Establishing zone regions in cyclic voltammetry using unsupervised machine learning

Cyclic voltammetry is widely used to establish mechanistic details associated with electrode processes. Commonly, an experienced electrochemist manually seeks to identify key feature maps to create zones of parameters present in simulated cyclic voltammograms that mimic features observed experimentally over a range of scan rates and concentrations. In this study, as an alternative to this tedious approach, an automated K-means time series clustering algorithm is applied as a form of unsupervised machine learning to sort and classify voltammetric information. In order to establish advantages and limitations of the K-means clustering approach, extensive simulations of six commonly encountered electrochemical reaction mechanisms using a wide range of parameters have been undertaken to generate zone regions relevant to each example. The K-means time series clustering together with a dynamic time warping metric successfully sort the simulated data into logical groups based on the features present.