Graph theory based approach to characterize self interstitial defect morphology

Bhardwaj, Utkarsh; Sand, Andrea E.; Warrier, M.

doi:10.1016/j.commatsci.2021.110474

Cited by 10 publications

(19 citation statements)

References 33 publications

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“…The database of 100 edge dislocations contains 34 defects found in a database of 230 collision cascades simulated with the three different potentials at energies ranging from 5 keV to 200 keV. The distribution of different morphologies and defect size distribution for each morphology for a subset of the database has been earlier shown in [1,6]. All the three potentials predict damage containing 100 dislocations, with almost all the defects having a size between 4 and 50 (the DND-BN potential has an outlier defect of size 147).…”

Section: Resultsmentioning

confidence: 99%

“…We track morphological changes using the SaVi algorithm [6] and use CSaransh [18] to visualize the defects. In addition to identifying when the morphological transition occurs, SaVi also outputs various parameters such as the number of dumbbells/crowdions in specific orientations at each step and the number of neighbors of each dumbbell/crowdion.…”

Section: Analysis Of MD Simulationmentioning

confidence: 99%

“…The 111 dumbbells and their clusters forming 111 dislocations are the most stable self-interstitial atom (SIA) defect morphologies in W. These constitute the majority of defects formed in W collision cascades [1]. In addition to the 111 dislocations, formation of 100 dislocations on high energy irradiation of W has been reported in experiments [2,3,4] as well as in MD simulations of collision cascades [5,6]. The 100 dislocations have been found in simulations carried out with various widely used inter-atomic potentials [1,7].…”

Section: Introductionmentioning

confidence: 99%

“…However, different potentials show different numbers and sizes of these defects [1]. In MD simulations, the 100 dislocations are observed to have non-100 dumbbells, and crowdions at the fringes of the defect [6].…”

Section: Introductionmentioning

confidence: 99%

“…To understand the reason behind irregularities, we analyze the internal configuration of defects using the SaVi [6] algorithm. The 100 dislocation defects are known to be composed of a bundle of 100 dumbbells surrounded by non-100 dumbbells on the fringes [6]. Two same size defects can have different number of 100 dumbbells.…”

Section: Introductionmentioning

confidence: 99%

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Stability of <100> Dislocations formed in W Collision Cascades

Bhardwaj,

Sand,

Warrier

2022

Preprint

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Experiments and simulations both have verified the presence of 100 dislocations in irradiated W. It is essential to know the properties and behavior of these defects to study the evolution of microstructures at higher scales. We study the thermal stability and transition mechanism of various 100 dislocations formed in a molecular dynamics (MD) database of 230 collision cascades using three different interatomic potentials. The energy of transition of 100 dislocations to more stable 111 dislocations is calculated for various defects that transition within the 100 nanosecond time scale readily accessible to MD.The stability of 100 dislocations increases with size, but the trend is not strict.The reasons for irregularities are the aspects of internal configuration such as (i) the arrangement of 100 directed crowdions within the defect, (ii) the presence and arrangement of non-100 crowdions on the fringes of the defect. We show the typical pathways of transitions and discuss the sources of instability in the defect configurations. We also discuss the similarities and differences in stability found across different interatomic potentials. Understanding transition mechanisms and internal morphology gives insights into the stability of 100 dislocations, useful in higher scale models such as Kinetic Monte Carlo (KMC).

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Section: Resultsmentioning

confidence: 99%

Section: Analysis Of MD Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Stability of <100> Dislocations formed in W Collision Cascades

Bhardwaj,

Sand,

Warrier

2022

Preprint

Self Cite

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Classification of clusters in collision cascades

Bhardwaj

Sand

Warrier

2020

Computational Materials Science

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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...

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