Title: Designing magnetism in Fe-based Heusler alloys: a machine learning approach,
Abstract: Combining material informatics and high-throughput electronic structure
calculations offers the possibility of a rapid characterization of complex
magnetic materials. Here we demonstrate that datasets of electronic properties
calculated at the ab initio level can be effectively used to identify and
understand physical trends in magnetic materials, thus opening new avenues for
accelerated materials discovery. Following a data-centric approach, we utilize
a database of Heusler alloys calculated at the density functional theory level
to identify the ideal ions neighbouring Fe in the $X_2$Fe$Z$ Heusler prototype.
The hybridization of Fe with the nearest neighbour $X$ ion is found to cause
redistribution of the on-site Fe charge and a net increase of its magnetic
moment proportional to the valence of $X$. Thus, late transition metals are
ideal Fe neighbours for producing high-moment Fe-based Heusler magnets. At the
same time a thermodynamic stability analysis is found to restrict $Z$ to main
group elements. Machine learning regressors, trained to predict magnetic moment
and volume of Heusler alloys, are used to determine the magnetization for all
materials belonging to the proposed prototype. We find that Co$_2$Fe$Z$ alloys,
and in particular Co$_2$FeSi, maximize the magnetization, which reaches values
up to 1.2T. This is in good agreement with both ab initio and experimental
data. Furthermore, we identify the Cu$_2$Fe$Z$ family to be a cost-effective
materials class, offering a magnetization of approximately 0.65T.