  Developing methods to understand and control defect formation in
nanomaterials offers a promising route for materials discovery. Monolayer MX2
phases represent a particularly compelling case for defect engineering of
nanomaterials due to the large variability in their physical properties as
different defects are introduced into their structure. However, effective
identification and quantification of defects remains a challenge even as
high-throughput scanning tunneling electron microscopy (STEM) methods improve.
This study highlights the benefits of employing first principles calculations
to produce digital twins for training deep learning segmentation models for
defect identification in monolayer MX2 phases. Around 600 defect structures
were obtained using density functional theory calculations, with each monolayer
MX2 structure being subjected to multislice simulations for the purpose of
generating the digital twins. Several deep learning segmentation architectures
were trained on this dataset, and their performances evaluated under a variety
of conditions such as recognizing defects in the presence of unidentified
impurities, beam damage, grain boundaries, and with reduced image quality from
low electron doses. This digital twin approach allows benchmarking different
deep learning architectures on a theory dataset, which enables the study of
defect classification under a broad array of finely controlled conditions. It
thus opens the door to resolving the underpinning physical reasons for model
shortcomings, and potentially chart paths forward for automated discovery of
materials defect phases in experiments.
