Berlin 2024 – scientific programme
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MM: Fachverband Metall- und Materialphysik
MM 3: Topical Session: Sustainable metallurgy
MM 3.4: Talk
Monday, March 18, 2024, 11:15–11:30, C 130
Development of an interatomic potential for iron and its oxides for direct reduction applications — •Baptiste Bienvenu1, Mira Todorova1, Jörg Neugebauer1, Matous Mrovec2, Yury Lysogorskiy2, Ralf Drautz2, and Dierk Raabe1 — 1Max-Planck-Insitut für Eisenforschung, Max-Planck-Straße 1, 40237 Düsseldorf, Germany — 2Interdisciplinary Centre for Advanced Materials Simulation, Ruhr Universität Bochum, 44780 Bochum, Germany
Atomistic modeling of iron oxides poses many great challenges, due to their combined structural and electronic complexities, down at the level of electronic structure calculations and up to the length and time scales relevant for the study of mechanisms involved, for instance, in the process of their direct reduction. To leverage these limitations in terms of accessible scales, one requires a physically justified interatomic potential with sufficient accuracy to correctly account for the complexity of iron-oxygen systems, which 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, with an explicit account of magnetism. The model is fitted on an extensive density functional theory database encompassing pure iron and the whole range of possible oxygen containing structures. We test the potential on a wide range of properties of iron and its oxides, including defects and relative stability between different crystal structures and magnetic orders, and demonstrate its ability to describe the thermodynamics of systems spanning the whole range of oxygen content and including magnetic degrees of freedom.
Keywords: Simulation; Interatomic potential; Oxide; Machine-learning; Green steel