Fig. 5: Prediction of flaw tolerance.

ML predictions show a degree of flaw tolerance, assessing fracture mechanics due to a pre-existing crack. The ML model predicts fracture paths given a 160 × 120 pixel images populated with values θ, representing sheets of graphene 32 × 24 nm with orientation angle θ. To introduce hole defects to the sample, a square region in the center of the image of size L × L is pre-labeled as “cracked” with the value −1 prior to starting fracture predictions. As the images show, b small hole defects placed in the center of graphene sheets only slightly affect the predicted fracture paths from the pristine samples. However, as flaw size increases to 3.2 nm, significant deviation occurs in the form of additional crack branching. This change in behavior at 3.2 nm is consistent with the threshold reported in the literature²⁹ for nanocrystalline graphene flaw tolerance—and may be the mechanism by which fracture strength is reportedly unaffected by flaw sizes below 3.2 nm but decreased with larger flaw sizes.