| About this Abstract |
| Meeting |
2023 TMS Annual Meeting & Exhibition
|
| Symposium
|
Advances in Multi-Principal Element Alloys II
|
| Presentation Title |
Combined Machine Learning – Graph Theory Based Framework for the Design of New High Entropy Alloy Chemistries |
| Author(s) |
Scott Broderick, Krishna Rajan |
| On-Site Speaker (Planned) |
Scott Broderick |
| Abstract Scope |
This work uses a graph representation approach to capture the thermodynamic and structural complexity of high entropy alloys (HEAs) to identify new chemistries with an enhanced combination of strength, ductility and environmental effects. The advantage of the graph-based approach is that it incorporates numerous criteria for design (including mechanical and environmental properties, as well as microstructure) to identify HEA compositions when there are trade-offs between the various criteria. By mapping the high dimensional nature of the systematics of elemental data embedded in the periodic table into the form of a network graph, one can uncover the influence of specific combinations elements on engineering properties of HEAs. In this way, mechanical and environmental properties are rationally designed through proposed chemical design rules across the entire HEA search space, resulting in a machine learning based representation of a periodic table based on HEA properties. |
| Proceedings Inclusion? |
Planned: |