| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
High Entropy Alloys IX: Structures and Modeling
|
| Presentation Title |
Machine Learning Enabled Prediction of Stacking Fault Energies in Concentrated Alloys |
| Author(s) |
Gaurav Arora, Anus Manzoor, Dilpuneet S. Aidhy |
| On-Site Speaker (Planned) |
Gaurav Arora |
| Abstract Scope |
A unique combination of high strength and high ductility in certain compositions of high entropy alloys (HEAs) has been observed which is attributed to the low stacking fault energy (SFE). While atomistic calculations successfully predict the SFE of pure metals, large variations up to 200 mJ/m2 have been observed in HEAs. The leading cause is the limited number of atoms that can be modeled in atomistic calculations; as a result, various nearest neighbor environments may not be adequately captured resulting in different SFE values. In this work, we use machine learning to overcome this limitation and provide a methodology to significantly reduce the variation and uncertainty in predicting SFEs. We show that the SFE can be accurately predicted across the composition ranges in binary alloys and in multi-elemental alloy. We also elucidate the underlying causes such as charge distribution, nearest neighbor environment, and the composition of an alloy on SFE. |
| Proceedings Inclusion? |
Planned: TMS Journal: Metallurgical and Materials Transactions |