arXiv subjects

Wenwu Jiang

Publications and source records attributed to Wenwu Jiang.

8 recordsLinked to original sources

arXiv2026

Strain and Twist Engineering of Interfacial Thermal Transport in Homo- and Hetero-Interfaces of Graphene and Hexagonal Boron Nitride

A dramatic difference between the vertical thermal conductance response of homogeneous and heterogeneous graphene/h-BN interfaces to external mechanical perturbations, is predicted. Homogeneous graphene and h-BN interfaces exhibit strong conductance reduction for both in-plane strain and interfacial twist. Conversely, the vertical thermal conductance of the heterogeneous graphene/h-BN junction is insensitive to twist deformations but shows significant increase or decrease under compressive or tensile strains, respectively. Our atomistic simulations predictions are rationalized by Fermi's golden rule and density of phonon modes analyses, indicating that vertical phonons and local stacking configurations have a central role in the interlayer heat transport behavior. A simple phenomenological model, based on local interlayer distance and stacking, captures well the dependence of vertical heat conductance on strain and twist deformations.

cond-mat.mes-hall↗

arXiv2026

Modular hybrid machine learning and physics-based potentials for scalable modeling of van der Waals heterostructures

Accurately modeling the structural reconstruction and thermodynamic behavior of van der Waals (vdW) heterostructures remains a significant challenge due to the limitations of conventional force fields in capturing their complex mechanical, thermal, electronic, and tribological properties. To address these limitations, we develop a hybrid framework that combines single-layer machine-learned potential ($s$MLP) with physics-based anisotropic interlayer potential (ILP), effectively decoupling intralayer and interlayer interactions. This $s$MLP+ILP approach modularizes the modeling of vdW heterostructures like assembling LEGOs, reducing the required training configurations by at least an order of magnitude compared to the pure MLP approach, while retaining predictive accuracy and computational efficiency. We validate our framework by accurately reproducing the mechanical and thermal transport properties of graphite and bulk hexagonal boron nitride ($h$-BN), and by resolving intricate Moiré patterns in graphene/$h$-BN bilayer and graphene/graphene/$h$-BN trilayer heterostructures, achieving excellent agreement with experimental observations. Leveraging the developed $s$MLP+ILP approach, we reveal the stacking order-dependent formation of Moir{é} superlattice in trilayer graphene/$h$-BN/MoS$_2$ heterostructures, demonstrating its ability to accurately model large-scale vdW systems comprising hundreds of thousands of atoms with near $ab$ $initio$ precision. These findings demonstrate that the hybrid $s$MLP+ILP framework remarkably outperforms existing pure machine-learned or empirical potentials, offering a scalable and transferable solution for accurately and extensively modeling complex vdW materials across diverse applications, including sliding ferroelectricity, thermal management, resistive switching, and superlubric nanodevices.

physics.comp-ph↗

arXiv2025

PYSED: A tool for extracting kinetic-energy-weighted phonon dispersion and lifetime from molecular dynamics simulations

Machine learning potential-driven molecular dynamics (MD) simulations have significantly enhanced the predictive accuracy of thermal transport properties across diverse materials. However, extracting phonon-mode-resolved insights from these simulations remains a critical challenge. Here, we introduce PYSED, a Python-based package built on the spectral energy density (SED) method, designed to efficiently compute kinetic-energy-weighted phonon dispersion and extract phonon lifetime from large-scale MD simulation trajectories. By integrating high-accuracy machine-learned neuroevolution potential (NEP) models, we validate and showcase the effectiveness of the implemented SED method across systems of varying dimensionalities. Specifically, the NEP-driven MD-SED accurately reveals how phonon modes are affected by strain in carbon nanotubes, as well as by interlayer coupling strengths and the twist angles in two-dimensional molybdenum disulfide. For three-dimensional systems, the SED method effectively establishes the thermal transport regime diagram for metal-organic frameworks, distinguishing between particlelike and wavelike propagation regions. Moreover, using bulk silicon as an example, we show that phonon SED can efficiently capture quantum dynamics based on path-integral trajectories. The PYSED package bridges MD simulations with detailed phonon-mode insights, delivering a robust tool for investigating thermal transport properties with detailed mechanisms across various materials.

physics.comp-ph↗

arXiv2025

Accurate Modeling of Interfacial Thermal Transport in van der Waals Heterostructures via Hybrid Machine Learning and Registry-Dependent Potentials

Two-dimensional transition metal dichalcogenides (TMDs) exhibit remarkable thermal anisotropy due to their strong intralayer covalent bonding and weak interlayer van der Waals (vdW) interactions. However, accurately modeling their thermal transport properties remains a significant challenge, primarily due to the computational limitations of density functional theory (DFT) and the inaccuracies of classical force fields in non-equilibrium regimes. To address this, we use a recently developed hybrid computational framework that combines machine learning potential (MLP) for intralayer interactions with registry-dependent interlayer potential (ILP) for anisotropic vdW interlayer interaction, achieving near quantum mechanical accuracy. This approach demonstrates exceptional agreement with DFT calculations and experimental data for TMD systems, accurately predicting key properties such as lattice constants, bulk modulus, moir\'e reconstruction, phonon spectra, and thermal conductivities. The scalability of this method enables accurate simulations of TMD heterostructures with large-scale moir\'e superlattices, making it a transformative tool for the design of TMD-based thermal metamaterials and devices, bridging the gap between accuracy and computational efficiency.

physics.comp-ph↗

arXiv2025

Moir\'e-Driven Interfacial Thermal Transport in Twisted Transition Metal Dichalcogenides

Cross-plane thermal conductivity in homogeneous transition metal dichalcogenides (TMDs) exhibits a strong dependence on twist angle, originating from atomic reconstruction within moir\'e superlattices. This reconstruction redistributes interlayer stacking modes, reducing high-efficiency thermal transport regions and softening the transverse acoustic phonon modes as the twist angle increases. We propose a general theoretical expression to capture this behavior, validated against non-equilibrium molecular dynamics simulations across both homo- and heterogeneous twisted TMDs structures, as well as homogeneous twisted graphene and hexagonal boron nitride stacks. Our model demonstrates that the interfacial thermal conductance (ITC) scales with the twist angle ($\theta$) as $\ln{\left(\text{ITC}\right)} \propto e^{-\sqrt{\theta}}$. These findings advance the understanding of twist-engineered interfacial thermal transport, offering design principles for optimizing thermal management in devices based on van der Waals layered materials.

cond-mat.mtrl-sci↗

arXiv2025

Probing the ideal limit of interfacial thermal conductance in two-dimensional van der Waals heterostructures

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 nanoelectronics. Using graphene/hexagonal boron nitride (Gr/$h$-BN), a structurally isomorphous heterostructure with minimal mass contrast, as a prototype, we develop an accurate yet highly efficient machine-learned potential (MLP) model, which drives nonequilibrium molecular dynamics (NEMD) simulations on a realistically large system with over 300,000 atoms, enabling us to report the ideal limit range of ITC for 2D heterostructures at room temperature. We further unveil an intriguing stacking-sequence-dependent ITC hierarchy in the Gr/$h$-BN heterostructure, which can be connected to moiré patterns and is likely universal in van der Waals layered materials. The underlying atomic-level mechanisms can be succinctly summarized as energy-favorable stacking sequences facilitating out-of-plane phonon energy transmission. This work demonstrates that MLP-driven MD simulations can serve as a new paradigm for probing and understanding thermal transport mechanisms in 2D heterostructures and other layered materials.

physics.comp-ph↗

arXiv2023

Anisotropic Interlayer Force Field for Group-VI Transition Metal Dichalcogenides

An anisotropic interlayer force field that describes the interlayer interactions in homogeneous and heterogeneous interfaces of group-VI transition metal dichalcogenides (MX2 where M = Mo, W and X = S, Se) is presented. The force field is benchmarked against density functional theory calculations for bilayer systems within the Heyd-Scuseria-Ernzerhof hybrid density functional approximation, augmented by a nonlocal many-body dispersion treatment of long-range correlation. The parametrization yields good agreement with reference calculations of binding energy curves and sliding potential energy surfaces. It is found to be transferable to TMD junctions outside the training set that contain the same atom types. Calculated bulk moduli agree with most previous dispersion corrected DFT predictions, which underestimate available experimental values. Calculated phonon spectra of the various junctions under consideration demonstrate the importance of appropriately treating the anisotropic nature of layered interfaces. Considering our previous parameterization for MoS2, the interlayer potential enables accurate and efficient large-scale simulations of the dynamical, tribological, and thermal transport properties of a large set of homogeneous and heterogeneous TMD interfaces.

cond-mat.mtrl-sci↗

arXiv2023

Twist-Dependent Anisotropic Thermal Conductivity in Homogeneous MoS$_2$ Stacks

Thermal transport property of homogeneous twisted molybdenum disulfide (MoS$_2$) is investigated using non-equilibrium molecular dynamics simulations with the state-of-art force fields. The simulation results demonstrate that the cross-plane thermal conductivity strongly depends on the interfacial twist angle, while it has only a minor effect on the in-plane thermal conductivity, exhibiting a highly anisotropic nature. A frequency-decomposed phonon analysis showed that both the cross-plane and in-plane thermal conductivity of MoS$_2$ are dominated by the low-frequency phonons below 15 THz. As the interfacial twist angle increases, these low-frequency phonons significantly attenuate the phonon transport across the interface, leading to impeded cross-plane thermal transport. However, the in-plane phonon transport is almost unaffected, which allows for maintaining high in-plane thermal conductivity. Additionally, our study revealed the strong size dependence for both cross-plane and in-plane thermal conductivities due to the low-frequency phonons of MoS$_2$. The maximum in-plane to cross-plane thermal anisotropy ratio is estimated as 400 for twisted MoS$_2$ from our simulation, which is in the same order of magnitude as recent experimental results (~900). Our study highlights the potential of twist engineering as a tool for tailoring the thermal transport properties of layered materials.

physics.comp-ph↗