  Analysis of Electrochemical Impedance Spectroscopy (EIS) data for
electrochemical systems often consists of defining an Equivalent Circuit Model
(ECM) using expert knowledge and then optimizing the model parameters to
deconvolute various resistance, capacitive, inductive, or diffusion responses.
For small data sets, this procedure can be conducted manually; however, it is
not feasible to manually define a proper ECM for extensive data sets with a
wide range of EIS responses. Automatic identification of an ECM would
substantially accelerate the analysis of large sets of EIS data. We showcase
machine learning methods to classify the ECMs of 9,300 impedance spectra
provided by QuantumScape for the BatteryDEV hackathon. The best-performing
approach is a gradient-boosted tree model utilizing a library to automatically
generate features, followed by a random forest model using the raw spectral
data. A convolutional neural network using boolean images of Nyquist
representations is presented as an alternative, although it achieves a lower
accuracy. We publish the data and open source the associated code. The
approaches described in this article can serve as benchmarks for further
studies. A key remaining challenge is the identifiability of the labels,
underlined by the model performances and the comparison of misclassified
spectra.

Machine Learning;Materials Science