Computational screening of magnetocaloric alloys

TL;DR

This paper presents a computational method to screen magnetocaloric alloys with atomic site disorder using density functional theory, enabling identification of promising materials with complex atomic arrangements.

Contribution

It extends high-throughput computational screening to disordered systems by thermodynamic averaging of magnetic deformation, capturing properties of complex solid solutions.

Findings

Successfully screened Mn(Co$_{1-x}$Fe$_x$)Ge and (Mn$_{1-x}$Ni$_x$)CoGe alloys.

Captured non-monotonic magnetocaloric properties of disordered alloys.

Demonstrated extension of DFT-based screening to disordered materials.

Abstract

An exciting development over the past few decades has been the use of high-throughput computational screening as a means of identifying promising candidate materials for a variety of structural or functional properties. Experimentally, it is often found that the highest-performing materials contain substantial atomic site disorder. These are frequently overlooked in high-throughput computational searches however, due to difficulties in dealing with materials that do not possess simple, well-defined crystallographic unit cells. Here we demonstrate that the screening of magnetocaloric materials with the help of the density functional theory-based magnetic deformation proxy can be extended to systems with atomic site disorder. This is accomplished by thermodynamic averaging of the magnetic deformation for ordered supercells across a solid solution. We show that the highly non-monotonic magnetocaloric properties of the disordered solid solutions Mn(Co$_{1-x}$Fe$_x$)Ge and (Mn$_{1-x}$Ni$_x$)CoGe are successfully captured using this method.