基于电化学阻抗谱的等效电路模型分类的机器学习基准
Machine Learning Benchmarks for the Classification of Equivalent Circuit Models from Electrochemical Impedance Spectra
AI总结：
本文针对电化学阻抗谱中等效电路模型的自动识别问题，基于QuantumScape提供的9,300个阻抗谱数据，比较了梯度提升树、随机森林和卷积神经网络等机器学习方法，并公开数据和代码作为后续研究基准。
摘要：
对电化学系统的电化学阻抗谱（EIS）数据进行分析，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。对于小型数据集，这一过程可以手动完成；然而，对于具有广泛EIS响应范围的大型数据集，手动定义合适的ECM是不可行的。自动识别ECM将大幅加速大规模EIS数据集的分析。我们展示了机器学习方法，用于对QuantumScape为BatteryDEV黑客马拉松提供的9,300个阻抗谱进行ECM分类。表现最佳的方法是梯度提升树模型，该模型利用一个库自动生成特征，其次是使用原始光谱数据的随机森林模型。本文还提出了一种使用Nyquist表示的二值图像的卷积神经网络作为替代方案，尽管其准确率较低。我们公开了数据并开源了相关代码。本文描述的方法可作为进一步研究的基准。一个关键且尚未解决的挑战是标签的可识别性，这一点通过模型性能以及误分类光谱的比较得到了强调。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
