In this work we present a systematic and elaborate approach for drift-diffusion modelling of perovskite solar cells. This approach enables the quantification of how much reliable, physically meaningful information can actually be extracted from experimental data, taking into account the spread in experimental data. It is aimed to go beyond just obtaining a single 'best-fit' result. We demonstrate its use by applying it to a use case with a state-of-the-art perovskite solar cell, where we studied the effect of chloride addition on the performance of the cells.

This dataset consists of the datafiles related to the use case with the state-of-the-art perovskite solar cell. It has the experimentally current density-voltage (J-V) curves for the three samples with varying amounts of chloride added, each measured under 5 light intensities. The datafile Input_simulations.txt contains the input/setup for the automated fitting of simulated to experimental data.