Simulations show anisotropy in graphene when under strong bending conditions

Two-dimensional materials such as graphene have a tendency to form bending structures such as scrolls, ripples, bubbles, and wrinkles, with manufacturing implications. Relatively little is understood, however, about the behavior of graphene under strong bending conditions. New work combining Raman spectroscopy and simulations looks to shed light on this process.

Zhou et al. have characterized the structure and phonon spectrum of bending graphene. Using the Deep Potential Model that employs machine learning, the group found that the curvature distributes uniformly when bent in the rounder armchair direction. When bent in zigzag shapes, the bending becomes highly localized, suggesting non-uniform properties of molecules in the material, called anisotropy.

In addition, the use of Raman spectroscopy — which uses light scattering patterns to analyze the collective excitation in a periodic, elastic arrangement of atoms or molecules in a material called phonons — points to a path forward for experimentally detecting bending anisotropy.

“This work has provided some suggestions for optimizing the fabrication methods of two-dimensional material devices,” said author Jun-Ding Zheng. “The different peeling directions may have a significant impact on the performance of two-dimensional materials. Choosing suitable directions may be more conducive to the fabrication of devices, even for those materials that appear to be isotropic.”

Graphene is known for its high symmetry, which accounts for its isotropy, or tendency to be molecularly uniform in all directions.

Raman spectroscopy showed instances of anisotropy when strong bending forces were applied. Localized curvature, as in zigzag bending, splits the G peak on graphene’s light scattering spectrum, a characteristic peak in the material’s molecular vibrations.

The group next looks to extend their approach to other materials as well as explore how anisotropy affects electronic structure.

Source: “Anisotropic deformation of strongly bent graphene: A study from machine learning,” by Song-Chuan Zhou, Jun-Ding Zheng, Zhi-Qiang Bao, and Chun-Gang Duan, APL Computational Physics (2026). The article can be accessed at https://doi.org/10.1063/5.0315652 .