SCAPS Empowered Machine Learning Modelling of Perovskite Solar Cells: Predictive Design of Active Layer and Hole Transport Materials

Abstract

Recently, organic–inorganic perovskites have manifested great capacity to enhance the performance of photovoltaic systems, owing to their impressive optical and electronic properties. In this simulation survey, we employed the Solar Cell Capacitance Simulator (SCAPS-1D) to numerically analyze the effect of different hole transport layers (HTLs) (Spiro, CIS, and CsSnI3) and perovskite active layers (ALs) (FAPbI3, MAPbI3, and CsPbI3) on the solar cells’ performance with an assumed configuration of FTO/SnO2/AL/HTL/Au. The influence of layer thickness, doping density, and defect density was studied. Then, we trained a machine learning (ML) model to perform predictions on the performance metrics of the solar cells. According to the SCAPS results, CsSnI3 (as HTL) with a thickness of 220 nm, a defect density of 5 × 1017 cm−3, and a doping density of 5 × 1019 cm−3 yielded the highest power conversion efficiency (PCE) of 23.90%. In addition, a 530 nm-FAPbI3 AL with a bandgap energy of 1.51 eV and a defect density of 1014 cm−3 was more favorable than MAPbI3 (1.55 eV) and CsPbI3 (1.73 eV) to attain a PCE of >24%. ML predicted the performance matrices of the investigated solar cells with ~75% accuracy. Therefore, the FTO/SnO2/FAPbI3/CsSnI3/Au structure would be suitable for experimental studies to fabricate high-performance photovoltaic devices.

Keywords

• perovskite
• hole transport layer
• solar cell
• external quantum efficiency
• SCAPS-1D
• machine learning