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.

翻译：电化学系统的电化学阻抗谱（EIS）数据分析通常包括利用专家知识定义等效电路模型（ECM），然后优化模型参数以解卷积各种电阻、电容、电感或扩散响应。对于小规模数据集，这一过程可以手动完成；但对于包含广泛EIS响应的大规模数据集而言，手动定义合适的ECM并不可行。自动识别ECM将大幅加速大量EIS数据集的分析过程。我们展示了针对QuantumScape为BatteryDEV黑客马拉松提供的9300条阻抗谱进行ECM分类的机器学习方法。性能最佳的方法是采用自动生成特征的梯度提升树模型，其次是基于原始谱数据的随机森林模型。作为替代方案，我们提出了使用奈奎斯特图布尔图像的卷积神经网络，尽管其准确率较低。我们公开发布了数据集及相关代码。本文所述方法可作为后续研究的基准。一个关键的剩余挑战是标签的可辨识性，这通过模型性能与错误分类谱图的对比得到印证。