Ternary half-Heusler compounds with widely tunable electronic structures, present a new platform to discover topological insulators. Due to time-consuming computations and synthesis procedures, the identification of new topological insulators is however a rough task. Here, we adopt a compressed-sensing approach to rapidly screen potential topological insulators in half-Heusler family, which is realized via a two-dimensional descriptor that only depends on the fundamental properties of the constituent atoms. Beyond the finite training data, the proposed descriptor is employed to screen many new half-Heusler compounds, including those with integer and fractional stoichiometry, and a larger number of possible topological insulators are predicted.
We propose a general rule for estimating the magnetic moments of Co$ 2$(cobalt)-based Heusler alloys, especially when doped with late transition metals. We come up with a descriptor that can characterise both pure Co$_2$YZ compounds and the doped ones with the chemical formula Co$_2$Y$_{1-x}$M$_x$Z (M is the dopant) using online data for magnetic moments of Heusler alloys with Co$_2$YZ structure and compressive sensing approach. The newly proposed descriptor not only depends on the number of valence electrons of the compound also it depends on the number of unoccupied d-electrons in the doping…
The Heusler compounds have provided a playground of material candidates for various technological applications based on their highly diverse and tunable properties, controlled by chemical composition and crystal structure. However, physical exploration of the Heusler chemical space en masse is impossible in practice, hindering the exploration of the chemical composition vs. proprieties relationship. Many of these applications are related to the Heuslers electron transport characteristics, which are embedded in their electronic band structure (EBS). Here we we created a Heuslers dataset using t…
Accurate Screening of Functional Materials with Machine-Learning Potential and Transfer-Learned Regressions: Heusler Alloy Benchmark
Research Center for Magnetic and Spintronic Materials, National Institute for Materials Science（磁性及自旋材料研究中心，物质材料研究机构）；Digital Transformation Initiative Center for Magnetic Materials (DXMag), National Institute for Materials Science（磁性材料数字化转型倡议中心（DXMag），物质材料研究机构）
Finding unprecedentedly low-thermal-conductivity half-Heusler semiconductors via high-throughput materials modeling
晶格热导率（κω）是化合物许多潜在应用的关键性质。由于高成本和耗时的合成程序，具有非常低或高κω的材料的发现仍然是实验挑战。高通量计算预筛选是一种有价值的方法，显着减少候选化合物的集合。在这篇文章中，我们介绍了可靠地估计大量化合物的体κω的有效方法。这些算法基于机器学习算法、物理见解和自动从头计算的组合。我们在AFLOWLIB.org数据库中扫描了大约79，000个半Heusler条目。在确定的450个机械稳定的有序半导体中，我们发现κω跨越两个数量级以上-比以前认为的范围大得多。对于在等价位置的元素具有大原子半径的化合物，κω最低。然后，我们对材料稳定性进行了彻底的筛选，从而将列表减少到77个系统。然后，我们可以为这个选定的系统范围提供κω的定量估计。提出了三种κω < 5 W /（mK）的半导体材料，供进一步的实验研究。
Chemical doping is one of the most important strategies for tuning electrical properties of semiconductors, particularly thermoelectric materials. Generally, the main role of chemical doping lies in optimizing the carrier concentration, but there can potentially be other important effects. Here, we show that chemical doping plays multiple roles for both electron and phonon transport properties in half-Heusler thermoelectric materials. With ZrNiSn-based half-Heusler materials as an example, we use high-quality single and polycrystalline crystals, various probes, including electrical transport m…
The predictive performance screening of novel compounds can significantly promote the discovery of efficient, cheap, and non-toxic thermoelectric materials. Large efforts to implement machine-learning techniques coupled to materials databases are currently being undertaken, but the adopted computational methods can dramatically affect the outcome. With regards to electronic transport and power factor calculations, the most widely adopted and computationally efficient method, is the constant relaxation time approximation (CRT). This work goes beyond the CRT and adopts the proper, full energy an…
