| About this Abstract |
| Meeting |
2020 TMS Annual Meeting & Exhibition
|
| Symposium
|
Innovations in High Entropy Alloys and Bulk Metallic Glasses: An SMD & FMD Symposium in Honor of Peter K. Liaw
|
| Presentation Title |
High-throughput Predicting and Machine-learning Solid-solution Formation |
| Author(s) |
Michael C. Gao, Zongrui Pei, Junqi Yin, Jeffrey Hawk, David Alman |
| On-Site Speaker (Planned) |
Michael C. Gao |
| Abstract Scope |
Various empirical rules are proposed to predict the formation of single-phase solid solution, but they are based on very small datasets and hence are of very limited predictability. In this work, we perform a machine-learning (ML) study on a large dataset consisting of 1252 alloys, including binary and high-entropy alloys, and we achieve a success rate of 93% in predicting single-phase solid solution. The present ML results suggest that the molar volume and bulk modulus are the most important features, and accordingly, a new physics-based thermodynamic rule is constructed. The new rule employs only the elemental properties and is nonetheless slightly less accurate (73%) than the ML algorithm. Finally, the advantages and pitfalls in applying high-throughput screening and ML versus CALPHAD calculations will be discussed. |
| Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |