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RESEARCH PAPER · Solar RRL · 2023

Passivation Engineering Using Ultrahydrophobic Donor–π–Acceptor Organic Dye with Machine Learning Insights for Efficient and Stable Perovskite Solar Cells

Mohamed M. Elsenety, Eleftherios Christopoulos, Polycarpos Falaras

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Passivation Engineering Using Ultrahydrophobic Donor–π–Acceptor Organic Dye with Machine Learning Insights for Efficient and Stable Perovskite Solar CellsOriginalPerformance Enhancement and Stability Improvement in Perovskite Solar Cells via Interface Functionalization16Insight into Controlled Surface Passivation of PEDOT:PSS for Defect Density Modulation and Efficient Charge Transport for Perovskite Solar Cells24Explainable optimized 3D-MoRSE descriptors for the power conversion efficiency prediction of molecular passivated perovskite solar cells through machine learning53-Thiophenemalonic Acid Additive Enhanced Performance in Perovskite Solar Cells10Construction frontier molecular orbital prediction model with transfer learning for organic materials36Design and predict the potential of imidazole-based organic dyes in dye-sensitized solar cells using fingerprint machine learning and supported by a web application11A comprehensive review of machine learning applications in perovskite solar cells: Materials discovery, device performance, process optimization and systems integration41Ultra-fast prediction of D-π-A organic dye absorption maximum with advanced ensemble deep learning models6Efficient Functional Compensation Layer Integrated with HTL for Improved Stability and Performance in Perovskite Solar Cells21Optimization and Scalability of Polymer‐Modified PSCs Investigated by Machine Learning4Double-Function Lewis-Base-Additive-Mediated Interfacial Configuration and Defect Mitigation in Perovskite Solar Cells3Harnessing machine learning to revolutionize perovskite solar cells: Optimizing RbTmCl3-based photovoltaic performance13Leveraging machine learning for the design and optimization of environmentally friendly K2CuCrCl6 perovskite solar cells25High‐Throughput Deep Learning Prediction of D‐π‐A Dye Absorption Maxima on Titania Surfaces0Accelerating Organic Solar Cell Development Through Artificial Intelligence–Driven Material Discovery and Optimization1Research progress on the application of machine learning in perovskite solar cells0Humidity-induced degradation kinetics of MAPbI3 perovskite films with machine learning insights0Hybrid machine learning and SCAPS-1D simulation for the design and performance optimization of Cs2HgPdI6-based perovskite solar cells0Raman and infrared spectra, novel normal coordinate analysis, vibrational assignments and DFT calculations of hybrid perovskites: trimethyl sulfonium lead halides0

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19 direct citing papers saved.

  1. Performance Enhancement and Stability Improvement in Perovskite Solar Cells via Interface Functionalization

    Electronics · 2023-08-03

    Christos Falaras, Elias Stathatos

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  2. Insight into Controlled Surface Passivation of PEDOT:PSS for Defect Density Modulation and Efficient Charge Transport for Perovskite Solar Cells

    ACS Applied Energy Materials · 2023-08-31

    Tanushree Majhi, M. Sridevi, Sanyam Jain, Avritti Srivastava, Mahesh Kumar, Sanjay Kumar Srivastava, Rajiv K. Singh

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  3. Explainable optimized 3D-MoRSE descriptors for the power conversion efficiency prediction of molecular passivated perovskite solar cells through machine learning

    Journal of Materials Chemistry A · 2024-01-01

    Xin Ye, Ningyi Cui, Wen Ou, Donghua Liu, Yufan Bao, Bin Ai, Yecheng Zhou

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  4. 3-Thiophenemalonic Acid Additive Enhanced Performance in Perovskite Solar Cells

    ACS Omega · 2024-01-04

    Samuel Abicho, Bekele Hailegnaw, Felix Mayr, Munise Cobet, Cigdem Yumusak, Teshome Abute Lelisho, Teketel Yohannes, Martin Kaltenbrunner, Niyazi Serdar Sariçiftçi, Markus Clark Scharber, Getachew Adam Workneh

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  5. Construction frontier molecular orbital prediction model with transfer learning for organic materials

    npj Computational Materials · 2024-09-11

    Xinyu Peng, Jiaojiao Liang, Kuo Wang, Xiaojie Zhao, Zhiyan Peng, Zhennan Li, Jinhui Zeng, Lan Xia Zheng, Min Lei, Di Huang

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  6. Design and predict the potential of imidazole-based organic dyes in dye-sensitized solar cells using fingerprint machine learning and supported by a web application

    Scientific Reports · 2024-11-03

    Mohamed M. Elsenety

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  7. A comprehensive review of machine learning applications in perovskite solar cells: Materials discovery, device performance, process optimization and systems integration

    Materials Today Energy · 2024-11-15

    Ling Mao, Changying Xiang

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  8. Ultra-fast prediction of D-π-A organic dye absorption maximum with advanced ensemble deep learning models

    Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · 2024-12-10

    Mohamed M. Elsenety

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  9. Efficient Functional Compensation Layer Integrated with HTL for Improved Stability and Performance in Perovskite Solar Cells

    Small · 2025-01-05

    Xiangrui Du, Fuqiang Li, Ying Li, Haicheng Xia, Eunhye Yang, Yoomi Ahn, Feng‐Wu Liu, Yongzan Chen, Bo Ram Lee, Sung Heum Park

    Read original paper ↗ ·

  10. Optimization and Scalability of Polymer‐Modified PSCs Investigated by Machine Learning

    Applied Research · 2025-04-01

    Mohamed M. Elsenety, Christos Falaras, Elias Stathatos, Yunjuan Niu, Linhua Hu

    Read original paper ↗ ·

  11. Double-Function Lewis-Base-Additive-Mediated Interfacial Configuration and Defect Mitigation in Perovskite Solar Cells

    ACS Applied Energy Materials · 2025-08-07

    Tanushree Majhi, M. Sridevi, Sanyam Jain, Rajiv K. Singh

    Read original paper ↗ ·

  12. Harnessing machine learning to revolutionize perovskite solar cells: Optimizing RbTmCl3-based photovoltaic performance

    Materials Research Bulletin · 2025-08-18

    Abderrahim Yousfi, Okba Saidani, Raouf Zerrougui, Tarek Assassi, Sagar Bhattarai, Md. Ferdous Rahman, Girija Shankar Sahoo

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  13. Leveraging machine learning for the design and optimization of environmentally friendly K2CuCrCl6 perovskite solar cells

    Materials Science and Engineering B · 2025-08-18

    Abderrahim Yousfi, Okba Saidani, Yehya Belhadad, Rafik Zouache, Abdelhalim Brahimi, Sagar Bhattarai, Girija Shankar Sahoo

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  14. High‐Throughput Deep Learning Prediction of D‐π‐A Dye Absorption Maxima on Titania Surfaces

    ChemistrySelect · 2025-09-01

    Mohamed M. Elsenety

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  15. Accelerating Organic Solar Cell Development Through Artificial Intelligence–Driven Material Discovery and Optimization

    International Journal of Energy Research · 2026-01-01

    Marwan Al‐Raeei

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  16. Research progress on the application of machine learning in perovskite solar cells

    Chinese Science Bulletin (Chinese Version) · 2026-01-27

    Aili Wang, Haoran Zhang, Kaihuai Du, Lvzhou Li, Jianning Ding

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  17. Humidity-induced degradation kinetics of MAPbI3 perovskite films with machine learning insights

    Solid State Sciences · 2026-05-15

    Mohamed M. Elsenety, Vasileios Foris, Polycarpos Falaras

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  18. Hybrid machine learning and SCAPS-1D simulation for the design and performance optimization of Cs2HgPdI6-based perovskite solar cells

    Journal of Optics · 2026-06-30

    Okba Saidani, Abderrahim Yousfi, Yehya Belhadad, Abdsamed Chenouf, Anis Chouya, Girija Shankar Sahoo

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  19. Raman and infrared spectra, novel normal coordinate analysis, vibrational assignments and DFT calculations of hybrid perovskites: trimethyl sulfonium lead halides

    Zeitschrift für Physikalische Chemie · 2026-10-13

    Tarek A. Mohamed, Mohamed M. Elsenety, Polycarpos Falaras

    Read original paper ↗ ·
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