Electrochemical Mechanistic Analysis from Cyclic Voltammograms Based on Deep Learning
Benjamin B. Hoar, Weitong Zhang, Shuangning Xu, Rana Deeba, Cyrille Costentin, Quanquan Gu, Chong Liu
Abstract
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.