Benchmarking the performance of Bayesian optimization across multiple experimental materials science...
Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains
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London: Nature Publishing Group UK
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Language
English
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London: Nature Publishing Group UK
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Bayesian optimization (BO) has been leveraged for guiding autonomous and high-throughput experiments in materials science. However, few have evaluated the efficiency of BO across a broad range of experimental materials domains. In this work, we quantify the performance of BO with a collection of surrogate model and acquisition function pairs across...
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Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains
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TN_cdi_doaj_primary_oai_doaj_org_article_bfc6d1e9f14b4b8bbc093f32c3360b8c
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_bfc6d1e9f14b4b8bbc093f32c3360b8c
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ISSN
2057-3960
E-ISSN
2057-3960
DOI
10.1038/s41524-021-00656-9
