About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
|
Computational Thermodynamics and Kinetics
|
Presentation Title |
Rapid Machine Learning Estimation of Grain Boundary Segregation Vibrational Entropy Spectra in Dilute Polycrystals |
Author(s) |
Nutth Tuchinda, Christopher A. Schuh |
On-Site Speaker (Planned) |
Nutth Tuchinda |
Abstract Scope |
The thermodynamics of grain boundary segregation must be quantitatively understood to design stabilized nanocrystalline alloys. While grain boundary segregation is a common phenomenon in polycrystalline materials, there is limited understanding of vibrational entropy effects for polycrystals with a multitude of solute segregation sites across the periodic table. We have therefore applied a machine learning accelerated method to efficiently sample and estimate vibrational effects within the harmonic approximation for dilute binary polycrystals with available interatomic potentials. The resulting substitutional segregation energy and vibrational entropy spectra are used with a spectral isotherm to demonstrate the average vibrational effects and generate a spectral grain boundary segregation database. This database can be applied to understand materials phenomena and design alloys involving grain boundary segregation at finite temperatures. |
Proceedings Inclusion? |
Planned: |
Keywords |
Computational Materials Science & Engineering, Modeling and Simulation, Nanotechnology |