Molecular Dynamics Simulations of Non-equilibrium Systems

Djurabekova, Flyura; Nordlund, K.

doi:10.1007/978-3-319-50257-1_119-1

Cited by 3 publications

(2 citation statements)

References 139 publications

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“…Sputtering has been widely simulated using both the binary collision approximation (BCA) [21,22] and molecular dynamics (MD) [23][24][25] approaches. The most commonly used BCA codes simulate only amorphous material [26][27][28] and are hence not suitable to examine how crystal structure may affect sputtering.…”

Section: Introductionmentioning

confidence: 99%

Investigation of surface orientation dependent sputtering of Ag

Nilsson,

Choupanian,

Ronning

et al. 2023

J. Phys.: Condens. Matter

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Sputtering of metal surfaces can be both a beneficial phenomenon, for instance in the coating industry, or an undesired side-effect, for instant materials subjected to irradiation. While the average sputtering yields are well known in common metals, recent studies have shown that the yields can depend on the crystallographic orientation of the surface much stronger than commonly appreciated. In this study, we investigate by computational means, molecular dynamics, the sputtering of single crystalline Ag surfaces under various incoming energies. The results at low and high energy are compared to experimental results for single crystalline Ag nanocubes of different orientations. We observe strong differences between the sputtering yields of different surface directions and ion energies. We analyze the results in terms of the atom cluster size of the sputtered materials, and show that the cluster size distribution is a key factor to understand the correspondence between simulations and experiments. At low energies mainly single atoms are sputtered, whereas at higher energies the sputtered material is mainly in atom clusters.

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Section: Introductionmentioning

confidence: 99%

Investigation of surface orientation dependent sputtering of Ag

Nilsson,

Choupanian,

Ronning

et al. 2023

J. Phys.: Condens. Matter

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“…The ability of describing or predicting the macro-scale effects of the irradiation on the material, to a great extent, hinges on understanding the early-stage damage caused by collision cascades, known as "primary damage" [7,8]. The fact that our understanding of the effects of particle irradiation on materials has taken its shape progressively over the years is partially due to the inherent nature of the atomic collision cascades; that this process is extremely fast and far from equilibrium [9,10]. This feature makes the observational examination of primary damage at the time scale it forms nearly impossible and leaves the burden of its description on the other methods, computer simulations being the most promising method during the past decades [11].…”

Section: Introductionmentioning

confidence: 99%

Primary radiation damage in silicon from the viewpoint of a machine learning interatomic potential

Hamedani

Byggmästar

Djurabekova

et al. 2021

Phys. Rev. Materials

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Characterization of the primary damage is the starting point in describing and predicting the irradiation-induced damage in materials. So far, primary damage has been described by traditional interatomic potentials in molecular dynamics simulations. Here, we employ a Gaussian approximation machine-learning potential (GAP) to study the primary damage in silicon with close to ab initio precision level. We report detailed analysis of cascade simulations derived from our modified Si GAP, which has already shown its reliability for simulating radiation damage in silicon. Major differences in the picture of primary damage predicted by machine-learning potential compared to classical potentials are atomic mixing, defect state at the heat spike phase, defect clustering, and re-crystallization rate. Atomic mixing is higher in the GAP description by a factor of two. GAP shows considerably higher number of coordination defects at the heat spike phase and the number of displaced atoms is noticeably greater in GAP. Surviving defects are dominantly isolated defects and small clusters, rather than large clusters, in GAP's prediction. The pattern by which the cascades are evolving is also different in GAP, having more expanded form compared to the locally compact form with classical potentials. Moreover, recovery of the generated defects at the heat spike phase take places with higher efficiency in GAP. We also provide the attributes of the new defect cluster that we had introduced in our previous study. A cluster of four defects, in which a central vacancy is surrounded by three split interstitials, where the surrounding atoms are all 4-folded bonded. The cluster shows higher occurrence in simulations with the GAP potential. The formation energy of the defect is 5.57 eV and it remains stable up to 700 K, at least for 30 ps. The Arrhenius equation predicts the lifetime of the cluster to be 0.0725 µs at room temperature.

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Interatomic Potentials for Nuclear Materials

Devanathan

2018

Handbook of Materials Modeling

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Molecular Dynamics Simulations of Non-equilibrium Systems

Cited by 3 publications

References 139 publications

Investigation of surface orientation dependent sputtering of Ag

Investigation of surface orientation dependent sputtering of Ag

Primary radiation damage in silicon from the viewpoint of a machine learning interatomic potential

Interatomic Potentials for Nuclear Materials

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