  Many of graphene's remarkable properties are intrinsically linked to its
inherent ripples. Defects, whether naturally present or artificially
introduced, are known to have a strong impact on the rippling of graphene.
However, how defects alter ripple dynamics in two-dimensional (2D) materials in
general, and graphene in particular, remains largely unexplored. Here, using
machine learning-driven molecular dynamics simulations, we reveal a fundamental
connection between defect concentration and ripple dynamics in freestanding
graphene sheets. Specifically, we find that at a critical concentration of
approximately 0.1%, dynamic rippling undergoes a transition from freely
propagating to static ripples. This is in quantitative alignment with the
experimentally observed turning point in the non-monotonic scaling of the
Young's modulus and emphasises the critical interplay between defects and
material dynamics. Our work not only unveils the significant impact of defects
on rippling dynamics in graphene but also paves the way to design
two-dimensional devices with tailored properties.
