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Computationally accelerated discovery of functional and structural Heusler materials

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MRS BULLETIN
卷 47, 期 6, 页码 559-572

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SPRINGER HEIDELBERG
DOI: 10.1557/s43577-022-00365-8

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  1. US Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design (CHiMaD) [70NANB19H005]
  2. Office of Naval Research [N00014-19-1-2073, N00014-21-1-2107]

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This article presents the crystal and electronic structures of Heusler compounds and its derivatives, as well as the state-of-the-art computational methods for materials discovery. It highlights the recent achievements in computational materials discoveries of Heusler compounds with promising applications in various fields. The article concludes with prospects for accelerated Heusler materials discovery and the challenges in predicting properties.
Heusler compounds are a large family of intermetallic materials with a variety of exciting physical properties and many appealing applications, such as spintronics, spin torque, superconductors, thermoelectrics, shape-memory behavior, and strengthening precipitates. The giant chemical space and structural variety of Heusler-phase compositions, however, presents a barrier to the rapid experimental exploration of new Heusler compounds and properties. With the aid of high-throughput computational materials discovery developed in recent years, many new Heusler compounds with desired properties have now been discovered or predicted. In this article, we describe the crystal and electronic structures of Heusler and its derivatives, the state-of-the-art computational methods for new materials discovery, and the primary properties of Heusler compounds that have been computationally predicted. We focus on recent achievements of computational materials discoveries on full Heusler, half Heusler, inverse Heusler, and quaternary Heusler compounds, with promising applications in thermoelectrics, piezoelectric, spintronics, optoelectronics, and precipitate hardening. Finally, we conclude with prospects for accelerated Heusler materials discovery based on data-driven methods and the challenges faced in computing synthesizability and predicting properties of Heusler compounds.

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