Deep generative learning for exploration in large electrochemical impedance dataset

The fields of energy storage, photocatalysis, and sensors have undergone substantial technological advancements, which have led to the generation of vast amounts of data on electrochemical impedance (EIS). The interpretation of large amounts of EIS data is a challenging task since the analysis of EIS data requires multiple steps to get a suitable equivalent circuit. Recently, some progress has been made in the machine learning (ML) model for EIS classification. However, most of the ML models are performed as a “black box” model, which provides only the classification result and lacks physical descriptor representation. Here, we apply variational autoencoders (VAE) to EIS data analysis, which includes classification, parameter prediction, and the visualization of physical descriptors. The VAE model performed well in the classification task, with an accuracy of 82.0%–92.4%. In the prediction task, VAE shows a high R-squared value on the Randles circuit. Additionally, the VAE model can map physical descriptors to the latent space, allowing the latent space to transform into a property space, which plays an important role in the optimization and exploration of novel materials research.