19 citations · 19 across the 2 of their papers we have counts for
9 papers
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…
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…
Device-Scale Atomistic Simulations of Heat Transport in Advanced Field-Effect Transistors
Ke Xu, Gang Wang, Ting Liang +8
Self-heating in next-generation, high-power-density field-effect transistor limits performance and complicates fabrication. Here, we introduce NEP-FET, a machine-learned framework…
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.…
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…
Moiré-Driven Interfacial Thermal Transport in Twisted Transition Metal Dichalcogenides
Wenwu Jiang, Ting Liang, Hekai Bu +2
Cross-plane thermal conductivity in homogeneous transition metal dichalcogenides (TMDs) exhibits a strong dependence on twist angle, originating from atomic reconstruction within m…