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February 12, 2026APL Computational Physics0 citationsOpen Access

Anisotropic deformation of strongly bent graphene: A study from machine learning

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SZSumei ZhouJZJun-Ding ZhengZBZhi-Qiang Bao

Key Points

  • This research aims to explore the anisotropic deformation of bent graphene and its effects on properties critical for applications.
  • Utilized machine learning techniques for deep potential modeling of bent graphene.
  • Investigated the structure and phonon spectrum associated with different bending directions.
  • Analyzed the mechanical anisotropy exhibited under strong bending conditions.
  • Found pronounced mechanical anisotropy with uniform curvature in the armchair direction and localized curvature in the zigzag direction.
  • Documented significant lattice distortion caused by localized curvature.
  • Observed splitting of the G peak in the Raman spectrum, indicating changes in phonon modes.

Abstract

Bent phenomena such as scrolls, ripples, and bubbles are inherent to the growth and application of graphene and critically influence its properties. The study of the bending behavior is, therefore, essential for the fabrication, functioning, and robustness of graphene-based devices. Here, we investigate the structure and phonon spectrum of bent graphene through deep potential modeling. Our results reveal a pronounced mechanical anisotropy under strong bending. The curvature is distributed uniformly when bent along the armchair direction but becomes highly localized along the zigzag direction. Localized curvature induces significant lattice distortion, which lifts the degeneracy of the longitudinal optical and transverse optical phonon modes. This lifting results in the splitting of the G peak in the Raman spectrum, providing a signature for experimental verification. Our work offers insights into the research on graphene bending and graphene-based devices.

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Cite This Study

Zhou et al. (2026) studied this question.

synapsesocial.com/papers/698d6eca5be6419ac0d54966https://doi.org/10.1063/5.0315652
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