Rapid Parameter Estimation from Photoluminescence Decays of Halide Perovskite Thin Films
Extracting material parameters from experimental data is often challenging if no invertible analytical equation can be used to link the data with the quantities of interest. If the link between experiment and material parameters is given mathematically by a set of non-linear differential equations, these must be solved repeatedly during the traditional fitting procedure, resulting in long optimization times and limited insight into parameter uncertainty. Here, we present a parameter estimation workflow specifically aimed at transient photoluminescence measurements performed on lead-halide perovskite films. This workflow is accelerated using artificial neural networks for rapid comparison between experiment and simulation. An advantage of the method is the ability to rapidly scan multidimensional material parameter spaces and identify correlations between parameters that provide insights into the physics of non-radiative recombination in halide perovskites. Finally, we compare steady-state and transient photoluminescence and show how uncertainty in parameters such as the defect density can be reduced by including steady-state data in the parameter estimation workflow.