Analyzing Sparse Data for Nitride Spinels Using Data Mining, Neural Networks, and Multiobjective Genetic Algorithms

Abstract

Nitride spinels are typically characterized by their unique AB2N4 structure containing a divalent cation A, a trivalent cation B, and an anion N. Numerous such species may exist as metals, semiconductors, or semimetals leading to their extensive usage in diverse scientific and engineering fields. Experimental and theoretical data on the physical or material properties of nitride spinels are, however, severely limited for coming up with a data driven, generic description for their material properties. In this study we have attempted to establish a methodology for handling such sparse data where the various features of some of the state of the art soft computing tools like Genetic Algorithms, Data Mining, and Neural Networks are used in tandem to construct some generic predictive models, in principle applicable to the nitride spinel structures at large, irrespective of their electronic characteristics. The paucity of the available data was circumvented in this work with a data mining strategy, important inputs were identified through an evolving neural net, and finally, the best possible tradeoffs between the bulk moduli and the relative stabilization energies of the nitride spinels were identified by constructing the Pareto-frontier for them through a Genetic Algorithms-based multiobjective optimization strategy.