Identifying sub-cascades from the primary damage state of collision cascades

Abstract

The morphology of a collision cascade is an important aspect in understanding 
the formation of defects and their distribution. While the number of 
sub-cascades is an essential parameter to describe the cascade morphology, the 
methods to compute this parameter are limited. We present a method to compute 
the number of sub-cascades from the primary damage state of the collision 
cascade. Existing methods analyze peak damage state or the end of ballistic 
phase to compute the number of sub-cascades which is not always available in 
collision cascade databases. We use density based clustering algorithm from 
unsupervised machine learning domain to identify the sub-cascades from the 
primary damage state. To validate the results of our method we first carry out 
a parameter sensitivity study of the existing algorithms. The study shows that 
the results are sensitive to input parameters and the choice of the time-frame 
analyzed. On a database of 100 collision cascades in W, we show that the method 
we propose, which analyzes primary damage state to predict number of 
sub-cascades, is in good agreement with the existing method that works on the 
peak state. We also show that the number of sub-cascades found with different 
parameters can be used to classify and group together the cascades that have 
similar time-evolution and fragmentation. It is seen that the number of SIA and
vacancies, fraction of defects in clusters and volume of the cascade, decrease with
increase in the number of sub-cascades.