About this Abstract |
Meeting |
2020 TMS Annual Meeting & Exhibition
|
Symposium
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High Entropy Alloys VIII
|
Presentation Title |
Unravelling Sluggish Diffusion of High-entropy Alloys through Machine Learning Methods |
Author(s) |
S. Mohadeseh Taheri-Mousavi, S. Sina Moeini-Ardakani, Ryan Penny, Ju Li, A. John Hart |
On-Site Speaker (Planned) |
S. Mohadeseh Taheri-Mousavi |
Abstract Scope |
High-entropy alloys (HEAs) can exhibit an exceptional combination of superior damage tolerance and strength at extreme temperatures. Since the advent of HEAs, their single-phase stabilization and the attributed mechanical properties are postulated to be enabled by sluggish diffusion of the alloying elements. Yet, due to the compositional complexity of HEAs, this hypothesis has never been systematically investigated and confirmed. Here, we present a newly-developed numerical framework whereby a machine-learning algorithm supervised by atomistic-scale simulations is used to explore the nanoscale features controlling the diffusivity of alloying components in HEAs. Analysis of all possible atomic configurations within a model HEA by the trained algorithm reveals how the size, and cohesive energy of alloying elements alter the diffusivity rate of the material. In the future, this understanding can be used to guide conventional processing or additive manufacturing, and could enable design of metals with tailored gradient diffusivity. |
Proceedings Inclusion? |
Planned: Supplemental Proceedings volume; Planned: Supplemental Proceedings volume |