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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

Universal machine learning interatomic potentials for large-scale molecular dynamics: Phase transitions in Barium Titanate and 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) offer a scalable approach for modeling interatomic interactions with near first-principles accuracy in complex material systems. We built a compact, diverse dataset by applying physical distortions to convex hull structures and trained a custom MLIP using an equivariant neural network. The model showed excellent performance in capturing structural and thermodynamic behavior in both Barium Titanate (BaTiO₃) and high-entropy ceramics (HECs). Encouraged by these results and the growing reputation of universal MLIPs, we benchmarked MACE, SevenNet, and MatterSim on the same test data—all three showed comparable accuracy. We selected SevenNet for large-scale molecular dynamics simulations based on its computational efficiency and compatibility. It successfully captured phase transition trends in BaTiO₃ and revealed how elemental segregation at HEC grain boundaries influences melting behavior. MLIPs further enable scalable simulations where system size is no longer a constraint, making them ideal for uncovering composition–structure–property relationships in complex ceramics.

Proceedings Inclusion?

Planned:

Keywords

Machine Learning, Modeling and Simulation, High-Temperature Materials

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