Where M is the cluster mass, and R "#$ its mass-weighted radius of gyration. In single structures we consider all atoms within a spherical volume extending from the center of mass of a cluster. For all atoms included into spherical volumes of increasing radii, we calculate the total mass and the mass-weighted radius of gyration. The fractal dimension is then calculated as the angular coefficient of the linear function obtained by fitting logjR "#$ k against log(M). This is repeated for all clusters consisting of at least three half-SBUs. We consider spherical shells ranging from the minimum distance between centers of half-SBUs in the cluster increased by 1 Å, to the maximum distance increased by 5 Å. This allows to have enough atoms to calculate a meaningful radius of gyration for all volumes, with the largest radius containing the entire cluster. A similar analysis is applied to clusters that spontaneously emerge during simulation. We observe the growth of a fractal pattern by plotting the size of clusters against the corresponding radii of gyration. Then, we use the power-law relationship to calculate the fractal dimension of clusters formed along the simulation trajectory. In this case, however, we substitute the total mass M with the number of half-SBUs in the cluster. The non-mass-weighed Rgyr of the cluster is computed from the positions of the centers of mass of the half-SBUs. Diffraction patterns. We calculate the X-Ray powder diffraction (XRD) patterns of clusters emerging throughout the simulation using PyMatGen . The wavelength of the Z-Ray source used is equal to Cuk-a radiation, l=1.54184 Å. We consider angles that differ less than 10 -5 radians to have the same intensity. We scale intensities so that the unity is the maximum value and scaled intensities less than 10 -5 were considered to be negligible. Hydrogen atoms are not present in the calculation to be consistent with the pattern of the experimental crystal structure. This analysis is performed for the five largest clusters at every ns of the simulation. Consequently, distance metrics can be used to evaluate the similarity of each structure with the reference . In this effort, we used various distance metrics such as the Euclidean, Hellinger, Cosine, City-Block (Manhattan), c 2 and Canberra distances . Similarity is calculated as the difference of the distance, normalized by its maximum value in all simulations, from the unity . Zero distance means highly similar to MIL-101(Cr) , while distance equal to the unity means patterns are not similar to MIL-101(Cr) . Effect of ions and solvent. Ions (Na + , F -) are added at concentrations of 0.035M in water and 0.075M in DMF. We chose small concentrations as it was suggested that this is the optimal balance between crystallinity and salt precipitation . Furthermore, a different solvent (DMF) can significantly affect the energetics of conformational transformations of the solute . In this context, we can assess the effect of guest molecules on the collective assembly of half-SBUs. Finally, there is experimental evidence that studying the solution in the early stages of assembly can significantly improve our understanding of the mechanism of MOF synthesis . Calculation of rates. We calculate nucleation and growth rate of cluster formation during self-assembly following the approach discussed by Yuhara et al. . In more detail, we calculate rates per unit volume, J. These are computed directly from unbiased simulations by estimating the partial derivative of the number of clusters formed, N(t), with respect to simulation time, t, and then normalizing it by the volume of the simulation box V: