Identifying self-interstitials of bcc and fcc crystals in molecular dynamics

Bukkuru, S.; Bhardwaj, Utkarsh; Warrier, M.; Rao, A. Sreenivasa; Valsakumar, M. C.

doi:10.1016/j.jnucmat.2016.12.010

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…The machine learning method described in [26,27] that uses max-space clustering on offsets of atoms, has O(NlogN) time-complexity but probably has faster implementation than k-d tree, requires no lattice or any other information. However, it can only be used to identify interstitials and not the vacancies.…”

Section: Iterate Over the Array Of Atomic Coordinatesmentioning

confidence: 99%

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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Section: Iterate Over the Array Of Atomic Coordinatesmentioning

confidence: 99%

Classification of clusters in collision cascades

Bhardwaj

Sand

Warrier

2020

Computational Materials Science

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“…LAMMPS [1] is an open source software capable of massively parallel atomistic simulations. We use it widely in our radiation damage studies, not only to simulate colision cascades [17,18], but also to simulate defect diffusion [19,20]. It has several package extensions amongst which the two temperature model (TTM) by Duffy et.al., is also implemented.…”

Section: Introductionmentioning

confidence: 99%

Inclusion and validation of electronic stopping in the open source LAMMPS code

Hemani,

Majalee,

Bhardwaj

et al. 2020

Preprint

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Electronic stopping (ES) of energetic atoms is not taken care of by the interatomic potentials used in molecular dynamics (MD) simulations when simulating collision cascades. The Lindhard-Scharff (LS) formula for electronic stopping is therefore included as a drag term for energetic atoms in the open source large scale atomic molecular massively parallel simulator (LAMMPS) code. In order to validate the ES implementatin, MD simulations of collision cascades at primary knock-on atom (PKA) energies of 5, 10 and 20 keV are carried out in W and Fe in 100 random directions. The total ES losses from the MD simulations are compared with the ES losses predicted by the Norgett-Robinson-Torrence (NRT) model. It is seen that the root mean square deviation of ES losses from the MD implementation is around 10 % for both W and Fe compared to the NRT model. The effect of ES on the number of defects in collision cascades with primary knock-on atom (PKA) energies of several tens of keV to more than 100 keV is presented.

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