Spinel nitride solid solutions: charting properties in the configurational space with explainable machine learning

Abstract

The thermodynamic and electronic properties of solid solutions are highly tuneable, based on ion arrangement. We use interpretable machine learning to explore the structure-property landscape of a spinel nitride and understand how crystal chemistry affects physical properties.