To address surface reaction network complexity using scaling relations machine learning and DFT calc...
To address surface reaction network complexity using scaling relations machine learning and DFT calculations
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Publisher
London: Nature Publishing Group UK
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Language
English
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London: Nature Publishing Group UK
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Contents
Surface reaction networks involving hydrocarbons exhibit enormous complexity with thousands of species and reactions for all but the very simplest of chemistries. We present a framework for optimization under uncertainty for heterogeneous catalysis reaction networks using surrogate models that are trained on the fly. The surrogate model is construc...
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Full title
To address surface reaction network complexity using scaling relations machine learning and DFT calculations
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TN_cdi_doaj_primary_oai_doaj_org_article_f70daa3691ba4e61869251a1b60cfe88
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_f70daa3691ba4e61869251a1b60cfe88
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ISSN
2041-1723
E-ISSN
2041-1723
DOI
10.1038/ncomms14621
