| About this Abstract |
| Meeting |
2024 TMS Annual Meeting & Exhibition
|
| Symposium
|
2024 Technical Division Student Poster Contest
|
| Presentation Title |
SPG-12: GenMG: A Tool for Predicting Novel Metallic Glasses with Application-specific Properties |
| Author(s) |
Jerry R. Howard, Dev Chidambaram, Leslie T. Mushongera, Krista Carlson |
| On-Site Speaker (Planned) |
Jerry R. Howard |
| Abstract Scope |
As metallic glasses (MGs) are typically discovered in multi-component systems with large numbers of principle elements, discovery of new MGs will likely involve many expensive simulations or experiments. For use in specific applications, researchers will need to discover alloys which are optimized for both glass forming ability (GFA) and relevant application-specific properties, which may be inversely related to GFA. GenMG is a genetic algorithm-based software package which couples empirical predictions of GFA with other methods of property prediction to efficiently search multi-component space for optimal compositions. Two cases are used to demonstrate the effectiveness of GenMG. In the first, high-temperature MGs in the TaNiCo alloy system for use as structural materials in extreme environments were located. In the second, GenMG was used to predict AlSiMn MG anodes for Li-ion batteries. These cases show that GenMG is a promising tool for MG researchers interested in applying these materials to diverse applications. |
| Proceedings Inclusion? |
Undecided |
| Keywords |
Additive Manufacturing, Titanium, Characterization |