About this Abstract |
Meeting |
2024 TMS Annual Meeting & Exhibition
|
Symposium
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Advances in Multi-Principal Element Alloys III: Mechanical Behavior
|
Presentation Title |
A Bayesian Approach to Explore Large Dimensional Compositionally Complex Alloy Spaces |
Author(s) |
Raymundo Arroyave |
On-Site Speaker (Planned) |
Raymundo Arroyave |
Abstract Scope |
In this talk, I will present recent advances in the development of frameworks to accelerate the exploration of vast alloy spaces. I will discuss novel approaches that combine Bayesian classification and Bayesian optimization. This framework(i) employs novel machine learning (ML) and data-driven search algorithms to identify efficiently the feasible regions amenable to optimization; (ii) exploits correlations to fuse simulations and experiments to obtain efficient ML models for predicting PSPP relations; (iii) uses Bayesian Optimization (BO) to make globally optimal iterative decisions regarding which region in the alloy space to explore/exploit, leveraging existing models and data; (iv) is capable of carrying out multiple optimal parallel queries to the design space. I present some examples in which this method has been used to efficiently discover novel printable refractory compositionally complex alloys. |
Proceedings Inclusion? |
Planned: |
Keywords |
High-Entropy Alloys, ICME, Machine Learning |