Inverse Design of Plasmonic Nanostructures Using Machine Learning for Optimized Prediction of Physical Parameters

Inverse Design of Plasmonic Nanostructures Using Machine Learning for Optimized Prediction of Physical Parameters

Plasmonic nanostructures have been widely studied for their unique optical properties, which are useful in sensing, photonics, and energy. However, the efficient design of these structures, considering the complex relationship between geometry, material, and optical response, remains a challenge. In...

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Bibliographic Details
Main Authors: Luana S. P. Maia, Darlan A. Barroso, Aêdo B. Silveira, Waleska F. Oliveira, André Galembeck, Carlos Alexandre R. Fernandes, Dayse G. C. Bandeira, Benoit Cluzel, Auzuir R. Alexandria, Glendo F. Guimarães
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Photonics
Subjects:
plasmonic
nanostructure
machine learning
inverse design
Online Access:https://www.mdpi.com/2304-6732/12/6/572
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Summary:Plasmonic nanostructures have been widely studied for their unique optical properties, which are useful in sensing, photonics, and energy. However, the efficient design of these structures, considering the complex relationship between geometry, material, and optical response, remains a challenge. In this study, we propose a machine learning-based approach to address the inverse design problem in nanostructures, using data generated by numerical simulations via the Finite Element Method (FEM). We used a dataset of over 140,000 entries to train the regression models CatBoost, Random Forest, and Extra Trees, capable of predicting physical parameters, such as the radius of the nanocylinder, based on the simulated optical response. The CatBoost model achieved the best performance, with a Mean Absolute Error below 0.3 nm on unseen data. In parallel, we applied a direct design approach to experimental data of metallic nanoparticles, focusing on the optical absorption prediction from particle size. In this case, Random Forest presented the best performance, with a lower risk of overfitting. The results indicate that machine learning models are promising tools for optimizing the design and characterization of plasmonic nanostructures, thus reducing the need for costly experimental techniques.
ISSN:2304-6732

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