Electrochemical Mechanistic Analysis from Cyclic Voltammograms Based on Deep Learning

For decades, employing cyclic voltammetry for mechanistic investigation has demanded manual inspection of voltammograms. Here, we report a deep-learning-based algorithm that automatically analyzes cyclic voltammograms and designates a probable electrochemical mechanism among five of the most common ones in homogeneous molecular electrochemistry. The reported algorithm will aid researchers' mechanistic analyses, utilize otherwise elusive features in voltammograms, and experimentally observe the gradual mechanism transitions encountered in electrochemistry. An automated voltammogram analysis will aid the analysis of complex electrochemical systems and promise autonomous high-throughput research in electrochemistry with minimal human interference.

Automated summary

This article introduces a deep-learning-based algorithm that automatically analyzes cyclic voltammograms and determines the probable electrochemical mechanism. The algorithm aids researchers in mechanistic analysis, allows observation of gradual mechanism transitions in electrochemistry, and enables analysis of complex electrochemical systems and high-throughput research with minimal human interference.