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This article discusses the use of Bayesian optimization, a machine-learning-based approach, to enhance the critical current properties of K-doped Ba122. The methodology involves a collaborative framework between researchers and data-driven processes, resulting in an optimal parameter set for fabricating permanent magnet prototypes. Experimental findings are compared to numerical models based on the finite element method.
