Adaptive learning implementations for materials design are challenged by the complex, nonlinear relationships between composition and properties, particularly in high-performance applications such as high-temperature compositionally complex refractory alloys. Traditional Bayesian optimization (BO) methods, which typically rely on a single Gaussian Process (GP) surrogate, often struggle to model heterogenous behaviors across the design domain. To address this limitation, we introduce collaborative BO as a multi-agent framework for materials discovery. In the context of optimizing compositions for desired properties, each agent models a specific subregion of the design space, where subregions share similar property trends, and exchanges information with the other agents to expedite exploration and design optimization. Comparative evaluations demonstrate that, when compared to single-agent BO and other approaches discussed in this article multi-agent BO allows flexible information-sharing protocols and effectively reduces iterations of adaptive learning while reliably delivering designs that meet the targeted mechanical properties. These findings provide novel insights into the behavior of refractory multi-component alloys, using the Hf-Ti-Ta-Nb system as a case study, and illustrate the potential of adaptive multi-agent learning in efficiently screening extensive materials libraries. Moreover, the framework is broadly applicable to other problems characterized by diverse data sources, where advanced optimization strategies are essential for accelerated materials discovery.
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