Data mining and informatics for crystal chemistry: establishing measurement techniques for mapping structure-property relationships

The present paper demonstrates how data mining techniques can be used to quantitatively assess multivariate material properties, such as electronic features and crystal structure parameters. Using AB(2)N(4) spinel nitrides as a template for the present study, the authors have assessed the statistical interdependency of each of the descriptors that may influence chemistry-structure-property relationships of spinel nitrides. Using principal component analysis, the authors demonstrate that classical versions of structure maps from the early work of Hill based on heuristic observations for this class of crystal chemistry can in fact be reproduced via data mining. The informatics approach also provides an alternative method for visualising structure maps as well as interpreting structure-property relationships. Apart from being able to reproduce earlier versions of structure maps, an example is also developed for the case of a new informatics based structure map for spinel nitrides, showing data clustering associated with site occupancy.