On the classification and quantification of crystal defects after energetic bombardment by machine learned molecular dynamics simulations

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Domínguez-Gutiérrez , F J , Byggmästar , J , Nordlund , K , Djurabekova , F & von Toussaint , U 2020 , ' On the classification and quantification of crystal defects after energetic bombardment by machine learned molecular dynamics simulations ' , Nuclear Materials and Energy , vol. 22 , 100724 . https://doi.org/10.1016/j.nme.2019.100724

Title: On the classification and quantification of crystal defects after energetic bombardment by machine learned molecular dynamics simulations
Author: Domínguez-Gutiérrez, F.J.; Byggmästar, J.; Nordlund, K.; Djurabekova, F.; von Toussaint, U.
Contributor: University of Helsinki, Materials Physics
University of Helsinki, Faculty of Science
University of Helsinki, Department of Physics
Date: 2020-01
Language: eng
Number of pages: 8
Belongs to series: Nuclear Materials and Energy
ISSN: 2352-1791
URI: http://hdl.handle.net/10138/314635
Abstract: The analysis of the damage on plasma facing materials (PFM), due to their direct interaction with the plasma environment, is needed to build the next generation of nuclear fusion reactors. After systematic analyses of numerous materials over the last decades, tungsten has become the most promising candidate for a nuclear fusion reactor. In this work, we perform molecular dynamics (MD) simulations using a machine learned interatomic potential, based on the Gaussian Approximation Potential framework, to model better neutron bombardment mechanisms in pristine W lattices. The MD potential is trained to reproduce realistic short-range dynamics, the liquid phase, and the material recrystallization, which are important for collision cascades. The formation of point defects is quantified and classified by a descriptor vector (DV) based method, which is independent of the sample temperature and its constituents, requiring only modest computational resources. The locations of vacancies are calculated by the k-d-tree algorithm. The analysis of the damage in the W samples is compared to results obtained by Finnis–Sinclair and Tersoff–Ziegler–Biersack–Littmark potentials, at a sample temperature of 300 K and a primary knock-on atom (PKA) energy range of 0.5–10 keV, where a good agreement with the reported number of Frenkel pair is observed. Our results provide information about the advantages and limits of the machine learned MD simulations with respect to the standard ones. The formation of dumbbell and crowdion defects as a function of PKA energy were identified and distinguished by our DV method.
Subject: Descriptor vectors
Gaussian approximation potentials
MD simulations
Machine learning
Material damage analysis
TUNGSTEN
Tungsten
VACANCY
114 Physical sciences
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