6 papers
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…
An Accurate 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…
High-dose long-time defect evolution in tungsten studied by atomistically informed Object Kinetic Monte Carlo simulations
Jintong Wu, Juan-Pablo Balbuena, Zhiwei Hu +4
Irradiation of materials in nuclear test reactors and power plants is known to alter the properties of the material. The irradiation event happening at pico- or nanosecond time sca…
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…