  Population and distribution of defects is one of the primary parameters
controlling materials functionality, are often non-ergodic and strongly
dependent on synthesis history, and are rarely amenable to direct theoretical
prediction. Here, dynamic electron beam-induced transformations in Si deposited
on a graphene monolayer are used to create libraries of the possible Si and
carbon vacancy defects. Automated image analysis and recognition based on deep
learning networks is developed to identify and enumerate the defects, creating
a library of (meta) stable defect configurations. The electronic properties of
the sample surface are further explored by atomically resolved scanning
tunneling microscopy (STM). Density functional theory is used to estimate the
STM signatures of the classified defects from the created library, allowing for
the identification of several defect types across the imaging platforms. This
approach allows automatic creation of defect libraries in solids, exploring the
metastable configurations always present in real materials, and correlative
studies with other atomically-resolved techniques, providing comprehensive
insight into defect functionalities. Such libraries will be of critical
importance in automated AI-assisted workflows for materials prediction and
atom-by atom manipulation via electron beams and scanning probes.
