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This content will become publicly available on August 9, 2017

Title: Imaging nanoscale lattice variations by machine learning of x-ray diffraction microscopy data

In this paper, we present a novel methodology based on machine learning to extract lattice variations in crystalline materials, at the nanoscale, from an x-ray Bragg diffraction-based imaging technique. By employing a full-field microscopy setup, we capture real space images of materials, with imaging contrast determined solely by the x-ray diffracted signal. The data sets that emanate from this imaging technique are a hybrid of real space information (image spatial support) and reciprocal lattice space information (image contrast), and are intrinsically multidimensional (5D). By a judicious application of established unsupervised machine learning techniques and multivariate analysis to this multidimensional data cube, we show how to extract features that can be ascribed physical interpretations in terms of common structural distortions, such as lattice tilts and dislocation arrays. Finally, we demonstrate this 'big data' approach to x-ray diffraction microscopy by identifying structural defects present in an epitaxial ferroelectric thin-film of lead zirconate titanate.
Authors:
 [1] ;  [2] ;  [3]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences; Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Inst. for Functional Imaging of Materials
  2. Argonne National Lab. (ANL), Argonne, IL (United States). X-ray Science Division
  3. Paul Scherrer Inst. (PSI), Villigen (Switzerland). Swiss Light Source
Publication Date:
OSTI Identifier:
1302903
Grant/Contract Number:
AC05-00OR22725; AC02-06CH11357
Type:
Accepted Manuscript
Journal Name:
Nanotechnology
Additional Journal Information:
Journal Volume: 27; Journal Issue: 37; Journal ID: ISSN 0957-4484
Publisher:
IOP Publishing
Research Org:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org:
USDOE Office of Science (SC)
Contributing Orgs:
Argonne National Lab. (ANL), Argonne, IL (United States); Paul Scherrer Inst. (PSI), Villigen (Switzerland)
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; 47 OTHER INSTRUMENTATION x-ray diffraction imaging; computer vision; machine learning; multivariate analysis; ferroelectric materials; epitaxial thin-film