Artificial Intelligence-Guided Discovery of Covalent Organic Frameworks for Next-Generation Polyfluoroalkyl Substances Removal.

The pervasive threat of per- and polyfluoroalkyl substances (PFAS) necessitates water treatment solutions capable of selective, trace-level removal within chemically complex, high-flux environments. Covalent organic frameworks (COFs) offer an unparalleled platform for such precision remediation through rational pore-surface engineering. However, industrial translation is currently constrained by a multiobjective optimization bottleneck, where adsorption performance must be balanced against manufacturability, end-of-use strategy, and economic viability. In this perspective, we bridge the gap between idealized laboratory studies and industrial requirements, positioning COFs not as bulk sorbent replacements, but as high-fidelity selective polishing layers within hybrid treatment trains. We argue that artificial intelligence (AI) and data-driven methodologies provide a critical framework for navigating this complex landscape, serving as an integrative layer to accelerate candidate screening, refine structure-performance descriptors through characterization-informed feedback, and enable process-aware optimization. Finally, we highlight the current absence of AI-guided discovery strategies explicitly tailored for COF-enabled PFAS removal in realistic aqueous matrices and propose a pragmatic roadmap toward interoperable data sets and closed-loop workflows linking molecular design to manufacturable architectures and real-water performance.