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Enabling Intrinsic Antiferroelectricity in Two-dimensional NbOCl2: Molecular Dynamics Simulations based on Deep Learning Interatomic Potential

  • Jiawei Mao (毛嘉伟)
  • , Yinglu Jia (贾瑛璐)
  • , Gaoyang Gou (缑高阳)*
  • , Shi Liu (刘仕)*
  • , Xiao Cheng Zeng*
  • *Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

Abstract

Compared to the well-studied two-dimensional (2D) ferroelectricity, the appearance of 2D antiferroelectricity is much rarer, where local dipoles from the nonequivalent sublattices within 2D monolayers are oppositely oriented. Using NbOCl2 monolayer with competing ferroelectric (FE) and antiferroelectric (AFE) phases as a 2D material platform, we demonstrate the emergence of intrinsic antiferroelectricity in NbOCl2 monolayer under experimentally accessible shear strain, along with new functionality associated with electric field-induced AFE-to-FE phase transition. Specifically, the complex configuration space accommodating FE and AFE phases, polarization switching kinetics, and finite temperature thermodynamic properties of 2D NbOCl2 are all accurately predicted by large-scale molecular dynamics simulations based on deep learning interatomic potential model. Moreover, room temperature stable antiferroelectricity with low polarization switching barrier and one-dimensional collinear polarization arrangement is predicted in shear-deformed NbOCl2 monolayer. The transition from AFE to FE phase in 2D NbOCl2 can be triggered by a low critical electric field, leading to a double polarization–electric (P–E) loop with small hysteresis. A new type of optoelectronic device composed of AFE-NbOCl2 is proposed, enabling electric “writing” and nonlinear optical “reading” logical operation with fast operation speed and low power consumption. © 2026 Chinese Physical Society and IOP Publishing Ltd.
Original languageEnglish
Article number010703
Number of pages7
JournalChinese Physics Letters
Volume43
Issue number1
Online published24 Nov 2025
DOIs
Publication statusPublished - Jan 2026

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 11574244 for G.Y.G.), the XJTU Research Fund for AI Science (Grant No. 2025YXYC011 for G.Y.G.), and the Hong Kong Global STEM Professorship Scheme (for X.C.Z.). We acknowledge Prof. Bin Xu from Soochow University for helpful discussion and Hefei Advanced Computing Center for computational support.

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