Data-driven strategies for decoding structure-reactivity relationships in porous electrocatalysis

Porous catalytic materials, including metal-organic frameworks (MOFs), covalent organic frameworks (COFs), zeolites, and porous carbons, provide structurally defined microenvironments for controlling reactivity and are increasingly being investigated in electrocatalysis. In electrochemical systems, potential-dependent adsorption energetics, electric double-layer structure, solvent effects, and mass transport within confined pores introduce additional layers of complexity beyond conventional heterogeneous catalysis. Decoding structure-reactivity relationships under such conditions therefore requires representation strategies that are explicitly aligned with reaction-relevant states. This review summarizes recent data-driven strategies used to interrogate porous catalysts, organized around three themes: (i) chemically informed, descriptor-based models that connect local structure to activity/selectivity/stability; (ii) graph-based representations that encode connectivity and topology to learn reactivity-relevant motifs; and (iii) multimodal and transferable learning approaches that integrate structural, spectroscopic and energetic information across material classes. Representative examples across MOFs/COFs, zeolites and porous carbons are discussed, with emphasis on studies that pair modelling with mechanistic reasoning and targeted experiments. Key bottlenecks remain, including the scarcity of reaction-resolved electrochemical datasets, limited treatment of dynamic restructuring under bias, and mismatches between computational descriptors and experimentally measurable observables. We conclude by outlining priorities for reaction-relevant descriptor design and model-experiment feedback loops to accelerate porous electrocatalyst development for sustainable chemical transformation.