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Using a fast and accurate neural network potential we are able tosystematically explore the energy landscape of large unit cells of bulkmagnesium oxide with the minima hopping method. The potential is trained with afocus on the near-stoichiometric compositions, in particular on suboxides,i.e., Mg$_x$O$_{1-x}$ with $0.50<x<0.60$. Our extensive explorationdemonstrates that for bulk stoichiometric compounds, there are several newlow-energy rocksalt-like structures in which Mg atoms are octahedrallysix--coordinated and form trigonal prismatic motifs with different stackingsequences. Furthermore, we find a dense spectrum of novel non-stoichiometriccrystal phases of Mg$_x$O$_{1-x}$ for each composition of $x$. These structuresare mostly similar to the rock salt structure with octahedral coordination andfive--coordinated Mg atoms. Due to the removal of one oxygen atom, the energylandscape becomes more glass-like with oxygen-vacancy type structures that alllie very close to each other energetically. For the same number of magnesiumand oxygen atoms our oxygen-deficient structures are lower in energy if thevacancies are aligned along lines or planes than rock salt structures withrandomly distributed oxygen vacancies. We also found the putative global minimaconfigurations for each composition of the non-stoichiometric suboxidestructures. These structures are predominantly composed of (111) slabs of therock salt structure which are terminated with Mg atoms at the top and bottom,and are stacked in different sequences along the $z$-direction. Like otherMagn\'eli-type phases, these structures have properties that differconsiderably from their stoichiometric counterparts such as low lattice thermalconductivity and high electrical conductivity.

physical review materials

Large Scale Structure Prediction of Near-Stoichiometric Magnesium Oxide  Based on a Machine-Learned Interatomic Potential: Novel Crystalline Phases  and Oxygen-Vacancy Ordering