  The structure of defect clusters formed in a displacement cascade plays a
significant role in the micro-structural evolution during irradiation.
Molecular dynamics simulations have been widely used to study collision
cascades and subsequent clustering of defects. We present a novel method to
pattern match and classify defect clusters. A cluster is characterized by the
geometrical and topological histograms of its angles and distances which can
then be used as similarity metrics. The technique is demonstrated by matching
similar clusters for different cluster shapes like ring, crowdions etc. in a
database of cascade damage configurations in Fe and W at different energies. We
further use graph based dimensionality reduction techniques and unsupervised
machine learning on the features of all the clusters present in the database to
find classes of clusters. The classification successfully separates out many
already known categories of clusters such as crowdions, planar crowdion pairs,
rings and perpendicular crowdions. The dimensionality and size of different
classes provides a broad categorization of classes. The distribution of
different classes of shapes among cascades of different elements and energies
shows the exclusivity of shapes to elements and energies. We discuss the key
points and computational efficiency of the algorithms along with the various
prominent results of their application. We discuss the motivation for using
machine learning and statistics for the problems and compare different
techniques. The algorithms along with the supporting analysis and
visualizations give an unsupervised approach for classification and study of
defect clusters in cascades. The distribution of cluster shapes and structures
along with the shape properties like diffusivity, stability, etc. can be used
as input to higher scale models in a multi-scale radiation damage study.
