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. EfimovaBelarus
Efimova Taisia A. – Postgraduate Student, Junior Researcher
68-2, Nezavisimosti Ave., 220072, Minsk
S. V. Gaponenko
Belarus
Gaponenko Sergey V. – Academician, D. Sc. (Physics and Mathematics), Professor, Chief Researcher
68-2, Nezavisimosti Ave., 220072, Minsk
References
1. Guzatov D. V., Vaschenko S. V., Stankevich V. V., Lunevich A. Y., Glukhov Y. F., Gaponenko S. V. Plasmonic enhancement of molecular fluorescence near silver nanoparticles: theory, modeling, and experiment. Journal of Physical Chemistry C, 2012, vol. 116, no. 19, pp. 10723–10733. https://doi.org/10.1021/jp301598w
2. Gaponenko S. V., Guzatov D. V. Colloidal plasmonics for active nanophotonics. Proceedings of the IEEE, 2020, vol. 108, no. 5, pp. 704–720. https://doi.org/10.1109/jproc.2019.2958875
3. Farsad M., Vernerey F. J., Park H. S. An extended finite element/level set method to study surface effects on the mechanical behavior and properties of nanomaterials. International Journal for Numerical Methods in Engineerin, 2010, vol. 84, no. 12, pp. 1466–1489. https://doi.org/10.1002/nme.2946
4. He J., He C., Zheng C., Wang Q., Ye J. Plasmonic nanoparticle simulations and inverse design using machine learning. Nanoscale, 2019, vol. 11, no. 37, pp. 17444–17459. https://doi.org/10.1039/c9nr03450a
5. Iyer V., Wilson B. A., Chen Y., Kildishev A. V., Shalaev V. M., Boltasseva A. Deep learning in photonic device development: nuances and opportunities. npj Nanophotonics, 2026, vol. 3, art. 5. https://doi.org/10.1038/s44310-025-00097-y
6. Wu C., Yang S., Zeng K., Dai X., Duan Y., Zhang S., Ma P., Guo X., Zhang S., Yang X., Dai Q. Physics-informed deep learning for plasmonic sensing of nanoscale protein dynamics in solution. Science Advances, 2025, vol. 11, no. 39, art. eadw0783. https://doi.org/10.1126/sciadv.adw0783
7. Gaponenko S. V., Efimova T. A. Optical nanotransceivers in photonics. Doklady Natsional’noi akademii nauk Belarusi = Doklady of the National Academy of Sciences of Belarus, 2024, vol. 68, no. 4, pp. 288–295 (in Russian). https://doi.org/10.29235/1561-8323-2024-68-4-288-295
8. Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V., Vanderplas J., Passos A., Cournapeau D., Brucher M., Perrot M., Duchesnay É. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 2011, vol. 12, pp. 2825–2830. Available at: https://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html (accessed 02 September 2026).
9. Shields M. D., Zhang J. The generalization of Latin hypercube sampling. Reliability Engineering & System Safety, 2016, vol. 148, pp. 96–108. https://doi.org/10.1016/j.ress.2015.12.002
10. Friedman J. H. Greedy function approximation: a gradient boosting machine. Annals of Statistics, 2001, vol. 29, no. 5, pp. 1189–1232. https://doi.org/10.1214/aos/1013203451
11. Thadson K., Sasivimolkul S., Suvarnaphaet P., Visitsattapongse S., Pechprasarn S. Measurement precision enhancement of surface plasmon resonance based angular scanning detection using deep learning. Scientific Reports, 2022, vol. 12, art. 2052. https://doi.org/10.1038/s41598-022-06065-2
12. Ahmed W. W., Cao H., Xu C., Farhat M., Amin M., Li X., Zhang X., Wu Y. Machine learning assisted plasmonic metascreen for enhanced broadband absorption in ultra-thin silicon films. Light: Science & Applications, 2025, vol. 14, art. 42. https://doi.org/10.1038/s41377-024-01723-8
13. Li X., Shu J., Gu W., Gao L. Deep neural network for plasmonic sensor modeling. Optical Materials Express, 2019, vol. 9, no. 9, pp. 3857–3862. https://doi.org/10.1364/ome.9.003857
Review
JATS XML





































