Construction of low energy effective Hamiltonians using supervised machine learning.
ORAL
Abstract
A crucial problem in modern physics is to derive a low energy effective model from a given high energy model. While perturbation theory is the most commonly used approach, there are many instances when such expansions break down. We propose a simple supervised machine learning (ML) algorithm to find the low energy spin Hamiltonian for a given labeled energy data-set from a “high energy” s-d model. The spin Hamiltonian obtained from the ML assisted approach reproduces the phase diagram of the s-d model and reveals the effective four-spin interactions that stabilize a magnetic field induced skyrmion crystal even in absence of spin anisotropy.
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Presenters
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Vikram Sharma
University of Tennessee
Authors
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Vikram Sharma
University of Tennessee
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Zhentao Wang
School of Physics and Astronomy, University of Minnesota, University of Minnesota, University of Tennessee
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Cristian Batista
University of Tennessee, Department of Physics & Astronomy, University of Tennessee, Knoxville, TN 37996, USA, Department of Physics and Astronomy, University of Tennessee, Physics and Astronomy, University of Tennessee, Oakridge National Laboratory, Department of Physics and astronomy, University of Tennessee, University of Tennessee, Knoxville, Department of Physics and Astronomy, University of Tennessee, Knoxville