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Center for Nonlinear Studies |
The Refractory High Entropy Alloy (RHEA) space is vast, and it is impossible to explore using conventional approaches to materials discovery. In this talk, I will present the Batch-wise Improvement in Reduced Design Space using a Holistic Optimization Technique (BIRDSHOT) framework. BIRDSHOT incorporates the strengths of ICME and combinatorial methods while addressing all their drawbacks, as it: (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 RHEA space to explore/exploit, leveraging existing models and data; (iv) is capable of carrying out multiple optimal parallel queries to the design space. We show how we have been using BIRDSHOT to search for next-generation refractory alloys for turbine engine applications.
Host: Jan Janssen, T-1