Materials property mapping from atomic scale imaging via machine learning based sub-pixel processing  

作　　者：Junghun Han Kyoung-June Go Jinhyuk Jang Sejung Yang Si-Young Choi 

机构地区：[1]Department of Biomedical Engineering,Yonsei University,Wonju,26493,Republic of Korea [2]Department of Materials Science and Engineering,Pohang University of Science and Technology,Pohang,37673,Republic of Korea

出　　处：《npj Computational Materials》2022年第1期1868-1876,共9页计算材料学（英文）

基　　金：This work was supported by the Global Frontier Hybrid Interface Materials of the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(2013M3A6B1078872);S.-Y.C.acknowledges the support of Korea Basic Science Institute(National research Facilities and Equipment Center)grant funded by the Ministry of Education.(2020R1A6C101A202).

摘　　要：Direct visualization of the atomic structure in scanning transmission electron microscopy has led to a comprehensive understanding of the structure-property relationship.However,a reliable characterization of the structural transition on a picometric scale is still challenging because of the limited spatial resolution and noise.Here,we demonstrate that the primary segmentation of atomic signals from background,succeeded by a denoising process,enables structural analysis in a sub-pixel accuracy.Poisson noise is eliminated using the block matching and three-dimensional filtering with Anscombe transformation,and remnant noise is removed via morphological filtering,which results in an increase of peak signal-to-noise ratio from 7 to 11 dB.Extracting the centroids of atomic columns segmented via K-means clustering,an unsupervised method for robust thresholding,achieves an average error of less than 0.7 pixel,which corresponds to 4.6 pm.This study will contribute to a profound understanding of the local structural dynamics in crystal structures.

关 键 词：PROPERTY FILTERING removed 

分 类 号：TB30[一般工业技术—材料科学与工程] TP181[自动化与计算机技术—控制理论与控制工程]