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. In this paper, 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. Lastly, this approach is illustrated for the La-doped BiFeO3 system.
- Authors:
-
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- South China Normal University, Guangzhou (China)
- Publication Date:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Materials Sciences & Engineering Division
- OSTI Identifier:
- 1550749
- Alternate Identifier(s):
- OSTI ID: 1546099
- Grant/Contract Number:
- AC05-00OR22725; AC02-05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Applied Physics Letters
- Additional Journal Information:
- Journal Volume: 115; Journal Issue: 5; Journal ID: ISSN 0003-6951
- Publisher:
- American Institute of Physics (AIP)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE
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. https://doi.org/10.1063/1.5109520
Ziatdinov, Maxim A., Nelson, Christopher, Vasudevan, Rama K., Chen, Deyang, and Kalinin, Sergei V. Fri .
"Building ferroelectric from the bottom up: The machine learning analysis of the atomic-scale ferroelectric distortions". United States. https://doi.org/10.1063/1.5109520. https://www.osti.gov/servlets/purl/1550749.
@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. In this paper, 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. Lastly, 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 = {Fri Aug 02 00:00:00 EDT 2019},
month = {Fri Aug 02 00:00:00 EDT 2019}
}
Web of Science
Figures / Tables:
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