相关论文: Simulating lattice thermal conductivity in semicon…
Amorphous silicon (a-Si) is an important thermal-management material and also serves as an ideal playground for studying heat transport in strongly disordered materials. Theoretical prediction of the thermal conductivity of a-Si in a wide…
Recent molecular dynamics simulation methods have enabled thermal conductivity of bulk materials to be estimated. In these simulations, periodic boundary conditions are used to extend the system dimensions to the thermodynamic limit. Such a…
High-efficient heat dissipation plays critical role for high-power-density electronics. Experimental synthesis of ultrahigh thermal conductivity boron arsenide (BAs, 1300 W m-1K-1) cooling substrates into the wide-bandgap semiconductor of…
The microscopic physics behind the lattice thermal conductivity of NiTiSn is investigated using first-principles-based anharmonic lattice dynamics. The calcu lated lattice thermal conductivity of bulk materials (5.3 W/m.K) is in good…
Uranium dioxide (UO2) is a prototypical nuclear fuel material, yet predicting its thermophysical properties across a wide temperature range remains challenging. One factor contributing to this difficulty is the complex magnetic ordering at…
Thermal properties of solid-state materials are a fundamental topic of study with important practical implications. For example, anisotropic displacement parameters (ADPs) are routinely used in physics, chemistry, and crystallography to…
The prediction of the atomistic structure and properties of crystals including defects based on ab-initio accurate simulations is essential for unraveling the nano-scale mechanisms that control the micromechanical and macroscopic behaviour…
Thermal phonon transport in square- and triangular-lattice Si phononic crystal (PnC) nanostructures with a period of 300 nm was investigated by measuring the thermal conductivity using micrometer-scale time-domain thermoreflectance. The…
Heat capacity, magnetic susceptibility, NMR, and resistivity of SrNi2P2 single crystals are presented, illustrating a purely structural transition at 325 K with no magnetism. Bulk superconductivity is found at 1.4 K. The magnitude of the…
Machine learning promises to accelerate the material discovery by enabling high-throughput prediction of desirable macro-properties from atomic-level descriptors or structures. However, the limited data available about precise values of…
The discovery and optimization of high-energy materials (HEMs) are constrained by the prohibitive computational expense and prolonged development cycles inherent in conventional approaches. In this work, we develop a general neural network…
Designing materials with targeted lattice thermal conductivity (LTC) demands electronic-level insight into chemical bonding. We introduce two bonding descriptors, namely normalized negative integrated crystal orbital Hamilton populations…
High-throughput computational screening has emerged as a critical component of materials discovery. Direct density functional theory (DFT) simulation of inorganic materials and molecular transition metal complexes is often used to describe…
High-temperature bad-metal transport has been recently studied both theoretically and in experiments as one of the key signatures of strong electronic correlations. Here we use the dynamical mean field theory (DMFT) and its cluster…
We study the accuracy of Kohn-Sham density functional theory (DFT) for warm- and hot-dense matter (WDM and HDM). Specifically, considering a wide range of systems, we perform accurate ab initio molecular dynamics simulations with…
Accurate prediction of the thermal and electrical conductivities of materials under extremely high temperatures is essential in high-energy-density physics. These properties govern processes such as stellar core dynamics, planetary magnetic…
Diminution of the thermal conductivity is a crucial aspect in thermoelectric research. We report a systematic and significant reduction of the cross-plane thermal conductivity in a model system consisting of DC sputtered TiNiSn and HfNiSn…
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…
The quest to improve the thermoelectric figure of merit has mainly followed the roadmap of lowering the thermal conductivity while keeping unaltered the power factor of the material. Ideally an electron-crystal phonon-glass system is…
Non-equilibrium molecular dynamics (NEMD) techniques are widely used for investigating lattice thermal conductivity. Recently, machine learning force fields (MLFFs) have emerged as a promising approach to enhance the precision in NEMD…