23 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…
Fast machine learned interatomic potential for hydrogen-induced embrittlement in α-Fe
Eetu Makkonen, Alvaro Lopez-Cazalilla, Flyura Djurabekova
In this work, we present a machine-learned interatomic potential for the -Fe-H system based on the tabulated Gaussian Approximation Potential (tabGAP) formalism. Trained on a D…
Coupled simulation of plasma-surface interactions during early stages of vacuum arcing
Roni Koitermaa, Andreas Kyritsakis, Tauno Tiirats +2
We describe fully coupled simulations that bridge atomistic cathode dynamics and plasma formation during the earliest stages of vacuum arcing. The model combines molecular dynamics…
Anisotropic Core-Shell Swift Heavy Ion Tracks in beta-Ga2O3
Huan He, Jiayu Liang, Shaowei He +8
Swift heavy ion (SHI) irradiation generates nanoscale ion tracks through intense electronic excitation, yet the microscopic mechanisms governing their morphology and phase stabilit…
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…
A Self-Evolving Machine-Learning-Based Kinetic Monte Carlo Method for Modelling Thin-Film Growth
Jyri Kimari, Flyura Djurabekova, Kostas Sarakinos
We present a kinetic Monte Carlo (KMC) simulation framework parameterized by automatically sampling machine-learning (ML) for modeling thin-film growth atom by atom. Given an inter…