Simulation and Machine Learning Driven Optimization of Rb 2 SnBr 6 -Based Lead-Free Perovskite Solar Cells Using Diverse ETLs for Enhanced Photovoltaic Performance

Abstract

In this work, a comprehensive simulation of n-i-p planar heterojunction perovskite solar cells (PSCs) was performed using SCAPS-1D, focusing on Rb 2 SnBr 6 as a lead-free, stable, and cost-effective absorber material. The simulated devices utilize fluorine-doped tin oxide (FTO) as the transparent leading substrate and gold (Au) as the rear contact. Three different electron transport layers (ETLs)-ZnSe, In 2 S 3 , and CdZnSe were evaluated to optimize device performance. The study assessed key parameters such as doping concentration, layer thickness, bulk and interface defect densities, and their effects on overall efficiency.Under AM 1.5G solar illumination, Device I (Au/ Rb 2 SnBr 6 /ZnSe/FTO/Al) achieved the highest power conversion efficiency (PCE) of 28.73%, with a fill factor (FF) of 86.86%, V OC of 0.8682 V, and a J SC of 38.094 mA/cm 2 . In comparison, Device II (with In 2 S 3 ) and Device III (with CdZnSe) recorded lower PCEs of 26.62% and 24.04%, respectively. Additionally, A Random Forest (RF) machine learning model was utilized to forecast the best PCE by examining various device attributes. Model interpretability was improved through the application of SHAP (Shapley Additive Explanations), which assessed the relative impact of each parameter on device performance. The correlation heatmap demonstrated little interparameter correlations, signifying limited multicollinearity among the input characteristics. The RF model attained a high coefficient of determination (R 2 = 0.8825), indicating robust predictive efficacy and dependability. The confusion matrix and parity plot demonstrated near-perfect categorization and strong concordance between predicted and actual values, underscoring the model's resilience and generalizability.These findings underscore the promising potential of Rb 2 SnBr 6 , especially Device I, for the development of high-efficiency, eco-friendly perovskite solar cells.

Article information

Article type
Paper
Submitted
25 Aug 2025
Accepted
08 Oct 2025
First published
10 Oct 2025
This article is Open Access
Creative Commons BY-NC license

Mater. Adv., 2025, Accepted Manuscript

Simulation and Machine Learning Driven Optimization of Rb 2 SnBr 6 -Based Lead-Free Perovskite Solar Cells Using Diverse ETLs for Enhanced Photovoltaic Performance

Md. S. Reza, A. Ghosh, A. I. Shimul, S. H. Nabil, M. Akter, A. R. Chaudhry, D. R. Sobuj, Y. Anil Kumar, S. Biswas, K. Alam and M. Maqsood, Mater. Adv., 2025, Accepted Manuscript , DOI: 10.1039/D5MA00955C

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