Computational Exploration of Magnetic Saturation and Anisotropy Energy for Nonstoichiometric Ferrite Compositions

TL;DR

This study uses density functional theory to systematically explore how nonstoichiometric ferrite compositions influence magnetic saturation and anisotropy energy, providing insights for optimizing magnetic materials in various applications.

Contribution

It computationally investigates the relationship between ferrite composition and magnetic properties across 571 structures, revealing how composition variations affect magnetic saturation and anisotropy energy.

Findings

Magnetic saturation and anisotropy energy vary significantly with composition.

Composition tuning can optimize magnetic properties for specific applications.

Variation in M_s is more impactful than MAE for application-specific optimization.

Abstract

A grand challenge in materials research is identifying the relationship between composition and performance. Herein, we explore this relationship for magnetic properties, specifically magnetic saturation (M$_s$) and magnetic anisotropy energy (MAE) of ferrites. Ferrites are materials derived from magnetite (which has the chemical formulae Fe$_3$O$_4$) that comprise metallic elements in some combination such as Fe, Mn, Ni, Co, Cu and Zn. They are used in a variety of applications such as electromagnetism, magnetic hyperthermia, and magnetic imaging. Experimentally, synthesis and characterization of magnetic materials is time consuming. In order to create insight to help guide synthesis, we compute the relationship between ferrite composition and magnetic properties using density functional theory (DFT). Specifically, we compute M$_s$ and MAE for 571 ferrite structures with the formulae M1$_x$M2$_y$Fe$_{3-x-y}$O$_4$, where M1 and M2 can be Mn, Ni, Co, Cu and/or Zn and 0 $\le$ x $\le$ 1 and y = 1 - x. By varying composition, we were able to vary calculated values of M$_s$ and MAE by up to 9.6$\times$10$^5$ A m$^{-1}$ and 14.1$\times$10$^5$ J m$^{-3}$, respectively. Our results suggest that composition can be used to optimize magnetic properties for applications in heating, imaging, and recording. This is mainly achieved by varying M$_s$, as these applications are more sensitive to variation in M$_s$ than MAE.