We demonstrate that this method can be applied to various defects, such as point and line defects in 2D materials and twin boundaries in 3D nanocrystals.Ĭrystallographic defects are interruptions of periodic patterns in crystals and play a key role in defining materials properties.
We introduce two schemes of image segmentation and data preprocessing, both of which involve taking the Patterson function of each segment as inputs. In this work, we develop an approach for defect detection with unsupervised machine learning based on a one-class support vector machine (OCSVM). Previous methods used for defect detection and classification were based on supervised learning, which requires human-labeled data. Furthermore, it is difficult to deal with crystal structures that have many atoms and low symmetries. However, like many other tasks related to object detection and identification in artificial intelligence, it is challenging to detect and identify defects from STEM images. Automatic detection and classification of defects in the STEM images are needed in order to handle the data in an efficient way. Crystallographic defects can now be routinely imaged at atomic resolution with aberration-corrected scanning transmission electron microscopy (STEM) at high speed, with the potential for vast volumes of data to be acquired in relatively short times or through autonomous experiments that can continue over very long periods.
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