Bayesian approach to cluster expansions
Name
Mueller-2009-Bayesian approach to.pdf
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497.79 KB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Mueller, Timothy K.
Ceder, Gerbrand
Date Issued
July 2009
Journal
Physical Review B
Publisher
American Physical Society
Citation
Mueller, Tim , and Gerbrand Ceder. “Bayesian approach to cluster expansions.” Physical Review B 80.2 (2009): 024103. (C) 2010 The American Physical Society.
Version
Final published version
Abstract
Cluster expansions have proven to be a valuable tool in alloy theory and other problems in materials science but the generation of cluster expansions can be a computationally expensive and time-consuming process. We present a Bayesian framework for developing cluster expansions that explicitly incorporates physical insight into the fitting procedure. We demonstrate how existing methods fit within this framework and use the framework to develop methods that significantly improve the predictive power of cluster expansions for a given training set size. The key to the methods is to apply physical insight and cross validation to develop physically meaningful prior probability distributions for the cluster expansion coefficients. We use the Bayesian approach to develop an efficient method for generating cluster expansions for low-symmetry systems such as surfaces and nanoparticles.
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
http://dx.doi.org/10.1103/PhysRevB.80.024103
