Comprehensive analysis of Sr3PCl3 absorber for solar cells using DFT, SCAPS-1D, and machine learning techniques
Md. Hafizur Rahman, Foysal Ahmed, Noureddine Elboughdiri, Karim Kriaa, Md. Sharif Uddin, Md. Azizur Rahman, Mst. Nazifa Tasnim, Imed Boukhris, Ali Akremi, Jothi Ramalingam Rajabathar, Mohd Taukeer Khan
Abstract
This study presents an integrated computational approach combining Density Functional Theory (DFT), SCAPS-1D simulations, and machine learning to design and optimize lead-free Sr 3 PCl 3 perovskite-based solar cells. Although perovskite solar cells exhibit outstanding optoelectronic properties. However, the environmental and health hazards associated with lead-based materials present a major limitation. To address this, Sr 3 PCl 3 is investigated as a potential absorber material. DFT calculations reveal that Sr 3 PCl 3 possesses a direct bandgap of 1.641 eV, high absorption coefficients, and excellent stability, making it a promising candidate for photovoltaic applications. Device performance was analyzed using SCAPS-1D, examining various electron transport layers (ETLs), including WS 2 , CdS, SnS 2 , and ZnS. Optimization of absorber thickness and defect density was performed to enhance efficiency. Among the tested configurations, the structure employing SnS 2 as the ETL achieved the highest power conversion efficiency (PCE) of 18.58 %. Other configurations using WS 2 , CdS, and ZnS exhibited PCEs of 17.70 %, 18.35 %, and 14.15 %, respectively. To further accelerate device optimization, machine learning models—specifically Ridge regression and CatBoost—were trained on 2187 SCAPS-1D simulation results. These models accurately predicted solar cell performance based on key parameters such as absorber thickness, defect density, and ETL characteristics. To enhance interpretability, techniques such as heatmaps and SHAP (SHapley Additive exPlanations) analysis were utilized to examine the influence of key parameters on device efficiency. This comprehensive framework, integrating first-principles calculations, numerical simulations, and machine learning, provides valuable insights into the development of stable, high-efficiency, lead-free perovskite solar cells. The findings underscore the potential of Sr 3 PCl 3 as an environmentally friendly absorber material, advancing its prospects for next-generation optoelectronic applications.