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Title: Building ferroelectric from the bottom up: The machine learning analysis of the atomic-scale ferroelectric distortions

Abstract

Recent advances in scanning transmission electron microscopy (STEM) have enabled direct visualization of the atomic structure of ferroic materials, enabling the determination of atomic column positions with approximately picometer precision. This, in turn, enabled direct mapping of ferroelectric and ferroelastic order parameter fields via the top-down approach, where the atomic coordinates are directly mapped on the mesoscopic order parameters. Here, we explore the alternative bottom-up approach, where the atomic coordinates derived from the STEM image are used to explore the extant atomic displacement patterns in the material and build the collection of the building blocks for the distorted lattice. This approach is illustrated for the La-doped BiFeO3 system.

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1];  [2]; ORCiD logo [1]
  1. ORNL
  2. University of California, Berkeley
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1550749
Alternate Identifier(s):
OSTI ID: 1546099
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Applied Physics Letters
Additional Journal Information:
Journal Volume: 115; Journal Issue: 5
Country of Publication:
United States
Language:
English

Citation Formats

Ziatdinov, Maxim A., Nelson, Christopher, Vasudevan, Rama K., Chen, Deyang, and Kalinin, Sergei V. Building ferroelectric from the bottom up: The machine learning analysis of the atomic-scale ferroelectric distortions. United States: N. p., 2019. Web. doi:10.1063/1.5109520.
Ziatdinov, Maxim A., Nelson, Christopher, Vasudevan, Rama K., Chen, Deyang, & Kalinin, Sergei V. Building ferroelectric from the bottom up: The machine learning analysis of the atomic-scale ferroelectric distortions. United States. doi:10.1063/1.5109520.
Ziatdinov, Maxim A., Nelson, Christopher, Vasudevan, Rama K., Chen, Deyang, and Kalinin, Sergei V. Thu . "Building ferroelectric from the bottom up: The machine learning analysis of the atomic-scale ferroelectric distortions". United States. doi:10.1063/1.5109520.
@article{osti_1550749,
title = {Building ferroelectric from the bottom up: The machine learning analysis of the atomic-scale ferroelectric distortions},
author = {Ziatdinov, Maxim A. and Nelson, Christopher and Vasudevan, Rama K. and Chen, Deyang and Kalinin, Sergei V.},
abstractNote = {Recent advances in scanning transmission electron microscopy (STEM) have enabled direct visualization of the atomic structure of ferroic materials, enabling the determination of atomic column positions with approximately picometer precision. This, in turn, enabled direct mapping of ferroelectric and ferroelastic order parameter fields via the top-down approach, where the atomic coordinates are directly mapped on the mesoscopic order parameters. Here, we explore the alternative bottom-up approach, where the atomic coordinates derived from the STEM image are used to explore the extant atomic displacement patterns in the material and build the collection of the building blocks for the distorted lattice. This approach is illustrated for the La-doped BiFeO3 system.},
doi = {10.1063/1.5109520},
journal = {Applied Physics Letters},
number = 5,
volume = 115,
place = {United States},
year = {2019},
month = {8}
}

Journal Article:
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Works referenced in this record:

Local polarization dynamics in ferroelectric materials
journal, April 2010

  • Kalinin, Sergei V.; Morozovska, Anna N.; Chen, Long Qing
  • Reports on Progress in Physics, Vol. 73, Issue 5, Article No. 056502
  • DOI: 10.1088/0034-4885/73/5/056502