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Optimization of optical nanotransceiver parameters using machine learning

https://doi.org/10.29235/1561-8323-2026-70-5-364-369

Abstract

The paper proposes an approach to the study and optimization of optical nanotransceiver parameters based on the “metal nanoparticle – quantum emitter” model using machine learning methods. To speed up multidimensional search, a surrogate model based on Gradient Boosting Machine (GBM) was built, for which 60,000 points were generated using the Latin Hypercube Sampling (LHS) method in a six-dimensional parameter space: excitation and emission wavelengths, nanoparticle radius, particle–emitter distance, refractive index of the medium, and intrinsic quantum yield of the emitter. The coefficient of determination R2 = 0.947 was reached in the test sample. Global optimization based on model predictions was performed, followed by refinement by direct calculation, and it was shown that the surrogate model is effective for multiparametric analysis and narrowing the search area for parameters necessary for optimizing the experiment.

About the Authors

T. A. Efimova
B. I. Stepanov Institute of Physics of the National Academy of Sciences of Belarus
Belarus

Efimova Taisia A. – Postgraduate Student, Junior Researcher 

68-2, Nezavisimosti Ave., 220072, Minsk 



S. V. Gaponenko
B. I. Stepanov Institute of Physics of the National Academy of Sciences of Belarus
Belarus

Gaponenko Sergey V. – Academician, D. Sc. (Physics and Mathematics), Professor, Chief Researcher

68-2, Nezavisimosti Ave., 220072, Minsk 



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ISSN 1561-8323 (Print)
ISSN 2524-2431 (Online)