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arXiv 2401.08076cond-mat.mtrl-sciphysics.comp-ph
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温度、掺杂和非晶化对铁的电子结构与磁阻尼的影响

Influence of temperature, doping, and amorphization on the electronic structure and magnetic damping of iron

Zhihao Jiang, Axel Hoffmann, André Schleife

AI总结:

本文通过第一性原理计算研究了体心立方铁的电子结构与磁阻尼,发现硼掺杂在6.25%时显著降低费米能级态密度,且降低幅度与原子几何和磁化强度相关,而温度和非晶化影响较小。

摘要:

近年来,混合磁振子量子系统因相干量子信息处理而受到越来越多的关注,但在使用金属磁体时,过大的磁阻尼一直是一个持续存在的问题。金属磁体的本征阻尼主要由自旋-轨道相互作用引起的电子-磁振子散射决定。在低散射极限下,阻尼由带内电子跃迁主导,理论上已证明其与费米能级处的电子态密度成正比。本文以体心立方铁作为典型铁磁材料,利用第一性原理密度泛函理论模拟系统地研究了其电子结构,并考虑了有限晶格温度、硼(B)掺杂和结构非晶化的影响。结果表明,温度引起的原子无序和非晶原子几何结构仅产生较小影响。相比之下,硼掺杂显著降低了费米能级附近的态密度,最佳掺杂浓度为6.25%。此外,我们还表明这种降低在不同原子几何结构之间存在显著差异,并发现最高的态密度降低与材料的大磁化强度相关。这可能提示在外部磁场下进行材料生长是实验中值得探索的途径。

英文摘要:

Hybrid magnonic quantum systems have drawn increased attention in recent years for coherent quantum information processing, but too large magnetic damping is a persistent concern when metallic magnets are used. Their intrinsic damping is largely determined by electron-magnon scattering induced by spin-orbit interactions. In the low scattering limit, damping is dominated by intra-band electronic transitions, which has been theoretically shown to be proportional to the electronic density of states at the Fermi level. In this work, we focus on body-centered-cubic iron as a paradigmatic ferromagnetic material. We comprehensively study its electronic structure using first-principles density functional theory simulations and account for finite lattice temperature, boron (B) doping, and structure amorphization. Our results indicate that temperature induced atomic disorder and amorphous atomic geometries only have a minor influence. Instead, boron doping noticeably decreases the density of states near the Fermi level with an optimal doping level of 6.25%. In addition, we show that this reduction varies significantly for different atomic geometries and report that the highest reduction correlates with a large magnetization of the material. This may suggest materials growth under external magnetic fields as a route to explore in experiment.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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