2 research outputs found
Single-image based deep learning for precise atomic defects identification
Defect engineering has been profoundly employed to confer desirable
functionality to materials that pristine lattices inherently lack. Although
single atomic-resolution scanning transmission electron microscopy (STEM)
images are widely accessible for defect engineering, harnessing atomic-scale
images containing various defects through traditional image analysis methods is
hindered by random noise and human bias. Yet the rise of deep learning (DL)
offering an alternative approach, its widespread application is primarily
restricted by the need for large amounts of training data with labeled ground
truth. In this study, we propose a two-stage method to address the problems of
high annotation cost and image noise in the detection of atomic defects in
monolayer 2D materials. In the first stage, to tackle the issue of data
scarcity, we employ a two-state transformation network based on U-GAT-IT for
adding realistic noise to simulated images with pre-located ground truth
labels, thereby infinitely expanding the training dataset. In the second stage,
atomic defects in monolayer 2D materials are effectively detected with high
accuracy using U-Net models trained with the data generated in the first stage,
avoiding random noise and human bias issues. In both stages, we utilize
segmented unit-cell-level images to simplify the model's task and enhance its
accuracy. Our results demonstrate that not only sulfur vacancies, we are also
able to visualize oxygen dopants in monolayer MoS2, which are usually
overwhelmed by random background noise. As the training was based on a few
segmented unit-cell-level realistic images, this method can be readily extended
to other 2D materials. Therefore, our results outline novel ways to train the
model with minimized datasets, offering great opportunities to fully exploit
the power of machine learning (ML) applicable to a broad materials science
community