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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

A design-focused machine learning framework for creep behavior in structural alloys

Author(s)

Madison Wenzlick, William Trehern, Wissam A. Saidi

On-Site Speaker (Planned)

Madison Wenzlick

Abstract Scope

Long-term mechanical properties including creep are critical metrics for meeting design specifications for high temperature and extreme environments. Due to experimental limitations and challenges with generalizability in empirical models, machine learning (ML) techniques have emerged as promising tools. However, to be useful for alloy design, ML methods must be generalizable and demonstrated to be accurate on unseen compositions. Further, the design space should be simple to query without requiring fabrication and testing of a material before prediction. In this work, we present a framework for creep prediction in 9-12% Cr ferritic-martensitic steels which addresses these needs of a ML model in a design-focused workflow based on high quality data. The design space is sampled and predictive trends are verified against experimental expectations. This workflow can be applied to optimize creep behavior leveraging only composition and processing values, making it ideal for rapidly querying a composition space and performing alloy design.

Proceedings Inclusion?

Planned:

Keywords

Iron and Steel, Machine Learning, Mechanical Properties

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