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- W3112828611 abstract "Abstract Rational design of technologically important exotic perovskites is hampered by the insufficient geometrical descriptors and costly and extremely high-pressure synthesis, while the big-data driven compositional identification and precise prediction entangles full understanding of the possible polymorphs and complicated multidimensional calculations of the chemical and thermodynamic parameter space. Here we present a rapid systematic data-mining-driven approach to design exotic perovskites in a high-throughput and discovery speed of the A 2 BB ’O 6 family as exemplified in A 3 TeO 6 . The magnetoelectric polar magnet Co 3 TeO 6 , which is theoretically recognized and experimentally realized at 5 GPa from the six possible polymorphs, undergoes two magnetic transitions at 24 and 58 K and exhibits helical spin structure accompanied by magnetoelastic and magnetoelectric coupling. We expect the applied approach will accelerate the systematic and rapid discovery of new exotic perovskites in a high-throughput manner and can be extended to arbitrary applications in other families." @default.
- W3112828611 created "2020-12-21" @default.
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- W3112828611 date "2020-12-09" @default.
- W3112828611 modified "2023-10-17" @default.
- W3112828611 title "Data-driven computational prediction and experimental realization of exotic perovskite-related polar magnets" @default.
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- W3112828611 doi "https://doi.org/10.1038/s41535-020-00294-2" @default.
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