Interfacial Magnetic Anisotropy of Iron-Adsorbed Ferroelectric Perovskites: First-Principles and Machine Learning Study
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866913985022918656 |
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| author | Jeong, Dameul Kang, Seoung-Hun Kwon, Young-Kyun |
| author_facet | Jeong, Dameul Kang, Seoung-Hun Kwon, Young-Kyun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_07953 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Interfacial Magnetic Anisotropy of Iron-Adsorbed Ferroelectric Perovskites: First-Principles and Machine Learning Study Jeong, Dameul Kang, Seoung-Hun Kwon, Young-Kyun Materials Science Mesoscale and Nanoscale Physics 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. |
| title | Interfacial Magnetic Anisotropy of Iron-Adsorbed Ferroelectric Perovskites: First-Principles and Machine Learning Study |
| topic | Materials Science Mesoscale and Nanoscale Physics |
| url | https://arxiv.org/abs/2306.07953 |