Fast screening of crystal defects via machine learning

Scientific Achievement: An unsupervised machine learning algorithm is developed for detecting crystallographic defects in atomic-resolution images.

Significance and Impact: The method enables fast screening of large volumes of atomic-resolution data of a variety of different crystal structures without needing manually labeled training images.

DOI: 10.1038/s41524-021-00642-1