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

Perovskite insulator SrTiO3 (STO) 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 (STO) films remains challenging due to the difficulty of precise stoichiometry control in perovskite oxide films. Here, to grow stoichiometric (STO) thin films by fine-tuning multiple growth conditions, we developed a new Bayesian optimization (BO)-based machine learning method that encourages 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), a highly insulating stoichiometric (STO) film with no absorption in the bandgap 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.