Abstract

Artificial intelligence has greatly accelerated the design and screening of metal–organic materials, particularly for crystalline systems with well-defined topologies and increasingly standardized structural databases. However, this success has also created a structure-centric design paradigm that is less suitable for metal–organic systems whose functions are governed by process history, interfacial assembly, and dynamic coordination rather than by a single idealized lattice. This Perspective proposes that artificial intelligence (AI)-guided design of metal–organic materials should expand beyond crystalline metal–organic frameworks (MOFs) to encompass a broader structural continuum, ranging from long-range ordered frameworks to dynamic, non-periodic coordination networks. Metal–polyphenol networks (MPNs) are used here as an experimentally tractable example within a broader family of structurally dynamic metal–organic materials, as they arise from coordination interactions between metal ions and polyphenolic ligands, generally lack long-range crystallographic periodicity, and exhibit functions that are governed by interfacial assembly, environmental responsiveness, and pathway-dependent structural evolution. These features challenge conventional descriptor design and database-driven prediction, but also create opportunities for AI approaches that are process-aware, interface-sensitive, and function-oriented. By placing MOFs and MPNs within a unified framework of structural order, this Perspective outlines how machine learning, multimodal characterization, active learning, and closed-loop experimentation could expand metal–organic materials design from topology prediction toward dynamic network optimization.

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