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Most of the current understanding of structure-property relations at themolecular and the supramolecular scales can be formulated in terms of thestability of and the interactions between a limited number of recurringstructural motifs (e.g., H-bonds, coordination polyhedra, and protein secondarystructure). Here we demonstrate an algorithm to automatically recognize suchpatterns, based on the identification of local maxima in the probabilitydistributions observed in atomistic computer simulations, which is robust tothe dimensionality and the sparsity of the reference atomistic data. We firstdiscuss its main features, demonstrating some on artificial data sets, and thenshow how it can be applied to identify coordination environments inLennard-Jones clusters and to recognize secondary-structure patterns in thesimulation of an oligopeptide. To assess the applicability of this algorithmfor motifs that involve several interdependent degrees of freedom, we alsoemploy it to identify groups of conformers of the cluster and the polypeptide,considered in their entirety. The motifs identified by analyzing atomisticsimulations can be used to interpret and rationalize the stability and behaviorof the system at hand, and also as a tool to accelerate sampling, inassociation with biased molecular dynamics schemes.

Recognizing Local and Global Structural Motifs at the Atomic Scale