Machine learning driven performance enhancement of perovskite solar cells with CNT as both hole transport layer and back contact

Abstract

We propose a machine learning-based approach to enhance the performance of perovskite solar cells (PSCs) using carbon nanotubes as both hole transport layer (HTL) and back contact. Carbon-based PSCs offer low fabrication costs, long-term mechanical stability, high charge transport, and broad wavelength transparency. Our study estimates and enhances power conversion efficiency (PCE) of carbon-based PSCs through machine learning. PSC with single-walled carbon nanotube (SWCNT)-based HTL and back contact is investigated in simulations using SCAPS-1D and achieved 14.24% PCE, closely aligning with experimental data. Additionally, we explore the impact of various easily tunable fabrication parameters, such as the band gap and electron affinity of SWCNT in HTL, HTL thickness, active layer thickness, electron transport layer (ETL) thickness, HTL dopant concentration, absorber defect density, and ETL dopant concentration, on PCE. We generate a dataset of 20,480 samples to train classical and neural network-based machine learning models to predict performance parameters of PSCs, including short-circuit current density (JSC), open-circuit voltage (VOC), fill factor (FF), and PCE (η). The current density vs. voltage (J-V) characteristics curve is well-fitted by the predicted JSC, VOC, and FF, as evidenced by an average root mean squared error (RMSE) of 0.03 and a goodness of fit (R2) value of 0.99. The fully connected artificial neural network performs best, with RMSE of 0.05% and R2 of 0.99998 for predicting PCE. Genetic algorithm is used to optimize PCE, yielding a maximum PCE of 20.92%. Furthermore, an analysis of feature dimension and dataset size on prediction performance guides future modeling approaches.