Post-processing interstitialcy diffusion from molecular dynamics simulations

Bhardwaj, Utkarsh; Bukkuru, S.; Warrier, M.

doi:10.1016/j.jcp.2015.10.034

Cited by 8 publications

(12 citation statements)

References 19 publications

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“…In this method, a interstitial-vacancy pair is said to have been created if the interstitial is away from its lattice position by a value r disp . We have used an unsupervised clustering algorithm (called Max-space clustering) to identify the value of r disp such that the displaced interstitials could be separated from the natural vibrational amplitudes of the atoms [17,18]. This was done as follows:…”

Section: Large Scale Data Analysis: MD Studies Of Directional Effectsmentioning

confidence: 99%

“…Distribution of the vacancies as a function of distance from the origin of the PKA for energies 1-5 keV in Cu number of parent nodes, and mark a link to the parent node which yields minimum cost. Details of the graph-tree algorithm and various cost functions explored are presented elsewhere [17].…”

Section: Interstitialcy Diffusion From MDmentioning

confidence: 99%

“…We have carried out MD simulations to determine (i) r disp , (ii) the migration energy E m and (iii) the diffusion coefficient for interstitialcy diffusion [18,17]. A 10 × 10 × 10 unit cell simulation box of various fcc and bcc metallic crystals of interest with a randomly placed single interstitial of the same specie was first equilibrated using a NPT MD simulation at various temperatures below the melting point of the crystal.…”

Section: Interstitialcy Diffusion From MDmentioning

confidence: 99%

“…In order to obtain the diffusion co-efficient of the defects, MD simulations for interstitialcy diffusion were carried out [17,18]. In interstitialcy diffusion the interstitial atom is same as the host material atom and the interstitial displaces a lattice atom and the lattice atom becomes the new interstitial.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore the identity of the atom keeps changing. In order to identify the interstitial and create a random walk from which the diffusion can be parametrized, we use unsupervised learning [19] and the graph-tree data structure [17,20]. This is described in Section.3.…”

Section: Introductionmentioning

confidence: 99%

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Multi-scale Modeling of Radiation Damage: Large Scale Data Analysis

Warrier¹,

Bhardwaj²,

Bukkuru³

2016

J. Phys.: Conf. Ser.

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Abstract. Modification of materials in nuclear reactors due to neutron irradiation is a multiscale problem. These neutrons pass through materials creating several energetic primary knock-on atoms (PKA) which cause localized collision cascades creating damage tracks, defects (interstitials and vacancies) and defect clusters depending on the energy of the PKA. These defects diffuse and recombine throughout the whole duration of operation of the reactor, thereby changing the micro-structure of the material and its properties. It is therefore desirable to develop predictive computational tools to simulate the micro-structural changes of irradiated materials. In this paper we describe how statistical averages of the collision cascades from thousands of MD simulations are used to provide inputs to Kinetic Monte Carlo (KMC) simulations which can handle larger sizes, more defects and longer time durations. Use of unsupervised learning and graph optimization in handling and analyzing large scale MD data will be highlighted.

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Section: Large Scale Data Analysis: MD Studies Of Directional Effectsmentioning

confidence: 99%

Section: Interstitialcy Diffusion From MDmentioning

confidence: 99%

Section: Interstitialcy Diffusion From MDmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-scale Modeling of Radiation Damage: Large Scale Data Analysis

Warrier¹,

Bhardwaj²,

Bukkuru³

2016

J. Phys.: Conf. Ser.

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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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