TY  - JOUR
AU  - Kipp, Jonathan
AU  - Lux, Fabian R
AU  - Pürling, Thorben
AU  - Morrison, Abigail
AU  - Blügel, Stefan
AU  - Pinna, Daniele
AU  - Mokrousov, Yuriy
TI  - Machine learning inspired models for Hall effects in non-collinear magnets
JO  - Machine learning: science and technology
VL  - 5
IS  - 2
SN  - 2632-2153
CY  - Bristol
PB  - IOP Publishing
M1  - FZJ-2024-04938
SP  - 025060 -
PY  - 2024
AB  - 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:001248895600001
DO  - DOI:10.1088/2632-2153/ad51ca
UR  - https://juser.fz-juelich.de/record/1029020
ER  -