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Informatics for Combinatorial Experiments: Accelerating Data Interpretation

Published online by Cambridge University Press:  26 February 2011

M. Stukowski
Affiliation:
Department of Materials Science and Engineering, Iowa State University, Ames, IA 50011, U.S.A.
C. Suh
Affiliation:
Department of Materials Science and Engineering, Iowa State University, Ames, IA 50011, U.S.A.
Krishna Rajan
Affiliation:
krajan@iastate.edu, Iowa State University, Materials Science and Engineering, HOOVER 2220S, AMES, IA, 50011, United States, 515-294-2670, 515-294-5444
P. D. Tall
Affiliation:
Department of Physics, Université Cheikh Anta Diop, Dakar, Sénégal
A. C. Beye
Affiliation:
Department of Mechanical and Aeronautical Engineering, Princeton University, Princeton, NJ 08544, U.S.A.
A. G. Ramirez
Affiliation:
Department of Mechanical Engineering, Yale University, New Haven, CT 06520, U.S.A.
W. O. Soboyejo
Affiliation:
Department of Mechanical and Aeronautical Engineering, Princeton University, Princeton, NJ 08544, U.S.A.
M. L. Benson
Affiliation:
Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN 37996, U.S.A.
P.K. Liaw
Affiliation:
Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN 37996, U.S.A.
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Abstract

Combinatorial experiments provide a means of generating large amounts of experimental data; however that does not necessarily lead to high throughput interpretation of that data. In this paper we provide a brief summary of how one can use informatics techniques to accelerate data interpretation from high throughput experiments. We provide examples from high throughput nanoindentation and diffraction experiments.

Type
Research Article
Copyright
Copyright © Materials Research Society 2006

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References

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