电化学阻抗光谱学的等效电路推荐:不同机器学习算法的基准

IF 4.1 3区 化学 Q1 CHEMISTRY, ANALYTICAL
Fermín Sáez-Pardo, Juan José Giner-Sanz, Valentín Pérez-Herranz
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引用次数: 0

摘要

电化学阻抗光谱法(EIS)是一种广泛应用于电化学领域的技术,可以评估电化学系统的动力学参数。一般来说,EIS 光谱分析采用两种方法:物理数学方法和等效电路 (EEC) 方法。物理数学方法包括建立和求解支配系统的微分方程集。相比之下,等效电路方法是用等效电路模型来描述 EIS 数据,从而降低了数学复杂性,并使 EIS 技术的使用平民化。在 EEC 方法中,最关键的任务是选择一个物理上合理的 EEC 模型。为了帮助选择 EEC 模型,Digby D. Macdonald 教授提议创建 "电化学基因组计划",该计划将由一个大型数据库和一个人工智能(AI)组成,人工智能(AI)能够在给定 EIS 频谱的情况下提出可信的 EEC 模型。迄今为止,已有几项研究探讨了使用机器学习(ML)为给定的 EIS 频谱推荐 EEC 的想法。事实上,有几位作者已经针对这项任务对不同的 ML 算法进行了基准测试。然而,这些基准通常报告的是每种算法所达到的精确度的点估计,但并不评估其不确定性。为此,我们完善了一个已有的数据库,得到了一个新的数据库。然后,使用新数据库训练不同的 ML 算法,优化其超参数,并评估其准确性。最后,比较不同优化算法的性能。文献中的大多数其他基准都是对准确度进行点估算,而在这项工作中,则是对准确度分布进行估算。通过这种方法,可以更可靠、更稳健地比较不同算法的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Equivalent Electrical Circuit recommendation for Electrochemical Impedance Spectroscopy: A benchmark of different Machine Learning algorithms

Equivalent Electrical Circuit recommendation for Electrochemical Impedance Spectroscopy: A benchmark of different Machine Learning algorithms
Electrochemical Impedance Spectroscopy (EIS) is a technique widely used in the electrochemistry field that allows assessing the kinetic parameters of electrochemical systems. In general, EIS spectra are analyzed using 2 methodologies: the physical–mathematical methodology and the Equivalent Electrical Circuit (EEC) methodology. The physical–mathematical methodology consists in developing and solving the set of differential equations that govern the system. In contrast, the EEC methodology describes EIS data in terms of EEC models, decreasing the math complexity and democratizing the use of the EIS technique. In the EEC methodology, the most critical task is selecting a physically sound EEC model. To help with the EEC model selection, Prof. Digby D. Macdonald proposed creating the Electrochemistry’s Genome Project, which would consist of a large database and an Artificial Intelligence (AI) able to suggest plausible EEC models given an EIS spectrum. Until now, several works have explored the idea of using Machine Learning (ML) for recommending an EEC for a given EIS spectrum. Indeed, several authors have benchmarked different ML algorithms for this task. However, these benchmarks generally report point estimates of the accuracy achieved by each algorithm, but do not assess its uncertainty.
In this work, we benchmarked different ML EEC recommendation algorithms. To achieve this, we refined a preexistent database, obtaining a new database. Then, the new database was used to train different ML algorithms, optimize their hyperparameters, and assess their accuracy. Finally, the performance of the different optimized algorithms were compared. Whereas most of the other benchmarks available in literature work with point estimates of the accuracies, in this work, the accuracy distributions have been estimated. This methodology allows to compare the performance of the different algorithms in a much more reliable and robust way.
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来源期刊
CiteScore
7.80
自引率
6.70%
发文量
912
审稿时长
2.4 months
期刊介绍: The Journal of Electroanalytical Chemistry is the foremost international journal devoted to the interdisciplinary subject of electrochemistry in all its aspects, theoretical as well as applied. Electrochemistry is a wide ranging area that is in a state of continuous evolution. Rather than compiling a long list of topics covered by the Journal, the editors would like to draw particular attention to the key issues of novelty, topicality and quality. Papers should present new and interesting electrochemical science in a way that is accessible to the reader. The presentation and discussion should be at a level that is consistent with the international status of the Journal. Reports describing the application of well-established techniques to problems that are essentially technical will not be accepted. Similarly, papers that report observations but fail to provide adequate interpretation will be rejected by the Editors. Papers dealing with technical electrochemistry should be submitted to other specialist journals unless the authors can show that their work provides substantially new insights into electrochemical processes.
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