Machine learning assisted derivation of minimal low-energy models for metallic magnets

Journal Article · 15 October 2023 · npj Computational Materials

DOI: https://doi.org/10.1038/s41524-023-01137-x · OSTI ID:2202501

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.

• Sponsoring Organization: USDOE
• Grant/Contract Number: SC0022311
• OSTI ID: 2202501
• Journal Information: npj Computational Materials, Journal Name: npj Computational Materials Journal Issue: 1 Vol. 9; ISSN 2057-3960
• Publisher: Nature Publishing GroupCopyright Statement
• Country of Publication: United Kingdom
• Language: English