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Machine learning is changing how we design and interpret experiments inmaterials science. In this work, we show how unsupervised learning, combinedwith ab initio modeling, improves our understanding of structural metastabilityin multicomponent alloys. We use the example case of Al-O-N alloys where theformation of aluminum vacancies in wurtzite AlN upon the incorporation ofsubstitutional oxygen can be seen as a general mechanism of solids wherecrystal symmetry is reduced to stabilize defects. The ideal AlN wurtzitecrystal structure occupation cannot be matched due to the presence of analiovalent hetero-element into the structure. The traditional interpretation ofthe c-lattice shrinkage in sputter-deposited Al-O-N films from X-raydiffraction (XRD) experiments suggests the existence of a solubility limit at8at.% oxygen content. Here we show that such naive interpretation ismisleading. We support XRD data with a machine learning analysis of ab initiosimulations and positron annihilation lifetime spectroscopy data, revealing nosigns of a possible solubility limit. Instead, the presence of a wide range ofnon-equilibrium oxygen-rich defective structures emerging at increasing oxygencontents suggests that the formation of grain boundaries is the most plausiblemechanism responsible for the lattice shrinkage measured in Al-O-N sputteredfilms.

Mapping the Structure of Oxygen-Doped Wurtzite Aluminum Nitride Coatings From Ab Initio Random Structure Search and Experiments