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Meeting

2026 TMS Annual Meeting & Exhibition

Symposium

AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling

Presentation Title

Machine-Learned Spin-Lattice Dynamic Interatomic Potential for Iron-Manganese Alloys

Author(s)

Robert D. H. Race, Doyl E. Dickel, Hala Ben Messaoud

On-Site Speaker (Planned)

Robert D. H. Race

Abstract Scope

Machine-learned interatomic potentials (ML IAP) for alloy systems have been sought after due to their ability to reduce experimentation costs and time, accelerating alloy development and discovery. However, the explicit inclusion of magnetism in these potentials has been both a difficult and important problem to solve, due to the complexity of spin-lattice dynamics and its significance in the properties of magnetic alloys. We present here the development of an explicitly magnetic Fe-Mn ML IAP using a physics informed neural network (PINN) extension of the rapid artificial neural network (RANN) formalism. It is shown that the potential is capable of reproducing a number of energetic, mechanical, and magnetic properties of the Fe-Mn system, including phase stability and magnetic ordering, as well as thermal and elastic properties. The presented formalism and potential should provide a useful platform for the exploration of magnetic alloy systems and their properties at the atomistic scale.

Proceedings Inclusion?

Planned:

Keywords

Computational Materials Science & Engineering, Machine Learning, Other

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