Related papers: Accurate estimation of interfacial thermal conduct…
In the present study we investigate the phase diagram of silicon within the framework of SNAP machine learning potential model. We show that the melting line of diamond phase of silicon is a linear function of pressure, which is in good…
Ultrahigh lattice thermal conductivity materials hold great importance since they play a critical role in the thermal management of electronic and optical devices. Models using machine learning can search for materials with outstanding…
In the present work we detail how the many-body potential energy landscape of interatomic potentials for carbon can be explored by utilising the nested sampling algorithm, allowing the calculation of their pressure-temperature phase diagram…
Understanding heat transfer across solid-liquid interfaces is central to thermal management and energy technologies, yet whether the interfacial thermal conductance (ITC) depends on the timescale of heating remains unclear. Here we use…
Understanding interfacial thermal transport is essential for improving thermal management in high-speed power electronic devices, where the efficient removal of excess heat is a critical challenge. In this study, a machine learning…
Accurately modeling interfacial thermal transport in van der Waals heterostructures is challenging due to the limited availability of interlayer interaction potentials. We develop a pairwise interlayer potential for graphene/germanene van…
While the effect of intrinsic defects on the electronic properties of half-Heusler compounds has been extensively discussed in literature, their effect on the lattice vibrations has received much less attention, due to the prohibitive…
The lattice thermal conductivity ($\kappa$) of two ceramic materials, cerium dioxide (CeO$_2$) and magnesium oxide (MgO), is computed up to 1500 K using first principles and the phonon Boltzmann Transport Equation (PBTE) and compared to…
Machine learned potentials (MLPs) have been widely employed in molecular dynamics (MD) simulations to study thermal transport. However, literature results indicate that MLPs generally underestimate the lattice thermal conductivity (LTC) of…
Metallic transition-metal nitrides (TMNs) are promising conductive ceramics for many applications, whose thermal transport is of great importance in device design. It is found metallic TiN and HfN hold anomalous thermal transport behaviors…
Improving interfacial thermal transport is crucial for heat dissipation in systems. Here, we design a strategy by utilizing the water adsorption-desorption process in porous metal-organic frameworks (MOFs) to tune the interfacial heat…
We present a new method for predicting effective thermal conductivity ($\kappa_{\textrm{eff}}$) in materials, informed by ${ab\,initio}$ material property simulations. Using the Boltzmann transport equation in a Self-Adjoint Angular Flux…
Using the atomic cluster expansion (ACE) framework, we develop a machine learning interatomic potential for fast and accurately modelling the phonon transport properties of wurtzite aluminum nitride. The predictive power of the ACE…
Medium-entropy alloys (MEAs) such as CoCrFeNi and CoCrNi are promising structural materials owing to their outstanding mechanical and thermal properties, which arise from complex chemical disorder and atomic-scale interactions. Although…
Ductile Regime Machining of semiconductors (DRM) offers higher quality of the resulting surfaces. To optimize this process, it is required to understand the thermal kinetics of silicon metallization under pressure. Such understanding is not…
We present experimental measurements of the thermal boundary conductance (TBC) from $77 - 500$ K across isolated heteroepitaxially grown ZnO films on GaN substrates. These data provide an assessment of the assumptions that drive the phonon…
In computational materials science, a common means for predicting macroscopic (e.g., mechanical) properties of an alloy is to define a model using combinations of descriptors that depend on some material properties (elastic constants,…
Machine-learned interatomic potentials (MLIPs) show promise in accurately describing the physical properties of materials, but there is a need for a higher throughput method of validation. Here, we demonstrate using that MLIPs and molecular…
Owing to their long phonon mean free paths (MFPs) and high thermal conductivity, carbon nanotubes (CNTs) are ideal candidates for, e.g., removing heat from electronic devices. It is unknown, however, how the intrinsic phonon MFPs depend on…
We compute atomistically the heat conductance for ultra-thin pristine silicon nanowires (SiNWs) with diameters ranging from 1 to 5 nm. The room temperature thermal conductance is found to be highly anisotropic: wires oriented along the…