Ferroelectric Domain and Switching Dynamics in Curved In2Se3: First-Principles and Deep Learning Molecular Dynamics Simulations
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Description
Despite its prevalence in experiments, the influence of complex strain on material properties remains understudied due to the lack of effective simulation methods. Here, the effects of bending, rippling, and bubbling on the ferroelectric domains are investigated in an In2Se3 monolayer by density functional theory and deep learning molecular dynamics simulations. Since the ferroelectric switching barrier can be increased (decreased) by tensile (compressive) strain, automatic polarization reversal occurs in α-In2Se3 with a strain gradient when it is subjected to bending, rippling, or bubbling deformations to create localized ferroelectric domains with varying sizes. The switching dynamics depends on the magnitude of curvature and temperature, following an Arrhenius-style relationship. This study not only provides a promising solution for cross-scale studies using deep learning but also reveals the potential to manipulate local polarization in ferroelectric materials through strain engineering.
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ID Code: | 245278 | ||||||||
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Item Type: | Contribution to Journal (Journal Article) | ||||||||
Refereed: | Yes | ||||||||
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Measurements or Duration: | 8 pages | ||||||||
Keywords: | 2D ferroelectric, deep learning potential, polarization switching, strain engineering, α-InSe | ||||||||
DOI: | 10.1021/acs.nanolett.3c03160 | ||||||||
ISSN: | 1530-6984 | ||||||||
Pure ID: | 152917033 | ||||||||
Divisions: | Current > QUT Faculties and Divisions > Faculty of Engineering Current > Schools > School of Mechanical, Medical & Process Engineering |
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Funding Information: | This work was supported by the Australian Research Council (Grant IC190100020 and DP200102546 & DP230101904), the National Natural Science Foundation of China (Grant 12202254), and the High-performance Computing (HPC) resources provided by the Queensland University of Technology (QUT). This research was undertaken with assistance of resources and services from the National Computational Infrastructure (NCI), which is supported by the Australian Government. | ||||||||
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Copyright Owner: | 2023 American Chemical Society | ||||||||
Copyright Statement: | This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au | ||||||||
Deposited On: | 19 Dec 2023 03:57 | ||||||||
Last Modified: | 18 Jul 2024 21:59 |
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