Insights into defect cluster formation in non-stoichiometric wustite (Fe1-xO) at elevated temperatures: accurate force field from deep learning

The limited understanding of the microstructure and dynamic evolution associated with the non-stoichiometric characteristics of wustite has constrained the comprehension of iron oxide properties, diffusion, and phase transformation behaviors. This study employs deep learning methods to train interatomic potential parameters for the Fe-O system, achieving precise atomic-scale simulations of the wustite phase structure and internal lattice defects. This approach addresses the shortcomings of large-scale molecular dynamics simulations in accurately describing the solid-phase structure of the Fe-O system. Utilizing these potential parameters, this research is the first to reveal the complex mechanisms underlying the non-stoichiometric nature of wustite (Fe1-xO). The study found that cation vacancy defects in wustite tend to aggregate, forming stable cluster structures. It also elucidated the formation mechanisms of interstitial iron atoms and typical defect clusters in wustite, establishing the formation preference for Koch-Cohen defect clusters. These potential parameters and research methods can be further applied in future studies on iron oxide reduction, phase transformation mechanisms, and related material development, thereby advancing fundamental research in metallurgy and related industries.