Machine‐Learning‐Driven Design of Dynamically Adaptive MOF Catalysts: Structural Evolution Mechanisms and Industrial Translation Pathways in Seawater Electrolysis

ABSTRACT Metal‐organic frameworks (MOFs) have emerged as promising electrocatalysts for seawater electrolysis, primarily owing to their structural tunability and high porosity. However, the practical application of MOFs is hindered by severe structural degradation under harsh seawater conditions, which includes chloride corrosion, microbial attack, pH fluctuations, and electric field‐induced reconstruction. This review systematically elucidates the dynamic evolution mechanisms of MOFs during seawater electrolysis, with a focus on corrosion‐driven degradation, electric field‐mediated valence oscillation, and ligand reconfiguration. We explore adaptive design strategies such as self‐healing, heterojunction engineering, defect control, coordination modulation, and spatial confinement that enhance the durability and activity of MOF‐based catalysts. Notably, the paper highlights the transformative role of machine learning in accelerating the discovery and optimization of corrosion‐resistant MOFs through high‐throughput screening, inverse design, and dynamic descriptor integration. By bridging fundamental mechanisms with industrial‐scale applications, this work provides a comprehensive roadmap for designing dynamically adaptive MOF catalysts, thereby paving the way for sustainable and efficient green hydrogen production from seawater.