Accelerated discovery of new magnets in the Heusler alloy family

Sanvito, Stefano; Oses, Corey; Xue, Junkai; Tiwari, Anurag; Žic, Mario; Archer, Thomas; Tozman, P.; Venkatesan, M.; Coey, J. M. D.; Curtarolo, Stefano

doi:10.1126/sciadv.1602241

Cited by 241 publications

(198 citation statements)

References 86 publications

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“…They are used for data storage, energy harvesting, magnetic cooling, and other applications. Nevertheless, the occurrence of magnetic ordering can be considered a rare phenomenon, with around 4% of the known inorganic compounds presenting such property [474]. The search for novel magnetic materials is not only a scientific interesting problem but an economic necessity.…”

Section: Magnetic Propertiesmentioning

confidence: 99%

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From DFT to machine learning: recent approaches to materials science–a review

et al. 2019

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Recent advances in experimental and computational methods are increasing the quantity and complexity of generated data. This massive amount of raw data needs to be stored and interpreted in order to advance the materials science field. Identifying correlations and patterns from large amounts of complex data is being performed by machine learning algorithms for decades. Recently, the materials science community started to invest in these methodologies to extract knowledge and insights from the accumulated data. This review follows a logical sequence starting from density functional theory as the representative instance of electronic structure methods, to the subsequent high-throughput approach, used to generate large amounts of data. Ultimately, data-driven strategies which include data mining, screening, and machine learning techniques, employ the data generated. We show how these approaches to modern computational materials science are being used to uncover complexities and design novel materials with enhanced properties. Finally, we point to the present research problems, challenges, and potential future perspectives of this new exciting field. characterized by its diverse and huge volume, usually ranging from terabytes to petabytes of data, being created in or near real-time. Such data is found either structured and unstructured in nature, and is exhaustive, usually aiming to capture entire populations in a scalable manner [7]. Simple tasks represent challenges in this scale: capture, curation, storage, search, sharing, analysis, and visualization of the data cannot be accomplished without the proper tools. Thus, it can be effectively summarized by the popular 'five V's': volume, velocity, variety, veracity, and value, shown in figure 3(right). A related sixth V is visualization, although not exclusive to Big Data, which requires different techniques to handle data with various characteristics.Striving to tackle the challenges imposed by Big Data, the field of Data Science has arisen. It is largely interdisciplinary being a combination of mathematics and statistics, computer science and programming, and domain knowledge for problem definition and solving, as shown in figure 3(left). Its objective is, roughly speaking, to deal with the whole process of data production, cleaning, preparation, and finally, analysis. Data science encompasses areas such as Big Data, which deals with large volumes of data, and data mining, which relates to analysis processes to discover patterns and extract knowledge from data, part of the so-called Knowledge Discovery in Databases (KDD).The analysis process within Data Science is challenging, as the techniques are very different from traditional static and rigid datasets, generated and analyzed under a predetermined hypothesis. The distinction from traditional data is based on the larger abundance, exhaustivity, and variety of Big Data. It is also much more dynamic, messy and uncertain, being highly relational [7]. Recently, the possibility of overcoming such a challenge slowly sta...

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Section: Magnetic Propertiesmentioning

confidence: 99%

“…Sanvito and coworkers [474] used high-throughput DFT calculations to construct a Heusler alloys database containing 236,115 entries. In order to search for novel high performing magnetic Heusler alloys, the convex Hull (binary and ternary) for 36,540 compounds was calculated to determine possible stable candidates.…”

Section: Magnetic Propertiesmentioning

confidence: 99%

From DFT to machine learning: recent approaches to materials science–a review

et al. 2019

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“…21 Focusing on the magnetic properties, Sanvito et al did HTP calculations on 36,540 Heusler alloys, leading to 248 thermodynami-cally stable compounds with 20 magnetic cases. 16 Moreover, 21 antiferromagnetic Heusler compounds with high Néel temperature have been proposed for spintronic applications. 18 Last but not least, among 286 Heusler compounds, HTP screening calculations suggest 62% have a tetragonal structure due to the peak-and-valley character in the density of states.…”

Section: Introductionmentioning

confidence: 99%

High-throughput screening for spin-gapless semiconductors in quaternary Heusler compounds

Gao

Opahle

Zhang

2019

Phys. Rev. Materials

102

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Based on high throughput density functional theory calculations, we performed systematic screening for spin-gapless semiconductors (SGSs) in quaternary Heusler alloys XX YZ (X, X , and Y are transition metal elements without Tc, and Z is one of B, Al, Ga, In, Si, Ge, Sn, Pb, P, As, Sb, and Bi). Following the empirical rule, we focused on compounds with 21, 26, or 28 valence electrons, resulting in 12, 000 possible chemical compositions. After systematically evaluating the thermodynamic, mechanical, and dynamical stabilities, we successfully identified 70 stable SGSs, confirmed by explicit electronic structure calculations with proper magnetic ground states. It is demonstrated that all four types of SGSs can be realized, defined based on the spin characters of the bands around the Fermi energy, and the type-II SGSs show promising transport properties for spintronic applications. The effect of spin-orbit coupling is investigated, resulting in large anisotropic magnetoresistance and anomalous Nernst effects. arXiv:1808.02684v1 [cond-mat.mtrl-sci]

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“…The subfamilies of HAs were well described somewhere else [1]. They showed the diverse physical properties such as half-metallicity, topological insulators, spin gapless semiconductors (SGS) including the superconductivity, shape memory effect induced by the magnetic field and the magnetocaloric effect [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. Co2-based Heusler compounds were synthesized first time in its ideal L21 structure in 1970 [17].…”

Section: Introductionmentioning

confidence: 99%

Effect of L21 and XA ordering on phase stability, half-metallicity and magnetism of Co2FeAl Heusler alloy: GGA and GGA + U approach

Ahmad

Srivastava

Das

2019

Journal of Magnetism and Magnetic Materials

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The generalized gradient approximation (GGA) scheme in the first-principles calculations are used to study the effect of L21 and XA ordering on the phase stability, halfmetallicity and magnetism of Co2FeAl (CFA) Heusler alloy. Various possible hypothetical structures: L21-I, L21-II, XA-I, and XA-II were prepared under the conventional L21 and inverse XA phases by altering the atomic occupancies at their Wyckoff sites. It is found that the XA-II phase of CFA is the most stable phase energetically among all the structures. The electronic structure calculations without U show the presence of half-metallic (HM) ground state only in L21-1 structure and the other structures are found to be metallic. However, the electronic structures of CFA are significantly modified in the presence of U, although the total magnetic moments per cell remained the same and consistent with the Slater-Pauling (SP) rule. The metallic ground states of CFA in L21-II and XA-II structures are converted into the half-metallic ground states in presence of U but remained the same (metallic) in XA-I structure. The results indicate that the electronic structures are not only dependent on the L21 and XA ordering of the atoms but also depend on the choice of U values. So experiments may only verify the superiority of GGA+U to GGA.

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Accelerated discovery of new magnets in the Heusler alloy family

Abstract: Advanced computer simulations and database access enable the design of novel magnetic materials at an unprecedented speed.

Cited by 241 publications

References 86 publications

From DFT to machine learning: recent approaches to materials science–a review

From DFT to machine learning: recent approaches to materials science–a review

High-throughput screening for spin-gapless semiconductors in quaternary Heusler compounds

Effect of L21 and XA ordering on phase stability, half-metallicity and magnetism of Co2FeAl Heusler alloy: GGA and GGA + U approach

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Accelerated discovery of new magnets in the Heusler alloy family

Abstract: Advanced computer simulations and database access enable the design of novel magnetic materials at an unprecedented speed.

Cited by 241 publications

References 86 publications

From DFT to machine learning: recent approaches to materials science–a review

From DFT to machine learning: recent approaches to materials science–a review

High-throughput screening for spin-gapless semiconductors in quaternary Heusler compounds

Effect of L21 and XA ordering on phase stability, half-metallicity and magnetism of Co2FeAl Heusler alloy: GGA and GGA + U approach

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Effect of L21 and XA ordering on phase stability, half-metallicity and magnetism of Co2FeAl Heusler alloy: GGA and GGA + U approach