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Quantitative Structure-Activity Relationships (QSARs) for Materials Science

Published online by Cambridge University Press:  17 March 2011

Krishna Rajan
Affiliation:
Combinatorial Materials Science and Informatics Laboratory Department of Materials Science & Eng. and Information Technology Program Rensselaer Polytechnic Institute, Troy NY 12180-3590USArajank@rpi.edu
Changwon Suh
Affiliation:
Combinatorial Materials Science and Informatics Laboratory Department of Materials Science & Eng. and Information Technology Program Rensselaer Polytechnic Institute, Troy NY 12180-3590USArajank@rpi.edu
Arun Rajagopalan
Affiliation:
Combinatorial Materials Science and Informatics Laboratory Department of Materials Science & Eng. and Information Technology Program Rensselaer Polytechnic Institute, Troy NY 12180-3590USArajank@rpi.edu
Xiang Li
Affiliation:
Combinatorial Materials Science and Informatics Laboratory Department of Materials Science & Eng. and Information Technology Program Rensselaer Polytechnic Institute, Troy NY 12180-3590USArajank@rpi.edu
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Abstract

The field of combinatorial synthesis and “artificial intelligence” in materials science is still in its infancy. In order to develop and accelerated strategy in the discovery of new materials and processes, requires the need to integrate both the experimental aspects of combinatorial synthesis with the computational aspects of information based design of materials. In biology and organic chemistry, this has been accomplished by developing descriptors which help to specify “quantitative structure- activity relationships” at the molecular level. If materials science is to adopt these strategies as well, a similar framework of “QSARs” is required. In this paper, we outline some approaches that can lay the foundations for QSARs in materials science.

Type
Research Article
Copyright
Copyright © Materials Research Society 2002

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