Computational optimization of MASnI 3 perovskite solar cells using SCAPS-1D simulations and machine learning techniques

Value of 0.9999, along with a low mean squared error (MSE) of 0.0092 and mean absolute error (MAE) of 0.051. In addition, the individual influence of several input parameters on the device efficiency has been assessed using the feature importance method. Among the evaluated features, defect density emerged as the most influential parameter, indicating that it plays a significant role in defining the overall performance of the photovoltaic (PV) device.