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Machine learning assisted derivation of minimal low-energy models for metallic magnets
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作者 Vikram Sharma Zhentao Wang Cristian D.Batista 《npj Computational Materials》 SCIE EI CSCD 2023年第1期353-364,共12页
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... 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. 展开更多
关键词 KONDO DYNAMICAL DERIVATION
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