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MonographDOI

Magnetism and Magnetic Materials

TLDR
In this paper, the authors introduce magnetostatics and magnetism of localized electrons on the atom, and apply it to spin electronics and magnetic recording, as well as applications of hard magnets.
Abstract
1. Introduction 2. Magnetostatics 3. Magnetism of electrons 4. Magnetism of localized electrons on the atom 5. Ferromagnetism and exchange 6. Antiferromagnetism and other magnetic order 7. Micromagnetism, domains and hysteresis 8. Nanoscale magnetism 9. Magnetic resonance 10. Experimental methods 11. Magnetic materials 12. Applications of soft magnets 13. Applications of hard magnets 14. Spin electronics and magnetic recording 15. Special topics Appendixes Index.

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Citations
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Journal ArticleDOI

Dispersible SmCo5 nanoparticles with huge coercivity.

TL;DR: The coercive mechanism is identified as nucleation and growth of 88 nm3 nucleation volumes in a low-anisotropy surface region about 15 nm thick and opens the prospect of new high-temperature magnet composites.
Journal ArticleDOI

Prediction of the new efficient permanent magnet SmCoNiFe 3

TL;DR: In this paper, the authors proposed a new efficient permanent magnet, which is a development of the well-known ${\mathrm{SmCo}}_{5}$ prototype, and showed by means of first-principles electronic-structure calculations that the new magnet has very favorable magnetic properties.
Journal ArticleDOI

Strong in-plane magnetic anisotropy in (111)-oriented CoFe2O4 thin film

TL;DR: In this article, the perfect (111)-oriented CoFe 2 O 4 thin films were grown on Pt(111)/Si substrate by pulsed laser deposition technique at substrate temperature of 600 ˚C.
Journal ArticleDOI

Mitigating realistic noise in practical noisy intermediate-scale quantum devices

TL;DR: In this paper, a stochastic quantum error mitigation (QEM) method was proposed for noisy intermediate-scale quantum (NISQ) devices, where each computation process, being either digital or analog, is described by a continuous time evolution.
Journal ArticleDOI

Predicting the Curie temperature of ferromagnets using machine learning

TL;DR: A new computing experiment suggests that machine-learning algorithms can accelerate the discovery and design of new magnetic materials as discussed by the authors, which is a promising direction for the future of magnetic materials discovery.