Machine-learning code was implemented on Jupyter notebooks using Python3 code. PyTorch machine learning frameworks were used to implement the various neural networks discussed in this work. The DL algorithms are trained to take either the first or second cycles of voltammograms for the same electrochemical system at various numbers of different scan rates ({v, i(E, σ)}n, n = 1 to 6, σ = 0.0 to 1.0) and yields the vector y = {y1, y2, y3, y4, y5}. Because different starting potentials of voltammograms create additional variations for the first cycle of the voltammograms in both simulated and experimental scenarios, algorithms trained by the second cycles of voltammograms are used for the results reported here. Graphs were generated using the