5 citations · 9 across the 7 of their papers we have counts for
Showing 2024Show all
3 papers · 1 filter
cond-mat.mtrl-sci2024★ 2 cited
Utilizing a machine-learned potential to explore enhanced radiation tolerance in the MoNbTaVW high-entropy alloy
Jiahui Liu, Jesper Byggmastar, Zheyong Fan +3
High-entropy alloys (HEAs) based on tungsten (W) have emerged as promising candidates for plasma-facing components in future fusion reactors, owing to their excellent irradiation r…
cond-mat.mtrl-sci2024★ 2 cited
Density dependence of thermal conductivity in nanoporous and amorphous carbon with machine-learned molecular dynamics
Yanzhou Wang, Zheyong Fan, Ping Qian +2
Disordered forms of carbon are an important class of materials for applications such as thermal management. However, a comprehensive theoretical understanding of the structural dep…
cond-mat.mtrl-sci2024★ 5 cited
Solute segregation in polycrystalline aluminum from hybrid Monte Carlo and molecular dynamics simulations with a unified neuroevolution potential
Keke Song, Jiahui Liu, Shunda Chen +3
One of the most effective methods to enhance the strength of aluminum alloys involves modifying grain boundaries (GBs) through solute segregation. However, the fundamental mechanis…