104 citations · 232 across the 13 of their papers we have counts for
12 papers · 1 filter
GPUMDkit: A User-Friendly Toolkit for GPUMD and NEP
Zihan Yan, Denan Li, Xin Wu +20
Machine-learned interatomic potentials have revolutionized molecular dynamics simulations by providing quantum-mechanical accuracy at empirical-potential speeds. The graphics proce…
Stabilisation of hBN/SiC Heterostructures with Vacancies and Transition-Metal Atoms
Arsalan Hashemi, Nima Ghafari Cherati, Sadegh Ghaderzadeh +3
When two-dimensional atomic layers of different materials are brought into close proximity to form van der Waals (vdW) heterostructures, interactions between adjacent layers signif…
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…
Structural and transport properties of LiTFSI/G3 electrolyte with machine-learned molecular dynamics
Chenyang Cao, Liyi Bai, Shuo Cao +4
The lithium bis(trifluoromethylsulfonyl)azanide-triglyme electrolyte plays a critical role in the performance of lithium-ion batteries. However, its solvation structure and transpo…