相关论文: Machine-Learning-based Prediction of Lattice Therm…

Safe and high-density storage of hydrogen, for a clean-fuel economy, can be realized by hydride-forming materials, but these materials should be able to store hydrogen at room temperature. Some high-entropy alloys (HEAs) have recently been…

Recently, $\mathrm{BaSn_2}$ is predicted to be a strong topological insulator by the first-principle calculations. It is well known that topological insulator has a close connection to thermoelectric material, such as $\mathrm{Bi_2Te_3}$…

While machine learning (ML) has shown increasing effectiveness in optimizing materials properties under known physics, its application in challenging conventional wisdom and discovering new physics still remains challenging due to its…

Spin caloritronics studies the interplay between charge-, heat- and spin-currents, which are initiated by temperature gradients in magnetic nanostructures. A plethora of new phenomena has been discovered that promises, e.g., to make wasted…

Machine Learning (ML) techniques are revolutionizing the way to perform efficient materials modeling. Nevertheless, not all the ML approaches allow for the understanding of microscopic mechanisms at play in different phenomena. To address…

Alkali metal-based compounds have garnered significant attention due to their exceptionally low lattice thermal conductivity, which is crucial for applications in thermoelectric energy conversion and thermal barrier coatings. However, the…

Predicting high temperature superconductors has long been a great challenge. A major difficulty is how to predict the transition temperature Tc of superconductors. Recently, progress in material informatics has led to a number of machine…

Understanding heat transport in semiconductors and insulators is of fundamental importance because of its technological impact in electronics and renewable energy harvesting and conversion. Anharmonic Lattice Dynamics provides a powerful…

The interplay between half-metallic ferromagnetism and spin-orbit coupling within the inversion symmetry-broken structure of half Heuslers provides an ideal platform for various spintronics functionalities. Taking advantage of good lattice…

Density functional theory is routinely applied to predict crystal structures. The most common exchange-correlation functionals used to this end are the Perdew-Burke-Ernzerhof (PBE) approximation and its variant PBEsol. We investigate the…

In this paper we focus on analyzing the thermal modality of tactile sensing for material recognition using a large materials database. Many factors affect thermal recognition performance, including sensor noise, the initial temperatures of…

Heusler compounds have always attracted a great deal of attention from researchers thanks to a wealth of interesting properties for technological applications. They are intermetallic ductile compounds, and some of them have been found to be…

First-principles computations are the driving force behind numerous discoveries of hydride-based superconductors, mostly at high pressures, during the last decade. Machine-learning (ML) approaches can further accelerate the future…

Double half-heusler alloys are the new class of compounds which can be seen as transmuted version of two single half-heusler with higher flexibility of tuning their properties. Here, we report a detailed study of thermoelectric (TE)…

Recently, machine learning has emerged as an alternative, powerful approach for predicting quantum-mechanical properties of molecules and solids. Here, using kernel ridge regression and atomic fingerprints representing local environments of…

Recently, machine-learning approaches have accelerated computational materials design and the search for advanced solid electrolytes. However, the predictors are currently limited to static structural parameters, which may not fully account…

Taking into considerations the wide compositional stretch of Heusler alloys, the first principles density functional theory based calculations are excellently suitable for estimating the multifaceted properties of alkali metal based LiVSb…

Hydrogen-rich superhydrides are believed to be very promising high-T$_c$ superconductors, with experimentally observed critical temperatures near room temperature, as shown in recently discovered lanthanide superhydrides at very high…

Data-driven methods, in particular machine learning, can help to speed up the discovery of new materials by finding hidden patterns in existing data and using them to identify promising candidate materials. In the case of superconductors,…

Metallic conductors that are optically transparent represent a rare breed of generally contraindicated physical properties that are nevertheless critically needed for application where both functionalities are crucial. Such rare materials…