镍基磁体设计中原子有序和磁各向异性的集成从头算模型

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL npj Computational Materials Pub Date : 2024-11-29 DOI:10.1038/s41524-024-01435-y
Christopher D. Woodgate, Laura H. Lewis, Julie B. Staunton
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引用次数: 0

摘要

我们描述了一种集成的建模方法,以加速寻找具有高磁晶各向异性(MCA)的新型单相多组分材料。对于一个给定的系统,我们预测原子有序的性质,它依赖于磁性状态,然后继续描述相应的MCA,磁化和磁性临界温度(居里温度)。至关重要的是,在我们的建模框架中,材料电子结构的相同从头开始描述决定了所有方面。我们通过研究Fe4Ni3X和Fe3Ni4X的一般化学计量学来研究FeNi中合金添加的影响,证明了这种整体方法,用于包括X = Pt, Pd, Al和Co在内的添加剂。添加这些元素预测的原子有序行为是确定材料MCA的基础,是丰富多样的。据报道,等原子FeNi需要铁磁顺序来建立适合于显著MCA的四方L10顺序。我们的研究结果表明,当合金添加到该材料中时,在外加磁场和/或低于材料的居里温度下退火也可以促进四方有序,同时对预测的硬磁性能有明显的影响。
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Integrated ab initio modelling of atomic ordering and magnetic anisotropy for design of FeNi-based magnets

We describe an integrated modelling approach to accelerate the search for novel, single-phase, multicomponent materials with high magnetocrystalline anisotropy (MCA). For a given system we predict the nature of atomic ordering, its dependence on the magnetic state, and then proceed to describe the consequent MCA, magnetisation, and magnetic critical temperature (Curie temperature). Crucially, within our modelling framework, the same ab initio description of a material’s electronic structure determines all aspects. We demonstrate this holistic method by studying the effects of alloying additions in FeNi, examining systems with the general stoichiometries Fe4Ni3X and Fe3Ni4X, for additives including X = Pt, Pd, Al, and Co. The atomic ordering behaviour predicted on adding these elements, fundamental for determining a material’s MCA, is rich and varied. Equiatomic FeNi has been reported to require ferromagnetic order to establish the tetragonal L10 order suited for significant MCA. Our results show that when alloying additions are included in this material, annealing in an applied magnetic field and/or below a material’s Curie temperature may also promote tetragonal order, along with an appreciable effect on the predicted hard magnetic properties.

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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
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