Utilizing machine learning to enhance performance of thin-film solar cells based on Sb(S Se ): investigating the influence of material properties.

Antimony chalcogenides (Sb(S Se )) have drawn attention as a potential semiconducting substance for heterojunction photovoltaic (PV) devices due to the remarkable optoelectronic properties and wide range of bandgaps spanning from 1.1 to 1.7 eV. In this investigation, SCAPS-1D simulation software is employed to design an earth abundant, non-toxic, and cost-effective antimony sulfide-selenide (Sb(S,Se))-based thin-film solar cell (TFSC), where tungsten disulfide (WS) and cuprous oxide (CuO) are used as an electron transport layer (ETL) and hole transport layer (HTL), respectively. The PV performance parameters such as power conversion efficiency, open-circuit voltage ( ), short-circuit current ( ), and fill factor (FF) are assessed through adjustments in material properties including thickness, acceptor concentration, bulk defect density of the absorber, defect state of absorber/ETL and HTL/absorber interfaces, operating temperature, work function of the rear electrode, and cell resistances. This analysis aims to validate their collective impact on the overall efficiency of the designed Ni/CuO/Sb(S,Se)/WS/FTO/Al TFSC. The optimized physical parameters for the Sb(S,Se) TFSC lead to impressive PV outputs with an efficiency of 30.18%, of 1.02 V, of 33.65 mA cm, and FF of 87.59%. Furthermore, an artificial neural network (ANN) machine learning (ML) algorithm predicts the optimal PCE by considering five semiconductor parameters: absorber layer thickness, bandgap, electron affinity, electron mobility, and hole mobility. This model, which has an approximate correlation coefficient ( ) of 0.999, is able to predict the data with precision. This numerical analysis provides valuable data for the fabrication of an environmentally friendly, economical, and incredibly non-toxic efficient heterojunction TFSC.