Inversion of the Impedance Response Towards Physical Parameter Extraction Using Interpretable Machine Learning

Abstract

Interpreting the impedance response of perovskite solar cells (PSCs) is challenging due to the complex coupling of ionic and electronic motion. While drift-diffusion (DD) modelling is a reliable method, its mathematical complexity makes directly extracting physical parameters from experimental data infeasible. This work uses DD modelling to generate a large synthetic dataset of impedance spectra for a standard TiO2/MAPI/spiro configuration. This dataset trains machine learning (ML) models to predict recombination and ionic parameters from impedance measurements. A Gradient Boosting Regressor, using features from a generalized equivalent circuit, showed the best performance. Interpretative analysis indicates that open-circuit impedance experiments best probe recombination losses, while short-circuit conditions are more adequate for extracting ionic features like concentrations and mobilities. The trained ML models were tested on experimental spectra, confirming that the inferred physical parameters could reproduce the data. For the studied configuration, predicted ion concentrations were (1.3–3.3) × 1017 cm−3, ion mobilities were (5–7) × 10− 1 1 cm2 V− 1 s− 1 , and surface recombination velocities were 7–9 and 23–40 ms−1 . This approach provides insights into the physical information extractable from impedance measurements and paves the way for ML models to unambiguously derive efficiency-determining parameters for solar cells.