Defects induce phase transition from dynamic to static rippling in graphene
Fabian L. Thiemann, Camille Scalliet, Erich A. M\"uller, Angelos, Michaelides

TL;DR
This study uses machine learning-driven molecular dynamics simulations to reveal how defect concentration causes a phase transition from dynamic to static ripples in graphene, impacting its properties and potential applications.
Contribution
It uncovers the defect-driven phase transition mechanism in graphene's rippling behavior using atomic-resolution simulations and identifies universal principles governing this transition.
Findings
Above a critical defect concentration, graphene transitions from dynamic to static ripples.
Elastic interactions between defects drive the phase transition.
The transition varies with defect types and can be controlled for material design.
Abstract
Two-dimensional (2D) materials display nanoscale dynamic ripples that significantly impact their properties. Defects within the crystal lattice are the elementary building blocks to tailor the material's morphology. While some studies have explored the link between defective structures and rippling dynamics in 2D materials, a comprehensive understanding of this relationship has yet to be achieved. Here, we address this using machine learning-driven molecular dynamics simulations. Specifically, we find that above a critical concentration of defects, free-standing graphene sheets undergo a dynamic transition from freely propagating to static ripples. Our computational approach captures the dynamics with atomic resolution, and reveals that the transition is driven by elastic interactions between defects. The strength of these interactions is found to vary across defect types and we…
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Taxonomy
TopicsForce Microscopy Techniques and Applications
