  Machine learning is changing how we design and interpret experiments in
materials science. In this work, we show how unsupervised learning, combined
with ab initio modeling, improves our understanding of structural metastability
in multicomponent alloys. We use the example case of Al-O-N alloys where the
formation of aluminum vacancies in wurtzite AlN upon the incorporation of
substitutional oxygen can be seen as a general mechanism of solids where
crystal symmetry is reduced to stabilize defects. The ideal AlN wurtzite
crystal structure occupation cannot be matched due to the presence of an
aliovalent hetero-element into the structure. The traditional interpretation of
the c-lattice shrinkage in sputter-deposited Al-O-N films from X-ray
diffraction (XRD) experiments suggests the existence of a solubility limit at
8at.% oxygen content. Here we show that such naive interpretation is
misleading. We support XRD data with a machine learning analysis of ab initio
simulations and positron annihilation lifetime spectroscopy data, revealing no
signs of a possible solubility limit. Instead, the presence of a wide range of
non-equilibrium oxygen-rich defective structures emerging at increasing oxygen
contents suggests that the formation of grain boundaries is the most plausible
mechanism responsible for the lattice shrinkage measured in Al-O-N sputtered
films.
