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  - Novita, Mega
AU  - Ristanto, Sigit
AU  - Saptaningrum, Ernawati
AU  - Supriyadi, Slemet
AU  - Marlina, Dian
AU  - Rondonuwu, Ferdy Semuel
AU  - Chauhan, Alok Singh
AU  - Walker, Benjamin
AU  - Ogasawara, Kazuyoshi
AU  - Piasecki, Michal
AU  - Brik, Mikhail G.
PY  - 2023
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/11098
AB  - The crystals of Mn4+-activated fluorides, such as those of the hexafluorometallate family, are widely known for their luminescence properties. The most commonly reported red phosphors are A2XF6: Mn4+ and BXF6: Mn4+ fluorides, where A represents alkali metal ions such as Li, Na, K, Rb, Cs; X=Ti, Si, Ge, Zr, Sn, B = Ba and Zn; and X = Si, Ge, Zr, Sn, and Ti. Their performance is heavily influenced by the local structure around dopant ions. Many well-known research organizations have focused their attention on this area in recent years. However, there has been no report on the effect of local structural symmetrization on the luminescence properties of red phosphors. The purpose of this research was to investigate the effect of local structural symmetrization on the polytypes of K2XF6 crystals, namely Oh-K2MnF6, C3v-K2MnF6, Oh-K2SiF6, C3v-K2SiF6, D3d-K2GeF6, and C3v-K2GeF6. These crystal formations yielded seven-atom model clusters. Discrete Variational Xα (DV-Xα) and Discrete Variational Multi Electron (DVME) were the first principles methods used to compute the Molecular orbital energies, multiplet energy levels, and Coulomb integrals of these compounds. The multiplet energies of Mn4+ doped K2XF6 crystals were qualitatively reproduced by taking lattice relaxation, Configuration Dependent Correction (CDC), and Correlation Correction (CC) into account. The 4A2g→4T2g (4F) and 4A2g→4T1g (4F) energies increased when the Mn-F bond length decreased, but the 2Eg → 4A2g energy decreased. Because of the low symmetry, the magnitude of the Coulomb integral became smaller. As a result, the decreasing trend in the R-line energy could be attributed to a decreased electron–electron repulsion. © 2023 by the authors.
T2  - Materials
T1  - Study on Local-Structure Symmetrization of K2XF6 Crystals Doped with Mn4+ Ions by First-Principles Calculations
VL  - 16
IS  - 11
DO  - 10.3390/ma16114046
ER  - 

@article{
author = "Novita, Mega and Ristanto, Sigit and Saptaningrum, Ernawati and Supriyadi, Slemet and Marlina, Dian and Rondonuwu, Ferdy Semuel and Chauhan, Alok Singh and Walker, Benjamin and Ogasawara, Kazuyoshi and Piasecki, Michal and Brik, Mikhail G.",
year = "2023",
abstract = "The crystals of Mn4+-activated fluorides, such as those of the hexafluorometallate family, are widely known for their luminescence properties. The most commonly reported red phosphors are A2XF6: Mn4+ and BXF6: Mn4+ fluorides, where A represents alkali metal ions such as Li, Na, K, Rb, Cs; X=Ti, Si, Ge, Zr, Sn, B = Ba and Zn; and X = Si, Ge, Zr, Sn, and Ti. Their performance is heavily influenced by the local structure around dopant ions. Many well-known research organizations have focused their attention on this area in recent years. However, there has been no report on the effect of local structural symmetrization on the luminescence properties of red phosphors. The purpose of this research was to investigate the effect of local structural symmetrization on the polytypes of K2XF6 crystals, namely Oh-K2MnF6, C3v-K2MnF6, Oh-K2SiF6, C3v-K2SiF6, D3d-K2GeF6, and C3v-K2GeF6. These crystal formations yielded seven-atom model clusters. Discrete Variational Xα (DV-Xα) and Discrete Variational Multi Electron (DVME) were the first principles methods used to compute the Molecular orbital energies, multiplet energy levels, and Coulomb integrals of these compounds. The multiplet energies of Mn4+ doped K2XF6 crystals were qualitatively reproduced by taking lattice relaxation, Configuration Dependent Correction (CDC), and Correlation Correction (CC) into account. The 4A2g→4T2g (4F) and 4A2g→4T1g (4F) energies increased when the Mn-F bond length decreased, but the 2Eg → 4A2g energy decreased. Because of the low symmetry, the magnitude of the Coulomb integral became smaller. As a result, the decreasing trend in the R-line energy could be attributed to a decreased electron–electron repulsion. © 2023 by the authors.",
journal = "Materials",
title = "Study on Local-Structure Symmetrization of K2XF6 Crystals Doped with Mn4+ Ions by First-Principles Calculations",
volume = "16",
number = "11",
doi = "10.3390/ma16114046"
}

Novita, M., Ristanto, S., Saptaningrum, E., Supriyadi, S., Marlina, D., Rondonuwu, F. S., Chauhan, A. S., Walker, B., Ogasawara, K., Piasecki, M.,& Brik, M. G.. (2023). Study on Local-Structure Symmetrization of K2XF6 Crystals Doped with Mn4+ Ions by First-Principles Calculations. in Materials, 16(11).
https://doi.org/10.3390/ma16114046

Novita M, Ristanto S, Saptaningrum E, Supriyadi S, Marlina D, Rondonuwu FS, Chauhan AS, Walker B, Ogasawara K, Piasecki M, Brik MG. Study on Local-Structure Symmetrization of K2XF6 Crystals Doped with Mn4+ Ions by First-Principles Calculations. in Materials. 2023;16(11).
doi:10.3390/ma16114046 .

Novita, Mega, Ristanto, Sigit, Saptaningrum, Ernawati, Supriyadi, Slemet, Marlina, Dian, Rondonuwu, Ferdy Semuel, Chauhan, Alok Singh, Walker, Benjamin, Ogasawara, Kazuyoshi, Piasecki, Michal, Brik, Mikhail G., "Study on Local-Structure Symmetrization of K2XF6 Crystals Doped with Mn4+ Ions by First-Principles Calculations" in Materials, 16, no. 11 (2023),
https://doi.org/10.3390/ma16114046 . .