A new method to accelerate material discovery

A technological breakthrough often requires the discovery of a new material with unprecedented physical properties. Ferromagnetic materials for example, enable a wide range of technologies with diverse applications ranging from energy production to computer hardware, memory storage, and transportation. As such, intense efforts are pursued worldwide to improve the magnetic properties of known materials, by, for example, reducing their economic or environmental cost, or to find new magnetic materials altogether. The search for entirely new families of ferromagnetic compounds is challenging, and, as a result, efforts are often focused on already known families that can be further improved. Accelerating material discovery remains one of the greatest challenges in material research.

A group of researchers from UC Davis in the group of Prof. Taufour, and collaborators from the Critical Material Institute (a US Department of Energy Innovation Hub), developed a method for discovering materials with specific magnetic properties. They demonstrate their approach by identifying several materials with room temperature ferromagnetism, or with easy-axis magnetic anisotropy. The identified compounds are then confirmed using band structure calculations, experimental synthesis, and characterization measurements. The method relies on statistics from a literature survey of the magnetic properties of thousands of cobalt-based compounds with different crystal structures. UC Davis graduate and undergraduate students in the group of Prof. Taufour co-authored the study that was recently published in Physical Review Materials as an Editors’ Suggestion. The manuscript presents the method, statistics, database, identification and confirmation of a few compounds.

https://doi.org/10.1103/PhysRevMaterials.6.063803


MaterialDiscoveryPicture1.png
The teaser picture selected by the editors of Physical Review Materials representing the wide variety of crystal structures, and Co-based ferromagnetic compounds.

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A visual breakdown of the overlapping categories in the database. Circles are not to scale.