Insights into the primary radiation damage of silicon by a machine learning interatomic potential

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http://hdl.handle.net/10138/317313

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Hamedani , A , Byggmästar , J , Djurabekova , F , Alahyarizadeh , G , Ghaderi , R , Minuchehr , A & Nordlund , K 2020 , ' Insights into the primary radiation damage of silicon by a machine learning interatomic potential ' , Materials Research Letters , vol. 8 , no. 10 , pp. 364-372 . https://doi.org/10.1080/21663831.2020.1771451

Title: Insights into the primary radiation damage of silicon by a machine learning interatomic potential
Author: Hamedani, A.; Byggmästar, J.; Djurabekova, F.; Alahyarizadeh, G.; Ghaderi, R.; Minuchehr, A.; Nordlund, K.
Contributor organization: Department of Physics
Materials Physics
Helsinki Institute of Physics
Date: 2020-10-02
Language: eng
Number of pages: 9
Belongs to series: Materials Research Letters
ISSN: 2166-3831
DOI: https://doi.org/10.1080/21663831.2020.1771451
URI: http://hdl.handle.net/10138/317313
Abstract: We develop a silicon Gaussian approximation machine learning potential suitable for radiation effects, and use it for the first ab initio simulation of primary damage and evolution of collision cascades. The model reliability is confirmed by good reproduction of experimentally measured threshold displacement energies and sputtering yields. We find that clustering and recrystallization of radiation-induced defects, propagation pattern of cascades, and coordination defects in the heat spike phase show striking differences to the widely used analytical potentials. The results reveal that small defect clusters are predominant and show new defect structures such as a vacancy surrounded by three interstitials. Impact statement Quantum-mechanical level of accuracy in simulation of primary damage was achieved by a silicon machine learning potential. The results show quantitative and qualitative differences from the damage predicted by any previous models.
Subject: Atomistic simulations
machine learning interatomic potential
primary radiation damage
silicon
THRESHOLD DISPLACEMENT ENERGIES
COLLISION CASCADES
SPUTTERING YIELDS
SIMULATIONS
DEFECTS
PHASE
APPROXIMATION
IRRADIATION
DEPENDENCE
METALS
114 Physical sciences
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion


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