Modular ensembled neural network approach to adjust and predict EIS parameters

Corrosion is one of the big problems in material science, and options to avoid and analyse it represent an active research area. Among the corrosion assessment methods and techniques, Electrochemical Impedance Spectroscopy (EIS) has an important role in determining and explaining the electrochemical mechanisms which occur in applications such as batteries, pipelines, structures, and so on. Its analysis is very specialised, and it can require a certain amount of time and sensibility to achieve high reliability. On the other hand, machine learning (ML) can represent a powerful tool to optimise how the data is analysed and processed. Recent works have brought ML to surface engineering, and the present work is aligned with it. This study presents a proof-of-concept machine learning workflow for the inversion of Electrochemical Impedance Spectroscopy (EIS) data, which specifically refers to the inverse mapping from synthetic EIS spectra to circuit parameters within a known-forward-model framework, using five predefined equivalent electrical circuits (EECs). The proposed approach employs a modular two-stage convolutional neural network (CNN) architecture (ML tandem): one establishes an initial guess (rough fit) and, sequentially, performs a fine-tuning fitting. To isolate methodological performance, the models are trained and evaluated on large, controlled synthetic datasets generated from the same forward EEC equations across five corrosion-relevant circuit topologies. Under these conditions, the architecture reveals a marked circuit-dependent difference in invertibility, which some EECs were identified accurately, whereas others exhibited systematic ambiguities due to overlapping spectral features within explored parameter ranges. Misclassification and inversion failures are also discussed. Overall, this work aims to evaluate the feasibility and limitations of a modular ML-based inversion strategy using controlled conditions and provides a transparent basis for future extensions to experimental EIS applications. • A brand-new ML architecture is proposed to analyse synthetic EIS data. • CNNs were configured in tandem to get an initial guess and fit the EIS data. • Model failure sources in classification and inversion are discussed.