  A crucial problem in achieving innovative high-throughput materials growth
with machine learning and automation techniques, such as Bayesian optimization
(BO) and robotic experimentation, has been a lack of an appropriate way to
handle missing data due to experimental failures. Here, we propose a new BO
algorithm that complements the missing data in the optimization of materials
growth parameters. The proposed method provides a flexible optimization
algorithm capable of searching a wide multi-dimensional parameter space. We
demonstrate the effectiveness of the method with simulated data as well as in
its implementation for actual materials growth, namely
machine-learning-assisted molecular beam epitaxy (ML-MBE) of SrRuO3, which is
widely used as a metallic electrode in oxide electronics. Through the
exploitation and exploration in a wide three-dimensional parameter space, while
complementing the missing data, we attained tensile-strained SrRuO3 film with a
high residual resistivity ratio of 80.1, the highest among tensile-strained
SrRuO3 films ever reported, in only 35 MBE growth runs.
