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Activity Number: 552
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #320930 View Presentation
Title: A Bayesian Approach to Modeling Atomic Structural Disorder
Author(s): Karl Pazdernik* and Brian J. Reich and Katharine Page
Companies: and North Carolina State University and Oak Ridge National Laboratory
Keywords: Bayesian ; local structure ; materials science ; pair distribution function ; Reverse Monte Carlo ; total scattering
Abstract:

Functional properties explored in materials science can be determined by an understanding of the local structure and disorder. Reverse Monte Carlo is a general method of atomic structural modelling that has made it possible to generate three-dimensional structural models whose properties are consistent with experimental data but are not unique. We propose a Bayesian approach that builds upon the ideas of the Reverse Monte Carlo algorithm, provides improved estimates of the variances of atomic coordinates, and succinctly incorporates multiple data sources.


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