Bayesian optimization with experimental failure for high-throughput materials growth

A crucial problem in achieving innovative high-throughput materials growth with machine learning, such as Bayesian optimization (BO), and automation techniques has been a lack of an appropriate way to handle missing data due to experimental failures. Here, we propose a BO algorithm that complements the missing data in optimizing materials growth parameters. The proposed method provides a flexible optimization algorithm that searches 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.

Automated summary

The lack of an appropriate way to handle missing data has been a crucial problem in achieving innovative high-throughput materials growth with machine learning and automation techniques. In this study, a Bayesian optimization algorithm is proposed to complement the missing data in optimizing materials growth parameters. The algorithm provides a flexible optimization approach that searches a wide multi-dimensional parameter space. The effectiveness of the method is demonstrated through simulated data and its implementation in the growth of SrRuO3 using machine-learning-assisted molecular beam epitaxy, achieving high-quality films in just 35 growth runs.