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The study of the nanomechanics of graphene $-$ and other 2D materials $-$ has led to the discovery of exciting new properties in 2D crystals, such as their remarkable in-plane stiffness and out of plane flexibility, as well as their unique frictional and wear properties at the nanoscale. Recently, nanomechanics of graphene has generated renovated interest for new findings on the pressure-induced chemical transformation of a few-layer thick epitaxial graphene into a new ultra-hard carbon phase, named diamene. In this work, by means of a machine learning technique, we provide a fast and efficient tool for identification of graphene domains (areas with a defined number of layers) in epitaxial and exfoliated films, by combining data from Atomic Force Microscopy (AFM) topography and friction force microscopy (FFM). Through the analysis of the number of graphene layers and detailed \r{A}-indentation experiments, we demonstrate that the formation of ultra-stiff diamene is exclusively found in 1-layer plus buffer layer epitaxial graphene on silicon carbide (SiC) and that an ultra-stiff phase is not observed in neither thicker epitaxial graphene (2-layer or more) nor exfoliated graphene films of any thickness on silicon oxide (SiO$_{2}$).

Layer dependence of graphene-diamene phase transition in epitaxial and exfoliated few-layer graphene using machine learning