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：电化学系统中电化学阻抗谱（EIS）数据的分析通常涉及利用专家知识定义等效电路模型（ECM），随后优化模型参数以解耦各类电阻、电容、电感或扩散响应。对于小规模数据集，该流程可手动完成；然而，面对包含广泛EIS响应的大规模数据集，手动定义合适的ECM并不可行。自动识别ECM将显著加速大量EIS数据的分析进程。本文展示了在BatteryDEV黑客马拉松期间开发的机器学习方法，用于分类QuantumScape提供的9300组EIS测量数据的ECM。最优方法采用基于梯度提升树模型，利用特征自动生成库提取特征，其次为基于原始谱数据的随机森林模型。作为替代方案，我们提出基于奈奎斯特图布尔图像的卷积神经网络，尽管其准确率较低。我们公开了数据及相应开源代码。本文所述方法可为后续研究提供基准。当前关键挑战在于，训练模型的表现揭示了标签数据存在不确定性及人为偏差。