  Measuring similarities/dissimilarities between atomic structures is important
for the exploration of potential energy landscapes. However, the cell vectors
together with the coordinates of the atoms, which are generally used to
describe periodic systems, are quantities not suitable as fingerprints to
distinguish structures. Based on a characterization of the local environment of
all atoms in a cell we introduce crystal fingerprints that can be calculated
easily and allow to define configurational distances between crystalline
structures that satisfy the mathematical properties of a metric. This distance
between two configurations is a measure of their similarity/dissimilarity and
it allows in particular to distinguish structures. The new method is an useful
tool within various energy landscape exploration schemes, such as minima
hopping, random search, swarm intelligence algorithms and high-throughput
screenings.
