203 citations · 778 across the 25 of their papers we have counts for
25 papers
Transitions between bulk and interfacial fracture in diamond/BN heterostructures
Wei Qiu, Feiyu Zhou, Xiaonan Wang +5
Whether an initially crack-free heterostructure fails at its interface or within an adjoining phase is controlled by the relative cohesion of competing atomic planes, but how inter…
Benchmarking Chemically Scalable Machine-Learning Interatomic Potentials for Large-Scale Simulations of Multicomponent Alloys
Fei Shuang, Penghua Ying, Kai Liu +5
Machine learning interatomic potentials (MLIPs) with broad chemical flexibility are essential for atomistic simulations of compositionally complex alloys, but their deployment in l…
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…
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…
Atomistic understanding of hydrogen bubble-induced embrittlement in tungsten enabled by machine learning molecular dynamics
Yu Bao, Keke Song, Jiahui Liu +4
Hydrogen bubble formation within nanoscale voids is a critical mechanism underlying the embrittlement of metallic materials, yet its atomistic origins remains elusive. Here, we pre…
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…
Interface phonon modes governing the ideal limit of thermal transport across diamond/cubic boron nitride interfaces
Xiaonan Wang, Xin Wu, Penghua Ying +2
Understanding the ideal limit of interfacial thermal conductance (ITC) across semiconductor heterointerfaces is crucial for optimizing heat dissipation in practical applications. B…
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…
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…
Softening of Vibrational Modes and Anharmonicity Induced Thermal Conductivity Reduction in a-Si:H at High Temperatures
Zhuo Chen, Yuejin Yuan, Yanzhou Wang +6
Hydrogenated amorphous silicon (a-Si:H) has garnered considerable attention in the semiconductor industry, particularly for its use in solar cells and passivation layers for high p…
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…
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…
Chemifriction and Superlubricity: Friends or Foes?
Penghua Ying, Xiang Gao, Amir Natan +2
The mechanisms underlying chemifriction, i.e. the contribution of interfacial bonding to friction in defected twisted graphene interfaces are revealed using fully atomistic machine…
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…
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…
Correcting force error-induced underestimation of lattice thermal conductivity in machine learning molecular dynamics
Xiguang Wu, Wenjiang Zhou, Haikuang Dong +5
Machine learned potentials (MLPs) have been widely employed in molecular dynamics (MD) simulations to study thermal transport. However, literature results indicate that MLPs genera…
Dissimilar thermal transport properties in -GaO and -GaO revealed by machine-learning homogeneous nonequilibrium molecular dynamics simulations
Xiaonan Wang, Jinfeng Yang, Penghua Ying +3
The lattice thermal conductivity (LTC) of GaO is an important property due to the challenge in the thermal management of high-power devices. We develop machine-learned neur…
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…
Combining linear-scaling quantum transport and machine-learning molecular dynamics to study thermal and electronic transports in complex materials
Zheyong Fan, Yang Xiao, Yanzhou Wang +3
We propose an efficient approach for simultaneous prediction of thermal and electronic transport properties in complex materials. Firstly, a highly efficient machine-learned neuroe…
Combining the D3 dispersion correction with the neuroevolution machine-learned potential
Penghua Ying, Zheyong Fan
Machine-learned potentials (MLPs) have become a popular approach of modelling interatomic interactions in atomistic simulations, but to keep the computational cost under control, a…
Accurate prediction of heat conductivity of water by a neuroevolution potential
Ke Xu, Yongchao Hao, Ting Liang +4
We propose an approach that can accurately predict the heat conductivity of liquid water. On the one hand, we develop an accurate machine-learned potential based on the neuroevolut…