| Abstract Scope |
The efficient exploration and exploitation of compositionally complex alloy spaces is extremely resource-intensive and most conventional approaches (e.g. traditional ICME and open-loop combinatorial methods) are not effective. Here, I present our recent work on the development of BIRDSHOT. 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) leverages the team’s newly developed batch modifications to BO that enable the parallel, iterative and optimal exploration/exploitation of materials spaces; and (v) is capable of simultaneously considering multiple objectives and constraints. |