203 citations · 458 across the 10 of their papers we have counts for
10 papers
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…
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…
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…
NEP-MB-pol: A unified machine-learned framework for fast and accurate prediction of water's thermodynamic and transport properties
Ke Xu, Ting Liang, Nan Xu +5
Water's unique hydrogen-bonding network and anomalous properties pose significant challenges for accurately modeling its structural, thermodynamic, and transport behavior across va…
Phonon coherence and minimum thermal conductivity in disordered superlattice
Xin Wu, Zhang Wu, Ting Liang +4
Phonon coherence elucidates the propagation and interaction of phonon quantum states within superlattice, unveiling the wave-like nature and collective behaviors of phonons. Taking…
Highly efficient path-integral molecular dynamics simulations with GPUMD using neuroevolution potentials: Case studies on thermal properties of materials
Penghua Ying, Wenjiang Zhou, Lucas Svensson +10
Path-integral molecular dynamics (PIMD) simulations are crucial for accurately capturing nuclear quantum effects in materials. However, their computational intensity and reliance o…
Lattice distortion leads to glassy thermal transport in crystalline CsBiICl
Zezhu Zeng, Zheyong Fan, Michele Simoncelli +5
The glassy thermal conductivities observed in crystalline inorganic perovskites such as CsBiICl is perplexing and lacking theoretical explanations. Here, we first e…
Molecular dynamics simulations of heat transport using machine-learned potentials: A mini review and tutorial on GPUMD with neuroevolution potentials
Haikuan Dong, Yongbo Shi, Penghua Ying +9
Molecular dynamics (MD) simulations play an important role in understanding and engineering heat transport properties of complex materials. An essential requirement for reliably pr…
General-purpose machine-learned potential for 16 elemental metals and their alloys
Keke Song, Rui Zhao, Jiahui Liu +25
Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their applicabil…
Mechanisms of temperature-dependent thermal transport in amorphous silica from machine-learning molecular dynamics
Ting Liang, Penghua Ying, Ke Xu +4
Amorphous silica (a-SiO) is a foundational disordered material for which the thermal transport properties are important for various applications. To accurately model the intera…