Decoding the unseen: unsupervised anomaly detection in metal–organic frameworks for discovery beyond the norm

, edge/node counts, degree dispersion) as the primary driver of anomalies, followed by window-limited geometry and linker-metal composition. We categorize results into three groups: (A) topologically unusual yet plausible candidates, (B) anomalies with chemically resolvable issues, and (C) likely structural artifacts. The full pipeline executes in under six minutes on standard CPUs and does not require 3D structure fitting or graph parsing. CHEM-AD generalizes to other porous materials, providing a scalable framework for discovery, database curation, and robust preprocessing in materials informatics.