The advancement of spin-based devices as a replacement for CMOS technology demands lower spin-switching energy in ferromagnetic (FM) materials. Ferroelectric (FE) materials offer a promising avenue for influencing FM properties, yet the mechanisms driving this interplay remain inadequately understood. In this study, we investigate iron-adsorbed FE ABO$_3$ perovskites using a combination of first-principles calculations and machine learning. Our findings reveal a universal correlation between the magnetic anisotropy energy (MAE) of iron and the induced magnetic dipole moments within the BO$_2$ layer and basal oxygen atoms of ABO$_3$ at the FE/FM interface. By identifying key material descriptors and achieving high predictive accuracy, this research provides a robust framework for selecting and optimizing ABO$_3$ substrates for energy-efficient spintronic devices. These insights contribute to the rational design of novel low-power spin-based technologies.
Categories: cond-mat.mtrl-sci cond-mat.mes-hall