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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 Agentic AI-Driven Design of Refractory Multi-Principal Element Alloys
Author(s) Kun Lu, Hamza Jabbar, Vedanta Somnathe, Caleb Seba, Xin Wang, Qiaofu Zhang, Shuozhi Xu, Xin Wang, Gregory Thompson
On-Site Speaker (Planned) Kun Lu
Abstract Scope Achieving a simultaneous optimization of high-temperature strength and room-temperature ductility remains an outstanding challenge for refractory multi-principal element alloys (RMPEAs). This challenge arises from the complex, high-dimensional design space defined by composition, microstructure, and processing, as well as the incomplete mechanistic understanding of deformation in chemically complex alloys. Agentic AI frameworks, powered by large language models (LLMs), enable the autonomous integration of key scientific workflows, including literature-based knowledge extraction and synthesis, hypothesis generation, real-time reasoning, tool utilization, and physics-aware modeling. Importantly, these frameworks also support human-in-the-loop oversight, allowing domain experts to monitor, guide, and validate the autonomous decision-making process. Together, these capabilities position the technology to significantly accelerate material discovery. This presentation will share our work on developing a multi-agent AI framework to design RMPEAs with optimal balance of strength and ductility. A team of agents, each powered by an LLM, will work collaboratively to propose design solutions.
Proceedings Inclusion? Planned:
Keywords High-Entropy Alloys, Machine Learning, Other

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