arXiv subjects

Ali Rajabpour

Publications and source records attributed to Ali Rajabpour.

At least 19 recordsLinked to original sources

arXiv2026

Interfacial thermal transport in Si/SiC and SiC/diamond heterostructures: effects of amorphous interlayers and SiC polytypes

This study examines phonon-mediated heat transfer across Si/SiC and SiC/diamond interfaces using non-equilibrium molecular dynamics simulations, emphasizing the influence of SiC polytypes and amorphous interlayers. For sharp interfaces, 4H-SiC exhibits considerably higher interfacial thermal conductance (ITC) than 3C-SiC, due to its broader active phonon spectrum and superior spectral matching with Si. While amorphous layers generally reduce ITC, a key observation is that an ultrathin 0.5-nm amorphous SiC (aSiC) layer can enhance heat transport in the Si/3C-SiC system: the ITC increases from 613 MW/m^2-K (sharp) to 716 MW/m^2-K, demonstrating a phonon-bridge effect. VDOS (vibrational density of states) analysis confirms that optimized ultrathin aSiC layers improve vibrational overlap and open additional phonon-transport channels. In contrast, thicker or silicon-rich amorphous layers significantly suppress ITC through enhanced inelastic phonon scattering. For SiC/diamond interfaces, any amorphous layer, particularly aSi, causes severe ITC degradation, highlighting the need for sharp, defect-free bonding to exploit diamond's high thermal conductivity.

cond-mat.mtrl-sci↗

arXiv2025

Interfacial Thermal Conductance Between a Polyethylene Glycol Polymer Chain and Water: A Molecular Dynamics Study

Understanding interfacial heat transfer between polymers and water is crucial for the design of biomaterials, drug delivery platforms, and nanofluidic systems. In this study, we employed all atom molecular dynamics (MD) simulations to quantify the interfacial thermal conductance between a polyethylene glycol (PEG) 36mer chain and explicit water over the temperature range of 280-350 K. To compare the conformational behavior of the PEG chain, we examined its radius of gyration and observed a temperature dependent chain collapse consistent with previous coarse grained models. By employing a transient non equilibrium MD approach, we imposed temperature difference across the interface and analyzed the energy relaxation behavior to compute heat transfer across the polymer water interfaces. Our results demonstrate that both temperature and interfacial interaction strength influence interfacial thermal conductance, with temperature playing the dominant role. Structural factors such as chain conformation and interfacial area were found to mediate the effect of interfacial interaction. Additional analysis of the vibrational density of states (VDOS) and the mean square displacement (MSD) reveal that vibrational coupling has minimal impact on thermal conductance across interfaces, whereas increased water thermal motion enhances energy transfer. These findings highlight the structural and dynamical origins of interfacial thermal conductance and provide atomistic insights into the tuning of interfacial heat transport in molecular systems through temperature and solvent interactions.

cond-mat.soft↗

arXiv2024

Accurate estimation of interfacial thermal conductance between silicon and diamond enabled by a machine learning interatomic potential

Thermal management at silicon-diamond interface is critical for advancing high-performance electronic and optoelectronic devices. In this study, we calculate the interfacial thermal conductance between silicon and diamond using machine learning (ML) interatomic potentials trained on density functional theory (DFT) data. Using non-equilibrium molecular dynamics (NEMD) simulations, we compute the interfacial thermal conductance (ITC) for various system sizes. Our results show a closer agreement with experimental data than those obtained using traditional semi-empirical potentials such as Tersoff and Brenner which overestimate ITC by a factor of about 3. In addition, we analyze the frequency-dependent heat transfer spectrum, providing insights into the contributions of different phonon modes to the interfacial thermal conductance. The ML potential accurately captures the phonon dispersion relations and lifetimes, in good agreement with DFT calculations and experimental observations. It is shown that the Tersoff potential predicts higher phonon group velocities and phonon lifetimes compared to the DFT results. Furthermore, it predicts higher interfacial bonding strength, which is consistent with higher interfacial thermal conductance as compared to the ML potential. This study highlights the use of the ML interatomic potential to improve the accuracy and computational efficiency of thermal transport simulations in complex material systems.

cond-mat.mtrl-sci↗

arXiv2024

Lattice thermal conductivity and mechanical properties of the single-layer penta-NiN2 explored by a deep-learning interatomic potential

Penta-NiN2, a novel pentagonal 2D sheet with potential nanoelectronic applications, is investigated in terms of its lattice thermal conductivity, stability, and mechanical behavior. A deep learning interatomic potential (DLP) is firstly generated from ab-initio molecular dynamics (AIMD) data and then utilized for classical molecular dynamics simulations. The DLP's accuracy is verified, showing strong agreement with AIMD results. The dependence of thermal conductivity on size, temperature, and tensile strain, reveals important insights into the material's thermal properties. Additionally, the mechanical response of penta-NiN2 under uniaxial loading is examined, yielding a Young's modulus of approximately 368 GPa. The influence of vacancy defects on mechanical properties is analyzed, demonstrating significant reduction in modulus, fracture stress, and ultimate strength. This study also investigates the influence of strain on phonon dispersion relations and phonon group velocity in penta-NiN2, shedding light on how alterations in the atomic lattice affect the phonon dynamics and, consequently, impact the thermal conductivity. This investigation showcases the ability of deep learning based interatomic potentials in studying the properties of 2D Penta-NiN2.

cond-mat.mtrl-sci↗

arXiv2024

Extreme near-field heat transfer between silica surfaces

Despite recent experiments exhibiting an impressive enhancement in radiative heat flux between parallel planar silica surfaces with gap sizes of about 10 nm, the exploration of sub-nanometric gap distances remains unexplored. In this work, by employing non-equilibrium molecular dynamics (NEMD) simulations, we study the heat transfer between two SiO2 plates in both their amorphous and crystalline forms. When the gap size is 2 nm, we find that the heat transfer coefficient experiences a substantial ~30-fold increase compared to the experimental value at the gap size of 10 nm confirming the dependence on the distance inversely quadratic as predicted by the fluctuational electrodynamics (FE) theory. Comparative analysis between NEMD and FE reveals a generally good agreement, particularly for amorphous silica. Spectral heat transfer analysis demonstrates the profound influence of gap size on heat transfer, with peaks corresponding to the resonances of dielectric function. Deviations from fluctuational electrodynamics theory at smaller gap sizes are interpreted in the context of acoustic phonon tunneling and the effects of a gradient of permittivity close to the surfaces.

cond-mat.mtrl-sci↗

arXiv2023

Enhanced interfacial thermal conductance in functionalized Boron Nitride/Polylactic acid nanocomposite: A molecular dynamics study

The relatively low thermal conductivity of biodegradable polylactic acid (PLA) has limited its applications in various fields. To address this issue, the incorporation of nanofillers, such as boron nitride nanosheets (BNNSs), has emerged as an effective method to enhance PLA's thermal properties. However, the thermal conduction of polymer-based nanocomposites is strongly influenced by interfacial thermal resistance. In this study, we investigate the impact of pristine and surface-treated BNNSs on the thermal behavior of PLA using molecular dynamics simulations. To enhance interfacial interactions and reduce chain mobility during heat transfer, we chemically modify the surface of BNNSs by introducing three different functional groups (NH2, OH, and COOH) with varying polarities. Our findings suggest that oxygen-containing groups, namely OH and COOH, exhibit stronger interfacial interactions compared to the other cases. We also systematically apply different percentages of these functional groups (i.e., 2.5, 5, and 7.5) and observe that a higher number of functional groups leads to a greater improvement in interfacial thermal transport, attributed to the enhanced phonon coupling effect. To complete the discussion, we thoroughly study the influence of random and agglomerated patterns of functional groups distribution.

cond-mat.mtrl-sci↗

arXiv2023

Enhanced Interfacial Thermal Conductance between Charged Nanoparticle and Aqueous Electrolyte

Heat transfer through the interface between a metallic nanoparticle and an electrolyte solution, has great importance in a number of applications, ranging from nanoparticle-based cancer treatments to nanofluids and solar energy conversion devices. However, the impact of surface charge and the dissolved ions on heat transfer has been scarcely explored so far. In this study, we compute the interface thermal conductance between hydrophilic and hydrophobic charged gold nanoparticles immersed in an electrolyte using equilibrium molecular dynamics simulations. Compared with an uncharged nanoparticle, we report a threefold increase of the Kapitza conductance for a nanoparticle surface charge +2 e/nm2. This enhancement is shown to be approximately independent of surface wettability, charge spatial distribution, and salt concentration. This allows us to express the Kapitza conductance enhancement in terms of surface charge density on a master curve. Finally, we interpret the increase of the Kapitza conductance as a combined result of a shift in the water density distribution toward the charged nanoparticle and an accumulation of the counter-ions around the nanoparticle surface which increase the Coulombic interaction between the liquid and the charged nanoparticle.

cond-mat.mes-hall↗

arXiv2022

Nonreciprocal nanoparticle refrigerators: design principles and constraints

We study the heat transfer between two nanoparticles held at different temperatures that interact through nonreciprocal forces, by combining molecular dynamics simulations with stochastic thermodynamics. Our simulations reveal that it is possible to construct nano refrigerators that generate a net heat transfer from a cold to a hot reservoir at the expense of power exerted by the nonreciprocal forces. Applying concepts from stochastic thermodynamics to a minimal under-damped Langevin model, we derive exact analytical expressions predictions for the fluctuations of work, heat, and efficiency, which reproduce thermodynamic quantities extracted from the molecular dynamics simulations. The theory only involves a single unknown parameter, namely an effective friction coefficient, which we estimate fitting the results of the molecular dynamics simulation to our theoretical predictions. Using this framework, we also establish design principles which identify the minimal amount of entropy production that is needed to achieve a certain amount of uncertainty in the power fluctuations of our nano refrigerator. Taken together, our results shed light on how the direction and fluctuations of heat flows in natural and artificial nano machines can be accurately quantified and controlled by using nonreciprocal forces.

cond-mat.stat-mech↗

arXiv2022

Lattice thermal conductivity and elastic modulus of XN4 (X=Be, Mg and Pt) 2D materials using machine learning interatomic potentials

The newly synthesized BeN4 monolayer has introduced a novel group of 2D materials called nitrogen-rich 2D materials. In the present study, the anisotropic mechanical and thermal properties of three members of this group, BeN4, MgN4, and PtN4, are investigated. To this end, a machine learning-based interatomic potential (MLIP) is developed on the basis of the moment tensor potential (MTP) method and utilized in classical molecular dynamics (MD) simulation. Mechanical properties are calculated by extracting the stress-strain curve and thermal properties by non-equilibrium molecular dynamics (NEMD) method. Acquired results show the anisotropic elastic modulus and lattice thermal conductivity of these materials. Generally, elastic modulus and thermal conductivity in the armchair direction are higher than in the zigzag direction. Also, the elastic anisotropy is almost constant at every temperature for BeN4 and MgN4, while for PtN4, this parameter is decreased by increasing the temperature. The findings of this research are not only evidence of the application of machine learning in MD simulations, but also provide information on the basic anisotropic mechanical and thermal properties of these newly discovered 2D nanomaterials.

cond-mat.mtrl-sci↗

arXiv2021

Interfacial thermal conductance between TiO2 nanoparticle and water: A molecular dynamics study

The interfacial thermal conductance (Kapitza conductance) between a TiO2 nanoparticle and water is investigated using transient non-equilibrium molecular dynamics. It is found that Kapitza conductance of TiO2 nanoparticles is one order of magnitude greater than other conventional nanoparticles such as gold, silver, silicon, platinum and also carbon nanotubes and graphene flakes. This difference can be explained by comparing the contribution of electrostatic interactions between the partially charged titanium and oxygen atoms and water atoms to the van der Waals interactions, which increases the cooling time by about 10 times. The effects of diameter and temperature of nanoparticle, surface wettability on the interfacial thermal conductance are also investigated. The results showed that by increasing the diameter of the nanoparticle from 4 to 9 nm, Kapitza conductance decreased slightly. Also, increasing the temperature of the heated nanoparticle from 400 K to 600 K led to thermal conductance enhancement. It has been found that increasing the coupling strength of Lennard-Jones (LJ) potential from 0.5 to 4 caused the increment of the Kapitza conductance about 20%. It is also shown that a continuum model which its input is provided by molecular dynamics can be a suitable approximation to describe the thermal relaxation of a nanoparticle in a liquid medium.

cond-mat.mtrl-sci↗

arXiv2021

Exploring thermal expansion of carbon-based nanosheets by machine-learning interatomic potentials

Examination of thermal expansion of two-dimensional (2D) nanomaterials is a challenging theoretical task with either ab-initio or classical molecular dynamics simulations. In this regard, while ab-initio molecular dynamics (AIMD) simulations offer extremely accurate predictions, but they are excessively demanding from computational point of view. On the other side, classical molecular dynamics simulations can be conducted with affordable computational costs, but without predictive accuracy needed to study novel materials and compositions. Herein, we explore the thermal expansion of several carbon-based nanosheets on the basis of machine-learning interatomic potentials (MLIPs). We show that passively trained MLIPs over inexpensive AIMD trajectories enable the examination of thermal expansion of complex nanomembranes over wide range of temperatures. Passively fitted MLIPs could also with outstanding accuracy reproduce the phonon dispersion relations predicted by density functional theory calculations. Our results highlight that the devised methodology on the basis of passively trained MLIPs is computationally efficient and versatile to accurately examine the thermal expansion of complex and novel materials and compositions using the molecular dynamics simulations.

cond-mat.mtrl-sci↗

arXiv2021

Learning the Hydrophilic, Hydrophobic and Aromatic Character of Amino Acids from their Interfacial Thermal Conductance in Water

In this study, the thermal relaxation of the 20 naturally occurring amino-acids in water is investigated using transient non-equilibrium molecular-dynamics simulations. By modeling the thermal relaxation process, the relaxation times of the amino-acids in water occurs over a timescale covering 2-5 ps. For the hydrophobic amino acids, the relaxation time is controlled by the size of the hydrocarbon side chain, while for hydrophilic amino acids, the number of hydrogen bonds do not significantly affect the timescales of the heat dissipation. Our results show that the interfacial thermal conductance at the amino-acid water interface is in the range of~40-80 MWm$^{-2}$K$^{-1}$. Hydrophobic and aromatic amino acids tend to have a lower interfacial thermal conductance. Notably, we reveal that amino acids can be classified, in terms of their thermal relaxation times and molar masses, into simply connected phases with the same hydrophilicity, hydrophibicity and aromaticity.

cond-mat.soft↗

arXiv2021

Thermal transport in van der Waals graphene/boron-nitride structure: a molecular dynamics study

Among the van der Waals heterostructures, graphene/h-BN heterostructure is an appropriate candidate for 2D nanoelectronic devices. In this paper, using non-equilibrium molecular dynamics simulation approach, heat transport in bilayer graphene/h-BN and graphene/h-BN van der Waals heterostructure (i.e. h-BN flakes periodically inserted on the top and bottom of a graphene layer) are explored. The results show that by increasing the length of the system, the thermal conductivity of bilayer graphene/h-BN increases. Furthermore, it was revealed that heat transport in graphene/h-BN heterostructure enhances compared to that in monolayer graphene or monolayer h-BN. The size effect analysis shows that the heat fluxes passing through each layer in bilayer graphene/h-BN converges when the size of the system is larger than 100 nm. The results can improve the understanding heat transfer phenomena in the van der Waals heterostructures and improve designing of heterostructures for better thermal management and heat dissipation.

cond-mat.mtrl-sci↗

arXiv2021

Enhanced local viscosity around colloidal nanoparticles probed by Equilibrium Molecular Dynamics Simulations

Nanofluids; dispersions of nanometer-sized particles in a liquid medium; have been proposed for a wide variety of thermal management applications. It is known that a solid-like nanolayer of liquid of typical thickness 0.5-1 nm surrounding the colloidal nanoparticles can act as a thermal bridge between the nanoparticle and the bulk liquid. Yet, its effect on the nanofluid viscosity has not been elucidated so far. In this article, we compute the local viscosity of the nanolayer using equilibrium molecular dynamics based on the Green-Kubo formula. We first assess the validity of the method to predict the viscosity locally. We apply this methodology to the calculation of the local viscosity in the immediate vicinity of a metallic nanoparticle for a wide range of solid-liquid interaction strength, where a nanolayer of thickness 1 nm is observed as a result of the interaction with the nanoparticle. The viscosity of the nanolayer, which is found to be higher than its corresponding bulk value, is directly dependent on the solid-liquid interaction strength. We discuss the origin of this viscosity enhancement and show that the liquid density increment alone cannot explain the values of the viscosity observed. Rather, we suggest that the solid-like structure of the distribution of the liquid atoms in the vicinity of the nanoparticle contributes to the nanolayer viscosity enhancement. Finally, we observe a failure of the Stokes-Einstein relation between viscosity and diffusion close to the wall, depending on the liquid-solid interaction strength, which we rationalize in terms of hydrodynamic slip.

cond-mat.soft↗

arXiv2021

Thermal transport in two-dimensional C3N/C2N superlattices: A molecular dynamics approach

Nanostructured superlattices have been the focus of many researchers due to their physical and manipulatable properties. They aim to find promising materials for new electronic and thermoelectric devices. In the present study, we investigate the thermal conductivity of two-dimensional (2D) C3N/ C2N superlattices using non-equilibrium molecular dynamics. We analyze the dependence of thermal conductivity on the total length, temperature, and the temperature difference between thermal baths for the superlattices. The minimum thermal conductivity and the phonon mean free path at a superlattice period of 5.2 nm are 23.2W/m.K and 24.7 nm, respectively. Our results show that at a specific total length, as the period increases, the number of interfaces decreases, thus the total thermal resistance decreases, and the effective thermal conductivity of the system increases. We found that at long lengths (L_x >80 nm), the high-frequency and low-wavelength phonons are scattered throughout the interfaces, while at short lengths, there is a wave interference that reduces the thermal conductivity. The combination of these two effects, i.e., the wave interference and the interface scattering, is the reason for the existence of a minimum thermal conductivity in superlattices.

cond-mat.mtrl-sci↗

arXiv2021

Effect of graphene and carbon-nitride nanofillers on the thermal transport properties of polymer nanocomposites: A combined molecular dynamics and finite element study

Low thermal conductivity of polymers, which is one of the considerable drawbacks of commonly used composite structures, has been the focus of many researchers aiming to achieve high-performance polymer-based nanocomposites through the inclusion of highly thermally conductive fillers inside the polymer matrices. Thus, in the present study, a multiscale scheme using non-equilibrium molecular dynamics (NEMD) and finite element (FE) method is developed to explore the impact of different nano-sized fillers (carbon-nitride and graphene) on the effective thermal conductivity of polyethylene-based nanocomposites. We show that the thermal conductivity of amorphous polyethylene at room temperature using the reactive bond order (REBO) interatomic potential is nearly 0.36 0.05W/mK. Also, the atomistic results predict that, compared to the C3N and graphene nanosheets, the C2N nanofilm presents a much stronger interfacial thermal conductance (ITC) with polyethylene. Furthermore, the results indicate that the effective thermal conductivity values of C2N-polyethylene, C3N-polyethylene, and graphene-polyethylene nanocomposite, at constant volume fractions of 1%, are about 0.47, 0.56, and 0.74 W/mK, respectively. In other words, the results of our models reveal that the thermal conductivity of fillers is the dominant factor that defines the effective thermal conductivity of nanocomposites.

cond-mat.mtrl-sci↗

arXiv2021

Thermo-mechanical properties of nitrogenated holey graphene (C2N): A comparison of machine-learning-based and classical interatomic potentials

Thermal and mechanical properties of two-dimensional nanomaterials are commonly studied by calculating force constants using the density functional theory (DFT) and classical molecular dynamics (MD) simulations. Although DFT simulations offer accurate estimations, the computational cost is high. On the other hand, MD simulations strongly depend on the accuracy of interatomic potentials. Here, we investigate thermal conductivity and elastic modulus of nitrogenated holey graphene (C2N) using passively fitted machine-learning interatomic potentials (MLIPs), which depend on computationally inexpensive ab-initio molecular dynamics trajectories. Thermal conductivity of C2N is investigated via MLIP-based non-equilibrium molecular dynamics simulations (NEMD). At room temperature, the lattice thermal conductivity of 85.5 W/m-K and effective phonon mean free path of 37.16 nm are found. By carrying out uniaxial tension simulations, the elastic modulus, ultimate strength, and fractural strain of C2N are predicted to be 390 GPa, 42 GPa, and 0.29, respectively. It is shown that the passively fitted MLIPs can be employed as an efficient interatomic potential to obtain the thermal conductivity and elastic modulus of C2N utilizing classical MD simulations. Moreover, the possibility of employing MLIPs to simulate C2N with point defects has been investigated. By training MLIP with point defect configurations, the mechanical properties of defective structures were studied. Although using the MLIP is more costly than classical interatomic potentials, it could efficiently predict the thermal and mechanical properties of 2D nanostructures.

cond-mat.mtrl-sci↗

arXiv2019

Thermal conductivity of graphene polymorphs and compounds: from C3N to graphdiyne lattices

Tremendous experimental and theoretical attempts to find carbon based two-dimensional semiconductors have yielded a wide variety of graphene polymorphs, such as carbon-nitride, carbonboride, graphyne and graphdiyne 2D materials with highly attractive physical and chemical properties. In this study, by conducting extensive non-equilibrium molecular dynamics simulations, we have calculated and compared the thermal conductivity of thirteen prominent carbon-based structures at different lengths and two main chirality directions. Acquired results show that the structures of C3N, C3B and C2N exhibit the highest thermal conductivity, respectively, which suggest them as suitable candidates for thermal management systems in order to enhance the heat dissipation rates. In contrast, generally graphdiyne lattices and in particular 18-6-Gdy graphdiyne yields the lowest thermal conductivity, which can be a promising feature for thermoelectric applications. As a remarkable finding, we could establish connections between the thermal conductivity and density or Young's modulus of carbon based 2D systems, which can be employed to estimate the thermal conductivity of other polymorphs. Those results can provide a comprehensive viewpoint on the thermal transport properties of the nonporous and exceedingly porous carbon based 2D materials and may be used as useful guides for future designs in thermal management.

cond-mat.mtrl-sci↗