金属间化合物
材料科学
合金
密度泛函理论
虚拟筛选
热电效应
纳米技术
冶金
分子动力学
化学
物理
计算化学
热力学
作者
Taewon Jin,Yousung Jung
摘要
Abstract Heusler alloy is an intermetallic alloy with a wide range of compositional space. Due to this compositional tunability, novel Heusler alloys have been studied for a variety of applications including permanent magnets, thermoelectric, and catalysis. Recently, computational approaches have been actively used to aid the fast discovery of novel Heusler alloys by identifying stable compositions with desired properties. In this mini‐review, we briefly overview several computational approaches used for Heusler alloys, namely, density functional theory (DFT) calculations, high‐throughput virtual screening (HTVS), and machine learning (ML) to further accelerate these screening processes. Future directions for the computational screening of Heusler alloy in other application fields are discussed.
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