ABSTRACT Key electrocatalytic reactions such as HER, OER, ORR, CO 2 RR, and NRR offer promising routes for storing renewable energy as chemical fuels. However, their widespread application is constrained due to the lack of highly active and stable catalysts. Grain boundaries (GBs), with their undercoordinated sites, lattice strain, and excess free volume, present a promising strategy to overcome the activity–stability trade‐off. Yet, systematic reviews on GB engineering in electrocatalysis remain limited. This review provides a comprehensive framework, beginning with the fundamental formation mechanisms of GBs, integrating experimental and theoretical insights into the factors governing boundary evolution. Six synthesis methods—hydrothermal, electrodeposition, vapor phase, ball milling, molten salt, and laser ablation—are critically evaluated in terms of process parameters, resulting boundary characteristics, and practical trade‐offs. Their applications across five major electrocatalytic reactions are examined, correlating boundary features such as misorientation angle, type, and lattice strain with catalytic outcomes in activity, selectivity, and stability. We also explore the emerging role of machine learning in the rational design of GB architectures, including property prediction, phase discovery, and interpretable modeling. By integrating these aspects, this review establishes a unified framework for a rational design of high‐performance, boundary‐rich electrocatalysts for sustainable energy conversion.
Gao et al. (2026) studied this question.