| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Algorithms Development in Materials Science and Engineering
|
| Presentation Title |
Elastic constants from charge density distribution in FCC high entropy alloys using CNN and DFT |
| Author(s) |
Nathan Linton, Ramin Soltanmohammadi, Hossein Mirzaee, Jacob Fischer, Serveh Kamrava, Pejman Tahmasebi, Dilpuneet S. Aidhy |
| On-Site Speaker (Planned) |
Dilpuneet S. Aidhy |
| Abstract Scope |
While high entropy alloys (HEAs) possess large compositional space, they also create computational challenge to trace the space, especially for the inherently expensive density functional theory calculations (DFT). Recent works have integrated machine learning (ML) to DFT to overcome these challenges. However, often these models require an intensive search of appropriate physics-based descriptors. In this paper, we employ a 3D convolutional neural network over just one descriptor, i.e., the charge density derived from DFT, to simplify and bypass the hunt for the descriptors. We show that the elastic constants of face-centered cubic multi-elemental alloys in the Ni-Cu-Au-Pd-Pt system can be predicted from charge density. In addition, using our recent PREDICT approach, we show that the model can be trained only on the charge densities of simpler binary and ternary alloys to predict elastic constants in complex multi-elemental alloys, thereby further enabling easier property-tracing of the larger compositional space of HEAs. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
High-Entropy Alloys, Computational Materials Science & Engineering, Machine Learning |