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pro vyhledávání: '"van der Giessen, Erik"'
Machine learning interatomic potentials (ML-IAPs) enable quantum-accurate, classical molecular dynamics simulations of large systems, beyond reach of density functional theory (DFT). Yet, their efficiency and ability to predict systems larger than DF
Externí odkaz:
http://arxiv.org/abs/2307.10072
The prediction of atomistic fracture mechanisms in body-centred cubic (bcc) iron is essential for understanding its semi-brittle nature. Existing atomistic simulations of the crack-tip deformation mechanisms under mode-I loading based on classical in
Externí odkaz:
http://arxiv.org/abs/2208.05912
Publikováno v:
In Biophysical Journal 15 October 2024 123(20):3533-3539
Publikováno v:
In Acta Materialia 15 May 2024 270
Publikováno v:
In Biophysical Journal November 2024
The yield surface in crystal plasticity can be approached from various directions during mechanical loading. We consider the competition between nanoindentation and tensile loading towards plastic yielding. For this purpose, we develop a two-dimensio
Externí odkaz:
http://arxiv.org/abs/1707.03915
Mechanical deformation of nanopillars displays features that are distinctly different from the bulk behavior of single crystals: Yield strength increases with decreasing size and plastic deformation comes together with strain bursts or/and stress dro
Externí odkaz:
http://arxiv.org/abs/1511.04613
Akademický článek
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Autor:
Van der Giessen, Erik
Publikováno v:
In European Journal of Mechanics / A Solids May-June 2019 75:277-283
Autor:
Jafarinia, Hamidreza1 (AUTHOR), Van der Giessen, Erik1 (AUTHOR), Onck, Patrick R.1 (AUTHOR) p.r.onck@rug.nl
Publikováno v:
Scientific Reports. 12/9/2022, Vol. 12 Issue 1, p1-11. 11p.