Year: 2026
Type: article
Abstract: Nanophotonic inverse design requires coordinated choices about geometry, optimization, and electromagnetic simulation. We present an agentic AI framework that combines literature-based reproduction, human-approved optimization strategies, adjoint optimization, and separate forward evaluation of binary candidates. Four metagrating strategies tested from a common initialization yield final diffraction efficiencies of 79.4–90.9%. Selecting by peak optimization objective and by final binary efficiency produces different rankings. A separate metagrating redesign increases diffraction efficiency from 81.2% to 94.6%. For a two-dimensional metalens, the minimum focusing efficiency across three visible wavelengths increases from 40.0% to 65.2% relative to a symmetrized reproduced reference under matched lossless evaluation. A Bayer color router improves mean channel routing from 50.3% to 54.7% under a 125 nm minimum-feature criterion. Separate workflow evaluations include five reproducibility-screening examples, seven literature-to-simulation cases, and repair outcomes across five solver errors. Solver-specific retrieval resolves an interface error left unresolved by every tested standalone configuration. These results demonstrate agent-assisted exploration of nanophotonic designs and show why candidate selection must use the optical performance of the final binary structure.
Source: Scientific Reports
Institutions: Hanyang University, Hankuk University of Foreign Studies
Language: English
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
Awards: G1767291611, G1816186749, G2124488252, G2199931666, G2365584765, G2424221239, G2581689735, G2805063684, G3363485770, G3812690899, G3904058931, G4248781092, G4848589832, G5068639314, G5396576536, G6098413186, G6107023710, G6451927723, G7747912143, G7967031600, G7997132224, G8363210920, G8470340488
OpenAlex ID: W7225468235