6 citations · 7 across the 2 of their papers we have counts for
2 papers
cond-mat.mtrl-sci2021★ 6 cited
Modeling refractory high-entropy alloys with efficient machine-learned interatomic potentials: defects and segregation
Jesper Byggmästar, Kai Nordlund, Flyura Djurabekova
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-centred cubic refra…
cond-mat.mtrl-sci2019★ 1 cited
On the classification and quantification of crystal defects after energetic bombardment by machine learned molecular dynamics simulations
F. J. Domínguez-Gutiérrez, J Byggmästar, K. Nordlund +2
The analysis of the damage on plasma facing materials (PFM), due to its direct interaction with the plasma environment, is needed to build the next generation of nuclear machines,…