| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
AI/Data informatics: Design of Structural Materials
|
| Presentation Title |
Using Machine Learning for Targeted Alloy Design in High Entropy Composition Spaces |
| Author(s) |
Tanner Kirk, Richard Couperthwaite, Guillermo Vazquez, Daniel Sauceda, Pejman Honarmandi, Prashant Singh, Raymundo Arroyave |
| On-Site Speaker (Planned) |
Tanner Kirk |
| Abstract Scope |
Alloy discovery in the large composition spaces associated with High Entropy Alloys can be a daunting task given the combinatorial explosion of potential compositions. However, a variety of machine learning techniques can reduce the design process to a tractable problem. These techniques are demonstrated to find suitable high strength alloys in each of two high entropy alloy spaces: the refractory, largely BCC, W-Mo-Nb-Ta-V-Al system and the largely FCC Fe-Mn-Cr-Co-Ni-V-Al system. High throughput CALPHAD modeling as well as analytical property models are compared to design requirements to identify feasible alloys. Dimensionality reduction techniques like t-distributed Stochastic Neighbor Embedding (t-SNE) can visualize the location of the feasible region in the total composition space. K-medoids clustering produces a representative subset of feasible alloys for more expensive modeling, like DFT, or experimentation. After characterization, models are updated and Batch Bayesian Optimization suggests further experiments based on design preferences, eventually arriving at the optimal alloy. |
| Proceedings Inclusion? |
Planned: |