TY  - JOUR
AU  - Novita, Mega
AU  - Chauhan, Alok Singh
AU  - Ujianti, Rizky Muliani Dwi
AU  - Marlina, Dian
AU  - Kusumo, Haryo
AU  - Anwar, Muchamad Taufiq
AU  - Piasecki, Michał
AU  - Brik, Mikhail G.
PY  - 2024
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/12869
AB  - In the pursuit of enhancing red phosphor materials, integrating Deep Learning (DL) and machine Learning (ML) techniques has emerged as a transformative avenue. Challenges persist, necessitating comprehensive exploration and detailed comparative analysis of methods, focusing on predictive accuracy, interpretability, and computational demands. The role of regression models and their coefficients in material property prediction requires in-depth investigation. A systematic approach was employed, leveraging literature reviews and comparative analyses. Relevant articles were meticulously selected, focusing on methodologies and algorithms in predicting material properties. The study aimed to explore the integration of DL and ML in advancing red phosphor materials, evaluating algorithms and seven different regression models. Linear Regression, Robust Regression, and Lasso Regression emerged as top-performing models in predicting red phosphor material properties, specifically the 2E energy of Mn4+ doped crystals, supported by comprehensive coefficient analysis. This research offers valuable insights, informing the selection of models for specific tasks and optimizing the integration of DL and ML techniques in the field of red phosphor materials.
T2  - Journal of Luminescence
T1  - Exploring deep learning and machine learning for novel red phosphor materials
VL  - 269
SP  - 120476
DO  - 10.1016/j.jlumin.2024.120476
ER  - 

@article{
author = "Novita, Mega and Chauhan, Alok Singh and Ujianti, Rizky Muliani Dwi and Marlina, Dian and Kusumo, Haryo and Anwar, Muchamad Taufiq and Piasecki, Michał and Brik, Mikhail G.",
year = "2024",
abstract = "In the pursuit of enhancing red phosphor materials, integrating Deep Learning (DL) and machine Learning (ML) techniques has emerged as a transformative avenue. Challenges persist, necessitating comprehensive exploration and detailed comparative analysis of methods, focusing on predictive accuracy, interpretability, and computational demands. The role of regression models and their coefficients in material property prediction requires in-depth investigation. A systematic approach was employed, leveraging literature reviews and comparative analyses. Relevant articles were meticulously selected, focusing on methodologies and algorithms in predicting material properties. The study aimed to explore the integration of DL and ML in advancing red phosphor materials, evaluating algorithms and seven different regression models. Linear Regression, Robust Regression, and Lasso Regression emerged as top-performing models in predicting red phosphor material properties, specifically the 2E energy of Mn4+ doped crystals, supported by comprehensive coefficient analysis. This research offers valuable insights, informing the selection of models for specific tasks and optimizing the integration of DL and ML techniques in the field of red phosphor materials.",
journal = "Journal of Luminescence",
title = "Exploring deep learning and machine learning for novel red phosphor materials",
volume = "269",
pages = "120476",
doi = "10.1016/j.jlumin.2024.120476"
}

Novita, M., Chauhan, A. S., Ujianti, R. M. D., Marlina, D., Kusumo, H., Anwar, M. T., Piasecki, M.,& Brik, M. G.. (2024). Exploring deep learning and machine learning for novel red phosphor materials. in Journal of Luminescence, 269, 120476.
https://doi.org/10.1016/j.jlumin.2024.120476

Novita M, Chauhan AS, Ujianti RMD, Marlina D, Kusumo H, Anwar MT, Piasecki M, Brik MG. Exploring deep learning and machine learning for novel red phosphor materials. in Journal of Luminescence. 2024;269:120476.
doi:10.1016/j.jlumin.2024.120476 .

Novita, Mega, Chauhan, Alok Singh, Ujianti, Rizky Muliani Dwi, Marlina, Dian, Kusumo, Haryo, Anwar, Muchamad Taufiq, Piasecki, Michał, Brik, Mikhail G., "Exploring deep learning and machine learning for novel red phosphor materials" in Journal of Luminescence, 269 (2024):120476,
https://doi.org/10.1016/j.jlumin.2024.120476 . .

TY  - JOUR
AU  - Srivastava, Alok M
AU  - Suchocki, Andrzej
AU  - Bulyk, Lev-Ivan
AU  - Zhydachevskyy, Yaroslav
AU  - Brik, Mikhail G.
AU  - Beers, William W
AU  - Cohen, Willian E
PY  - 2024
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/13124
AB  - We have measured the excitation and emission spectra, and lifetime for Fe3+ in the Ca8Mg(SiO4)4Cl2 compound. The spectroscopic data of the ferric ion (Fe3+) is interpreted within the framework of the Tanabe-Sugano crystal field theory for ions with the d5 electronic configuration. Quantitative evaluation of the Racah parameters B and C and the crystal field parameter (10 Dq) is provided. The emission and excitation data are consistent for Fe3+ in a tetrahedral-site occupancy. The red emission is assigned to the 4T1 (4G)→6A1 (6S) crystal field transition. The remarkable feature of the luminescence is the positioning of the emission band; it is centered at 623 nm with a Full Width at Half Maximum of 51 nm. To the best of our knowledge this is one of the highest energy emission for the tetrahedral-site Fe3+ ion. The practical implication of this spectral power distribution is discussed. The temperature dependency of the luminescence lifetime and emission intensity are reported.
T2  - Optical Materials
T1  - Narrowband red luminescence of tetrahedral-site Fe3+ In Ca8Mg(SiO4)4Cl2
VL  - 150
SP  - 115290
DO  - 10.1016/j.optmat.2024.115290
ER  - 

@article{
author = "Srivastava, Alok M and Suchocki, Andrzej and Bulyk, Lev-Ivan and Zhydachevskyy, Yaroslav and Brik, Mikhail G. and Beers, William W and Cohen, Willian E",
year = "2024",
abstract = "We have measured the excitation and emission spectra, and lifetime for Fe3+ in the Ca8Mg(SiO4)4Cl2 compound. The spectroscopic data of the ferric ion (Fe3+) is interpreted within the framework of the Tanabe-Sugano crystal field theory for ions with the d5 electronic configuration. Quantitative evaluation of the Racah parameters B and C and the crystal field parameter (10 Dq) is provided. The emission and excitation data are consistent for Fe3+ in a tetrahedral-site occupancy. The red emission is assigned to the 4T1 (4G)→6A1 (6S) crystal field transition. The remarkable feature of the luminescence is the positioning of the emission band; it is centered at 623 nm with a Full Width at Half Maximum of 51 nm. To the best of our knowledge this is one of the highest energy emission for the tetrahedral-site Fe3+ ion. The practical implication of this spectral power distribution is discussed. The temperature dependency of the luminescence lifetime and emission intensity are reported.",
journal = "Optical Materials",
title = "Narrowband red luminescence of tetrahedral-site Fe3+ In Ca8Mg(SiO4)4Cl2",
volume = "150",
pages = "115290",
doi = "10.1016/j.optmat.2024.115290"
}

Srivastava, A. M., Suchocki, A., Bulyk, L., Zhydachevskyy, Y., Brik, M. G., Beers, W. W.,& Cohen, W. E.. (2024). Narrowband red luminescence of tetrahedral-site Fe3+ In Ca8Mg(SiO4)4Cl2. in Optical Materials, 150, 115290.
https://doi.org/10.1016/j.optmat.2024.115290

Srivastava AM, Suchocki A, Bulyk L, Zhydachevskyy Y, Brik MG, Beers WW, Cohen WE. Narrowband red luminescence of tetrahedral-site Fe3+ In Ca8Mg(SiO4)4Cl2. in Optical Materials. 2024;150:115290.
doi:10.1016/j.optmat.2024.115290 .

Srivastava, Alok M, Suchocki, Andrzej, Bulyk, Lev-Ivan, Zhydachevskyy, Yaroslav, Brik, Mikhail G., Beers, William W, Cohen, Willian E, "Narrowband red luminescence of tetrahedral-site Fe3+ In Ca8Mg(SiO4)4Cl2" in Optical Materials, 150 (2024):115290,
https://doi.org/10.1016/j.optmat.2024.115290 . .