Designing and screening novel electrocatalysts, understanding
electrocatalytic
mechanisms at an atomic level, and uncovering scientific insights
lie at the center of the development of electrocatalysis. Despite
certain success in experiments and computations, it is still difficult
to achieve the above objectives due to the complexity of electrocatalytic
systems and the vastness of the chemical space for candidate electrocatalysts.
With the advantage of machine learning (ML) and increasing interest
in electrocatalysis for energy conversion and storage, data-driven
scientific research motivated by artificial intelligence (AI) has
provided new opportunities to discover promising electrocatalysts,
investigate dynamic reaction processes, and extract knowledge from
huge data. In this Perspective, we summarize the recent applications
of ML in electrocatalysis, including the screening of electrocatalysts
and simulation of electrocatalytic processes. Furthermore, interpretable
machine learning methods for electrocatalysis are discussed to accelerate
knowledge generation. Finally, the blueprint of machine learning is
envisaged for future development of electrocatalysis.