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Title: Accurate, rapid identification of dislocation lines in coherent diffractive imaging via a min-max optimization formulation

Abstract

Defects such as dislocations impact materials properties and their response during external stimuli. Imaging these defects in their native operating conditions to establish the structure-function relationship and, ultimately, to improve performance via defect engineering has remained a considerable challenge for both electron-based and x-ray-based imaging techniques. While Bragg coherent x-ray diffractive imaging (BCDI) is successful in many cases, nuances in identifying the dislocations has left manual identification as the preferred method. Derivative-based methods are also used, but they can be inaccurate and are computationally inefficient. Here we demonstrate a derivative-free method that is both more accurate and more computationally efficient than either derivative-or human-based methods for identifying 3D dislocation lines in nanocrystal images produced by BCDI. We formulate the problem as a min-max optimization problem and show exceptional accuracy for experimental images. We demonstrate a 227x speedup for a typical experimental dataset with higher accuracy over current methods. We discuss the possibility of using this algorithm as part of a sparsity-based phase retrieval process. We also provide MATLAB code for use by other researchers.

Authors:
ORCiD logo [1];  [2];  [2]
  1. Materials Science Division, Argonne National Laboratory, Lemont, IL 60439, USA
  2. Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL 60439, USA
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1425269
DOE Contract Number:
AC02-06CH11357
Resource Type:
Journal Article
Resource Relation:
Journal Name: AIP Advances; Journal Volume: 8; Journal Issue: 1
Country of Publication:
United States
Language:
English

Citation Formats

Ulvestad, A., Menickelly, M., and Wild, S. M. Accurate, rapid identification of dislocation lines in coherent diffractive imaging via a min-max optimization formulation. United States: N. p., 2018. Web. doi:10.1063/1.5017596.
Ulvestad, A., Menickelly, M., & Wild, S. M. Accurate, rapid identification of dislocation lines in coherent diffractive imaging via a min-max optimization formulation. United States. doi:10.1063/1.5017596.
Ulvestad, A., Menickelly, M., and Wild, S. M. Mon . "Accurate, rapid identification of dislocation lines in coherent diffractive imaging via a min-max optimization formulation". United States. doi:10.1063/1.5017596.
@article{osti_1425269,
title = {Accurate, rapid identification of dislocation lines in coherent diffractive imaging via a min-max optimization formulation},
author = {Ulvestad, A. and Menickelly, M. and Wild, S. M.},
abstractNote = {Defects such as dislocations impact materials properties and their response during external stimuli. Imaging these defects in their native operating conditions to establish the structure-function relationship and, ultimately, to improve performance via defect engineering has remained a considerable challenge for both electron-based and x-ray-based imaging techniques. While Bragg coherent x-ray diffractive imaging (BCDI) is successful in many cases, nuances in identifying the dislocations has left manual identification as the preferred method. Derivative-based methods are also used, but they can be inaccurate and are computationally inefficient. Here we demonstrate a derivative-free method that is both more accurate and more computationally efficient than either derivative-or human-based methods for identifying 3D dislocation lines in nanocrystal images produced by BCDI. We formulate the problem as a min-max optimization problem and show exceptional accuracy for experimental images. We demonstrate a 227x speedup for a typical experimental dataset with higher accuracy over current methods. We discuss the possibility of using this algorithm as part of a sparsity-based phase retrieval process. We also provide MATLAB code for use by other researchers.},
doi = {10.1063/1.5017596},
journal = {AIP Advances},
number = 1,
volume = 8,
place = {United States},
year = {Mon Jan 01 00:00:00 EST 2018},
month = {Mon Jan 01 00:00:00 EST 2018}
}