Machine-learning-assisted thin-film growth: Bayesian optimization in molecular beam epitaxy of SrRuO3 thin films

@article{Wakabayashi2019MachinelearningassistedTG,
  title={Machine-learning-assisted thin-film growth: Bayesian optimization in molecular beam epitaxy of SrRuO3 thin films},
  author={Yuki K. Wakabayashi and Takuma Otsuka and Yoshiharu Krockenberger and Hiroshi Sawada and Yoshitaka Taniyasu and Hideki Yamamoto},
  journal={APL Materials},
  year={2019}
}
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… 

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