Searching and Ranking Similar Clusters of Polyhedra in Crystal Structures

Abstract

A graph-based method is described for searching and ranking clusters of polyhedra in large crystallographic databases. First, embeddings of topologically equivalent substructures are determined for a given target cluster based upon a graph representation of polyhedral networks. Then, an algorithm for solving the problem of absolute orientation is applied in order to rank topologically equivalent clusters according to their geometric similarity.