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A Bayesian approach to modeling diffraction profiles and application to ferroelectric materials
Author(s) -
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.
Publication year - 2017
Publication title -
journal of applied crystallography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.429
H-Index - 162
ISSN - 1600-5767
DOI - 10.1107/s1600576716020057
Subject(s) - markov chain monte carlo , ferroelectricity , materials science , diffraction , lead zirconate titanate , bayesian inference , monte carlo method , crystallite , electric field , bayesian probability , algorithm , statistical physics , computer science , optics , statistics , physics , mathematics , artificial intelligence , optoelectronics , dielectric , quantum mechanics , metallurgy
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 0.3 Ti 0.7 O 3 and a bulk commercial PZT polycrystalline ferroelectric. The new method offers a unique uncertainty quantification of the model parameters that can be readily propagated into new calculated parameters.