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Combinatorial Materials Design through Database Science

Published online by Cambridge University Press:  01 February 2011

Changwon Suh ,
Arun Rajagopalan ,
Xiang Li  and
Krishna Rajan
Changwon Suh
Affiliation:
Department of Materials Science and Engineering Combinatorial Materials Science and Materials Informatics Laboratory, Rensselaer Polytechnic Institute, Troy NY 12180–3590 Email: rajank@rpi.edu URL: http://www.rpi.edu/∼rajank/materialsdiscovery and http://cosmic.rpi.edu
Arun Rajagopalan
Affiliation:
Department of Materials Science and Engineering Combinatorial Materials Science and Materials Informatics Laboratory, Rensselaer Polytechnic Institute, Troy NY 12180–3590 Email: rajank@rpi.edu URL: http://www.rpi.edu/∼rajank/materialsdiscovery and http://cosmic.rpi.edu
Xiang Li
Affiliation:
Department of Materials Science and Engineering Combinatorial Materials Science and Materials Informatics Laboratory, Rensselaer Polytechnic Institute, Troy NY 12180–3590 Email: rajank@rpi.edu URL: http://www.rpi.edu/∼rajank/materialsdiscovery and http://cosmic.rpi.edu
Krishna Rajan
Affiliation:
Department of Materials Science and Engineering Combinatorial Materials Science and Materials Informatics Laboratory, Rensselaer Polytechnic Institute, Troy NY 12180–3590 Email: rajank@rpi.edu URL: http://www.rpi.edu/∼rajank/materialsdiscovery and http://cosmic.rpi.edu
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Abstract

Large scale materials databases have been traditionally used for search and retrieval of experimental and theoretical data. In this paper, three different cases are used to illustrate applications of statistical techniques in databases that extend beyond searching. A complete large scale database of molten salts is visualized for pattern seeking. In the second case, a large virtual combinatorial library of chalcopyrite semiconductors is developed from a small experimental and theoretical dataset. This involves selecting statistically appropriate parameters based on the physics of the materials. In the third case, ‘secondary’ descriptors are developed for a zeolites database to better understand the topology of mesoporous structures and as a materials design tool. These examples serve to demonstrate how databases can be used to identify important combinations of parameters relevant to combinatorial experimentation.

Type
Research Article
Information
MRS Online Proceedings Library (OPL) , Volume 804: Symposium JJ – Combinatorial and Artificial Intelligence Methods in Materials Science II , 2003 , JJ9.23
Copyright
Copyright © Materials Research Society 2004

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References

REFERENCES

1. Janz, G. J., Ward, A. T., and Reeves, R. D., ‘Electrical conductance, Density, and Viscosity’, Technical bulletin series, Rensselaer Polytechnic Institute, (1964)Google Scholar
2. Janz, G., Molten Salts Handbook Academic Press, NY (1967)Google Scholar
3. Suh, C., Rajagopalan, A., Li, X., and Rajan, K., in International Symposium on Ionic Liquids; Festschrift in honor of Prof M. Gaune-Escard', eds. Øye, H.A. and Jagtøyen, A.; pp. 587–599; publ. Norwegian University of Science and Technology (2003)Google Scholar
4. Villars, P. and Hulliger, F., J Less-common Met., 132, 289 (1987)Google Scholar
5. Suh, C. and Rajan, K., Applied Surface Science, In Press (2003)Google Scholar
6. Rajagopalan, A., Suh, C., Li, X. and Rajan, K., Applied Catalysis A: General, 254, Issue 1 (2003 Google Scholar
7. Eriksson, L., Johansson, E., Kettaneh-Wold, N., and Wold, S., ‘Multi- and Megavariate Data Analysis-Principles and Applications’, Umetrics Academy, (1999)Google Scholar