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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

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.

Authors who are presenting talks have a * after their name.

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