Bayesian optimization with experimental failure for high-throughput materials growth
نویسندگان
چکیده
Abstract 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 experimental failures. Here, we propose BO algorithm that complements the optimizing parameters. The proposed method provides flexible searches wide multi-dimensional parameter space. We demonstrate effectiveness simulated well its implementation for actual growth, namely machine-learning-assisted molecular beam epitaxy (ML-MBE) SrRuO 3 , which is widely used metallic electrode oxide electronics. Through exploitation exploration three-dimensional space, while complementing data, attained tensile-strained film high residual resistivity ratio 80.1, highest among films ever reported, only 35 MBE runs.
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ژورنال
عنوان ژورنال: npj computational materials
سال: 2022
ISSN: ['2057-3960']
DOI: https://doi.org/10.1038/s41524-022-00859-8