Exploring the use of machine learning for interpreting electrochemical impedance spectroscopy data: evaluation of the training dataset size
Exploring the use of machine learning for interpreting electrochemical impedance spectroscopy data: evaluation of the training dataset size
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DOI：
10.1016/j.corsci.2022.110119
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发表时间：
2022-01
影响因子：
8.3
通讯作者：
V. Bongiorno;S. Gibbon;E. Michailidou;M. Curioni
中科院分区：
文献类型：
--
作者：
V. Bongiorno;S. Gibbon;E. Michailidou;M. Curioni
电化学阻抗谱（EIS）的解释通常基于通过等效电路对腐蚀系统的响应进行建模。虽然有效，但该方法难以自动化，并且在工业环境中的应用有限。机器学习（ML）算法可以在训练过程后解决复杂的任务，这项工作探索了使用ML解释EIS数据的可能性。考虑了两种情况：分类，即识别哪个等效电路与EIS谱相关联，以及拟合，即估计等效电路的组件的数值。
Electrochemical impedance spectroscopy (EIS) interpretation is generally based on modelling the response of a corroding system by an equivalent circuit. Although effective, the approach is difficult to automate and uptake in an industrial context is limited. Machine Learning (ML) algorithms can solve complex tasks after a training process and this work explores the possibility of using ML to interpret EIS data. Two scenarios are considered: classification, i.e. identifying which equivalent circuit is associated to an EIS spectrum, and fitting, i.e. estimating the numeric values of the components of an equivalent circuit.