Exploring deep learning and machine learning for novel red phosphor materials
Само за регистроване кориснике
2024
Аутори
Novita, MegaChauhan, Alok Singh
Ujianti, Rizky Muliani Dwi
Marlina, Dian
Kusumo, Haryo
Anwar, Muchamad Taufiq
Piasecki, Michał
Brik, Mikhail G.
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
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.
Кључне речи:
2E / DL / ML / Mn4+ / Phosphor / Red phosphor materialИзвор:
Journal of Luminescence, 2024, 269, 120476-Финансирање / пројекти:
- Penelitian Kompetitif Nasional skema Penelitian Kerjasama Dalam Negeri [contract No. 0536/E5/ PG.02.00/2023, decision letter No. 0536/E5/PG.02.00/2023]
- DRTPM and LLDIKT [No. 182/ E5/PG.02.00. PL/2023]
- LLDIKTI and LPPM [No. 0031/LL6/PB/AL.04/2023]
- LPPM and the Researchers [No. 13/161038/PG/SP2H/PB/2023_PB]
- National Natural Science Foundation of China [Grant Nos. 52161135110 and 12274048]
- Polish NCN [2021/40/Q/ST5/00336]
- Overseas Talents Plan of Chongqing Association for Science and Technology, China [(Grant No. 2022[60]]
- Estonian Research Council [grant PRG2031]
- National Science Centre, Poland [grant SHENG number 2021/40/Q/ST5/00336]
- Министарство науке, технолошког развоја и иновација Републике Србије, институционално финансирање - 200017 (Универзитет у Београду, Институт за нуклеарне науке Винча, Београд-Винча) (RS-MESTD-inst-2020-200017)
Колекције
Институција/група
VinčaTY - 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 . .