PAMM is a pattern recognition framework designed to identify molecular motifs based on their meta-stability. The core idea behind the PAMM scheme consists into partitioning the probability distribution function (PDF) of structures sampled from an atomistic simulation or collected from experiments. The input is simply a series of N (preferably high-dimensional) vectors representing local or global environments. This could be any function of the atomic coordinates, such as distances, angles or more sophisticated descriptors. Then one needs to perform a kernel density estimation (KDE) on a grid extracted using farthest point sampling (FPS), which has proven to be well suited for selecting the most widely spread set of landmarks from the initial set. Combining FPS with KDE allows for linear scaling with the number of samples and for the use of very large datasets, which is usually a bottleneck for most clustering algorithms. Extensive details on PAMM are available in the SI.