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- Title
Machine learning assisted derivation of minimal low-energy models for metallic magnets.
- Authors
Sharma, Vikram; Wang, Zhentao; Batista, Cristian D.
- Abstract
We consider the problem of extracting a low-energy spin Hamiltonian from a triangular Kondo Lattice Model (KLM). The non-analytic dependence of the effective spin-spin interactions on the Kondo exchange excludes the use of perturbation theory beyond the second order. We then introduce a Machine Learning (ML) assisted protocol to extract effective two- and four-spin interactions. The resulting spin model reproduces the phase diagram of the original KLM as a function of magnetic field and single-ion anisotropy and reveals the effective four-spin interactions that stabilize the field-induced skyrmion crystal phase. Moreover, this model enables the computation of static and dynamical properties with a much lower numerical cost relative to the original KLM. A comparison of the dynamical spin structure factor in the fully polarized phase computed with both models reveals a good agreement for the magnon dispersion even though this information was not included in the training data set.
- Subjects
KLM Royal Dutch Airlines (Company); SPIN-spin interactions; PERTURBATION theory; MAGNETS; MACHINE learning; PHASE diagrams; MAGNETIC fields
- Publication
NPJ Computational Materials, 2023, Vol 9, Issue 1, p1
- ISSN
2057-3960
- Publication type
Article
- DOI
10.1038/s41524-023-01137-x