26 citations · 29 across the 7 of their papers we have counts for
7 papers
Beyond dpa: an atomistic framework for a quantitative description of radiation damage in YBa2Cu3O7
Federico Ledda, Daniele Torsello, Davide Gambino +8
Radiation damage in high-temperature cuprate superconductors represents one of the main technological challenges for their deployment in harsh environments, such as fusion reactors…
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…
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…
Spontaneous damage annealing reactions as a possible source of low energy excess in semiconductor detectors
Kai Nordlund, Fanhao Kong, Flyura Djurabekova +4
In semiconductor detectors designed for capturing dark matter particles or neutrinos, when the detection threshold is constantly improved to increasingly low energies, an "excess"…
Probing the dark matter velocity distribution via daily modulation
Matti Heikinheimo, Kai Nordlund, Sebastian Sassi +1
We consider dark matter velocity distributions with an anisotropic component, and analyze how the velocity structure can be probed in a solid state ionization detector with no dire…
Multiscale machine-learning interatomic potentials for ferromagnetic and liquid iron
Jesper Byggmästar, Giorgos Nikoulis, Aslak Fellman +3
We develop and compare four interatomic potentials for iron: a simple machine-learned embedded atom method (EAM) potential, a potential with machine-learned two- and three-body-dep…