| About this Abstract |
| Meeting |
TMS Specialty Congress 2025
|
| Symposium
|
8th World Congress on Integrated Computational Materials Engineering (ICME 2025)
|
| Presentation Title |
Exploring Novel Alloys With Superior Specific Hardness Using Data-Driven Approaches |
| Author(s) |
Taeyeop Kim, Wook Ha Ryu, Geun Hee Yoo, Donghyun Park, Ji Young Kim, Eun Soo Park, Dongwoo Lee |
| On-Site Speaker (Planned) |
Dongwoo Lee |
| Abstract Scope |
The discovery of advanced alloys using experimental data-driven approaches is hindered by the challenges of managing large compositional design spaces and the risk of overfitting machine learning (ML) models. This study combines ML predictions with thin-film base high-throughput experimental verification to accelerate the discovery of novel ternary alloy systems with exceptional specific hardness. By applying ensemble learning to a dataset from combinatorial experiments, we efficiently explored a composition space involving 28 metallic elements and discovered tens of new compositions exhibiting superior specific hardness compared with previously reported alloys. The property was consistently observed in 2 mm thick ribbon samples, demonstrating scalability. Explainable AI revealed that elemental dissimilarities significantly enhance solid-solution strengthening and phase formation, offering key insights into the underlying mechanisms. This iterative ML-driven process provides a reliable approach for discovering high-performance alloys and could serve as a useful framework for future materials development. |
| Proceedings Inclusion? |
Undecided |