arXiv subjects

Zhicheng Zong

Publications and source records attributed to Zhicheng Zong.

7 recordsLinked to original sources

arXiv2026

The substitutional atomic distance model for predicting lattice thermal conductivity in alloys

Understanding phonon transport in alloys is crucial for the design of high-performance electronic and thermoelectric devices. However, conventional theoretical models fail to provide a clear physical picture of phonon scattering caused by atomic disorder in alloys, and their prediction accuracy is limited. In this work, a new substitutional atomic distance model for alloys is proposed, providing an intuitive physical picture. SiGe and InGaAs alloys are taken as representative systems, and their thermal conductivities are calculated, showing good agreement with previous experimental measurements. The results indicate that alloy scattering plays a dominant role in reducing thermal conductivity. This study provides new insights into phonon transport in alloys and offers guidance for tailoring thermal properties through compositional engineering.

cond-mat.mtrl-sci↗

arXiv2025

Machine learning potential for predicting thermal conductivity of θ-phase and amorphous Tantalum Nitride

Tantalum nitride (TaN) has attracted considerable attention due to its unique electronic and thermal properties, high thermal conductivity, and applications in electronic components. However, for the θ-phase of TaN, significant discrepancies exist between previous experimental measurements and theoretical predictions. In this study, deep potential models for TaN in both the θ-phase and amorphous phase were developed and employed in molecular dynamics simulations to investigate the thermal conductivities of bulk and nanofilms. The simulation results were compared with reported experimental and theoretical results, and the mechanism for differences were discussed. This study provides insights into the thermal transport mechanisms of TaN, offering guidance for its application in advanced electronic and thermal management devices.

cond-mat.mtrl-sci↗

arXiv2025

Using Targeted Phonon Excitation to Modulate Thermal Conductivity of Boron Nitride

Recent advancements in thermal conductivity modulating strategies have shown promising enhancements to the thermal management capabilities of two-dimensional materials. In this article, both iterative Boltzmann transport equation solution and two-temperature model were employed to investigate the efficacy of targeted phonon excitation applied to hexagonal boron nitride. The results indicate significant modifications to hBN's thermal conductivity, achieving increases of up to 30.1% as well as decreases of up to 59.8%. These findings validate the reliability of the strategy, expand its scope of applicability, and establish it as a powerful tool for tailoring thermal properties across a wider range of fields.

cond-mat.mtrl-sci↗

arXiv2025

Unveiling the thermal transport mechanism in compressed plastic crystals assisted by deep potential

The unique properties of plastic crystals highlight their potential for use in solid-state refrigeration. However, their practical applications are limited by thermal hysteresis due to low thermal conductivity. In this study, the effect of compressive strain on the thermal transport properties of plastic crystal [(CH3)4N][FeCl4] was investigated using molecular dynamic simulation with a deep neural network potential. It is found that a 9% strain along [001] direction enhances thermal conductivity sixfold. The underlying mechanisms are analyzed through vibrational density of states, spectral energy densities, and mean square displacements. The enhancement in thermal conductivity is primarily due to increased group velocity and reduced phonon scattering, driven by volume compression within the 0-1 THz. These findings offer theoretical insights for the practical application of plastic crystals in thermal management systems.

cond-mat.mtrl-sci↗

arXiv2024

Deep potential for interaction between hydrated Cs+ and graphene

The influence of hydrated cation-π interaction forces on the adsorption and filtration capabilities of graphene-based membrane materials is significant. However, the lack of interaction potential between hydrated Cs+ and graphene limits the scope of adsorption studies. Here, it is developed that a deep neural network potential function model to predict the interaction force between hydrated Cs+ and graphene. The deep potential has DFT-level accuracy, enabling accurate property prediction. This deep potential is employed to investigate the properties of the graphene surface solution, including the density distribution, mean square displacement, and vibrational power spectrum of water. Furthermore, calculations of the molecular orbital electron distributions indicate the presence of electron migration in the molecular orbitals of graphene and hydrated Cs+, resulting in a strong electrostatic interaction force. The method provides a powerful tool to study the adsorption behavior of hydrated cations on graphene surfaces and offers a new solution for handling radionuclides.

physics.comp-ph↗

arXiv2023

Enhancing interfacial thermal conductance of Si/PVDF by strengthening atomic couplings

The thermal transport across inorganic/organic interfaces attracts interest for both academic and industry due to its widely applications in flexible electronics etc. Here, the interfacial thermal conductance of inorganic/organic interfaces consisting of silicon and polyvinylidene fluoride is systematically investigated by molecular dynamics simulations. Interestingly, it is demonstrated that a modified silicon surface with hydroxyl groups can drastically enhance the conductance by 698%. These results are elucidated based on interfacial couplings and lattice dynamics insights. This study not only provides feasible strategies to effectively modulate the interfacial thermal conductance of inorganic/organic interfaces but also deepens the understanding of the fundamental physics underlying phonon transport across interfaces.

cond-mat.mtrl-sci↗

arXiv2023

Modulating Thermal Conductivity via Targeted Phonon Excitation

Thermal conductivity is a critical material property in numerous applications, such as those related to thermoelectric devices and heat dissipation. Effectively modulating thermal conductivity has become a great concern in the field of heat conduction. In this study, a quantum strategy is proposed to modulate thermal conductivity by exciting targeted phonons. The results show that the thermal conductivity of graphene can be tailored in the range of 1559 W/m-K (49%) to 4093 W/m-K (128%), compared with the intrinsic value of 3189 W/m-K. A similar trend is also observed for graphene nanoribbons. The results are obtained through both ab initio calculations and molecular dynamics simulations. This brand-new quantum strategy to modulate thermal conductivity paves a way for quantum heat conduction.

cond-mat.mtrl-sci↗