203 citations · 693 across the 29 of their papers we have counts for
30 papers
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…
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…
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…
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…
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…
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…
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.…
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…
Optimizing thermoelectric performance of graphene antidot lattices via quantum transport and machine-learning molecular dynamics simulations
Yang Xiao, Yuqi Liu, Zihan Tan Bohan Zhang +5
Thermoelectric materials, which can convert waste heat to electricity or be utilized as solid-state coolers, hold promise for sustainable energy applications. However, optimizing t…
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…
Thermal transport of amorphous hafnia across the glass transition
Zezhu Zeng, Xia Liang, Zheyong Fan +3
Heat transport in glasses across a wide range of temperature is vital for applications in gate dielectrics and heat insulator. However, it remains poorly understood due to the chal…
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…
Insight into the effect of force error on the thermal conductivity from machine-learned potentials
Wenjiang Zhou, Nianjie Liang, Xiguang Wu +3
Machine-learned potentials (MLPs) have been extensively used to obtain the lattice thermal conductivity via atomistic simulations. However, the impact of force errors in various ML…
Million-atom heat transport simulations of polycrystalline graphene approaching first-principles accuracy enabled by neuroevolution potential on desktop GPUs
Xiaoye Zhou, Yuqi Liu, Benrui Tang +5
First-principles molecular dynamics simulations of heat transport in systems with large-scale structural features are challenging due to their high computational cost. Here, using…
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…
Density dependence of thermal conductivity in nanoporous and amorphous carbon with machine-learned molecular dynamics
Yanzhou Wang, Zheyong Fan, Ping Qian +2
Disordered forms of carbon are an important class of materials for applications such as thermal management. However, a comprehensive theoretical understanding of the structural dep…
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…
Solute segregation in polycrystalline aluminum from hybrid Monte Carlo and molecular dynamics simulations with a unified neuroevolution potential
Keke Song, Jiahui Liu, Shunda Chen +3
One of the most effective methods to enhance the strength of aluminum alloys involves modifying grain boundaries (GBs) through solute segregation. However, the fundamental mechanis…
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…