Використання інформації про умови експерименту для прогнозування властивостей метаматеріалів

Pavlo I. Krysenko; Maksym Olehovych Zoziuk

doi:10.20535/2523-4455.mea.287808

Vol. 28 · Issue 3 · 2023 Dec 27, 2023 Electronic Systems and Signals

Using Information about Experimental Conditions to Predict Properties of Metamaterials

Pavlo I. Krysenko Corresponding Національний технічний університет України "Київський політехнічний інститут імені Ігоря Сікорського" Ukraine

Maksym Olehovych Zoziuk Національний технічний університет України "Київський політехнічний інститут імені Ігоря Сікорського" Ukraine

Pages287808.1-287808.8 PublishedDec 27, 2023

PDF (Ukrainian)

VOL 28 · ISS 3 · 2023 To the 125th Anniversary of Igor Sikorsky Kyiv Polytechnic Institute View issue

Abstract

In this work, a method of increasing the amount of data for training neural networks is proposed using the possibility of using information about the experimental conditions of measuring the properties of metamaterials. It is shown that the method is flexible and effective. The results of predicting the transmission coefficient of the metamaterial for different angles of incidence of radiation and type of polarization are presented. Using the architecture presented in the work, a high rate of learning and generation of new data was obtained with an error that does not exceed 12% for experiments in one frequency range and does not exceed 31% if all experiments are used for training. The architecture of the neural network and the method by which it is possible to easily change the number and types of experimental conditions are presented.

Keywords

#metamaterials #3D convolutional neural network #experimental conditions

References

H. K. D. H. Bhadeshia, “Neural Networks in Materials Science.,” ISIJ International, vol. 39, no. 10, pp. 966–979, 1999, DOI: https://doi.org/10.2355/isijinternational.39.966
P. Reiser et al., “Graph neural networks for materials science and chemistry,” Commun Mater, vol. 3, no. 1, p. 93, Nov. 2022, DOI: https://doi.org/10.1038/s43246-022-00315-6
W. Sha and K. L. Edwards, “The use of artificial neural networks in materials science based research,” Mater Des, vol. 28, no. 6, pp. 1747–1752, Jan. 2007, DOI: https://doi.org/10.1016/j.matdes.2007.02.009
J. Parsons, C. P. Burrows, J. R. Sambles, and W. L. Barnes, “A comparison of techniques used to simulate the scattering of electromagnetic radiation by metallic nanostructures,” J Mod Opt, vol. 57, no. 5, pp. 356–365, Mar. 2010, DOI: https://doi.org/10.1080/09500341003628702
M. Locarno and D. Brinks, “Analytical calculation of plasmonic resonances in metal nanoparticles: A simple guide,” Am J Phys, vol. 91, no. 7, p. 538, Jul. 2023, DOI: https://doi.org/10.1119/5.0094967
P. R. Wiecha et al., “‘pyGDM’ - new functionalities and major improvements to the python toolkit for nano-optics full-field simulations,” Comput Phys Commun, vol. 270, p. 108142, Jan. 2022, DOI: https://doi.org/10.1016/j.cpc.2021.108142
B. Guo, L. Deng, and H. Zhang, “Non-local generative machine learning-based inverse design for scattering properties,” Opt Express, vol. 31, no. 13, p. 20872, Jun. 2023, DOI: https://doi.org/10.1364/OE.492361
J. R. Capers, S. J. Boyes, A. P. Hibbins, and S. A. R. Horsley, “Designing the collective non-local responses of metasurfaces,” Commun Phys, vol. 4, no. 1, p. 209, Sep. 2021, DOI: https://doi.org/10.1038/s42005-021-00713-1
P. Naseri and S. V. Hum, “A Generative Machine Learning-Based Approach for Inverse Design of Multilayer Metasurfaces,” IEEE Trans Antennas Propag, vol. 69, no. 9, pp. 5725–5739, Sep. 2021, DOI: https://doi.org/10.1109/TAP.2021.3060142
C. Niu, M. Phaneuf, and P. Mojabi, “A Diffusion Model for Multi-Layered Metasurface Unit Cell Synthesis,” IEEE Open Journal of Antennas and Propagation, vol. 4, pp. 654–666, 2023, DOI: https://doi.org/10.1109/OJAP.2023.3292149
S. Wang et al., “Innovative design of metamaterial perfect absorbers via residual fully connected neural network modeling,” Opt Commun, vol. 545, p. 129732, Oct. 2023, DOI: https://doi.org/10.1016/j.optcom.2023.129732
C. Zhang, A. Ridard, M. Kibsey, and Y. F. Zhao, “Variant design generation and machine learning aided deformation prediction for auxetic metamaterials,” Mechanics of Materials, vol. 181, p. 104642, Jun. 2023, DOI: https://doi.org/10.1016/j.mechmat.2023.104642
P. Jain et al., “Multiband Metamaterial absorber with absorption prediction by assisted machine learning,” Mater Chem Phys, vol. 307, p. 128180, Oct. 2023, DOI: https://doi.org/10.1016/j.matchemphys.2023.128180
P. P. Meyer, C. Bonatti, T. Tancogne-Dejean, and D. Mohr, “Graph-based metamaterials: Deep learning of structure-property relations,” Mater Des, vol. 223, p. 111175, Nov. 2022, DOI: https://doi.org/10.1016/j.matdes.2022.111175
S. Telvari, M. Sayyafzadeh, J. Siavashi, and M. Sharifi, “Prediction of two-phase flow properties for digital sandstones using 3D convolutional neural networks,” Adv Water Resour, vol. 176, p. 104442, Jun. 2023, DOI: https://doi.org/10.1016/j.advwatres.2023.104442
A. P. Garland, B. C. White, B. H. Jared, M. Heiden, E. Donahue, and B. L. Boyce, “Deep Convolutional Neural Networks as a Rapid Screening Tool for Complex Additively Manufactured Structures,” Addit Manuf, vol. 35, p. 101217, Oct. 2020, DOI: https://doi.org/10.1016/j.addma.2020.101217
M. O. Zoziuk and O. I. Yurikov, “Convolutional Neural Network to Predict the Penetration Coefficient of Metamaterials Based on Their Structure and Composition,” Microsystems, Electronics and Acoustics, vol. 28, no. 1, Apr. 2023, DOI: https://doi.org/10.20535/2523-4455.mea.271444
F. Ding, Z. Wang, S. He, V. M. Shalaev, and A. V. Kildishev, “Broadband High-Efficiency Half-Wave Plate: A Supercell-Based Plasmonic Metasurface Approach,” ACS Nano, vol. 9, no. 4, pp. 4111–4119, Apr. 2015, DOI: https://doi.org/10.1021/acsnano.5b00218
C. Huang, “Efficient and broadband polarization conversion with the coupled metasurfaces,” Opt Express, vol. 23, no. 25, p. 32015, Dec. 2015, DOI: https://doi.org/10.1364/OE.23.032015
A. Shaltout, J. Liu, A. Kildishev, and V. Shalaev, “Photonic spin Hall effect in gap–plasmon metasurfaces for on-chip chiroptical spectroscopy,” Optica, vol. 2, no. 10, p. 860, Oct. 2015, DOI: https://doi.org/10.1364/OPTICA.2.000860
N. K. Grady et al., “Terahertz Metamaterials for Linear Polarization Conversion and Anomalous Refraction,” Science (1979), vol. 340, no. 6138, pp. 1304–1307, Jun. 2013, DOI: https://doi.org/10.1126/science.1235399
L. Cong, Y. K. Srivastava, and R. Singh, “Inter and intra-metamolecular interaction enabled broadband high-efficiency polarization control in metasurfaces,” Appl Phys Lett, vol. 108, no. 1, Jan. 2016, DOI: https://doi.org/10.1063/1.4939564
V. Nair and G. E. Hinton, “Rectified Linear Units Improve Restricted Boltzmann Machines,” in Proceedings of the 27th International Conference on International Conference on Machine Learning, 2010, pp. 807–814, DOI: https://doi.org/10.5555/3104322.3104425
“PyTorch documentation — PyTorch 2.0 documentation.” [Online]. Available: https://pytorch.org/docs/stable/index.html

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[1] H. K. D. H. Bhadeshia, “Neural Networks in Materials Science.,” ISIJ International, vol. 39, no. 10, pp. 966–979, 1999, DOI: https://doi.org/10.2355/isijinternational.39.966

[2] P. Reiser et al., “Graph neural networks for materials science and chemistry,” Commun Mater, vol. 3, no. 1, p. 93, Nov. 2022, DOI: https://doi.org/10.1038/s43246-022-00315-6

[3] W. Sha and K. L. Edwards, “The use of artificial neural networks in materials science based research,” Mater Des, vol. 28, no. 6, pp. 1747–1752, Jan. 2007, DOI: https://doi.org/10.1016/j.matdes.2007.02.009

[4] J. Parsons, C. P. Burrows, J. R. Sambles, and W. L. Barnes, “A comparison of techniques used to simulate the scattering of electromagnetic radiation by metallic nanostructures,” J Mod Opt, vol. 57, no. 5, pp. 356–365, Mar. 2010, DOI: https://doi.org/10.1080/09500341003628702

[5] M. Locarno and D. Brinks, “Analytical calculation of plasmonic resonances in metal nanoparticles: A simple guide,” Am J Phys, vol. 91, no. 7, p. 538, Jul. 2023, DOI: https://doi.org/10.1119/5.0094967

[6] P. R. Wiecha et al., “‘pyGDM’ - new functionalities and major improvements to the python toolkit for nano-optics full-field simulations,” Comput Phys Commun, vol. 270, p. 108142, Jan. 2022, DOI: https://doi.org/10.1016/j.cpc.2021.108142

[7] B. Guo, L. Deng, and H. Zhang, “Non-local generative machine learning-based inverse design for scattering properties,” Opt Express, vol. 31, no. 13, p. 20872, Jun. 2023, DOI: https://doi.org/10.1364/OE.492361

[8] J. R. Capers, S. J. Boyes, A. P. Hibbins, and S. A. R. Horsley, “Designing the collective non-local responses of metasurfaces,” Commun Phys, vol. 4, no. 1, p. 209, Sep. 2021, DOI: https://doi.org/10.1038/s42005-021-00713-1

[9] P. Naseri and S. V. Hum, “A Generative Machine Learning-Based Approach for Inverse Design of Multilayer Metasurfaces,” IEEE Trans Antennas Propag, vol. 69, no. 9, pp. 5725–5739, Sep. 2021, DOI: https://doi.org/10.1109/TAP.2021.3060142

[10] C. Niu, M. Phaneuf, and P. Mojabi, “A Diffusion Model for Multi-Layered Metasurface Unit Cell Synthesis,” IEEE Open Journal of Antennas and Propagation, vol. 4, pp. 654–666, 2023, DOI: https://doi.org/10.1109/OJAP.2023.3292149

[11] S. Wang et al., “Innovative design of metamaterial perfect absorbers via residual fully connected neural network modeling,” Opt Commun, vol. 545, p. 129732, Oct. 2023, DOI: https://doi.org/10.1016/j.optcom.2023.129732

[12] C. Zhang, A. Ridard, M. Kibsey, and Y. F. Zhao, “Variant design generation and machine learning aided deformation prediction for auxetic metamaterials,” Mechanics of Materials, vol. 181, p. 104642, Jun. 2023, DOI: https://doi.org/10.1016/j.mechmat.2023.104642

[13] P. Jain et al., “Multiband Metamaterial absorber with absorption prediction by assisted machine learning,” Mater Chem Phys, vol. 307, p. 128180, Oct. 2023, DOI: https://doi.org/10.1016/j.matchemphys.2023.128180

[14] P. P. Meyer, C. Bonatti, T. Tancogne-Dejean, and D. Mohr, “Graph-based metamaterials: Deep learning of structure-property relations,” Mater Des, vol. 223, p. 111175, Nov. 2022, DOI: https://doi.org/10.1016/j.matdes.2022.111175

[15] S. Telvari, M. Sayyafzadeh, J. Siavashi, and M. Sharifi, “Prediction of two-phase flow properties for digital sandstones using 3D convolutional neural networks,” Adv Water Resour, vol. 176, p. 104442, Jun. 2023, DOI: https://doi.org/10.1016/j.advwatres.2023.104442

[16] A. P. Garland, B. C. White, B. H. Jared, M. Heiden, E. Donahue, and B. L. Boyce, “Deep Convolutional Neural Networks as a Rapid Screening Tool for Complex Additively Manufactured Structures,” Addit Manuf, vol. 35, p. 101217, Oct. 2020, DOI: https://doi.org/10.1016/j.addma.2020.101217

[17] M. O. Zoziuk and O. I. Yurikov, “Convolutional Neural Network to Predict the Penetration Coefficient of Metamaterials Based on Their Structure and Composition,” Microsystems, Electronics and Acoustics, vol. 28, no. 1, Apr. 2023, DOI: https://doi.org/10.20535/2523-4455.mea.271444

[18] F. Ding, Z. Wang, S. He, V. M. Shalaev, and A. V. Kildishev, “Broadband High-Efficiency Half-Wave Plate: A Supercell-Based Plasmonic Metasurface Approach,” ACS Nano, vol. 9, no. 4, pp. 4111–4119, Apr. 2015, DOI: https://doi.org/10.1021/acsnano.5b00218

[19] C. Huang, “Efficient and broadband polarization conversion with the coupled metasurfaces,” Opt Express, vol. 23, no. 25, p. 32015, Dec. 2015, DOI: https://doi.org/10.1364/OE.23.032015

[20] A. Shaltout, J. Liu, A. Kildishev, and V. Shalaev, “Photonic spin Hall effect in gap–plasmon metasurfaces for on-chip chiroptical spectroscopy,” Optica, vol. 2, no. 10, p. 860, Oct. 2015, DOI: https://doi.org/10.1364/OPTICA.2.000860

[21] N. K. Grady et al., “Terahertz Metamaterials for Linear Polarization Conversion and Anomalous Refraction,” Science (1979), vol. 340, no. 6138, pp. 1304–1307, Jun. 2013, DOI: https://doi.org/10.1126/science.1235399

[22] L. Cong, Y. K. Srivastava, and R. Singh, “Inter and intra-metamolecular interaction enabled broadband high-efficiency polarization control in metasurfaces,” Appl Phys Lett, vol. 108, no. 1, Jan. 2016, DOI: https://doi.org/10.1063/1.4939564

[23] V. Nair and G. E. Hinton, “Rectified Linear Units Improve Restricted Boltzmann Machines,” in Proceedings of the 27th International Conference on International Conference on Machine Learning, 2010, pp. 807–814, DOI: https://doi.org/10.5555/3104322.3104425

[24] “PyTorch documentation — PyTorch 2.0 documentation.” [Online]. Available: https://pytorch.org/docs/stable/index.html

Using Information about Experimental Conditions to Predict Properties of Metamaterials

Abstract

Keywords

References

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