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Computational optimization of MASnI3 perovskite solar cells using SCAPS-1D simulations and machine learning
Benjer Islam1, Tanvir Mahtab Khan1, Md Mountasir Rahaman2
1Department of Electrical, Electronic and Communication Engineering, Pabna University of Science and Technology Pabna 6600 Bangladesh tanvirshaikat92@gmail.com rashel@pust.ac.bd.
RSC Advances
|January 5, 2026
Summary
This study explores lead-free methylammonium tin iodide (MASnI3) perovskite solar cells (PSCs). The optimized MASnI3 device achieved a 32.30% efficiency, with defect density significantly impacting performance.
Area of Science:
- Materials Science
- Renewable Energy
- Computational Physics
Background:
- Lead-free perovskite solar cells (PSCs) are gaining traction as sustainable alternatives to lead-based devices.
- Methylammonium tin iodide (MASnI3) offers an ideal band gap and eco-friendly composition for PSC applications.
Purpose of the Study:
- To computationally design and evaluate novel PSC structures using MASnI3 as the absorber layer.
- To investigate the impact of various material properties and operational parameters on device performance.
- To compare machine learning models for predicting PSC efficiency.
Main Methods:
- Utilized the SCAPS-1D simulator to model and optimize PSC architectures.
- Investigated the influence of MASnI3 layer thickness, carrier density, defect concentration, and carrier lifetime.
- Analyzed effects of interface recombination, temperature, and capacitance characteristics (C-V, C-F).
- Employed three machine learning algorithms (including XGBoost) for efficiency prediction and feature importance analysis.
Main Results:
- An optimized MASnI3-based device (Al/FTO/WS2/MASnI3/Zn3P2/Ni) demonstrated high efficiency (32.30%), fill factor (87.35%), Jsc (34.19 mAcm-2), and Voc (1.08 V).
- Defect density in the MASnI3 layer was identified as the most critical factor influencing device efficiency.
- The XGBoost model accurately predicted device efficiency with R2=0.9999, MSE=0.0092, and MAE=0.051.
Conclusions:
- The proposed MASnI3 PSC architecture shows significant potential for high-performance, lead-free solar energy conversion.
- Controlling defect density in the perovskite layer is crucial for maximizing photovoltaic performance.
- Machine learning, particularly XGBoost, offers a powerful tool for predicting and optimizing PSC efficiency.
