71 citations · 201 across the 10 of their papers we have counts for
10 papers
Effect of Near-surface Thermal Spikes on Radiation Hardness of Gallium Oxide
Tomás Fernández Bouvier, Umutcan Bektas, Alexander Azarov +10
Gallium oxide (GaO) stands out as an extraordinary high radiation-tolerant semiconductor, because its lattice displacements induce polymorph transitions, holding materi…
Threshold displacement energies in refractory high-entropy alloys
Jesper Byggmästar, Flyura Djurabekova, Kai Nordlund
Refractory high-entropy alloys show promising resistance to irradiation, yet little is known about the fundamental nature of radiation-induced defect formation. Here, we simulate t…
Fast and accurate machine-learned interatomic potentials for large-scale simulations of Cu, Al and Ni
Aslak Fellman, Jesper Byggmästar, Fredric Granberg +2
Machine learning (ML) has become widely used in the development of interatomic potentials for molecular dynamics simulations. However, most ML potentials are still much slower than…
Threshold displacement energy map of Frenkel pair generation in from machine-learning-driven molecular dynamics simulations
Huan He, Junlei Zhao, Jesper Byggmästar +4
phase gallium oxide (-) demonstrates tremendous potential for electronics applications and offers promising prospects for integration into future space systems…
Crystallization Instead of Amorphization in Collision Cascades in Gallium Oxide
Junlei Zhao, Javier García Fernández, Alexander Azarov +6
Disordering of solids typically leads to amorphization, but polymorph transitions, facilitated by favorable atomic rearrangements, may temporarily help to maintain long-range perio…
Analysis of lattice locations of deuterium in tungsten and its application for predicting deuterium trapping conditions
Xin Jin, Flyura Djurabekova, Etienne A. Hodille +2
Retention of hydrogen isotopes (protium, deuterium and tritium) in tungsten is one of the most severe issues in design of fusion power plants, since significant trapping of tritium…
Simple machine-learned interatomic potentials for complex alloys
Jesper Byggmästar, Kai Nordlund, Flyura Djurabekova
Developing data-driven machine-learning interatomic potentials for materials containing many elements becomes increasingly challenging due to the vast configuration space that must…
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,…
Electronic effects in high-energy radiation damage in tungsten
Eva Zarkadoula, Dorothy M. Duffy, Kai Nordlund +4
Although the effects of the electronic excitations during high-energy radiation damage processes are not currently understood, it is shown that their role in the interaction of rad…
What is the real driving force of ion beam mixing?
P. Süle, M. Menyhárd, K. Nordlund
Molecular dynamics simulations have been used to study the driving force of ion irradiation induced interfacial mixing in metal bilayers in which the relative mass of the constitue…