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

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 Zr₆-oxo cluster and pyrene-tetrabenzoate linker but differ in topology. To remove variability arising from in situ precursor formation, we employed preformed Zr₆-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.