相关论文: Unlocking Thermoelectric Potential: A Machine Lear…

Machine learning (ML) can facilitate efficient thermoelectric (TE) material discovery essential to address the environmental crisis. However, ML models often suffer from poor experimental generalizability despite high metrics. This study…

Predicting the melting temperature (Tm) of multi-component and high-entropy alloys (HEAs) is critical for high-temperature applications but computationally expensive using traditional CALPHAD or DFT methods. In this work, we develop a…

The half-Heusler compound has drawn attention in a variety of fields as a candidate material for thermoelectric energy conversion and spintronics technology. This is because it has various electronic structures, such as semi-metals,…

We report the machine learning (ML)-based approach allowing thermoelectric generator (TEG) efficiency evaluation directly from 5 parameters: 2 physical properties - carriers density and energy gap, and 3 engineering parameters - external…

Half Heusler (HH) thermoelectric alloys provide a wide platform to choose materials with non-toxic and earth abundant elements. This article presents an ab-initio theoretical evaluation of electrical and thermal transport properties of…

Half-Heuslers are a promising family for thermoelectric (TE) applications, yet only a small fraction of their potential chemistries has been experimentally explored. In this work, we introduce a distinct computational high-throughput…

Random forest (RF) regression model is used to predict the lattice constant, magnetic moment and formation energies of full Heusler alloys, half Heusler alloys, inverse Heusler alloys and quaternary Heusler alloys based on existing as well…

A novel phenomenological framework for an efficient estimation of the thermo-electric properties at room temperature and elevated temperatures of body-centered cubic (BCC) transition metal concentrated alloys is proposed in this work. The…

In this study, we attempted to model vacancy ordered half Heusler compounds with 18 valence electron count (VHH) derived from 19 VEC compounds such as TiNiSb such that the compositions will be Ti0.75NiSb, Zr0.75NiSb and Hf0.75NiSb with…

The reason behind the remarkable properties of High-Entropy Alloys (HEAs) is rooted in the diverse phases and the crystal structures they contain. In the realm of material informatics, employing machine learning (ML) techniques to classify…

Thermoelectric materials can be used to construct devices which recycle waste heat into electricity. However, the best known thermoelectrics are based on rare, expensive or even toxic elements, which limits their widespread adoption. To…

In the ongoing search for innovative thermoelectric (TE) materials with superior TE performance globally, we aim to investigate the possible use of half-Heusler alloy CoHfSi in TE applications. We analyzed the structure stability,…

Half-Heusler (HH) phases have garnered much attention as thermally stable and non-toxic thermoelectric materials for power conversion. The most studied alloys to date utilize Hf, Zr, and Ti as the base components. These alloys can achieve a…

In this study, theoretical investigation on structural, electronic, magnetic, elastic and thermoelectric properties of the full Heusler Co$_2$YPb (Y = Tc, Ti, Zr and Hf) alloys have been performed within density functional theory (DFT). The…

Half-Heusler (HH) alloys are potential thermoelectric materials for use at elevated temperatures due to their high Seebeck coefficient and superior mechanical and thermal stability. However, their enhanced lattice thermal conductivity is…

This study investigates the application of Random Forest Regression for predicting mechanical properties of alloy steel-Elongation, Tensile Strength, and Yield Strength-from material composition features including Iron (Fe), Chromium (Cr),…

The most favorable structure of the synthesized TiCoSb half-Heusler alloy is explored theoretically and experimentally, and the best structure for thermoelectric conversion is reported. Rietveld refinement of the X-ray diffraction data…

A half-Heusler material FeNb$_{0.8}$Ti$_{0.2}$Sb has been identified as a promising thermoelectric material due to its excellent thermoelectric performance at high temperatures. The origins of the efficient thermoelectric performance are…

A variety of statistical and machine learning methods are used to model crash frequency on specific roadways with machine learning methods generally having a higher prediction accuracy. Recently, heterogeneous ensemble methods (HEM),…

The thermoelectric performance of materials exhibits complex nonlinear dependencies on both elemental types and their proportions, rendering traditional trial-and-error approaches inefficient and time-consuming for material discovery. In…