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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title AI-Enabled High Throughput Discovery of High Entropy Alloys for Extreme Environments
Author(s) Marshall Donald Allen, Michael Miller, Jianliang Lin, Anton Zahradnik, Mirella Vargas, William Watson, John Macha
On-Site Speaker (Planned) Marshall Donald Allen
Abstract Scope This work details a high-throughput methodology for the design and discovery of novel high entropy alloys (HEAs). The computational component of this methodology leverages AI-enabled literature mining with large language models, materials data curation, data-driven material property modeling, and CALPHAD. Meanwhile, the experimental component is comprised of physical vapor deposition (PVD) of combinatorial thin-film libraries for compositional screening with quantitative elemental analysis, X-ray diffraction (XRD), and nanoindentation. Subsequently, promising compositions are synthesized as bulk specimens to validate macroscale performance. We demonstrate the initial deployment of this pipeline, including promising results in metalloid-containing HEA systems targeted for high-strength structural materials at extreme temperatures. The presentation will focus on early screening insights, the practical hurdles of bridging computational and experimental components, and our roadmap for workflow automation.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, High-Entropy Alloys, High-Temperature Materials

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