arXiv subjects

Yangjun Qin

Publications and source records attributed to Yangjun Qin.

9 recordsLinked to original sources

arXiv2025

Higher-Order-Phonon Scattering Governs Targeted Control of Heat Conduction in Bulk Boron Arsenide

Conventional approaches for modulating thermal conductivity usually rely on structural modifications and therefore cannot achieve reversible in situ regulation. Targeted phonon excitation has recently emerged as a promising strategy for dynamically tuning thermal transport, but its applicability has so far been demonstrated mainly in two-dimensional systems. Here, we extend this strategy to a three-dimensional bulk material by taking boron arsenide (BAs) as a representative example. Based on first-principles calculations and phonon Boltzmann transport analysis, we show that targeted phonon excitation modulates the thermal conductivity of bulk BAs in a strongly frequency-dependent manner. Within the three-phonon-only framework, the modulation at 300 K is weak but clearly bidirectional. However, once four-phonon scattering is included, the modulation changes qualitatively to a predominantly suppressive behavior. In the combined three-phonon plus four-phonon (3ph+4ph) framework, the strongest suppression occurs at 20.5 THz, where the relative thermal conductivity decreases to 0.828 and 0.415 for excitation intensities of 5 and 25, respectively. By comparing the 3ph-only and 3ph+4ph results, we show that four-phonon scattering plays a decisive role in determining the net modulation effect by raising the intrinsic scattering background and promoting a more systematic excitation-induced increase in the scattering of low-frequency heat-carrying phonons.

cond-mat.mtrl-sci↗

arXiv2025

Machine learning potential for predicting thermal conductivity of {\theta}-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 {\theta}-phase of TaN, significant discrepancies exist between previous experimental measurements and theoretical predictions. In this study, deep potential models for TaN in both the {\theta}-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

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

Achieving ultra-high anisotropy in thermal conductivity of plastic crystal through megapascal pressure of hot pressing

Plastic crystals, owing to their exceptional properties, are gradually finding applications in solid-state refrigeration and ferroelectric fields. However, their inherently low thermal conductivity restricts their utilization in electronic devices. This study demonstrates that applying megapascal pressure of hot pressing can enhance the thermal conductivity of plastic crystal films. Most importantly, it induces significant anisotropy in thermal conductivity. Such anisotropy in thermal conductivity is beneficial for specialized thermal management applications, such as directing heat flow paths in electronic devices. In this study, [(CH3)4N][FeCl4] PCs films were prepared by hot pressing. At a pressure of 16 MPa, the ratio of in-plane to cross-plane thermal conductivity in the film reaches a remarkable 5.5. This is attributed to the preferential orientation along the (002) crystal plane induced by uniaxial pressure, leading to the formation of a layered structure and the creation of a flat and dense film. Furthermore, according to molecular dynamics simulations, the thermal conductivity along the [100] and [010] directions (parallel to the (002) crystal plane) is higher than in other directions. Therefore, significant modulation of anisotropy in thermal conductivity is achieved in [(CH3)4N][FeCl4] films by applying uniaxial hot pressing pressure. This phenomenon has the potential to greatly broaden the application of plastic crystals in the field of flexible electronic devices.

cond-mat.mtrl-sci↗

arXiv2024

Deep potential for interaction between hydrated Cs+ and graphene

The influence of hydrated cation-{\pi} 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

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↗

arXiv2023

The effect of dataset size and the process of big data mining for investigating solar-thermal desalination by using machine learning

Machine learning's application in solar-thermal desalination is limited by data shortage and inconsistent analysis. This study develops an optimized dataset collection and analysis process for the representative solar still. By ultra-hydrophilic treatment on the condensation cover, the dataset collection process reduces the collection time by 83.3%. Over 1,000 datasets are collected, which is nearly one order of magnitude larger than up-to-date works. Then, a new interdisciplinary process flow is proposed. Some meaningful results are obtained that were not addressed by previous studies. It is found that Radom Forest might be a better choice for datasets larger than 1,000 due to both high accuracy and fast speed. Besides, the dataset range affects the quantified importance (weighted value) of factors significantly, with up to a 115% increment. Moreover, the results show that machine learning has a high accuracy on the extrapolation prediction of productivity, where the minimum mean relative prediction error is just around 4%. The results of this work not only show the necessity of the dataset characteristics' effect but also provide a standard process for studying solar-thermal desalination by machine learning, which would pave the way for interdisciplinary study.

physics.app-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↗

arXiv2022

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↗