However, these are appreciably lower than the ones in pure water; hence DMF decreases the fractal dimension related with self-assembly. Spectator ions in DMF further induce a slight decrease in the fractal dimension related with growth. Diffraction pattern. In this section, we assess structural similarity based on the diffraction patterns of clusters formed during the simulation and that of MIL-101(Cr) (reference structure). We perform this analysis both on clusters formed at the end of our simulations as well as the 5 largest clusters emerging every nanosecond during the whole simulation trajectory. Clusters formed out of MLA units are more similar to the reference based on cosine similarity. We note that the cosine similarity differentiates between the clusters emerging over time under various system compositions and it is widely used to compare spectra. Nevertheless, other similarity metrics have been assessed and they are provided in the SI, section VIII, p. 26-32. These do not show considerable differences between the various systems; hence they will not be discussed in this text. This is more pronounced in the case of pure water. Consequently, MLA promotes the crystallinity of clusters formed during assembly in pure water. Also, we see that in pure water some degree of crystallinity is observed for only the larger clusters identified in the simulation, while for a completely different system composition (MLA/B/C in DMF with ions), smaller clusters with some degree of crystallinity are formed. Principal Component Analysis. Descriptors of the nucleation process, extracted from simulations, can be projected on principal components, and help identify new collective variables (named PCs). The 1 st PC is calculated from pro-jecting the data to the 1 st principal component, i.e., the eigenvector with the largest eigenvalue, the 2 nd PC from the 2 nd principal component, i.e., corresponding to the 2 nd largest eigenvalue, etc. The nucleation rate and fractal dimension related to growth are correlated with both PCs. The average graph degree (tied to the local coordination environment) is mostly correlated with the 2 nd PC. Cosine similarity (a descriptor of crystallinity), cluster size, spherical radius, radius of gyration, assortativity coefficient, and transitivity are predominantly correlated with the 1 st PC. In summary, the 1 st PC describes long-range order as it is strongly correlated with the similarity between the XRD patterns of emerging clusters and MIL-101(Cr). The 2 nd PC describes the local coordination environment as it is strongly correlated with the average graph degree. At last, the 3 rd PC is strongly correlated with the growth rate. Systems projected on the PCs, correlation coefficients between properties and the PCs are presented in Figure . An explanation of how we performed this analysis and PCA at the very early stages of assembly are provided in the SI, section IX, p. 33-35.