The electrochemical interface, where the adsorption of
reactants
and electrocatalytic reactions take place, has long been a focus of
attention. Some of the important processes on it tend to possess relatively
slow kinetic characteristics, which are usually beyond the scope of ab initio molecular dynamics. The newly emerging technique,
machine learning methods, provides an alternative approach to achieve
thousands of atoms and nanosecond time scale while ensuring precision
and efficiency. In this Perspective, we summarize in detail the recent
progress and achievements made by the introduction of machine learning
to simulate electrochemical interfaces, and focus on the limitations
of current machine learning models, such as accurate descriptions
of long-range electrostatic interactions and the kinetics of the electrochemical
reactions occurring at the interface. Finally, we further point out
the future directions for machine learning to expand in the field
of electrochemical interfaces.