About this Abstract |
Meeting |
2024 TMS Annual Meeting & Exhibition
|
Symposium
|
Advances in Multi-Principal Element Alloys III: Mechanical Behavior
|
Presentation Title |
D-3: Correlated Lattice Distortion and Vacancy Formation Energies in Multi-principal Element Alloys from DFT and Machine Learning |
Author(s) |
Nathan Linton, Dilpuneet Aidhy |
On-Site Speaker (Planned) |
Nathan Linton |
Abstract Scope |
Multi-principal element alloys (MPEAs) consist of multiple elements randomly distributed on a crystal lattice resulting in large variations in chemical environments and local lattice distortions. These features cause large variations in vacancy formation energies, even within a given alloy composition. Consequently, not only is there a well-known large phase space challenge, but there is also a lattice space challenge for DFT calculations to accurately predict formation energies. In this work, we employ our recently developed PREDICT framework (PRedict properties from Existing Database In Complex materials Territory), to predict the vacancy formation energies in ternary, quaternary, and quinary MPEAs via simply using the DFT database of binary alloys in face-centered cubic (FCC) systems. This approach bypasses expensive DFT calculations and enables tracing the phase and lattice spaces in complex alloys. We incorporate chemical environment, bond length, and charge distortion as the key descriptors to the machine learning models. |
Proceedings Inclusion? |
Planned: |
Keywords |
Computational Materials Science & Engineering, Machine Learning, High-Entropy Alloys |