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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

Comparing universal machine learning interatomic potentials (uMLIP) in calculating selected properties in Co–Ni–Ru family alloys

Author(s)

Subah Mubassira, Thu Nguyen, Anvesh Nathani, Shoutian Sun, Ming Hu, Yanqing Su, Bin Wang, Iman Ghamarian, Shuozhi Xu

On-Site Speaker (Planned)

Subah Mubassira

Abstract Scope

Multi-principal element alloys (MPEAs) are composed of three or more elements in near-equiatomic ratios and offer exceptional strength, ductility, and thermal stability. Recent advancements in machine learning interatomic potentials (MLIPs) have made it possible to achieve near–ab initio accuracy. Building on this progress, the field is transitioning toward universal MLIPs (uMLIPs)—foundational models trained on chemically and structurally diverse datasets, reducing the need for system-specific retraining. Here we assess three state-of-the-art uMLIP frameworks: SevenNet, DeePMD, and ORB, for their abilities to predict key mechanical descriptors of Co–Ni–Ru family MPEAs. We compute properties including relaxed lattice parameters, elastic constants, and generalized stacking-fault energies for face-centered cubic (FCC) and hexagonal close-packed (HCP) structured CoNiRu, FCC Co₂Ni₂Ru, HCP CoNiRu₂, and FCC pure Ni which are quantitatively benchmarked against density functional theory (DFT) results. This study offers insights into the strengths and limitations of current uMLIPs for accelerating high-throughput, in-silico design of advanced MPEAs.

Proceedings Inclusion?

Planned:

Keywords

Machine Learning, Modeling and Simulation, Computational Materials Science & Engineering

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