Fast \textit{ab initio} design of high-entropy magnetic thin films

TL;DR

This paper demonstrates that extit{ab initio} density functional theory calculations within the coherent potential approximation can efficiently predict magnetic properties of high-entropy alloys, aligning well with experimental data and enabling high-throughput exploration.

Contribution

It introduces a method to accurately model magnetic properties of HEAs using DFT within CPA, facilitating rapid predictions across compositional space.

Findings

DFT within CPA captures magnetic property trends in HEAs.

Predicted magnetic properties align with experimental values.

Method enables high-throughput screening of HEA magnetic behavior.

Abstract

We show that the magnetic properties of high-entropy alloys (HEAs) can be captured by \textit{ab initio} calculations within the coherent potential approximation, where the atomic details of the high-entropy mixing are considered as an effective medium that possesses the translational symmetry of the lattice. This is demonstrated using the face-centered cubic (FCC) phase of $\textrm{FeCoNiMnCu}$ and the $L1_0$ phase of $\textrm{(FeCoNiMnCu)Pt}$ by comparing the density functional theory (DFT) results with the experimental values. Working within the first Brillouin zone and the primitive unit cell, we show that DFT can capture the smooth profile of magnetic properties such as the saturation magnetization, the Curie temperature and the magnetic anisotropy, using only a sparse set of sampling points in the vast compositional space. The smooth profiles given by DFT indeed follow the experimental trend, demonstrating the promising potential of using machine learning to explore the magnetic properties of HEAs, by establishing reasonably large datasets with high-throughput calculations using density-functional theory.