Machine-learning-assisted thin-film growth: Bayesian optimization in molecular beam epitaxy of SrRuO3 thin films
Materials informatics exploiting machine learning techniques, e.g., Bayesian optimization (BO), have the potential to reduce the number of thin-film growth runs for optimization of thin-film growth conditions through incremental updates of machine learning models in accordance with newly measured da...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
AIP Publishing LLC
2019-10-01
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| Series: | APL Materials |
| Online Access: | http://dx.doi.org/10.1063/1.5123019 |