AMINO is a method that can be used to cluster a large set of Order Parameters (OPs) and then obtain a representative one from each of the clusters. The aim is to remove non-significant OPs and reduce their redundancy, in order to efficiently select a Reaction Coordinate (RC) by employing the representative OPs found as an input for other methods (e.g. PCA, TICA, RAVE).
The required input consists in a large set of OPs obtained from either a short unbiased simulation or from a biased one appropriately reweighted. Then AMINO steps can be summarized as follows:
OPs are clustered
A representative OP is determined for each cluster
The appropriate number of clusters is determined
OPs are clustered using a Mutual Information based distance matrix with a modified KMeans clustering approach employing a dissimilarity matrix for centroid initialization. Finally, the proper number of clusters is determined using rate distortion theory as the driving principle.
Automatic mutual information noise omission (AMINO) was used in the following examples: