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Perovskite insulator SrTiO3 is expected to be applied to the next generationof electronic and photonic devices as high-k capacitors and photocatalysts.However, reproducible growth of highly insulating stoichiometric SrTiO3 filmsremains challenging due to the difficulty of the precise stoichiometry controlin perovskite oxide films. Here, to grow stoichiometric SrTiO3 thin films byfine-tuning multiple growth conditions, we developed a new Bayesianoptimization (BO)-based machine learning method that encourages the explorationof the search space by varying the prior mean to get out of suboptimal growthcondition parameters. Using simulated data, we demonstrate the efficacy of thenew BO method, which reproducibly reaches the global best conditions. With theBO method implemented in machine-learning-assisted molecular beam epitaxy(ML-MBE), highly insulating stoichiometric SrTiO3 film with no absorption inthe band gap was developed in only 44 MBE growth runs. The proposed algorithmprovides an efficient experimental design platform that is not as dependent onthe experience of individual researchers and will accelerate not only oxideelectronics but also various material syntheses.

Stoichiometric growth of SrTiO3 films via Bayesian optimization with adaptive prior mean