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TEMImageNet, AtomSegNet and TomoFillNet, open-source libraries and models that enable defect localization in 2D and 3D atomic resolution images

Published online by Cambridge University Press:  30 July 2021

Huolin Xin
Affiliation:
University of California, Irvine, United States
Chad Manson
Affiliation:
University of California, Irvine, United States
Chunyang Wang
Affiliation:
University of California, Irvine, California, United States

Abstract

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Type
Defects in Materials: How We See and Understand Them
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

References

TEMImageNet Training Library and AtomSegNet Deep-Learning Models for High-Precision Atom Segmentation, Localization, Denoising, and Deblurring of Atom-Resolution Images, Lin, R, Zhang, R, Wang, C, Yang, X, Xin, HL, Scientific Reports, in pressGoogle Scholar
0.7 Å resolution electron tomography enabled by deep learning aided information recovery, Wang, Chunyang, Ding, Guanglei, Liu, Yitong, Xin, Huolin L, Advanced Intelligent Systems, 2, 2000152 (2020)Google Scholar
Three-Dimensional Atomic Structure of Grain Boundaries Resolved by Atomic-Resolution Electron Tomography, Wang, Chunyang, Duan, Huichao, Chen, Chunjin, Wu, Peng, Qi, Dongqing, Ye, Hengqiang, Jin, Hai-Jun, Xin, Huolin L., Du, Kui, Matter, 3, 1999–2011 (2020)Google Scholar
A joint deep learning model to recover information and reduce artifacts in missing-wedge sinograms for electron tomography and beyond, Ding, Guanglei, Liu, Yitong, Zhang, Rui, Xin, Huolin L., Scientific Reports, 9, 12803 (2019)Google Scholar
This presentation is based on materials that is primarily is supported by H.L.X.'s startup funding and the Early Career Research Program, Materials Science and Engineering Divisions, Office of Basic Energy Sciences of the U.S. Department of Energy, under award no. DE-SC0021204 (program manager Dr. Jane Zhu). C.M.'s effort was supported by the UC Irvine MRSEC, Center for Complex and Active Materials, under National Science Foundation award DMR-2011967Google Scholar