Machine learning study of highly spin-polarized Heusler alloys at finite temperature

Abstract

A huge magnetoresistance (MR) ratio exceeding 2000% at cryogenic temperature that was reported for half-metallic Heusler alloy based magnetic tunnel junctions showed large degradation at room temperature, which impedes practical application of Heusler alloy based MR devices. This motivates us to explore alternative Heusler alloys that show high spin polarization at finite temperatures. Here, we propose half-metallic Heusler alloys based on finite-temperature first-principles calculation via the disordered local moment method together with machine learning. We found several prospective materials at room temperature such as ${\mathrm{Co}}_{2}{\mathrm{MnGa}}_{0.2}{\mathrm{As}}_{0.8}$ and ${\mathrm{Co}}_{2}{\mathrm{FeAl}}_{0.4}{\mathrm{Sn}}_{0.6}$. We also investigated two combinatorial series, ${\mathrm{Co}}_{2}{\mathrm{MnGa}}_{y}{\mathrm{As}}_{1\text{\ensuremath{-}}y}$ and ${\mathrm{Co}}_{2}{\mathrm{FeAl}}_{y}{\mathrm{Sn}}_{1\text{\ensuremath{-}}y}$, to understand the effect of alloy mixing on temperature dependence and found that Fermi level tuning significantly improved the spin polarization and its temperature dependence, especially in ${\mathrm{Co}}_{2}{\mathrm{FeAl}}_{y}{\mathrm{Sn}}_{1\text{\ensuremath{-}}y}$.