相关论文: High-Throughput Computational Screening of thermal…
We develop a technique for predicting the Curie temperature of magnetic materials using density functional theory calculations suitable to include in high-throughput frameworks. We apply four different models, including physically relevant…
Theoretically predicted linear correlation between the volume coefficient of thermal expansion and the thermal heat capacity was investigated for highly symmetrical atomic arrangements. Normalizing the data of these thermodynamic parameters…
Ab initio Green-Kubo (aiGK) simulations of heat transport in solids allow for assessing lattice thermal conductivity in anharmonic or complex materials from first principles. In this work, we present a detailed account of their practical…
Reliable artificial-intelligence models have the potential to accelerate the discovery of materials with optimal properties for various applications, including superconductivity, catalysis, and thermoelectricity. Advancements in this field…
For a given crystal structure, say body-centred-cubic, the many-body Hamiltonian in which nuclear and electron motions are to be treated from the outset on the same footing, has parameters, for the elements, which can be classified as (i)…
The discovery of novel materials for thermoelectric energy conversion has potential to be accelerated by data-driven screening combined with high-throughput calculations. One way to increase the efficacy of successfully choosing a candidate…
The efficient and economical exploitation of polymers with high thermal conductivity is essential to solve the issue of heat dissipation in organic devices. Currently, the experimental preparation of functional thermal conductivity polymers…
Ab initio calculations based on the Density Functional Theory are used to show that the Debye frequency is a linear function of density to a high accuracy for several elemental solids at pressures (at least) up to 360 GPa. This implies that…
We present measurements of the low temperature thermal conductivity for materials useful in the construction of cryogenic supports for scientific instrumentation and in the fabrication of flat flexible cryogenic cabling. The materials we…
An uncoupled multi-scale homogenization approach is used to estimate the effective thermal conductivities of plain weave C/C composites with a high degree of porosity. The geometrical complexity of the material system on individual scales…
The optical conductivity, $\sigma(\omega)$, of the two dimensional one-band Hubbard model is calculated at finite temperature using exact diagonalization techniques on finite clusters. The in-plane d.c. resistivity, $\rho_{ab}$, is also…
The structural, electronic, optical and thermodynamic properties of Mo2Ga2C are investigated using density functional theory (DFT) within the generalized gradient approximation (GGA). The optimized crystal structure is obtained and the…
The excellent magnetic entropy change ($\Delta S_T$) in the temperature range of 20 $\sim$ 77 K due to the first-order phase transition makes $Pr_2In$ an intriguing candidate for magnetocaloric hydrogen liquefaction. As an equally important…
Predicting microstructure evolution during thermomechanical treatment is essential for determining the final mechanical properties of a material, yet conventional simulations based on Partial Differential Equations (PDEs) remain…
An analysis of thermal transients from non-equilibrium ab initio molecular-dynamics simulations can be used to calculate the thermal conductivity of materials with a short phonon mean-free path. We adapt the approach-to-equilibrium…
We present a computational framework that integrates machine learning with high-throughput \textit{ab initio} calculations to screen over 2.8 million compounds for metallic transport. We identify several intermetallic candidates with…
We propose an efficient scheme, which combines density functional theory (DFT) with deep potentials (DP), to systematically study the convergence issues of the computed electronic thermal conductivity of warm dense Al (2.7 g/cm$^3$,…
Materials with a low thermal conductivity are important for a variety of applications such as thermal barrier coatings and thermoelectrics, and understanding the underlying mechanisms of low heat transport, and relating them to structural…
The Cold Posterior Effect (CPE) is a phenomenon in Bayesian Deep Learning (BDL), where tempering the posterior to a cold temperature often improves the predictive performance of the posterior predictive distribution (PPD). Although the term…
By combining ab initio quantum mechanics calculation and Drude model, electron temperature and lattice temperature dependent electron thermal conductivity is calculated and implemented into a multiscale model of laser material interaction,…