Tracking Self-Assembly. The first tool presented is a method to track soft matter self-assembly. The Spatial Clustering Protocol (SCP) algorithm provides a fast way to label molecules based on the cluster or aggregate in which they reside during a self-assembly process. We make use of simple graph theory to represent molecules as a graph, where each molecule is a node and if the distance between specified atoms of any two given molecules is less than the defined cutoff, an edge is added between these two nodes in the graph. In this representation, clusters are rapidly identifiable as connected subgraphs. This makes this analysis suitably fast, such that the dynamical self-assembly process can be quickly rationalized over an entire trajectory. The algorithms returns a pandas dataframe which contains the molecule resids for each cluster and the cluster size at each time step. More details about the application of graph theory to investigating self-assembly can be found in the Electronic Supplementary Information (ESI).