To apply such a classification we built a dictionary: we attempted to create the most complete dictionary for icosahedral, decahedral, and octahedral NPs' AEs by choosing the most different environments from various minimized Au NPs. To enrich our dictionary, together with the NPs that we effectively simulated in this work, we also included larger size NPs possessing a higher variety of AEs in their ideal state. We obtained a dictionary of 47 elements. To simplify its usage, we hierarchically classified its elements using the hierarchical clustering algorithms implemented in scipy . First of all, we used the Equation (1) for calculating the distance between each of the environments belonging to the dictionary. Then we created a binary tree that represents this classification by using the "complete" algorithm for hierarchical clustering, which at each step couples the closest elements in the set and assigns to the newly formed couple the largest distance from each remaining element of the set, and uses the new distance in the next steps until it has completed the classification. We represent this tree in the dendrogram in Figure , where we show that we have chosen to apply a cut at the distance of 0.08 [dSOAP]. This cut leads to the creation of 10 different groups of dictionary entries (that can be seen more clearly in Figure ) with similar geometrical characteristics, from the original 47 environments. During the MD simulations analysis, we assigned an environment to one of these 10 clusters in two steps. The first step is to classify it as one of the 47 elements of the original environment dictionary: we do this simply by assigning it to the closest element of the dictionary in terms of the SOAP distance (using Equation ( )). The second step is to classify our analyzed environment by assigning it to the cluster to which its closest reference belongs.