Combinatorial Survey of Structural Phase Distribution and Magnetism in Fe-Ge-Te Composition-spread Thin Film Libraries

TL;DR

This study employs combinatorial synthesis, high-throughput characterization, and machine learning to map the relationship between composition, structure, and magnetism in Fe-Ge-Te thin film libraries, identifying key structural prerequisites for ferromagnetism.

Contribution

It introduces a high-throughput, ML-assisted framework for rapidly exploring and identifying ferromagnetic materials within a broad compositional space of Fe-Ge-Te thin films.

Findings

Hexagonal structure is essential for ferromagnetism in Fe-Ge-Te.

Unexplored hexagonal phases can be efficiently identified as potential ferromagnetic materials.

The workflow enables rapid mapping of composition, structure, and magnetic properties.

Abstract

Recently, magnetic 2-dimensional (2D) van der Waals (vdW) materials have garnered tremendous attention. The vdW ferromagnet Fe5Ge1Te2 has a Curie temperature Tc of ~ 270 K, which is tailorable by tuning the stoichiometry and the Fe deficiency to reach room temperature. To explore the expanded compositional space, we implemented combinatorial synthesis and high-throughput characterization to investigate the structural phase distribution and ferromagnetism of a Fe-Ge-Te thin film library. The library was prepared by magnetron co-sputtering followed by annealing in vacuum or in an inert environment. Composition and structural phase distribution of the 177 pads in the library were characterized using high-throughput wavelength dispersive spectroscopy (WDS), X-ray diffraction (XRD), and two-point probe resistance measurements. We leverage unsupervised machine learning to cluster the XRD dataset into groups of compositions with similar structural phases, and further study the ferromagnetic properties via SQUID magnetometry and X-ray magnetic circular dichroism (XMCD) across different clusters. The results are compared against magnetization and structural models calculated using DFT. Our results demonstrate that the hexagonal crystal structure is a critical prerequisite for ferromagnetism in this system, and that unexplored materials adopting this structure can be efficiently identified as possible ferromagnetic materials using our high-throughput, ML-assisted framework. This workflow based on the combinatorial strategy allows us to rapidly capture the composition-structure-magnetic property map across a broad compositional landscape of novel magnetic materials.