Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/10374
Title: Data-driven computational prediction and experimental realization of exotic perovskite-related polar magnets
Authors: Han, Yifeng
Wu, Meixia
Gui, Churen
Zhu, Chuanhui
Sun, Zhongxiong
Zhao, Mei-Huan
Li, Man-Rong
Han, Yifeng
Wu, Meixia
Gui, Churen
Zhu, Chuanhui
Sun, Zhongxiong
Zhao, Mei-Huan
Li, Man-Rong
Izmir Institute of Technology
Issue Date: 2020
Publisher: Nature Publishing Group
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 Co3TeO6, 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.
URI: https://doi.org/10.1038/s41535-020-00294-2
https://hdl.handle.net/10374
ISSN: 2397-4648
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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