Bayesian optimization with experimental failure for high-throughput materials growth
Bayesian optimization with experimental failure for high-throughput materials growth
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Ithaca: Cornell University Library, arXiv.org
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English
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Publisher
Ithaca: Cornell University Library, arXiv.org
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Contents
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 i...
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Bayesian optimization with experimental failure for high-throughput materials growth
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TN_cdi_proquest_journals_2649833120
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2649833120
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E-ISSN
2331-8422
DOI
10.48550/arxiv.2204.05452
