioners.

Université Clermont Auvergne (FR), Stanford University (US)
Openalex Percentile: Top 95%
Modular Robots and Swarm Intelligence
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Automated and high speed freeform metasurface design using an agentic large language model framework — Chenkai Mao, Jonathan A. Fan, et al. · (2025) | TGRS Research Map | TGRS