About this Abstract |
Meeting |
2nd World Congress on High Entropy Alloys (HEA 2021)
|
Symposium
|
2nd World Congress on High Entropy Alloys (HEA 2021)
|
Presentation Title |
ON DEMAND: Predict Solid Solution Formation Using Machine Learning |
Author(s) |
Michael C. Gao, Zongrui Pei, Junqi Yin, Jeffrey Hawk, David Alman |
On-Site Speaker (Planned) |
Michael C. Gao |
Abstract Scope |
Predicting solid solution formation remains essential in the high entropy alloys research. Various empirical rules are proposed to predict the formation of single-phase solid solution, but many are based on very small datasets and hence are of very limited predictability. In this project, 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 is nonetheless slightly less accurate (73%) than the ML algorithm but employs only the elemental properties and is thus convenient in applications. Finally, the advantages and pitfalls in applying high-throughput screening and ML versus CALPHAD calculations will be discussed. |
Proceedings Inclusion? |
Undecided |