Controlling Polymorphism and Particle Size in Zr‐Based Metal‐Organic Frameworks Through Preformed Nodes and Machine‐Learning‐Assisted Synthesis

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Abstract
Framework topology and particle size jointly govern pore architecture, diffusion limitations, and accessible surface area, making their simultaneous control essential for functional metal–organic framework (MOF) design. Achieving reproducible control over both remains a central challenge in MOF chemistry, particularly for systems capable of crystallizing in multiple polymorphs. Here, we systematically explore the synthesis of two Zr‐based MOFs, NU‐1000 and NU‐901, which share the same Zr 6 ‐oxo cluster and pyrene‐tetrabenzoate linker but differ in topology. To remove variability arising from in situ precursor formation, we employed preformed Zr 6 ‐oxo clusters as well‐defined nodes. Building on this controlled starting point, multiple particle‐size regimes (<100 nm to ∼1 µm) were accessed through systematic variation of modulator identity and loading, co‐modulator content, linker equivalents, temperature, and dilution. Machine‐learning (ML) analysis quantitatively disentangles the multivariate influences governing phase selection and particle‐size evolution. This integrated approach enables predictive navigation of the synthesis parameter space, providing reproducible access to MOFs with defined size regimes and controlled topology. Together, these results establish a generalizable experimental‐computational workflow that transforms empirical MOF synthesis into a predictable, designable process, enabling rational control over polymorphism and growth in structurally adaptable framework materials.
Funding Information
  • Deutsche Forschungsgemeinschaft

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