Improving the design efficiency of electrocatalysts for nitrate reduction by data-driven machine learning insight

The electrocatalytic nitrate reduction reaction (NO₃⁻RR) offers a compelling route for the simultaneous removal of nitrate from contaminated water and convert nitrate into value-added ammonia products under mild conditions. To advance electrocatalysts discovery and design, data-driven machine learning (ML) strategies are reshaping traditional catalysts discovery workflows. This Perspective highlights the transformative advancement of ML data foundations from isolated NH₃ faradaic efficiency to multidimensional descriptor hierarchies tailored to NO₃⁻RR, incorporating catalyst features, product distributions, parasitic reactions, and microenvironmental parameters. It then presents ML-driven design workflows using supervised surrogate models to explore high-dimensional spaces of NO₃⁻RR catalyst architectures and reaction environments. Finally, the significance of emerging paradigms that move beyond black-box predictors toward interpretable, physics‑informed, and ultimately active learning closed-loop frameworks integrating ML with density functional theory (DFT), molecular dynamics (MD), microkinetics, and automated experimentation is underscored. Together, these developments point to ML-native electrocatalyst discovery pathways capable of accelerating the development of robust design rules and practical NO₃⁻RR technologies.