State-of-art ResNet models of 18 residual learning layers, i.e. ResNet-18, were trained and validated by simulated cyclic voltammograms {v, i(E, σ)}n. When n = 6 and σ = 0.3, satisfactory accuracy (>90%) was achieved among {v, i(E, σ)}n when more than 3,000 electrochemical systems were included for each mechanism type in the training set (Fig. ). After 1000 epochs of training to improve accuracy (Fig. ), a voting process that contains eight ResNet-18 models, designated as ResNet{v, i(E, σ)}n, achieved an overall accuracy of 98.5% for {v, i(E, σ)}n (n = 6, σ = 0.3) and generated a confusion matrix with nearly zero off-diagonal components (Fig. ). In comparison, alternative machine-learning algorithms including linear classification, the vanilla multilayer perceptron (MLP), the MLP using attention mechanism to aggregate the extracted features of each curve ("MLP & attention mechanism"), and the MLP sharing same parameters/weights on the first layer ("MLP & parameter sharing") only yielded lower accuracies of 88.7%, 89.6%, 92.3%, and 90.7%, respectively (Fig. to S2f). We also built a DL model under the same protocol without the v values as input, ResNet{i(E, σ)}n (n = 6, σ = 0.3), in which the model's inputs contain the n number of voltammograms but not the exact v values. Only an overall accuracy of 92.0% was achieved and the corresponding confusion matrix contains noticeable non-zero off-diagonal entries (Fig. ). Consistent with manual inspection, the exact values of v are critical in DL to fully differentiate electrochemical mechanisms.