Combinatorial materials design through database science

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.