Understanding Metal-Organic Framework Nucleation from a Solution with Evolving Graphs.

Area of Science:

• Materials Science
• Chemical Engineering
• Computational Chemistry

Background:

• Understanding metal-organic framework (MOF) synthesis and scale-up is challenging due to complex building block interactions.
• MIL-101(Cr) serves as a model system to investigate early-stage MOF nucleation and assembly.
• Current knowledge gaps hinder predictable MOF production.

Purpose of the Study:

• To investigate the influence of synthesis conditions on MOF nucleation and cluster formation.
• To apply graph theory to analyze MOF self-assembly processes.
• To identify key molecular descriptors for controlling MOF synthesis.

Main Methods:

• Large-scale molecular dynamics simulations were employed to model MOF building unit assembly.
• The study analyzed the impact of solvent choice (water, DMF), ion presence (Na+, F-), and half-SBU isomer populations.
• MOF nuclei self-assembly was interpreted as the time evolution of an undirected graph.

Main Results:

• Solvent, ions, and half-SBU isomer ratios significantly affect cluster formation, size, and morphology.
• Pure solvents promote rapid formation of large clusters, while ions in water lead to smaller clusters and slower nucleation.
• Graph theory analysis revealed that descriptors like average coordination number and fractal dimension capture assembly diversity.

Conclusions:

• Graph theory provides a powerful framework for understanding complex MOF self-assembly processes.
• Key molecular descriptors, identifiable through simulations and experiments, can be used to control MOF synthesis.
• This work offers insights into tailoring MOF properties by manipulating synthesis conditions.