Exploring physics of ferroelectric domain walls via Bayesian analysis of atomically resolved STEM data

Christopher T. Nelson, Rama K. Vasudevan, Xiaohang Zhang, Maxim Ziatdinov, Eugene A. Eliseev, Ichiro Takeuchi, Anna N. Morozovska and Sergei V. Kalinin ()
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Christopher T. Nelson: Oak Ridge National Laboratory
Rama K. Vasudevan: Oak Ridge National Laboratory
Xiaohang Zhang: University of Maryland
Maxim Ziatdinov: Oak Ridge National Laboratory
Eugene A. Eliseev: Institute for Problems of Materials Science, National Academy of Sciences of Ukraine
Ichiro Takeuchi: University of Maryland
Anna N. Morozovska: Institute of Physics, National Academy of Sciences of Ukraine
Sergei V. Kalinin: Oak Ridge National Laboratory

Nature Communications, 2020, vol. 11, issue 1, 1-12

Abstract: Abstract 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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-19907-2

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DOI: 10.1038/s41467-020-19907-2

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