Machine learning interatomic potential for the structural properties of iron oxides

Iron oxides constitute an important class of materials, exhibiting a rich and intricate range of behaviors. Despite their significance, their structural and mechanical properties, particularly Hematite ($$\alpha$$-Fe$$\phantom{0}_2$$O$$\phantom{0}_3$$), have been scarcely investigated in the literature from a theoretical standpoint. At the same time, recent developments in machine learning for interatomic potentials have revolutionized computational materials science by enabling highly accurate and efficient simulations of atomic interactions. Traditional methods, such as density functional theory (DFT) and classical force fields, often struggle with high computational costs or lack the flexibility to generalize across diverse chemical environments. ML-based approaches, have emerged as powerful alternatives, learning complex potential energy surfaces from quantum-mechanical data. These models can achieve DFT accuracy at a fraction of the computational cost, facilitating large-scale molecular dynamics (MD) simulations. In this work, we present a graph neural network interatomic potential for hematite. The model was trained on datasets generated from DFT+U calculations to account for strong electronic correlations, using atomic configurations sampled across a wide range of temperatures and pressures. Our potential accurately reproduces fundamental material properties, including the elastic moduli, anisotropic elastic constants, vibrational frequencies, and surface energies. Furthermore, we demonstrate its transferability to other bulk iron oxides. This work enables large-scale molecular dynamics (MD) simulations of iron-based materials with ab initio accuracy at a computational cost comparable to that of classical potentials, opening new opportunities for investigating these complex systems.