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physics.comp-ph2022★ 463 cited
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…
physics.comp-ph2022★ 22 cited
Variable thermal transport in black, blue, and violet phosphorene from extensive atomistic simulations with a neuroevolution potential
Penghua Ying, Ting Liang, Ke Xu +6
Phosphorus has diverse chemical bonds and even in its two-dimensional form there are three stable allotropes: black phosphorene (Black-P), blue phosphorene (Blue-P), and violet pho…
physics.comp-ph2022
Abnormally High Thermal Conductivity in Fivefold Twinned Diamond Nanowires
Ting Liang, Ke Xu, Meng Han +5
Fivefold twins (5FTs), discovered nearly 200 years ago, are a common multiply twinned structure that usually dramatically deteriorate the thermal transport properties of nanomateri…