463 citations · 699 across the 8 of their papers we have counts for
10 papers
Understanding compression-extension error asymmetry in universal machine-learning interatomic potentials
Hongwei Du, Dingyang Lv, Boyan Zhang +11
Universal machine-learning interatomic potentials are increasingly used across diverse materials, yet their reliability under directional deformation remains unclear. We compare co…
A Transferable Machine-Learned Potential Enabling Large-Scale Copper-Carbon Simulations from Equilibrium to Extreme Conditions
Feiyu Zhou, Wei Qiu, Yan Chen +4
Cu-C materials and interfaces are central to diamond thermal management, metallization, graphene growth and carbon-coating technologies, but atomistic simulations across these appl…
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…
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…
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…
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…
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…
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…
Large-scale machine-learning molecular dynamics simulation of primary radiation damage in tungsten
Jiahui Liu, Jesper Byggmastar, Zheyong Fan +2
Simulating collision cascades and radiation damage poses a long-standing challenge for existing interatomic potentials, both in terms of accuracy and efficiency. Machine-learning b…
GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations
Zheyong Fan, Yanzhou Wang, Penghua Ying +17
We present our latest advancements of machine-learned potentials (MLPs) based on the neuroevolution potential (NEP) framework introduced in [Fan et al., Phys. Rev. B 104, 104309 (2…