Exploratory analysis of polarized Raman spectra of ZnGeP₂ crystals using machine learning methods | Izvestiya vuzov. Fizika. 2025. № 11. DOI: 10.17223/00213411/68/11/9

Exploratory analysis of polarized Raman spectra of ZnGeP₂ crystals using machine learning methods

The exploratory analysis of polarization Raman scattering spectra of ZnGeP₂ crystal samples was performed using machine learning methods. The experimental data were obtained using a setup for recording Raman spectra in three configurations: natural light, parallel polarized light, and perpendicular polarized light. Machine learning methods included reducing the dimensionality of spectral data using the principal component method, block least squares method, and stochastic neighbor embedding with t-distribution. The separability of the data into groups depending on the crystallographic direction and polarization of the crystals was shown. The results can be used to build predictive models for determining the orientations of ZnGeP₂ crystals.

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Keywords

ZnGeP₂, raman scattering, machine learning, polarization, phonon modes

Authors

Name	Organization	E-mail
Vrazhnov Denis A.	V.E. Zuev Institute of Atmospheric Optics of the Siberian Branch of the Russian Academy of Sciences	vda@iao.ru
Knyazkova Anastasia I.	V.E. Zuev Institute of Atmospheric Optics of the Siberian Branch of the Russian Academy of Sciences	knyazkova@iao.ru
Snegerev Mikhail S.	Tomsk State University	snegerev@mail.tsu.ru
Raspopin Georgy K.	V.E. Zuev Institute of Atmospheric Optics of the Siberian Branch of the Russian Academy of Sciences	RaspopinGK@mail.tsu.ru
Kistenev Yury V.	V.E. Zuev Institute of Atmospheric Optics of the Siberian Branch of the Russian Academy of Sciences	yuk@iao.ru

Всего: 5

References

Yudin N. et al. // Crystals. - 2023. - V. 13. - No. 3. - P. 440. - DOI: 10.3390/cryst13030440.

Grechin S.G., Muravev I.A. // Photonics. - 2024. - V. 11. - No. 5. - P. 450. - DOI: 10.3390/photonics11050450.

Voevodin V.I. et al. // Photonics. - 2023. - V. 10. - No. 7. - P. 827. - DOI: 10.3390/photonics10070827.

Dhar S., Nag B.R. // J. Cryst. Growth. - 1978. - V. 43. - No. 1. - P. 120-122. - DOI: 10.1016/0022-0248(78)90376-7.

Younes K. et al. // Gels. - 2023. - V. 9. - No. 4. - P. 304. - DOI: 10.3390/gels9040304.

Lee L.C., Liong C.Y., Jemain A.A. // Analyst. - 2018. - V. 143. - No. 15. - P. 3526-3539. - DOI: 0.1039/C8AN00599K.

Asri M.N.M. et al. // Chemometrics and Intelligent Laboratory Systems. - 2022. - V. 225. - P. 104557. - DOI: 10.1016/j.chemolab.2022.104557.

Stevens F. et al. // J. Chemometrics. - 2024. - V. 38. - No. 4. - P. e3544. - DOI: 10.1002/cem.3544.

Hasan B.M.S., Abdulazeez A.M. // J. Soft Comput. Data Mining. - 2021. - V. 2. - No. 1. - P. 20-30. - DOI: 10.30880/jscdm.2021.02.01.003.

Ruiz-Perez D. et al. // BMC Bioinformatics. - 2020. - V. 21. - Suppl 1. - P. 2. - DOI: 10.1186/s12859-019-3310-7.

Palo H.K., Sahoo S., Subudhi A.K. // Data Anal. Bioinform.: A Machine Learning Perspective. - 2021. - P. 77-107.

Barreto D.F. An exploratory analysis using t-SNE. - 2018.

Zhang Z.M., Chen S., Liang Y.Z. // Analyst. - 2010. - V. 135. - No. 5. - P. 1138-1146. - DOI: 10.1039/b922045c.