The relationship between apparently disparate sets of data is a critical component of interpreting materials' behavior, especially in terms of assessing the impact of the microscopic characteristics of materials on their macroscopic or engineering behavior. In this paper we demonstrate the value of principal component analysis of property data associated with high temperature superconductivity to examine the statistical impact of the materials' intrinsic characteristics on high temperature superconducting behavior.

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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.

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.