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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title Development of Machine-Learning Interatomic Potential forSolute–Dislocation Interactions in Fe–C–Cu Steels
Author(s) Zizheng Cao, Junya Inoue
On-Site Speaker (Planned) Zizheng Cao
Abstract Scope A machine-learning interatomic potential (MLIP) has been constructed for Fe–C–Cu ternary Fe-based alloys to investigate solute–dislocation interactions in steels. This Fe–C–Cu MLIP is expected to retain density functional theory(DFT)-level accuracy while enabling large-scale molecular dynamics (MD) simulations. To build a comprehensive training dataset, representative configurations are included, such as bcc α-Fe, interstitial carbon, substitutional copper, strained lattices, and defect environments relevant to dislocation cores. Furthermore, universal machine-learning interatomic potentials (UMLIPs) are also explored as efficient surrogate models of DFT for specific configurations, which would further accelerate the generation of training dataset. The resulting Fe–C–Cu MLIP enables efficient atomistic simulations of solute-mediated dislocation behavior and to clarify the contributions of interstitial and substitutional solutes to strengthening mechanisms in Fe-based steels.
Proceedings Inclusion? Planned:
Keywords Iron and Steel, Machine Learning, Mechanical Properties

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