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

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

Ferroelectric domain wall profiles can be modeled by phenomenological Ginzburg-Landau theory, with different candidate models and parameters. Here, the authors solve the problem of model selection by developing a Bayesian inference framework allowing for uncertainty quantification and apply it to at...

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Main Authors: Christopher T. Nelson, Rama K. Vasudevan, Xiaohang Zhang, Maxim Ziatdinov, Eugene A. Eliseev, Ichiro Takeuchi, Anna N. Morozovska, Sergei V. Kalinin
Format: Article
Language:English
Published: Nature Publishing Group 2020-12-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-020-19907-2
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spelling doaj-7f7cee60a5a54273b04953277d9b026c2021-01-31T13:49:54ZengNature Publishing GroupNature Communications2041-17232020-12-0111111210.1038/s41467-020-19907-2Exploring physics of ferroelectric domain walls via Bayesian analysis of atomically resolved STEM dataChristopher T. Nelson0Rama K. Vasudevan1Xiaohang Zhang2Maxim Ziatdinov3Eugene A. Eliseev4Ichiro Takeuchi5Anna N. Morozovska6Sergei V. Kalinin7The Center for Nanophase Materials Sciences, Oak Ridge National LaboratoryThe Center for Nanophase Materials Sciences, Oak Ridge National LaboratoryDepartment of Materials Science and Engineering, University of MarylandThe Center for Nanophase Materials Sciences, Oak Ridge National LaboratoryInstitute for Problems of Materials Science, National Academy of Sciences of UkraineDepartment of Materials Science and Engineering, University of MarylandInstitute of Physics, National Academy of Sciences of UkraineThe Center for Nanophase Materials Sciences, Oak Ridge National LaboratoryFerroelectric domain wall profiles can be modeled by phenomenological Ginzburg-Landau theory, with different candidate models and parameters. Here, the authors solve the problem of model selection by developing a Bayesian inference framework allowing for uncertainty quantification and apply it to atomically resolved images of walls. This analysis can also predict the level of microscope performance needed to detect specific physical phenomena.https://doi.org/10.1038/s41467-020-19907-2
collection DOAJ
language English
format Article
sources DOAJ
author Christopher T. Nelson
Rama K. Vasudevan
Xiaohang Zhang
Maxim Ziatdinov
Eugene A. Eliseev
Ichiro Takeuchi
Anna N. Morozovska
Sergei V. Kalinin
spellingShingle Christopher T. Nelson
Rama K. Vasudevan
Xiaohang Zhang
Maxim Ziatdinov
Eugene A. Eliseev
Ichiro Takeuchi
Anna N. Morozovska
Sergei V. Kalinin
Exploring physics of ferroelectric domain walls via Bayesian analysis of atomically resolved STEM data
Nature Communications
author_facet Christopher T. Nelson
Rama K. Vasudevan
Xiaohang Zhang
Maxim Ziatdinov
Eugene A. Eliseev
Ichiro Takeuchi
Anna N. Morozovska
Sergei V. Kalinin
author_sort Christopher T. Nelson
title Exploring physics of ferroelectric domain walls via Bayesian analysis of atomically resolved STEM data
title_short Exploring physics of ferroelectric domain walls via Bayesian analysis of atomically resolved STEM data
title_full Exploring physics of ferroelectric domain walls via Bayesian analysis of atomically resolved STEM data
title_fullStr Exploring physics of ferroelectric domain walls via Bayesian analysis of atomically resolved STEM data
title_full_unstemmed Exploring physics of ferroelectric domain walls via Bayesian analysis of atomically resolved STEM data
title_sort exploring physics of ferroelectric domain walls via bayesian analysis of atomically resolved stem data
publisher Nature Publishing Group
series Nature Communications
issn 2041-1723
publishDate 2020-12-01
description Ferroelectric domain wall profiles can be modeled by phenomenological Ginzburg-Landau theory, with different candidate models and parameters. Here, the authors solve the problem of model selection by developing a Bayesian inference framework allowing for uncertainty quantification and apply it to atomically resolved images of walls. This analysis can also predict the level of microscope performance needed to detect specific physical phenomena.
url https://doi.org/10.1038/s41467-020-19907-2
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AT maximziatdinov exploringphysicsofferroelectricdomainwallsviabayesiananalysisofatomicallyresolvedstemdata
AT eugeneaeliseev exploringphysicsofferroelectricdomainwallsviabayesiananalysisofatomicallyresolvedstemdata
AT ichirotakeuchi exploringphysicsofferroelectricdomainwallsviabayesiananalysisofatomicallyresolvedstemdata
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