Computational Design of Rare-Earth Reduced Permanent Magnets

Kovacs, Alexander; Fischbacher, Johann; Gusenbauer, Markus; Oezelt, Harald; Herper, Heike C.; Vekilova, Olga Yu.; Nieves, P.; Arapan, Sergiu; Schrefl, T.

doi:10.1016/j.eng.2019.11.006

Cited by 28 publications

(17 citation statements)

References 26 publications

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“…The demand for permanent magnets for modern application technology is very high. Permanent magnets are used in automating and robotics industry and constitute a parts of an electric vehicles, wind turbines, drives, and storage and magnetic cooling devices [1][2][3][4][5][6][7] . Application grade materials should exhibit particular intrinsic properties, in first line a high saturation magnetization (M s ) in combination with a large uniaxial anisotropy and a sufficiently high Curie temperature (T C ).…”

Section: Introductionmentioning

confidence: 99%

Impact of local arrangement of Fe and Ni on the phase stability and magnetocrystalline anisotropy in Fe-Ni-Al Heusler alloys

Sokolovskiy¹,

Мирошкина²,

Buchelnikov³

et al. 2021

Preprint

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On the basis of density functional calculations, we report on a comprehensive study of the influences of atomic arrangement and Ni substitution for Al on the ground state structural and magnetic properties for Fe2Ni1+xAl1−x Heusler alloys. We discuss systematically the competition between five Heusler-type structures formed by shuffles of Fe and Ni atoms and their thermodynamic stability. All Ni-rich Fe2Ni1+xAl1−x tend to decompose into a dual-phase mixture consisting of Fe2NiAl and FeNi. The successive replacement of Ni by Al leads to a change of ground state structure and eventually an increase in magnetocrystalline anisotropy energy (MAE). We predict for stoichiometric Fe2NiAl a ground state structure with nearly cubic lattice parameters but alternating layers of Fe and Ni possessing an uniaxial MAE which is even larger than tetragonal L10-FeNi. This opens an alternative route for improving the phase stability and magnetic properties in FeNi-based permanent magnets.

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Section: Introductionmentioning

confidence: 99%

Impact of local arrangement of Fe and Ni on the phase stability and magnetocrystalline anisotropy in Fe-Ni-Al Heusler alloys

Sokolovskiy¹,

Мирошкина²,

Buchelnikov³

et al. 2021

Preprint

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“…In real magnets, to approach theoretical maximum energy product and coercivity it is essential to have an optimized microstructure. The intergranular structure between the grains plays a significant role determining the magnetic MnAl τ -phase, etc [91]. In Fig.…”

Section: Micromagneticsmentioning

confidence: 99%

Database of novel magnetic materials for high-performance permanent magnet development

Nieves

Arapan

Maudes

et al. 2019

Computational Materials Science

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This paper describes the open Novamag database that has been developed for the design of novel Rare-Earth free/lean permanent magnets. The database software technologies, its friendly graphical user interface, advanced search tools and available data are explained in detail. Following the philosophy and standards of Materials Genome Initiative, it contains significant results of novel magnetic phases with high magnetocrystalline anisotropy obtained by three computational high-throughput screening approaches based on a crystal structure prediction method using an Adaptive Genetic Algorithm, tetragonally distortion of cubic phases and tuning known phases by doping. Additionally, it also includes theoretical and experimental data about fundamental magnetic material properties such as magnetic moments, magnetocrystalline anisotropy energy, exchange parameters, Curie temperature, domain wall width, exchange stiffness, coercivity and maximum energy product, that can be used in the study and design of new promising high-performance Rare-Earth free/lean permanent magnets. The results therein contained might provide some insights into the ongoing debate about the theoretical performance limits beyond Rare-Earth based magnets. Finally, some general strategies are discussed to design possible experimental routes for exploring most promising theoretical novel materials found in the database. NOVAMAG H c > M r /2 [5]. Extrinsic properties also depend on temperature, in fact they typically decrease as temperature increases, reducing the magnet's performance, especially close to the Curie temperature T C (i.e. ferromagnetic-paramagenetic transition). The macroscopic behavior is tightly linked to the microscopic properties called intrinsic. Main magnetic intrinsic properties are atomic magnetic moment µ at (the magnetic moment per volume gives the maximum theoretical M s ), exchange interactions J ij (which determine the magnetic order and T C )and magnetocrystalline anisotropy K 1 (that can enhance H c and it is indispensable in modern magnets to get H c > M r /2) [6]. PMs should have high atomic mangetic moments per volume (> 0.1µ B /Å 3 ), strong ferromangetic exchange interactions (able to give T C > 600 K) and high easy axis magnetocrystalline anisotropy (K 1 > 1 MJ/m 3 ) in order to exhibit good extrinsic properties suitable for PM applcations. In particular, magnetic materials with hardness parameter κ = K 1 /(µ 0 M 2 s ) > 1 (called "hard" magnets) are very valuable since can be used to make efficient magnets of any shape [6]. At mesoscopic scale, intergranular structure between the grains and crystallographic defects can strongly affect the performance of a magnet [7]. Therefore, the optimization of the material's microstructure is also very important in the design and devel-

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“…[24] Later, the development in computational materials science and the application of ab initio theoretical calculations have contributed significantly to the overall scientific effort, in conjuction with experimental synthesis and characterization. [25][26][27][28][29] The application of advanced computational tools enhances the relevant research in the field by screening out phases with possibly unsuitable properties, allowing costly and time-consuming experimental research to focus on the most promising systems. In additon, the feedback of experimental results to theoretical research may lead to the improvement of the latter, by fine-tuning basic parameters and boundary conditions with limited-up to the present-knowledge.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Magnetic Materials

Katsikas

Sarafidis

Kioseoglou

2021

Physica Status Solidi (b)

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The technological advancements of every era of human civilization owe themselves to the materials available at the time. Despite the substantial interest in the discovery of novel materials, materials research remains a very delicate and time‐exhaustive procedure. Over the last 30 years, ab initio computational methods based on density functional theory (DFT) have allowed researchers to explore materials with ease and expect above‐experiment measurement precision. However, DFT‐based detailed investigation of novel materials is generally computationally intensive and greatly time‐consuming. This review presents machine learning methods applied to DFT simulation data to uncover connections between material structure, chemical composition, and magnetization, a target property chosen for its great industrial demand. Models are developed that can partially circumvent the need for simulation, guiding researchers in the design of magnetic materials. Specifically, the Materials Project database is examined and it is concluded that Eu, Gd, Pu, Fe, Np, Mn, U, Cr, Co, and Ce are amongst the most common elements found in magnetic materials, and that materials of the same composition may have different magnetization depending on their space group. A neural network capable of predicting magnetization with a standard error of 8.3 × 10−3 μ B Å−3 is created.

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Computational Design of Rare-Earth Reduced Permanent Magnets

Cited by 28 publications

References 26 publications

Impact of local arrangement of Fe and Ni on the phase stability and magnetocrystalline anisotropy in Fe-Ni-Al Heusler alloys

Impact of local arrangement of Fe and Ni on the phase stability and magnetocrystalline anisotropy in Fe-Ni-Al Heusler alloys

Database of novel magnetic materials for high-performance permanent magnet development

Machine Learning in Magnetic Materials

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