Accelerated Process Development of YBa₂Cu₃O₇ film with Bayesian Optimization Augmented by Transferred Data

Efficient process optimization is a key to the development of YBa 2 Cu 3 O 7 films, whose properties are influenced by many factors such as substrate, nanostructure, and composition. The simplest optimization is exhaustive search, which requires many experiments. Bayesian optimization is more efficient but requires initial data. Here we propose the Bayesian optimization augmented by domain-aligned sampling, data transfer, and peak screening (transfer-based Bayesian optimization), which efficiently acquires the initial data. In this method, the preliminary data in a system with a consistent process nature is used as the initial data. The preliminary data of the critical current density ( J c ) were obtained in the YBa 2 Cu 3 O 7 /SrTiO 3 by exhaustive search, and the J c of YBa 2 Cu 3 O 7 /LaAlO 3 was optimized. A Gaussian process regression model was constructed from the preliminary data of YBa 2 Cu 3 O 7 /SrTiO 3 and the domain-aligned sampling data of the YBa 2 Cu 3 O 7 /LaAlO 3 . The initial local maxima were included as candidates for the global maximum in the peak screening to avoid overlooking potentially high J c . The transfer-based Bayesian optimization found an optimal J c as high as that by the exhaustive search with a smaller number of iterations. Thus, the optimal property was obtained efficiently by transfer-based Bayesian optimization, making the optimization of coated conductors and advanced materials more efficient.