- Title : Deep generative learning for exploration in large electrochemical impedance dataset
- Authors : Dulyawat Doonyapisut , Byeongkyu Kim , Jung Kyu Kim , Eunseok Lee , Chan-Hwa Chung*

 - Engineering Applications of Artificial Intelligence

 - DOI  : 10.1016/j.engappai.2023.107027


Abstract
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.