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

Hemani, H.; Majalee, A.; Bhardwaj, U.; Arya, A.; Nordlund, K.; Warrier, M.

doi:10.48550/arxiv.2005.11940

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…The simulations were carried out at an initial temperature of 300 Kelvin and evolved for 40 ps. Electronic stopping was applied to atoms with energies above 10 eV [18]. A PKA was selected at the center from the lattice atoms of the cubic simulation cell.…”

Section: Molecular Dynamics Simulation Datasetmentioning

confidence: 99%

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

Bhardwaj,

Warrier

2024

Modelling Simul. Mater. Sci. Eng.

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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.

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Section: Molecular Dynamics Simulation Datasetmentioning

confidence: 99%

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

Bhardwaj,

Warrier

2024

Modelling Simul. Mater. Sci. Eng.

View full text Add to dashboard Cite

show abstract

“…The simulations were carried out at an initial temperature of 300 Kelvin and evolved for 40ps. Electronic stopping was applied to atoms with energies above 10 eV [17]. A PKA was selected at the center from the lattice atoms of the cubic simulation cell.…”

Section: Molecular Dynamics Simulation Datasetmentioning

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

Cited by 2 publications

References 17 publications

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

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

Classification of clusters in collision cascades

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