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20232025
most citedSolute segregation in polycrystalline aluminum from hybrid Monte Carlo and molecular dynamics simulations with a unified neuroevolution potential

5 citations · 7 across the 3 of their papers we have counts for

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Showing cond-mat.mtrl-sciShow all

6 papers · 1 filter

cond-mat.mtrl-sci20252 cited

Atomistic understanding of hydrogen bubble-induced embrittlement in tungsten enabled by machine learning molecular dynamics

Yu Bao, Keke Song, Jiahui Liu +4

Hydrogen bubble formation within nanoscale voids is a critical mechanism underlying the embrittlement of metallic materials, yet its atomistic origins remains elusive. Here, we pre…

cond-mat.mtrl-sci2025

Revealing the impact of chemical short-range order on radiation damage in MoNbTaVW high-entropy alloys using a machine-learning potential

Jiahui Liu, Shuo Cao, Yanzhou Wang +4

The effect of chemical short-range order (CSRO) on primary radiation damage in MoNbTaVW high-entropy alloys is investigated using hybrid Monte Carlo/molecular dynamics simulations…

cond-mat.mtrl-sci202522 cited

NEP89: Universal neuroevolution potential for inorganic and organic materials across 89 elements

Ting Liang, Ke Xu, Eric Lindgren +16

While machine-learned interatomic potentials offer near-quantum-mechanical accuracy for atomistic simulations, many are material-specific or computationally intensive, limiting the…

cond-mat.mtrl-sci20242 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-sci20245 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…

cond-mat.mtrl-sci2023

General-purpose machine-learned potential for 16 elemental metals and their alloys

Keke Song, Rui Zhao, Jiahui Liu +25

Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their applicabil…