相关论文: Wenzhou TE: a first-principles calculated thermoel…
This manuscript proposes an optimization framework to find the tailor-made functionally graded material (FGM) profiles for thermoelastic applications. This optimization framework consists of (1) a random profile generation scheme, (2) deep…
One of the ultimate goals of computational modeling in condensed matter is to be able to accurately compute materials properties with minimal empirical information. First-principles approaches such as the density functional theory (DFT)…
This chapter provides a tutorial overview of first principles methods to describe the properties of matter at the ground state or equilibrium. It begins with a brief introduction to quantum and statistical mechanics for predicting the…
In this Letter, we show thermoelectric conversion efficiency is \emph{exactly} determined by \emph{three} independent material parameters $Z_{\rm gen}$, $\tau$, and $\beta$. Each parameter is a figure of merit hence improving $\tau$ or…
Perovskite material such as BCZT (Ba$_{0.875}$Ca$_{0.125}$(Zr$_{0.125}$Ti$_{0.875}$)O$_{3}$) is well known for its high value of piezoceramic properties and Curie temperature which has potential applications in sensors, actuators,…
The structure and energy of grain boundaries (GBs) are essential for predicting the properties of polycrystalline materials. In this work, we use high-throughput density functional theory calculations workflow to construct the Grain…
Although the convergence of high-performance computing, automation, and machine learning has significantly altered the materials design timeline, transformative advances in functional materials and acceleration of their design will require…
Modern materials science has historically been founded on combining restricted subsets of the periodic table, favoring high-purity, few-element systems. However, the demands of an emerging circular economy, together with the need to…
Machine Learning (ML) has offered innovative perspectives for accelerating the discovery of new functional materials, leveraging the increasing availability of material databases. Despite the promising advances, data-driven methods face…
Efficiently predicting properties of porous crystalline materials has great potential to accelerate the high throughput screening process for developing new materials, as simulations carried out using first principles model are often…
Accelerated cooling is a key technology in producing thermomechanically controlled processed (TMCP) steel plates. In a TMCP process hot plates are subjected to a strong cooling what results in a complex microstructure leading to increased…
Latent heat thermal energy storage (LHTES) systems are compelling candidates for energy storage, primarily owing to their high storage density. Improving their performance is crucial for developing the next-generation efficient and cost…
Technologies that function at room temperature often require magnets with a high Curie temperature, $T_\mathrm{C}$, and can be improved with better materials. Discovering magnetic materials with a substantial $T_\mathrm{C}$ is challenging…
We study thermoelectric properties of inhomogeneous nanogranular materials for weak tunneling conductance between the grains, g_t < 1. We calculate the thermopower and figure of merit taking into account the shift of the chemical potential…
In this work, we study the suitability of different thermoelectric materials like $Bi_2Te_3$, $Sb_{2}Te_{3}$, $PbTe$, $TAGS$, $CeFe_{4}Sb_{12}$, $SiGe$ and $TiO_{1.1}$ in estimation of thermoelectric generator's (TEG) efficiency. The…
Low dimensional structures have demonstrated improved thermoelectric (TE) performance because of a drastic reduction in their thermal conductivity, {\kappa}l. This has been observed for a variety of materials, even for traditionally poor…
The electronic transport behaviour of materials determines their suitability for technological applications. We develop an efficient method for calculating carrier scattering rates of solid-state semiconductors and insulators from first…
The availability of big data in materials science offers new routes for analyzing materials properties and functions and achieving scientific understanding. Finding structure in these data that is not directly visible by standard tools and…
Thermoelectric energy conversion is an attractive technology for generating electricity from waste heat and using electricity for solid-state cooling. However, conventional manufacturing processes for thermoelectric devices are costly and…
We propose an efficient approach for simultaneous prediction of thermal and electronic transport properties in complex materials. Firstly, a highly efficient machine-learned neuroevolution potential is trained using reference data from…