Large-Scale Structure Prediction of Near-Stoichiometric Magnesium Oxide based on a Machine-Learned Interatomic Potential

Using a fast and accurate neural network potential, we are able to systematically
explore the energy landscape of large unit cells of bulk magnesium oxide with the
minima hopping method. The potential is trained with a focus on the near-
stoichiometric compositions, in particular on suboxides, i.e., Mg x O 1−x with 0.50 < x <
0.60. Our extensive exploration demonstrates that for bulk stoichiometric
compounds, there are several new low-energy rock-salt-like structures in which Mg
atoms are octahedrally six-coordinated and form trigonal prismatic motifs with
different stacking sequences.
Furthermore, we find a dense spectrum of novel nonstoichiometric crystal phases of
Mg x O 1−x for each composition of x. These structures are mostly similar to the rock-salt
structure with octahedral coordination and five-coordinated Mg atoms. Due to the
removal of one oxygen atom, the energy landscape becomes more glass-like with
oxygen-vacancy type structures that all lie very close to each other energetically. For
the same number of magnesium and oxygen atoms, our oxygen-deficient structures
are lower in energy if the vacancies are aligned along lines or planes than rock-salt
structures with randomly distributed oxygen vacancies. We also found the putative
global minima configurations for each composition of the nonstoichiometric suboxide
structures. These structures are predominantly composed of MgO(111) layers of the
rock-salt structure which are terminated with Mg atoms at the top and bottom and
are stacked in different sequences along the z direction. Like for other materials,
these Magnéli-type phases have properties that differ considerably from their
stoichiometric counterparts such as high electrical conductivity