The training dataset used for constructing the ANN potential consisted of C-H cluster structures, varying in size from 10 to 71 atoms. Employing cluster structures allowed for a broad sampling of atomic configurations within C-H systems, thereby enhancing the transferability of the ANN potential. The initial dataset was constructed by about 7000 fully optimized defective graphene nanoflakes obtained from our local database (Initial Training Data Generation in Fig. ). These structures were generated by introducing 1 -24 C vacancies in a zigzag graphene flake. The original pristine graphene flake contains 54 C atoms, with 18 H atoms passivating the edges. The geometries of these defective structures underwent optimization using DFT implemented in the Gaussian 16 package. Generally, the creation of vacancies induces structural instability, leading to a transition toward amorphization at higher vacancy concentrations. Consequently, these defective structures exhibited significant structural reconstructions after geometry optimization, resembling amorphous phases.