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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Autores principales:	Christopher T. Nelson, Rama K. Vasudevan, Xiaohang Zhang, Maxim Ziatdinov, Eugene A. Eliseev, Ichiro Takeuchi, Anna N. Morozovska, Sergei V. Kalinin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/7f7cee60a5a54273b04953277d9b026c
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spelling	oai:doaj.org-article:7f7cee60a5a54273b04953277d9b026c2021-12-02T13:24:15ZExploring physics of ferroelectric domain walls via Bayesian analysis of atomically resolved STEM data10.1038/s41467-020-19907-22041-1723https://doaj.org/article/7f7cee60a5a54273b04953277d9b026c2020-12-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-19907-2https://doaj.org/toc/2041-1723Ferroelectric 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.Christopher T. NelsonRama K. VasudevanXiaohang ZhangMaxim ZiatdinovEugene A. EliseevIchiro TakeuchiAnna N. MorozovskaSergei V. KalininNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-12 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q 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
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.
format	article
author	Christopher T. Nelson Rama K. Vasudevan Xiaohang Zhang Maxim Ziatdinov Eugene A. Eliseev Ichiro Takeuchi Anna N. Morozovska Sergei V. Kalinin
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 Portfolio
publishDate	2020
url	https://doaj.org/article/7f7cee60a5a54273b04953277d9b026c
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Exploring physics of ferroelectric domain walls via Bayesian analysis of atomically resolved STEM data

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