Inclusion of Multiple Cycling of the Potential into Deep Neural Network Classification of Voltammetric Reaction Mechanisms

The use of Deep Neural Networks (DNNs) for the classification of electrochemical mechanisms using simulated voltammograms with one cycle of the potential for training has previously been reported. In this paper, it is shown how valuable additional patterns for mechanism distinction become available when a new DNN with advanced architecture is trained simultaneously on images obtained from three cycles of potential using tensor inputs. Significant improvement relative to the single cycle training in achieving correct classification of E, EC1st and EC2nd mechanisms (E = electron transfer step, C1st and C2nd are first and second order follow up chemical reactions, respectively) is demonstrated with noisy simulated data for conditions where all mechanisms are close to chemically reversible and hence difficult to distinguish even by an experienced electrochemist. Challenges anticipated in applying the new DNN to classification of experimental data are highlighted. Directions for future development are also discussed.