Predicting magnetic anisotropy energies using site-specific spin-orbit coupling energies and machine learning: Application to iron-cobalt nitrides
Timothy Liao, Weiyi Xia, Masahiro Sakurai, Renhai Wang, Chao Zhang, Huaijun Sun, Kai‐Ming Ho, Cai‐Zhuang Wang, James R. Chelikowsky
Abstract
The authors present a promising machine learning model, which focuses on site-magnetic-properties for rapid screening in materials design and accelerates computational screening of candidate materials that possess high magnetizations and large magnetic anisotropy energies.
Topics & Concepts
Materials scienceAnisotropyCobaltSpin–orbit interactionCoupling (piping)Condensed matter physicsMagnetic anisotropySpin (aerodynamics)NitrideMagnetizationNanotechnologyMetallurgyMagnetic fieldThermodynamicsPhysicsLayer (electronics)OpticsQuantum mechanicsMachine Learning in Materials ScienceElectronic and Structural Properties of OxidesElectron and X-Ray Spectroscopy Techniques