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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

Large Language Model assisted Design of Superalloys via Olson-Flow Block Diagrams

Author(s)

Hyun Gi Min, Heechan Jung, Dongsoo Kang, Eun Soo Park

On-Site Speaker (Planned)

Hyun Gi Min

Abstract Scope

The development of Ni‑based superalloys under extreme conditions necessitates simultaneous control of multiple microstructural and performance attributes. Although machine learning offers accelerated exploration, its impact is limited by poorly defined or heuristic descriptors, leading to inefficient sampling and suboptimal discovery. To overcome this, we employ large language models to systematically construct Olson‑Flow Block Diagrams, capturing critical causal relationships in PSPP From scientific literature, the LLM-derived OFBD extracts physically meaningful features—such as lattice misfit, γ′ volume fraction, solvus temperature, and creep resistance—and structurally encodes them into the design space. These features then serve as robust inputs for downstream ML models, enabling more targeted and interpretable alloy screening. Our results show that LLM‑assisted OFBD facilitates clear feature establishment and enhances ML model efficiency, reducing compositional search space while improving performance prediction accuracy. We discuss implications for inverse design, high‑temperature alloy development, and AI‑augmented materials engineering.

Proceedings Inclusion?

Planned:

Keywords

Machine Learning, High-Temperature Materials,

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