DFT and Machine Learning Analysis of Eco‐Friendly Formamidinium Tin Iodide Perovskite Solar Cells: Optimizing Charge Transport Layers Through SCAPS‐1D Simulations

ABSTRACT Tin‐based solar cells are the eco‐friendly alternative to the toxic lead‐based cells that are currently in use. In this context, Formamidinium Tin Iodide (FASnI 3 ) emerges as a promising candidate due to its superior performance, exhibiting higher efficiency compared to other options. FASnI 3 is a lead‐free tin‐based perovskite that has attracted considerable interest as a potential alternative to lead‐containing photovoltaic absorbers. However, its practical application remains subject to challenges associated with Sn 2+ oxidation and environmental stability. The present study introduces density functional theory (DFT) for structural, electronic, and optical property computations using the Quantum Espresso Code. The electronic properties of FASnI 3 exhibit a direct band gap of 0.96/1.41 eV with PBE/HSE function. The optical properties of this material emphasize its good dielectric qualities, a lower loss function, and enhanced absorption under visible light. Consequently, the FASnI 3 perovskite layer is simulated with multiple charge transport layers (HTLs and ETLs) to ensure the ascertainment of the optimal conjunction. Furthermore, the thickness, dopant density, and other simulation parameters of the absorber and charge transport layers are altered using SCAPS‐1D computational tool to ascertain the output parameters of FASnI 3 ‐based perovskite solar cells (PSCs). Additionally, machine learning (ML) regression models (Gradient Boosting, Random Forest, XGBoost, and LightGBM), trained on the SCAPS‐generated dataset, are employed to predict and interpret key device parameters (PCE, Voc, Jsc, and FF) as a function of absorber thickness, doping density, and defect density, serving as a complementary, interpolation‐based tool alongside the device simulations rather than an independent validation of them, and demonstrating strong predictive accuracy and feature importance insights. The final optimized structure, Au/Cu 2 O/FASnI 3 /WS 2 /ITO, achieves a PCE of 27.71%, Voc of 1.03 V, Jsc of 31.14 mA/cm 2 , and FF of 86.00%, a competitive result relative to several previously reported lead‐free FASnI 3 ‐ and MASnI 3 ‐based device architectures. This work establishes a feasible approach for the improvement of toxin‐free, stable perovskite photovoltaics through a combination of computational guidance and the predicted device architecture, providing a computationally testable framework for future experimental investigation.