Mechanism-Guided and Data-Driven Design of COF/MOF Photocatalysts Toward H2O2 Production.

Hydrogen peroxide (H2O2) is a green oxidant and potential energy carrier, and the photosynthesis of H2O2 from water and oxygen offers a sustainable alternative to the conventional anthraquinone process. Due to the tunable skeletons, adjustable pore microenvironments, and designable active sites, covalent organic frameworks (COFs) and metal-organic frameworks (MOFs) have emerged as crystalline porous photocatalysts for H2O2 production. This review summarizes recent advances in COF/MOF photocatalysts for H2O2 production, focusing on density functional theory (DFT)-based mechanistic insights, descriptor construction, and data-driven catalyst discovery enabled by artificial intelligence (AI) and machine learning (ML). We first discuss how DFT calculations elucidate active sites, charge-transfer pathways, and reaction mechanisms by revealing electronic structures, excited-state charge distributions, adsorption behavior, and reaction free-energy profiles. We then highlight how DFT-derived descriptors can bridge mechanistic understanding with materials screening. Finally, we discuss how AI and ML approaches integrate experimental performance, reaction conditions, structural features, and theoretical descriptors to enable candidate screening and design-rule extraction. Overall, the integration of DFT calculations with AI/ML methods provides a promising route toward mechanism-guided and data-driven discovery of COF/MOF photocatalysts for solar-driven H2O2 production.