We conclude that less than a third of molecular descriptors suffice to account for 90% of the variance of the dataset. Consequently, we successfully identify nucleation descriptors that capture the evolution of both local and extended features. The 1 st principal component is correlated with the cosine similarity that can be calculated from powder diffraction patterns, and measures long-range order. The latter can be monitored by time-resolved diffraction. The 2 nd principal component is strongly correlated with the local structural environment of the half-SBU, a variable that is accessible through X-Ray absorption and scattering methods. The 3 rd principal component characterizes the rate of cluster growth that can be experimentally measured. Ultimately, these descriptors can also form a basis for collective variables to simulate nucleation via enhanced sampling. This opens up the exciting possibility to monitor the evolution of these graphs both experimentally and computationally.