About this Abstract |
Meeting |
2022 TMS Annual Meeting & Exhibition
|
Symposium
|
Algorithm Development in Materials Science and Engineering
|
Presentation Title |
Development of Segregation Energy Predictions Utilizing Advanced Descriptors of Local Atomic Environments |
Author(s) |
Jacob P. Tavenner, Ankit Gupta, Garritt J Tucker |
On-Site Speaker (Planned) |
Jacob P. Tavenner |
Abstract Scope |
The addition of new chemical species to an existing bulk metal significantly changes its properties, allowing for alloys to be designed to attain specific properties useful for advanced engineering systems. However, simulation of segregation behavior in metallic alloys is still computationally expensive. By analyzing atomic sites preferred for segregation as a function of the instantaneous local structure using a new descriptor framework known as Strain Functional Descriptors (SFDs), an improved model is implemented for analyzing alloy systems. Since this technique only relies upon instantaneous snapshots of the local atomic environments, computationally complex iteration over each site in the structure and/or Monte-Carlo methods can be bypassed. These descriptors also improve our understanding of specific relationships between atomic environments and their underlying physics when implemented alongside modern machine learning techniques. Extension of these techniques beyond the dilute limit will be addressed. |
Proceedings Inclusion? |
Planned: |
Keywords |
Computational Materials Science & Engineering, Modeling and Simulation, Machine Learning |