Development of an Atomic Cluster Expansion potential for iron and its oxides

TL;DR

This paper introduces a machine-learning interatomic potential based on Atomic Cluster Expansion for iron and its oxides, explicitly including magnetism to improve modeling accuracy across various properties.

Contribution

The work develops the first physically justified, magnetism-aware atomic cluster expansion potential for iron-oxygen systems, enabling more accurate atomistic simulations.

Findings

Accurately describes thermodynamics of iron oxides with varying oxygen content

Successfully models magnetic degrees of freedom in iron systems

Demonstrates broad applicability across different iron-oxide properties

Abstract

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.