Exploring physics of ferroelectric domain walls via Bayesian analysis of atomically resolved STEM data
Journal Article
·
· Nature Communications
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS)
- Univ. of Maryland, College Park, MD (United States)
- National Academy of Sciences of Ukraine, Kyiv (Ukraine)
The physics of ferroelectric domain walls is explored using the Bayesian inference analysis of atomically resolved STEM data. We demonstrate that domain wall profile shapes are ultimately sensitive to the nature of the order parameter in the material, including the functional form of Ginzburg-Landau-Devonshire expansion, and numerical value of the corresponding parameters. The preexisting materials knowledge naturally folds in the Bayesian framework in the form of prior distributions, with the different order parameters forming competing (or hierarchical) models. Here, we explore the physics of the ferroelectric domain walls in BiFeO3 using this method, and derive the posterior estimates of relevant parameters. More generally, this inference approach both allows learning materials physics from experimental data with associated uncertainty quantification, and establishing guidelines for instrumental development answering questions on what resolution and information limits are necessary for reliable observation of specific physical mechanisms of interest.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Science (CNMS)
- Sponsoring Organization:
- European Union (EU); National Institute of Standards and Technology (NIST); National Science Foundation (NSF); USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division
- Grant/Contract Number:
- AC05-00OR22725
- OSTI ID:
- 1787254
- Alternate ID(s):
- OSTI ID: 1870263
- Journal Information:
- Nature Communications, Journal Name: Nature Communications Journal Issue: 1 Vol. 11; ISSN 2041-1723
- Publisher:
- Nature Publishing GroupCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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