1 citations · 1 across the 6 of their papers we have counts for
7 papers
Trillion-atom molecular dynamics simulations with ab initio accuracy
Pengfei Suo, Wudi Cao, Xingxing Wu +14
Material properties are fundamentally dictated by multiscale phenomena, which often reach mesoscale in size. The μm mesoscale is also the size which can be observed directly under…
Benchmarking Chemically Scalable Machine-Learning Interatomic Potentials for Large-Scale Simulations of Multicomponent Alloys
Fei Shuang, Penghua Ying, Kai Liu +5
Machine learning interatomic potentials (MLIPs) with broad chemical flexibility are essential for atomistic simulations of compositionally complex alloys, but their deployment in l…
Nine-element machine-learned interatomic potentials for multiphase refractory alloys
Jesper Byggmästar, Tiago Lopes, Zheyong Fan +1
New refractory alloys are being continuously designed and characterised for applications requiring good high-temperature mechanical properties and stability. Computational design f…
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…
Lattice thermal conductivity of 16 elemental metals from molecular dynamics simulations with a unified neuroevolution potential
Shuo Cao, Ao Wang, Zheyong Fan +4
Metals play a crucial role in heat management in electronic devices, such as integrated circuits, making it vital to understand heat transport in elementary metals and alloys. In t…
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…