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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

H-9: Universal Machine Learning Interatomic Potentials for Large-Scale Molecular Dynamics: Phase Transitions in Barium Titanate and Grain Boundary Segregation, Melting Behavior in High-Entropy Ceramics

Author(s)

Marium Mostafiz Mou, Tarek Haque, Sam Daigle, Donald Brenner

On-Site Speaker (Planned)

Marium Mostafiz Mou

Abstract Scope

Machine-learning interatomic potentials (MLIPs) enable near–first-principles accuracy at molecular-dynamics scales, making them ideal for studying complex ceramics where DFT is size-limited. We built a compact, distortion-augmented dataset from convex-hull structures and trained an equivariant neural-network MLIP, then benchmarked universal MLIPs (MACE, SevenNet, MatterSim) on the same test cases. All showed comparable accuracy; SevenNet was used for efficient large-scale perovskite simulations, while MACE was ultimately selected for high-entropy carbides (HECs) due to stronger transferability for interfacial chemistry. NPT MD captures BaTiO₃’s orthorhombic→tetragonal→cubic phase sequence and associated cation off-centering, and preliminary BiFeO₃ results suggest correlated, multi-domain-like ferroelectric states. For HECs, coupled Monte Carlo/MD reveals grain-boundary segregation trends and how low-melting solutes can promote interfacial premelting. A simple mixing–disorder metric is used to explain composition-dependent interfacial behavior and guide grain-boundary-aware alloy design.

Proceedings Inclusion?

Planned:

Keywords

Machine Learning, Modeling and Simulation, High-Temperature Materials

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