activity
20232026
most citedGeneral-purpose machine-learned potential for 16 elemental metals and their alloys

203 citations · 693 across the 29 of their papers we have counts for

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5 papers · 1 filter

physics.comp-ph2026

Wisevolve Potential Engine: Machine-Learning Molecular Dynamics at 100 Million Atom-Steps per Second on a Single GPU

Hekai Bu, Zheyong Fan

General-purpose molecular dynamics software supports diverse interatomic potentials and simulation requirements. Within a simulation stage, the loaded potential model and the requi…

physics.comp-ph2025★ 4 cited

Accurate Modeling of Interfacial Thermal Transport in van der Waals Heterostructures via Hybrid Machine Learning and Registry-Dependent Potentials

Wenwu Jiang, Hekai Bu, Ting Liang +4

Two-dimensional transition metal dichalcogenides (TMDs) exhibit remarkable thermal anisotropy due to their strong intralayer covalent bonding and weak interlayer van der Waals (vdW…

physics.comp-ph2025★ 28 cited

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.…

physics.comp-ph2025★ 2 cited

Modular hybrid machine learning and physics-based potentials for scalable modeling of van der Waals heterostructures

Hekai Bu, Wenwu Jiang, Penghua Ying +3

Accurately modeling the structural reconstruction and thermodynamic behavior of van der Waals (vdW) heterostructures remains a significant challenge due to the limitations of conve…

physics.comp-ph2025★ 17 cited

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…