Prediction magnetocrystalline anisotripy Fe-Rh thin films via machine leaning

Eun Sung Jekal; Hyunwoo Park

Prediction magnetocrystalline anisotripy Fe-Rh thin films via machine leaning

Materials Science 2019-08-14 v1

Authors: Eun Sung Jekal , Hyunwoo Park

Abstract

Least absolute shrinkage and selection operator (Lasso) was originally formulated for least squares models and this simple case reveals a substantial amount about the behavior of the estimator. It also shows that the coefficient estimates need not be unique if covariates are collinear. Using this Lasso technique, we analyze a magnetocrystalline anisotropy energy which is a long-standing issue in transition-metal thin films, expectially for Fe-Rh thin film systems on a MgO substrate. Our LASSO regression took advantage of the data obtained from first principles calculations for single slabs with seven atomic-layers of binary Fe-Rh films on MgO(001). In the case of Fe-Rh thin films, we have successfully found a linear behavior between the MCA energy and the anisotropy of orbital moments.

Keywords

magnetocrystalline anisotropy fe-rh thin film lasso regression machine learning first principles calculation orbital moment

Cite

@article{arxiv.1908.04762,
  title  = {Prediction magnetocrystalline anisotripy Fe-Rh thin films via machine leaning},
  author = {Eun Sung Jekal and Hyunwoo Park},
  journal= {arXiv preprint arXiv:1908.04762},
  year   = {2019}
}

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