The article presents a framework for targeted materials discovery using Bayesian algorithm execution (BAX). This approach uses user-defined filtering algorithms and Bayesian optimization to efficiently identify materials with specific properties. The framework includes three strategies: SwitchBAX, InfoBAX, and MeanBAX, which help navigate complex design spaces by guiding experiments towards optimal conditions. Demonstrations on TiO2 nanoparticle synthesis and magnetic materials characterization show significant efficiency improvements over traditional methods. This methodology accelerates the discovery of advanced materials by simplifying the acquisition function design process.
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