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