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Information-Based Development of New Radiation Detection Materials

Published online by Cambridge University Press:  26 February 2011

Kim Ferris,
bobbie-jo m webb-robertson and
dumont m jones
Kim Ferris
Affiliation:
kim.ferris@pnl.gov, pacific northwest national laboratory, computational and informational sciences, p.o. box 999, richland, wa, 99352, United States
bobbie-jo m webb-robertson
Affiliation:
bobbie-jo.webb-robertson@pnl.gov, pacific northwest national laboratory, computational and information sciences, United States
dumont m jones
Affiliation:
dumont.jones@prxt.com, proximate technologies, llc, United States
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Abstract

With our present concern for a secure environment, the development of new radiation detection materials has focused on the capability of identifying potential radiation sources at increased sensitivity levels. As the initial framework for a materials-informatics approach to radiation detection materials, we have explored the use of both supervised (Support Vector Machines – SVM and Linear Discriminant Analysis – LDA) and unsupervised (Principal Component Analysis – PCA) learning methods for the development of structural signature models. Application of these methods yields complementary results, both of which are necessary to reduce parameter space and variable degeneracy. Using a crystal structure classification test, the use of the nonlinear SVM significantly increases predictive performance, suggesting trade-offs between smaller descriptor spaces and simpler linear models.

Keywords

crystallographic structureradiation effectscombinatorial synthesis

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