TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhys.17.5.145
TI  - Kernel polynomial method for linear spin wave theory
PY  - 2024/11/26
UR  - https://scipost.org/SciPostPhys.17.5.145
JF  - SciPost Physics
JA  - SciPost Phys.
VL  - 17
IS  - 5
SP  - 145
A1  - Lane, Harry
AU  - Zhang, Hao
AU  - Dahlbom, David
AU  - Quinn, Sam
AU  - Somma, Rolando
AU  - Mourigal, Martin
AU  - Batista, Christian D.
AU  - Barros, Kipton
AB  - Calculating dynamical spin correlations is essential for matching model magnetic exchange Hamiltonians to momentum-resolved spectroscopic measurements. A major numerical bottleneck is the diagonalization of the dynamical matrix, especially in systems with large magnetic unit cells, such as those with incommensurate magnetic structures or quenched disorder. In this paper, we demonstrate an efficient scheme based on the kernel polynomial method for calculating dynamical correlations of relevance to inelastic neutron scattering experiments. This method reduces the scaling of numerical cost from cubic to linear in the magnetic unit cell size.
ER  -

@Article{10.21468/SciPostPhys.17.5.145,
	title={{Kernel polynomial method for linear spin wave theory}},
	author={Harry Lane and Hao Zhang and David Dahlbom and Sam Quinn and Rolando D. Somma and Martin Mourigal and Cristian D. Batista and Kipton Barros},
	journal={SciPost Phys.},
	volume={17},
	pages={145},
	year={2024},
	publisher={SciPost},
	doi={10.21468/SciPostPhys.17.5.145},
	url={https://scipost.org/10.21468/SciPostPhys.17.5.145},
}