| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
AI/Data informatics: Tools for Accelerated Design of High-temperature Alloys
|
| Presentation Title |
Revealing Nanoscale Features Controlling Diffusion Within Multi-component Alloys through Machine Learning |
| Author(s) |
S. Mohadeseh Taheri-Mousavi, S. Sina Moeini-Ardakani, Ryan W. Penny, Ju Li, A. John Hart |
| On-Site Speaker (Planned) |
S. Mohadeseh Taheri-Mousavi |
| Abstract Scope |
The immense compositional breadth of non-dilute multi-component and concentrated alloys has made their well-targeted design extremely challenging. Here, we present a newly developed numerical framework whereby deep learning algorithms supervised by atomistic-scale simulations are used to explore the nanoscale features controlling the diffusivity of atomic components in heavily alloyed compounds. Due to inherent non-linear optimization of the machine learning algorithms, the prediction accuracy is at least 10-fold improved over a conventional clustering method. Analysis of all possible atomic configurations and compositions within a model NiAl alloy reveals how the propensity of Al to form short-range-order near vacancies correlates with the generalized stacking fault energy of configurations with mobile Ni atoms. In the future, this approach can guide the selection of composition and processing parameters for conventional as well as additive manufacturing techniques, and it could enable design of metals with tailored gradient diffusivity for high temperature applications. |
| Proceedings Inclusion? |
Planned: |