activity
20232026
most citedMolecular dynamics simulations of heat transport using machine-learned potentials: A mini review and tutorial on GPUMD with neuroevolution potentials

104 citations · 378 across the 22 of their papers we have counts for

collaborators

29 papers

cond-mat.soft2026

Structurally Triggered Breakdown of the Phonon Gas Model in Crystalline Metal-Organic Frameworks

Penghua Ying, Ting Liang, Yun Chen +5

While crystalline materials with glass-like thermal conductivity are fundamentally intriguing, structurally triggering the transition from propagating to diffusive heat transport w…

physics.chem-ph20251 cited

Thermal conductivities of monolayer graphene oxide from machine learning molecular dynamics simulations

Bohan Zhang, Biyuan Liu, Penghua Ying +6

Graphene oxide (GO) exhibits rich chemical heterogeneity that strongly influences its structural, thermal, and mechanical properties, yet quantitatively linking reduction chemistry…

cond-mat.mtrl-sci2025

Heat transport in superionic materials via machine-learned molecular dynamics

Wenjiang Zhou, Benrui Tang, Zheyong Fan +3

Precise modeling and understanding of heat transport in the superionic phase are of great interest. Although simulations combining Green-Kubo (GK) molecular dynamics with machine-l…

cond-mat.mtrl-sci20254 cited

Anisotropic and isotropic elasticity and thermal transport in monolayer C networks from machine-learning molecular dynamics

Qing Li, Haikuan Dong, Penghua Ying +1

Two-dimensional fullerene networks have recently attracted increasing interest due to their diverse bonding topologies and mechanically robust architectures. In this work, we devel…

physics.comp-ph20254 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-ph202528 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.…