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. Here, we showcase machine learning methods developed during the BatteryDEV hackathon to classify the ECMs of 9,300 EIS measurements provided by QuantumScape. 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 that the labels contain uncertainty and human bias, underlined by the performance of the trained models.

翻译：对电子化学系统的电化学阻碍光谱学(EIS)数据的分析往往包括利用专家知识界定等效电路模型(ECM),然后优化模型参数,以解析各种抗力、电能、感应或扩散反应;对于小型数据集,这一程序可以人工操作;然而,为广泛的数据组以广泛的EIS反应人工定义适当的ECM是不可行的;自动确定ECM将大大加快对大量EIS数据的分析。在这里,我们展示了在BatteryDEV黑客中开发的机器学习方法,以对QaantumScape提供的9 300 EIS测量ECM进行分类。最佳表现方式是使用图书馆自动生成特征的梯度加速树模型,然后使用原始光谱数据随机森林模型。使用Nyquist表示的布利安图像的革命性神经网络作为一种替代方法,尽管其精确度较低。我们公布了数据和公开源相关代码。这一工具中描述的、经过培训的不确定性模型可以作为人类绩效基准。