The integration of theoretical calculations, machine learning, and experimentation offers a powerful approach to develop practical, selective, and stable electrocatalysts. By combining machine learning and DFT calculations, researchers can potentially reduce the time needed to perform screening while progressively increasing the fidelity of simulated results. In particular, machine learning holds the promise of improving the mapping reaction pathways, of finding materials that break traditional scaling relationships, and of modeling more complex materials behavior. However, it is important to keep in mind that electrocatalytic processes are extremely complex and cannot be entirely modeled. Local environmental conditions such as pH, dissolved oxygen/hydrogen concentrations, and temperature at the electrode surface could deviate significantly from the bulk conditions at applied potentials , and it is challenging to measure these parameters and model them over extended time scales through computations. Nevertheless, historical successes and new developments in ML offer the possibility of continued extensions of what is possible in virtual catalyst design.