Kai Nordlund

collaborators

4 papers

cond-mat.mtrl-sci2024

Threshold displacement energies in refractory high-entropy alloys

Jesper ByggmÃ¤star, 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…

cond-mat.mtrl-sci2024

Fast and accurate machine-learned interatomic potentials for large-scale simulations of Cu, Al and Ni

Aslak Fellman, Jesper ByggmÃ¤star, 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…

cond-mat.mtrl-sci2024

Crystallization Instead of Amorphization in Collision Cascades in Gallium Oxide

Junlei Zhao, Javier GarcÃa FernÃ¡ndez, 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…

cond-mat.mtrl-sci2024

Threshold displacement energy map of Frenkel pair generation in $G a_{2} O_{3}$ from machine-learning-driven molecular dynamics simulations

Huan He, Junlei Zhao, Jesper ByggmÃ¤star +4

$I^^{2}$ phase gallium oxide ( $I^^{2}$ - $G a_{2} O_{3}$ ) demonstrates tremendous potential for electronics applications and offers promising prospects for integration into future space system…

Threshold displacement energies in refractory high-entropy alloys

Fast and accurate machine-learned interatomic potentials for large-scale simulations of Cu, Al and Ni

Crystallization Instead of Amorphization in Collision Cascades in Gallium Oxide

Threshold displacement energy map of Frenkel pair generation in Ga2​O3​ from machine-learning-driven molecular dynamics simulations

Threshold displacement energy map of Frenkel pair generation in $G a_{2} O_{3}$ from machine-learning-driven molecular dynamics simulations