Accelerated Exploration\nof Empty Material Compositional\nSpace: Mg–Fe–B Ternary Metal Borides
Borides are a family of materials with valuable properties\nfor\nvarious applications. Their diverse structures and compositions, yet\ndisparity in the constituent chemical elements for the known compounds,\ngive elemental substitutions for prototypes great potential for material\ndiscovery. To explore uncharted material compositional space, we develop\na workflow that joins high-throughput crystal structure prediction\nand automated diffraction pattern matching to discover new compounds\nwith significant prediction and synthesis hurdles. Utilizing the workflow,\nwe explore the empty Mg–Fe–B ternary compositional space,\npreviously uncharted largely due to the immiscibility of Mg and Fe,\nas a paradigm. A total of 275 ternary boride prototypes are classified,\nusing which we predict 23 (158) stable and metastable ternary phases\nwithin 50 (200) meV/atom above the convex hull. We identify Gd<sub>2</sub>(FeB)<sub>7</sub>-type Mg<sub>2</sub>Fe<sub>7</sub>B<sub>7</sub> and ZrCo<sub>3</sub>B<sub>2</sub>-type MgFe<sub>3</sub>B<sub>2</sub> to match previously unsolved experimental powder X-ray diffraction\n(PXRD) patterns. The discovered Mg<sub>2</sub>Fe<sub>7</sub>B<sub>7</sub> and related channeled structures feature mismatched Mg and\n(FeB) sublattice periods, for which we conduct structural analyses\nwith respect to the PXRD. They are predicted to exhibit exceptionally\nfast superionic transport of Mg and outstanding electrochemical performance,\nwhich serve as post-Li-ion battery candidate electrode materials.\nThis result opens a new avenue for borides’ potential applications\nas electrode materials and fast ionic conductors. This work also portrays\nthe map and landscape of ternary metal borides with similar chemical\nenvironments. With high efficiency, the prototype- and PXRD-assisted\ncrystal structure prediction workflow opens a new avenue for exploring\nvarious material compositional spaces across the periodic table.
Publication Details
- Journal
- Figshare
- Published
- 2024-11-20
- DOI
- https://doi.org/10.1021/jacs.4c12648.s001
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00