  Materials informatics exploiting machine learning techniques, e.g., Bayesian
optimization (BO), has the potential to offer high-throughput optimization of
thin-film growth conditions through incremental updates of machine learning
models in accordance with newly measured data. Here, we demonstrated BO-based
molecular beam epitaxy (MBE) of SrRuO3, one of the most-intensively studied
materials in the research field of oxide electronics, mainly owing to its
unique nature as a ferromagnetic metal. To simplify the intricate search space
of entangled growth conditions, we ran the BO for a single condition while
keeping the other conditions fixed. As a result, high-crystalline-quality
SrRuO3 film exhibiting a high residual resistivity ratio (RRR) of over 50 as
well as strong perpendicular magnetic anisotropy was developed in only 24 MBE
growth runs in which the Ru flux rate, growth temperature, and
O3-nozzle-to-substrate distance were optimized. Our BO-based search method
provides an efficient experimental design that is not as dependent on the
experience and skills of individual researchers, and it reduces experimental
time and cost, which will accelerate materials research.
