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Title: A Bayesian approach to modeling diffraction profiles and application to ferroelectric materials

Abstract

A new statistical approach for modeling diffraction profiles is introduced, using Bayesian inference and a Markov chain Monte Carlo (MCMC) algorithm. This method is demonstrated by modeling the degenerate reflections during application of an electric field to two different ferroelectric materials: thin-film lead zirconate titanate (PZT) of composition PbZr 0.3Ti 0.7O 3and a bulk commercial PZT polycrystalline ferroelectric. Here, the new method offers a unique uncertainty quantification of the model parameters that can be readily propagated into new calculated parameters.

Authors:
 [1];  [2];  [2];  [2];  [2];  [2];  [3];  [3];  [3];  [4];  [2]
  1. North Carolina State Univ., Raleigh, NC (United States); King Mongkut's Univ. of Technology North Bangkok, Bangkok (Thailand)
  2. North Carolina State Univ., Raleigh, NC (United States)
  3. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  4. Georgia Inst. of Technology, Atlanta, GA (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1342701
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Applied Crystallography (Online)
Additional Journal Information:
Journal Name: Journal of Applied Crystallography (Online); Journal Volume: 50; Journal Issue: 1; Journal ID: ISSN 1600-5767
Publisher:
International Union of Crystallography
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; ferroelectric materials; Bayesian inference; domain switching fraction; modeling diffraction profiles

Citation Formats

Iamsasri, Thanakorn, Guerrier, Jonathon, Esteves, Giovanni, Fancher, Chris M., Wilson, Alyson G., Smith, Ralph C., Paisley, Elizabeth A., Johnson-Wilke, Raegan, Ihlefeld, Jon F., Bassiri-Gharb, Nazanin, and Jones, Jacob L. A Bayesian approach to modeling diffraction profiles and application to ferroelectric materials. United States: N. p., 2017. Web. doi:10.1107/S1600576716020057.
Iamsasri, Thanakorn, Guerrier, Jonathon, Esteves, Giovanni, Fancher, Chris M., Wilson, Alyson G., Smith, Ralph C., Paisley, Elizabeth A., Johnson-Wilke, Raegan, Ihlefeld, Jon F., Bassiri-Gharb, Nazanin, & Jones, Jacob L. A Bayesian approach to modeling diffraction profiles and application to ferroelectric materials. United States. doi:10.1107/S1600576716020057.
Iamsasri, Thanakorn, Guerrier, Jonathon, Esteves, Giovanni, Fancher, Chris M., Wilson, Alyson G., Smith, Ralph C., Paisley, Elizabeth A., Johnson-Wilke, Raegan, Ihlefeld, Jon F., Bassiri-Gharb, Nazanin, and Jones, Jacob L. Wed . "A Bayesian approach to modeling diffraction profiles and application to ferroelectric materials". United States. doi:10.1107/S1600576716020057. https://www.osti.gov/servlets/purl/1342701.
@article{osti_1342701,
title = {A Bayesian approach to modeling diffraction profiles and application to ferroelectric materials},
author = {Iamsasri, Thanakorn and Guerrier, Jonathon and Esteves, Giovanni and Fancher, Chris M. and Wilson, Alyson G. and Smith, Ralph C. and Paisley, Elizabeth A. and Johnson-Wilke, Raegan and Ihlefeld, Jon F. and Bassiri-Gharb, Nazanin and Jones, Jacob L.},
abstractNote = {A new statistical approach for modeling diffraction profiles is introduced, using Bayesian inference and a Markov chain Monte Carlo (MCMC) algorithm. This method is demonstrated by modeling the degenerate reflections during application of an electric field to two different ferroelectric materials: thin-film lead zirconate titanate (PZT) of composition PbZr0.3Ti0.7O3and a bulk commercial PZT polycrystalline ferroelectric. Here, the new method offers a unique uncertainty quantification of the model parameters that can be readily propagated into new calculated parameters.},
doi = {10.1107/S1600576716020057},
journal = {Journal of Applied Crystallography (Online)},
number = 1,
volume = 50,
place = {United States},
year = {2017},
month = {2}
}

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