People | Locations | Statistics |
---|---|---|
Naji, M. |
| |
Motta, Antonella |
| |
Aletan, Dirar |
| |
Mohamed, Tarek |
| |
Ertürk, Emre |
| |
Taccardi, Nicola |
| |
Kononenko, Denys |
| |
Petrov, R. H. | Madrid |
|
Alshaaer, Mazen | Brussels |
|
Bih, L. |
| |
Casati, R. |
| |
Muller, Hermance |
| |
Kočí, Jan | Prague |
|
Šuljagić, Marija |
| |
Kalteremidou, Kalliopi-Artemi | Brussels |
|
Azam, Siraj |
| |
Ospanova, Alyiya |
| |
Blanpain, Bart |
| |
Ali, M. A. |
| |
Popa, V. |
| |
Rančić, M. |
| |
Ollier, Nadège |
| |
Azevedo, Nuno Monteiro |
| |
Landes, Michael |
| |
Rignanese, Gian-Marco |
|
Byggmästar, Jesper
University of Helsinki
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (16/16 displayed)
- 2024Understanding the RBS/c spectra of irradiated tungsten : A computational study
- 2024Understanding the RBS/c spectra of irradiated tungsten
- 2024Solubility of Hydrogen in a WMoTaNbV High-Entropy Alloycitations
- 2024Nanoindentation of tungsten : From interatomic potentials to dislocation plasticity mechanismscitations
- 2024Interatomic force fields for zirconium based on the embedded atom method and the tabulated Gaussian Approximation Potential
- 2023Nanoindentation of tungstencitations
- 2023Simple machine-learned interatomic potentials for complex alloyscitations
- 2023Self-ion irradiation of high purity ironcitations
- 2023Comprehensive structural changes in nanoscale-deformed silicon modelled with an integrated atomic potentialcitations
- 2022Simple machine-learned interatomic potentials for complex alloyscitations
- 2021Modeling refractory high-entropy alloys with efficient machine-learned interatomic potentialscitations
- 2021Modeling refractory high-entropy alloys with efficient machine-learned interatomic potentials : Defects and segregationcitations
- 2021Machine-learning interatomic potential for W-Mo alloyscitations
- 2020Insights into the primary radiation damage of silicon by a machine learning interatomic potentialcitations
- 2019Cascade overlap with vacancy-type defects in Fecitations
- 2018Defect structures and statistics in overlapping cascade damage in fusion-relevant bcc metalscitations
Places of action
Organizations | Location | People |
---|
article
Modeling refractory high-entropy alloys with efficient machine-learned interatomic potentials
Abstract
<p>We develop a fast and accurate machine-learned interatomic potential for the Mo-Nb-Ta-V-W quinary system and use it to study segregation and defects in the body-centered-cubic refractory high-entropy alloy MoNbTaVW. In the bulk alloy, we observe clear ordering of mainly Mo-Ta and V-Wbinaries at low temperatures. In damaged crystals, our simulations reveal clear segregation of vanadium, the smallest atom in the alloy, to compressed interstitial-rich regions such as radiation-induced dislocation loops. Vanadium also dominates the population of single self-interstitial atoms. In contrast, due to its larger size and low surface energy, niobium segregates to spacious regions such as the inner surfaces of voids. When annealing samples with supersaturated concentrations of defects, we find that in complete contrast to W, interstitial atoms in MoNbTaVW cluster to create only small (similar to 1 nm) experimentally invisible dislocation loops enriched by vanadium. By comparison to W, we explain this by the reduced but three-dimensional migration of interstitials, the immobility of dislocation loops, and the increased mobility of vacancies in the high-entropy alloy, which together promote defect recombination over clustering.</p>