First-principles investigation and device simulation of TlPbI3-based perovskite solar cells with machine learning-driven efficiency prediction

Abstract

The development of sustainable, cost-effective, and environmentally friendly photovoltaic absorbers is essential for advancing next-generation solar cell technologies. In this study, we investigate the thallium-based halide perovskite TlPbI₃ through a synergistic combination of density functional theory (DFT), solar cell capacitance simulator in one dimension (SCAPS-1D) device simulation, and machine learning (ML). Structural optimization, tolerance factor, formation energy, and phonon dispersion curve confirmed the structural, dynamic, thermodynamic, and mechanical stability of cubic TlPbI₃, with elastic constants fulfilling Born's stability criteria. The calculated direct band gap of 1.26 eV at the R point falls within the optimal range for single-junction photovoltaics. Charge density mapping indicated mixed ionic-covalent bonding, ensuring structural robustness. Mechanical analysis confirmed ductility and thermal stability, with an estimated melting temperature of ∼757 K. Optical results showed strong absorption in the visible region, a high static dielectric constant (ε₀ ≈ 4.6), and low reflectivity, underlining the suitability of TlPbI₃ for optoelectronic devices. SCAPS-1D simulations were performed on different heterojunction configurations, optimizing absorber thickness, doping density, defect density, and buffer layers. The best-performing device, fluorine-doped tin oxide (FTO)/cadmium sulfide (CdS)/thallium lead triiodide (TlPbI₃)/copper (Cu), delivered a power conversion efficiency (PCE) of 22.20% with open-circuit voltage (V_OC) = 0.7987 V, short-circuit current density (J_SC) = 34.56 mA cm⁻², and fill factor (FF) = 80.40%, confirming the strong photovoltaic potential of TlPbI₃. Machine learning models, trained on simulation datasets, successfully identified absorber thickness and defect density as the most critical factors influencing device performance. This integrated computational framework demonstrates the potential of TlPbI₃ as a viable absorber material for next-generation solar cells and provides valuable predictive insights to guide experimental development.