Journal Article

FZJ-2024-04938

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Machine learning inspired models for Hall effects in non-collinear magnets

Kipp, J. (Corresponding author)FZJ* ; Lux, F. R. ; Pürling, T. ; Morrison, A. ; Blügel, S.FZJ* ; Pinna, D. ; Mokrousov, Y.FZJ*

2024
IOP Publishing Bristol

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Please use a persistent id in citations: doi:10.1088/2632-2153/ad51ca  doi:10.34734/FZJ-2024-04938

Abstract: The anomalous Hall effect has been front and center in solid state research and material science forover a century now, and the complex transport phenomena in nontrivial magnetic textures havegained an increasing amount of attention, both in theoretical and experimental studies. However, a clear path forward to capturing the influence of magnetization dynamics on anomalous Hall effect even in smallest frustrated magnets or spatially extended magnetic textures is still intensively sought after. In this work, we present an expansion of the anomalous Hall tensor into symmetrically invariant objects, encoding the magnetic configuration up to arbitrary power of spin. We show that these symmetric invariants can be utilized in conjunction with advanced regularization techniques in order to build models for the electric transport in magnetic textures which are, on one hand, complete with respect to the point group symmetry of the underlying lattice, and on the other hand, depend on a minimal number of order parameters only. Here, using a four-band tight-binding model on a honeycomb lattice, we demonstrate that the developed method can be used to address the importance and properties of higher-order contributions to transverse transport. The efficiency and breadth enabled by this method provides an ideal systematic approach to tackle the inherent complexity of response properties of noncollinear magnets, paving the way to the exploration of electric transport in intrinsically frustrated magnets as well as large-scale magnetic textures.

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Contributing Institute(s):

1. Quanten-Theorie der Materialien (PGI-1)
2. Computational and Systems Neuroscience (IAS-6)
3. Computational and Systems Neuroscience (INM-6)

Research Program(s):

1. 5211 - Topological Matter (POF4-521) (POF4-521)
2. DFG project 437337265 - Spin+AFM-Dynamik: Antiferromagnetismus durch Drehimpulsströme und Gitterdynamik (A11) (437337265) (437337265)
3. DFG project 444844585 - Statische und dynamische Kopplung von Gitter- und magnetischen Eigenschaften in zweidimensionalen Materialien mit niedriger Symmetrie (B06) (444844585) (444844585)
4. 3D MAGiC - Three-dimensional magnetization textures: Discovery and control on the nanoscale (856538) (856538)
5. DFG project 403235169 - Magnetochirale Transporteffekte in Skyrmionen (403235169) (403235169)

Appears in the scientific report 2024

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