Object-detecting deep learning for mechanism discernment in multi-redox cyclic voltammograms

In electrochemical analysis, mechanism assignment is fundamental to understanding the chemistry of a system. The detection and classification of electrochemical mechanisms in cyclic voltammetry set the foundation for subsequent quantitative evaluation and practical application, but are often based on relatively subjective visual analyses. Deep-learning (DL) techniques provide an alternative, automated means that can support experimentalists in mechanism assignment. Herein, we present a custom architecture based on Faster R-CNN (Regional Convolutional Neural Network), dubbed as EchemNet, capable of assigning both voltage windows and mechanism classes to electrochemical events within multi-redox cyclic voltammograms. The developed technique detects over 96% of all electrochemical events in simulated testing data and shows a classification accuracy of up to 97.2% on redox events with 8 known mechanisms. Further, the overall inference F1 score, a combined measure of accuracy and sensitivity in statistical analysis, achieves 0.937, relaying high reliability for detecting and classifying all electrochemical events within complicated voltammograms. This newly developed DL model, the first of its kind, proves the feasibility of redox-event detection and electrochemical mechanism classification with minimal a priori knowledge. The DL model will augment human researchers’ productivity and constitute a critical component in a general-purpose autonomous electrochemistry laboratory