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Meeting	2025 TMS Annual Meeting & Exhibition
Symposium	Artificial Intelligence Applications in Integrated Computational Materials Engineering
Presentation Title	Magnetic RANN Interatomic Potential for Iron
Author(s)	Hala Ben Messaoud, Mashroor Nitol, Doyl Dickel
On-Site Speaker (Planned)	Hala Ben Messaoud
Abstract Scope	Developing an accurate interatomic potential for iron that incorporates its the magnetic properties is crucial for understanding the behavior of steels. However, the complexity of magnetic properties presents challenges to their effective integration. Therefore, our study aims to construct a robust predictive machine learning model capable of accurately describing iron behavior and validating its effectiveness. In this work, we develop a Rapid Neural Network (RANN) style machine-learned interatomic potential using both nonmagnetic and magnetic structural fingerprints. Utilizing ab initio calculations based on spin density functional theory to build a comprehensive training dataset, we perform collinear and non-collinear spin simulations across various magnetic and crystal structures. The resulting potential enables lattice and spin dynamics simulations, while capturing essential mechanical properties and thermodynamic properties of iron. The potential correctly reproduces properties such as elastic constants, phonon and magnon spectra for all iron phases with different magnetic structures at finite temperature.
Proceedings Inclusion?	Planned:
Keywords	Magnetic Materials, Machine Learning, Computational Materials Science & Engineering

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