| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Advances in Multi-Principal Element Alloys IV: Mechanical Behavior
|
| Presentation Title |
Large-Language Model-Assisted High Entropy Alloy Design: Knowledge Transfer and Hypothesis Generation |
| Author(s) |
Quanliang Liu, Maciej Piotr Polak, So Yeon Kim, MD Al Amin Shuvo, Hrishikesh Shridhar Deodhar, Jeongsoo Han, Dane Morgan, Hyunseok Oh |
| On-Site Speaker (Planned) |
Hyunseok Oh |
| Abstract Scope |
While the expanding volume of information across different materials domains provides opportunities for generating diverse design hypotheses and making disruptive discoveries by transferring knowledge across fields, it also makes it difficult for researchers to stay abreast of the latest advancements. A notable example is high entropy alloys (HEAs), as evidenced by the exponential growth in scholarly publications and frequent synergistic integration of scientific principles across domains for developing new HEAs. With in-context learning capabilities, large-language models (LLMs) provide a new opportunity for streamlining and synthesizing literature to generate new scientific insights. In this presentation, we will showcase various types of LLM-assisted alloy design activities, including summarized information extraction and alloy design hypothesis generation not previously established in the literature for cryogenic HEAs. This approach, guided by materials system charts, enhances the potential for disruptive discoveries of new materials while maintaining scientific and engineering plausibility. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
High-Entropy Alloys, Mechanical Properties, Computational Materials Science & Engineering |