We envision that machine-learning algorithms such as those of deep-learning (DL) are capable of aiding mechanism categorization in cyclic voltammetry. In electrochemistry, the kinetics of E and/or C steps formulate the set of partial differential equations (PDE) and boundary conditions that dictate the i-E characteristics recorded in the cyclic voltammograms under a collection of different v values ({v, i(E)}n, n, number of different v values) (Fig. ). Such a mathematically bijective function between electrochemical mechanisms ({Ei, Cj}) and the electrochemically accessible parameter space of the combined voltammograms {v, i(E)}n suggests that it is feasible to employ DL algorithms to designate discrete mechanisms from sufficiently sampled cyclic voltammograms with minimal ambiguity (Fig. ). Indeed, this bijective relationship enables numerical simulations based on finite-element methods, and recently by artificial neural networks, to be used as a tool to efficiently search the parameter space of cyclic voltammograms and fit kinetic parameters upon a mechanism determined a priori by manual inspections of voltammogram. Yet, electrochemical mechanistic investigations remain commonly trial-and-error because the determination of the aforementioned a priori mechanism before any quantitative studies still relies on manual inspection. We posit that a set of cyclic voltammograms simulated from finite-element methods based on preset mechanism designations will suffice in the first-order approximation for the establishment of a DL model that analyzes cyclic voltammograms and qualitatively categorizes mechanisms provided a large enough sampling of {v, i(E)}n. Research by Bond and coworkers tested the concept of DLbased automatic analysis of a single simulated voltammogram for a selection of 3 mechanism types with overall accuracies just below 90%. The exploration of this concept with experimental data, which commonly includes multiple voltammograms at different v values, has yet to be conducted for mechanistic studies. We advocate the broader use of DL-based analysis and hypothesize that the bijective relationship between {Ei, Cj} and {v, i(E)}n enables the establishment of DL algorithms that detect and utilize subtle voltammogram features, ones not commonly used as mechanistic discriminants by humans, and observe the evolution of "edge" cases when two mechanisms co-exist and/or one mechanism is transitioning into another one, challenging scenarios for manual analysis in cyclic voltammetry. The established algorithms can be continuously refined and improved from experimental data, potentially including data contributed from the electrochemistry community. The algorithms will find their use in analyzing complex mechanistic scenarios and addressing the current paucity of automatic, high-throughput mechanistic analysis in electrochemistry.