  The analysis of defects and defect dynamics in crystalline materials is
important for fundamental science and for a wide range of applied engineering.
With increasing system size the analysis of molecular-dynamics simulation data
becomes non-trivial. Here, we present a workflow for semi-automatic
identification and classification of defects in crystalline structures,
combining a new approach for defect description with several already existing
open-source software packages. Our approach addresses the key challenges posed
by the often relatively tiny volume fraction of the modified parts of the
sample, thermal motion and the presence of potentially unforeseen atomic
configurations (defect types) after irradiation. The local environment of any
atom is converted into a rotation-invariant descriptive vector ('fingerprint'),
which can be compared to known defect types and also yields a distance metric
suited for classification. Vectors which cannot be associated to known
structures indicate new types of defects. As proof-of-concept we apply our
method on an iron sample to analyze the defects caused by a collision cascade
induced by a 10 keV primary-knock-on-atom. The obtained results are in good
agreement with reported literature values.
