Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-11-25T15:44:17.303Z Has data issue: false hasContentIssue false
  • English
  • Français

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:
[email protected], pacific northwest national laboratory, computational and informational sciences, p.o. box 999, richland, wa, 99352, United States
bobbie-jo m webb-robertson
Affiliation:
[email protected], pacific northwest national laboratory, computational and information sciences, United States
dumont m jones
Affiliation:
[email protected], proximate technologies, llc, United States
Get access

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
Type
Research Article
Information
MRS Online Proceedings Library (OPL) , Volume 894: Symposium LL – Combinatorial Methods and Informatics in Materials Science , 2005 , 0894-LL09-02
Copyright
Copyright © Materials Research Society 2006

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1. Moses, W., Nucl. Instr. Meth. A 487, 123 (2002).Google Scholar
2. Weber, M.J., J. Lumin. 100, 35 (2002).Google Scholar
3. Derenzo, S., Weber, M., Nucl. Instr. Meth. A 422, 111 (1999).Google Scholar
4. Winston, P.H., Artificial Intelligence, 3rd Ed., (Addison-Wesley, 1993).Google Scholar
5. Austern, M.H., Generic Programming and the STL, (Addison-Wesley, 1999).Google Scholar
6. Pearson, K., Principal component analysis. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science 6(2), 559 (1901).Google Scholar
7. Cristianini, C. and Shawe-Taylor, J. An introduction to support vector machines and other kernel-based learning methods (Cambridge University Press, 2000).Google Scholar
8. Vapnik, V.N., The Nature of Statistical Learning Theory (Springer, 1995).Google Scholar
9. GIST software - http://svm.sdsc.edu/svm-intro.html. Neither the author nor the Materials Research Society warrants or assumes liability for the content or availability of the URL referenced above.Google Scholar
10. Pettifor, D.G., Mat. Sci. Tech. 4, 675 (1988).Google Scholar
11. Rabe, K.M, Phillips, J.C., Villars, P., and Brown, I.D., Phys. Rev. B 45, 7650 (1992).Google Scholar
12. Villars, P., Brandenburg, K., Berndt, M., LeClair, S., Jackson, A., Pao, Y.-H., Igelnik, B., Oxley, M., Bakshi, B., Chen, P., Iwata, S., Eng. Appl. Artif. Intell. 13, 497 (2000).Google Scholar
13. Morgan, D., Rodgers, J., and Ceder, G., J. Phys.: Condens. Matter 15, 4361 (2003).Google Scholar