Advanced machine learning decision policies for diameter control of carbon nanotubes
Advanced machine learning decision policies for diameter control of carbon nanotubes
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London: Nature Publishing Group UK
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English
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London: Nature Publishing Group UK
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The diameters of single-walled carbon nanotubes (SWCNTs) are directly related to their electronic properties, making diameter control highly desirable for a number of applications. Here we utilized a machine learning planner based on the Expected Improvement decision policy that mapped regions where growth was feasible vs. not feasible and further...
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Advanced machine learning decision policies for diameter control of carbon nanotubes
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TN_cdi_doaj_primary_oai_doaj_org_article_405301fc6d5648b1b8d827b52675e7b9
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_405301fc6d5648b1b8d827b52675e7b9
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ISSN
2057-3960
E-ISSN
2057-3960
DOI
10.1038/s41524-021-00629-y
