203 citations · 517 across the 13 of their papers we have counts for
5 papers · 1 filter
PYSED: A tool for extracting kinetic-energy-weighted phonon dispersion and lifetime from molecular dynamics simulations
Ting Liang, Wenwu Jiang, Ke Xu +4
Machine learning potential-driven molecular dynamics (MD) simulations have significantly enhanced the predictive accuracy of thermal transport properties across diverse materials.…
Optimizing thermoelectric performance of graphene antidot lattices via quantum transport and machine-learning molecular dynamics simulations
Yang Xiao, Yuqi Liu, Zihan Tan Bohan Zhang +5
Thermoelectric materials, which can convert waste heat to electricity or be utilized as solid-state coolers, hold promise for sustainable energy applications. However, optimizing t…
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…
Probing the ideal limit of interfacial thermal conductance in two-dimensional van der Waals heterostructures
Ting Liang, Ke Xu, Penghua Ying +9
Probing the ideal limit of interfacial thermal conductance (ITC) in two-dimensional (2D) heterointerfaces is of paramount importance for assessing heat dissipation in 2D-based nano…
Advances in modeling complex materials: The rise of neuroevolution potentials
Penghua Ying, Cheng Qian, Rui Zhao +4
Interatomic potentials are essential for driving molecular dynamics (MD) simulations, directly impacting the reliability of predictions regarding the physical and chemical properti…