Predicting cation ordering in MgAl₂O₄ using genetic algorithms and density functional theory

ABSTRACT

Genetic algorithms (GAs) together with classical pair potentials and density functional theory (DFT) are used to investigate cation order in MgAl₂O₄ (Spinel). To efficiently locate the global minimum/minima on the system potential energy surface, corresponding to the ordered and fully equilibrated low-temperature phase, local structural optimizations are essential. Such energy minimizations are expensive at the DFT level, but a comparison of the distribution of the energy minima from DFT and popular classical pair potentials allows one to rapidly tune the GA parameters. We show that GAs are able to find, not only the global minimum on the potential energy, but also other low-energy cation configurations representing possible frozen-in disordered or metastable phases after quenching. The nature of these low-energy configurations can help to interpret the extent of kinetic trapping which hampers the comparison between different experimental studies.

KEYWORDS:

• Alloys
• cation-ordering
• crystal-structure
• DFT
• genetic-algorithms
• minerals
• spinel
• structure-prediction