Automatically identifying electrode reaction mechanisms using deep neural networks

At present, electrochemical mechanisms are most commonly identified subjectively based on the experience of the researcher. This subjectivity is reflected in bias to particular mechanisms as well as lack of quantifiable confidence in the chosen mechanism compared to potential alternative mechanisms. In this paper we demonstrate that a deep neural network trained to recognize dc cyclic voltammograms for three commonly encountered mechanisms provides correct classifications within 5 ms without the problem of subjectivity. To mimic experimental data, the impact of noise, uncompensated resistance, and dependence on scan rate, factors that are relevant to practical studies, has also been investigated. Outcomes with two experimental data sets are also presented.