| About this Abstract |
| Meeting |
3rd World Congress on High Entropy Alloys (HEA 2023)
|
| Symposium
|
HEA 2023
|
| Presentation Title |
Bond-stiffness Based ML Approach to Predict Atomic Level Properties in MPEAs |
| Author(s) |
Nathan Linton, Dharmendra Pant, Dilpuneet S. Aidhy |
| On-Site Speaker (Planned) |
Dilpuneet S. Aidhy |
| Abstract Scope |
On the one hand, the presence of multiple elements in large proportions in multi-principal element alloys (MPEAs) present opportunities to unravel novel properties, whereas on the other, they pose a large computational challenge due to the large phase space, especially for density functional theory (DFT) calculations, that are inherently very expensive. We present PREDICT (PRedict properties from Existing Database In Complex alloys Territory), a machine learning framework coupled with DFT whereby properties in MPEAs could be predicted simply by learning from the binary alloys database. The physics is included via bond stiffness derived from DFT to predict elastic constants, vibrational entropy and other related properties in FCC based MPEAs leading to accurate predictions. This approach enables probing any MPEA composition by just including DFT information of constituting binary alloys, thereby altogether bypassing DFT calculations in MPEAs. |
| Proceedings Inclusion? |
Planned: Metallurgical and Materials Transactions |