https://doi.org/10.1140/epjp/s13360-025-06937-2
Regular Article
Enhancing RbSnBr3 perovskite solar cells performances: machine learning-driven optimization of active layers and electron transport materials using SCAPS-1D
1
Department of Electrical and Electronic Engineering, Begum Rokeya University, 5400, Rangpur, Bangladesh
2
Department of Mathematics, Lamar University, 77705, Beaumont, TX, USA
3
Department of Mechanical Engineering, Lamar University, Beaumont, TX, USA
4
Department of Electrical and Computer Engineering, Lamar University, 77710, Beaumont, TX, USA
5
Department of Management Information System, Lamar University, Beaumont, TX, USA
6
Department of Physics, College of Science, University of Bisha, P.O. Box 551, 61922, Bisha, Saudi Arabia
7
Department of Industrial and Systems Engineering, Lamar University, 77710, Beaumont, TX, USA
a
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Received:
21
June
2025
Accepted:
5
October
2025
Published online:
16
October
2025
Abstract
The perovskite solar cell (PSC), recognized as a novel and prospective technology, has been the subject of extensive research. This investigation involved the development of a model for a PSC fabricated utilizing the SCAPS-1D model tool in ambient air. Its findings are the capabilities of a novel solar cell configuration comprising ITO/WS2/RbSnBr3. RbSnBr3, a lead-free and environmentally benign perovskite, presents a viable replacement for lead-based traditional perovskites, owing to its non-toxic properties and stable optoelectronic performance. The integration of these materials within the ITO/WS2/RbSnBr3 framework was analyzed through sophisticated computational methods to assess critical performance indicators, such as power conversion efficiency (PCE), short-circuit current density (JSC), open-circuit voltage (VOC), and fill factor (FF). The optimized PV parameters of this structure are VOC of 0.89 V, JSC of 47.14 mA/cm2, FF of 78.92%, and PCE of 33.25%, respectively. The findings reveal that the ITO/WS2/RbSnBr3 solar cell attains an ideal equilibrium of carrier extraction, transport, and diminished defect densities at the interfaces, resulting in improved photovoltaic effectiveness. This study establishes the foundation for advancing perovskite of lead-free solar cells (SCs), contributing to developing more sustainable and efficient photovoltaic technologies. We next built a machine learning (ML) model to predict the performance characteristics of the solar cells. ML predicted the performance matrix of the solar cells under study with an accuracy rate of around 80%. The practical design of this study and the important discoveries it makes might lead to an inexpensive RbSnBr3 thin-film solar cell.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2025
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
