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Population and distribution of defects is one of the primary parameterscontrolling materials functionality, are often non-ergodic and stronglydependent on synthesis history, and are rarely amenable to direct theoreticalprediction. Here, dynamic electron beam-induced transformations in Si depositedon a graphene monolayer are used to create libraries of the possible Si andcarbon vacancy defects. Automated image analysis and recognition based on deeplearning networks is developed to identify and enumerate the defects, creatinga library of (meta) stable defect configurations. The electronic properties ofthe sample surface are further explored by atomically resolved scanningtunneling microscopy (STM). Density functional theory is used to estimate theSTM signatures of the classified defects from the created library, allowing forthe identification of several defect types across the imaging platforms. Thisapproach allows automatic creation of defect libraries in solids, exploring themetastable configurations always present in real materials, and correlativestudies with other atomically-resolved techniques, providing comprehensiveinsight into defect functionalities. Such libraries will be of criticalimportance in automated AI-assisted workflows for materials prediction andatom-by atom manipulation via electron beams and scanning probes.

Building and exploring libraries of atomic defects in graphene: scanning transmission electron and scanning tunneling microscopy study