We chose ResNet 14 , a widely used network architecture evolved from convolutional neural networks, to extract intrinsic features from high-dimensional data. The ResNet architecture utilizes skip connections within its convolutional layers for deeper networks for greater feature extraction. Such architecture is critical for training successes as it alleviates the problems of both vanishing and exploding gradients during the training process which mitigates the risk of training failure while maintaining the low overfitting risk inherent of convolutional architectures. The neural network is trained to take either the first or the second cycle of voltammograms for the same electrochemical system at various numbers of different scan rates ({v, i(E, σ)}n, n = 1 to 6) and yields the vector y = {y1, y2, y3, y4, y5} (Fig. , see Methods), in which each component in y is a surrogate of the electrochemical system's probability or fraction towards mechanisms of Er, ErCr, CrEr, ECE, and DISP1, respectively. The classification process is completed by designating the mechanism of the largest component in y as the most probable or most prominent one for the studied electrochemical system. Furthermore, the proposed model can estimate the "importance" of different parts of the voltammograms to the prediction (Fig. ), by visualizing the relative magnitudes of gradients of the logits on input data after feeding through the neural network. We hypothesize that such a visual guide of algorithm's "importance" will illustrate how DL models analyze cyclic voltammograms and offer a comparative study between manual inspection and the ResNet-based one.