Classification of clusters in collision cascades

Abstract

The structure of defect clusters formed in a displacement cascade plays a significant role in the microstructural 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.The defects formed during the displacement cascades due to irradiation are the primary source of radiation damage [1,2,3,4,5,6,7]. The defects in metals with body-centered cubic structure are produced in the form of single point defects (interstitials and vacancies) or clusters of such defects. The point defects and glissile clusters diffuse after the cascade to either annihilate or form bigger defect clusters. The structural details of primary point defect clusters (formed as a direct consequence of the cascade) define the diffusion, recombination, thermal stability and their other characteristics [2,8,9,10] which in the long term determine the micro-structural changes in the material [11,12,13,14,15]. These properties have an affect on the results of higher scale models like Monte Carlo methods, rate theories etc. [13,14,7,16]. The glissile clusters can move and interact with other defects and grain boundaries whereas the sessile clusters can be nucleation centers for defect-growth. The interaction of these clusters with other defects will decide the micro-structural changes due to irradiation. Classification and taxonomy of all possible clusters in diff...

Modelling the primary damage in Fe and W: influence of the short-range interactions on the cascade properties: Part 2 – multivariate multiple linear regression analysis of displacement cascades

“…Our work thus shows that the methodology used until now by many groups to identify two defects as belonging to the same cluster, which relies only on the distance between the defects, is not sufficient to properly characterize the cluster. Indeed, beyond the number of point defects, the shape (loops, C15 and all kinds of imperfect structures) is also very important, raising the question of the classification of defects as shown for instance in [34,35]. Here, we describe in detail our methodology to be able to compare with the work reported in the literature, but the purpose here is not to propose a new methodology which would require a significant amount of statistics and which is out of the scope of the present study.…”

An estimate for thermal diffusivity in highly irradiated tungsten using Molecular Dynamics simulation

“…Existing methods for distinguishing athermal atoms from bulk crystal atoms include analysing bond angle distributions, common neighbour analysis and graphs of connected bonds [22,23]. Progress has also been made recently to detect athermal atoms based on Machine Learning [24].…”

Graph Theory Based Approach to Characterize Self Interstitial Defect Morphology

“…In an energetic collision cascade, defect clusters of self-interstitial atoms (SIAs) and vacancies are formed. Vacancy cluster formation is relatively rare in the MD cascade simulations and they show lesser morphological variety than those of SIA clusters [1]. The morphology of a SIA cluster is an important aspect for its diffusion profile, thermal stability and interaction with other defects which in turn decide the long-term radiation damage [2,3,4,5,6,7].…”