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Two-dimensional (2D) ferroelectric (FE) materials with relatively low switching barrier and large polarization are promising candidates for next-generation miniaturized nonvolatile memory devices. Herein, we screen out 39 new 2D ferroelectric materials, MX (M: Group III-V elements; X: Group V-VII elements), in three phosphorus-analogue phases including black phosphorene-like α-phase, blue phosphorus-like β-phase, and GeSe-like γ-phase using high-throughput calculations. Seven materials (α-SbP, γ-AsP, etc.) exhibit FE switching barriers lower than 0.3 eV/f.u., ferroelectric polarization larger than 2 × 10−10 C/m, and high thermodynamic stability with energy above hull smaller than 0.2 eV/atom. We find that the larger the electronegativity difference between M and X, the larger the ferroelectric polarization. Moreover, larger electronegativity differences result in lower in-plane piezoelectric stress tensor (e11) for MX consisting of Group IV and VI elements and larger e11 for those consisting of Group V elements. Further calculations predict a giant tunneling electroresistance in ferroelectric tunnel junction α-Sb(Sn)P/α-SbP/α-Sb(Te)P (1.26 × 104%) and large piezoelectric strain coefficient in α-SnTe (396 pm/V), providing great opportunities to the design of non-volatile resistive memories, and high-performance piezoelectric devices.

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Publication history
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Acknowledgements

Publication history

Received: 13 September 2022
Revised: 15 October 2022
Accepted: 15 October 2022
Published: 09 December 2022
Issue date: April 2023

Copyright

© Tsinghua University Press 2022

Acknowledgements

Acknowledgements

This work was supported by grants from the National Natural Science Foundation of China (Nos. 52272172, 61888102, and 52102193), the Major Program of National Natural Science Foundation of China (No. 92163206), and the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDB30000000), and the Fundamental Research Funds for the Central Universities. Computational resources were provided by the National Supercomputing Center in Tianjin.

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