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 acquir...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.Automatic detection and classification of defects in the STEM images are needed in order to handle the data in an efficient way.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.Furthermore,it is difficult to deal with crystal structures that have many atoms and low symmetries.Previous methods used for defect detection and classification were based on supervised learning,which requires human-labeled data.In this work,we develop an approach for defect detection with unsupervised machine learning based on a one-class support vector machine(OCSVM).We introduce two schemes of image segmentation and data preprocessing,both of which involve taking the Patterson function of each segment as inputs.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.展开更多
基金This effort is primarily based upon work supported by the U.S.Department of Energy(DOE),Office of Science,Basic Energy Sciences(BES),Materials Sciences and Engineering Division(Y.G.,S.V.K.,and A.R.L.).Electron microscopy with Nion UltraSTEM 100 and TEM sample preparation were performed at the Oak Ridge National Laboratory’s Center for Nanophase Materials Sciences(CNMS),a U.S.Department of Energy Office of Science User Facility.S.V.K.and A.V.D.acknowledge support through the Materials Genome Initiative funding allocated to NIST.
文摘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.Automatic detection and classification of defects in the STEM images are needed in order to handle the data in an efficient way.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.Furthermore,it is difficult to deal with crystal structures that have many atoms and low symmetries.Previous methods used for defect detection and classification were based on supervised learning,which requires human-labeled data.In this work,we develop an approach for defect detection with unsupervised machine learning based on a one-class support vector machine(OCSVM).We introduce two schemes of image segmentation and data preprocessing,both of which involve taking the Patterson function of each segment as inputs.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.
