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RESEARCH PAPER · Journal of Luminescence · 2024

Exploring deep learning and machine learning for novel red phosphor materials

Mega Novita, Alok Singh Chauhan, Rizky Muliani Dwi Ujianti, Dian Marlina, Haryo Kusumo, Muchamad Taufiq Anwar, Michał Piasecki, M.G. Brik

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11 direct citing papers in this saved network. Explore the next layer with second-order citations.

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42 direct citing works

Exploring deep learning and machine learning for novel red phosphor materialsOriginalSpectroscopic analysis of Mn4+-activated phosphors: Unveiling the relationship between energy levels and Racah parameters6A review of recent developments in rare earth-doped nanophosphors for emerging technological applications79Spectroscopic characteristics and characterizations of CaAl12O19-based phosphors for anti-counterfeiting applications and performance predictions4Machine learning-based predictions of the 4fn-4fn-15d1 UV absorption for Pr3+ and Ce3+ ions in fluoride and oxide compounds6Predicting Abrasion Resistance in Thermoplastic Polyurethanes Using Machine Learning7Explainable machine learning-enabled discovery for high-efficiency red-emitting phosphor under data constraints5A Stacking Ensemble Model for Improving the Prediction Performance of Luminescent Color of Phosphor1Composition-based machine learning for predicting and designing Mn 4+ -doped phosphors3A review of artificial intelligence approaches for the development of upconversion and downconversion phosphors1Machine learning for cracking the molecular code of room-temperature organic phosphorescent materials: From structure-property relationship to performance prediction0Graph neural networks for predicting and ranking drug loading in mesoporous silica nanoparticles0

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11 direct citing papers saved.

  1. Spectroscopic analysis of Mn4+-activated phosphors: Unveiling the relationship between energy levels and Racah parameters

    Journal of Luminescence · 2024-12-24

    Jiquan Huang, Qiufeng Huang, Jian Chen, Zhonghua Deng, Zhuguang Liu, Wang Guo

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  2. A review of recent developments in rare earth-doped nanophosphors for emerging technological applications

    RSC Advances · 2025-01-01

    R. Kiran, Nagaraj Kamath, M.I. Sayyed, Aljawhara H. Almuqrin, Sudha D. Kamath

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  3. Spectroscopic characteristics and characterizations of CaAl12O19-based phosphors for anti-counterfeiting applications and performance predictions

    Advanced Powder Technology · 2025-05-06

    Shifa Wang, Yuanyuan Zhang, Yujia Jin, Xinmiao Yu, Xianlun Yu, Xinxin Zhao, Huajing Gao, Xianju Zhou, Dengfeng Li, Hua Yang, Leiming Fang, Asad Syed

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  4. Machine learning-based predictions of the 4fn-4fn-15d1 UV absorption for Pr3+ and Ce3+ ions in fluoride and oxide compounds

    Journal of Luminescence · 2025-06-11

    G. Dima, Andrei V. Racu, Chong‐Geng Ma, A.M. Srivastava, Željka M. Antić, Miroslav D. Dramićanin, M.G. Brik

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  5. Predicting Abrasion Resistance in Thermoplastic Polyurethanes Using Machine Learning

    Journal of Applied Polymer Science · 2025-07-15

    Sara Zahmatkesh, Hamed Esmaeili, Alfonso Tavares Valerio, Reza R. Rizvi

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  6. Explainable machine learning-enabled discovery for high-efficiency red-emitting phosphor under data constraints

    Chemical Engineering Journal · 2025-08-11

    Shaoan Zhang, Yang Lv, Zhenzhang Li, Chao He, Zonglong Guo, Ximei An, Shengzhi Sun, Zaijin Fang, Yang Li

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  7. A Stacking Ensemble Model for Improving the Prediction Performance of Luminescent Color of Phosphor

    ChemPhysChem · 2025-11-08

    Shufang Li, Tinglun Ao, Kunpeng Gao, Junqi Tang

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  8. Composition-based machine learning for predicting and designing Mn 4+ -doped phosphors

    RSC Advances · 2026-01-01

    Ngo T. Que, Vu Dinh Huan, Le Thanh Duy, Vu Ngoc Bao, Vu L. Minh, Mai Xuan Trang, Anh Duc Phan, Phạm Thành Huy

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  9. A review of artificial intelligence approaches for the development of upconversion and downconversion phosphors

    Journal of Alloys and Compounds · 2026-02-12

    David Chávez, Jorge Roberto Oliva-Uc, Carlos Eduardo Rodríguez-García, Mario Castelán

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  10. Machine learning for cracking the molecular code of room-temperature organic phosphorescent materials: From structure-property relationship to performance prediction

    Green Energy & Environment · 2026-08-01

    Yan Hu, Senlin Tian, Yingjie Li

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  11. Graph neural networks for predicting and ranking drug loading in mesoporous silica nanoparticles

    Science China Materials · 2026-09-30

    Wancheng Zhang, Xinya Zhang, Zhiwen Ding, Yeqiong Mao, Dapeng Zhang, Yongsheng Li, Dechao Niu

    Read original paper ↗ ·
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