Hostname: page-component-586b7cd67f-rcrh6 Total loading time: 0 Render date: 2024-11-24T23:54:39.979Z Has data issue: false hasContentIssue false

Rules-Driven Materials Design Using an Informatics-Based Approach

Published online by Cambridge University Press:  26 February 2011

Joan T. Muellerleile
Affiliation:
[email protected], Battelle Memorial Institute, 505 King Avenue, Columbus, OH, 43201, United States
Kim F. Ferris
Affiliation:
[email protected], Pacific Northwest National Laboratory, United States
Dumont M. Jones
Affiliation:
[email protected], Proximate Technologies, LLC, United States
Roger W. Hyatt
Affiliation:
[email protected], Battelle Memorial Institute, United States
Get access

Abstract

A rules-driven, informatics-based approach to multiply-constrained materials design is outlined, employing the example of polymer coating design for silica fibers. This approach to the inverse mapping problem of structure generation from design constraints and quantitative structure-property relationships (QSPRs) emphasizes design rule generation and analysis. Using this approach addresses several issues in new materials discovery: 1) factoring a larger design problem into tractable components, 2) integrating physical and non-physical requirements (such as cost), 3) identifying information gaps that must be resolved to complete a design, and 4) identifying situations in which a solution consistent with known information is not feasible.

Type
Research Article
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. Kernighan, , Brian, W. and Pike, , Rob, , “The Unix Programming Environment”, Prentice-Hall: New York (1984).Google Scholar
2. Hoskuldsson, A., J. Chemometrics, 2, (1998) 211.Google Scholar
3. Goldberg, , David, Genetic Algorithms in Search, Optimization, and Machine LearningAddison-Wesley (1989).Google Scholar
4. Churchwell, C., Rintoul, M.D., Martin, S., Visco, D.P., Kotu, A., Larson, R.S., Sillerud, L.O., Brown, D.C., Faulon, J.-L., J., Mol.Graph.Model. 22 (2004) 263273.Google Scholar
5. Weis, D.C., Paulon, J.-L., LeBorne, R.C., Visco, D.P., Ind. Eng. Chem. Res. 44 (2005), 8883.Google Scholar
6. Venkatasubramanian, K., Chan, K., Caruthers, J.M., J Chem. Inf. Comput Sci. 35 (1995), 188.Google Scholar
7. Sheridan, R.P. and Kearsley, S.K., J. Chem. Inf. Comput. Sci 25 (1995) 310.Google Scholar
8. Bicerno, J., “Prediction of Polymer PropertiesMarcel-Dekker: New York (1996).Google Scholar
9. van Krevelen, D.W., “Properties of Polymers, Their Correlation with Chemical Structure”, 3rd Edition, Elsevier: New York (1990).Google Scholar
10. Vogel, A., J. Chem. Soc. (1948) 1833.Google Scholar