105 citations · 341 across the 16 of their papers we have counts for
5 papers · 1 filter
Surface-mediated reduction of ion-irradiation-induced damage in tungsten revealed by advanced ion channeling analysis
Xin Jin, Fredric Granberg, Kai Nordlund +3
Tungsten is a leading candidate material for plasma-facing components in future fusion reactors. In this work, we integrate advanced ion channeling analysis with large-scale molecu…
A General-Purpose and Efficient Machine-Learned Potential for SiC from Ambient to Extreme Environments
Jintong Wu, Zhuang Shao, Junlei Zhao +5
Silicon carbide (SiC) polymorphs are widely employed as nuclear materials, mechanical components, and wide-bandgap semiconductors. The rapid advancement of SiC-based applications h…
Effects of training machine-learning potentials for radiation damage simulations using different pseudopotentials
A. Fellman, J. Byggmästar, F. Granberg +2
Machine learning (ML) has become a commonplace approach in the development of interatomic potentials for molecular dynamics simulations, and its use also for radiation effect model…
Radiation damage and phase stability of AlCrCuFeNi alloys using a machine-learned interatomic potential
Aslak Fellman, Jesper Byggmästar, Fredric Granberg +2
We develop a machine-learned interatomic potential for AlCrCuFeNi high-entropy alloys (HEA) using a diverse set of structures from density functional theory calculated including ma…
Four regimes of primary radiation damage in tungsten
Jesper Byggmästar, Ville-Markus Yli-Suutala, Aslak Fellman +3
We observe for the first time in silico the transition to a linear regime in the primary damage production in tungsten. As the critical plasma-facing material in fusion reactors, r…