  The combined structural and electronic complexity of iron oxides poses many
challenges to atomistic modeling. To leverage limitations in terms of the
accessible length and time scales, one requires a physically justified
interatomic potential which is accurate to correctly account for the complexity
of iron-oxygen systems. Such a potential is not yet available in the
literature. In this work, we propose a machine-learning potential based on the
Atomic Cluster Expansion for modeling the iron-oxygen system, which explicitly
accounts for magnetism. We test the potential on a wide range of properties of
iron and its oxides, and demonstrate its ability to describe the thermodynamics
of systems spanning the whole range of oxygen content and including magnetic
degrees of freedom.
