Machine learned features from density of states for accurate adsorption energy prediction
Machine learned features from density of states for accurate adsorption energy prediction
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London: Nature Publishing Group UK
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English
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Publisher
London: Nature Publishing Group UK
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Materials databases generated by high-throughput computational screening, typically using density functional theory (DFT), have become valuable resources for discovering new heterogeneous catalysts, though the computational cost associated with generating them presents a crucial roadblock. Hence there is a significant demand for developing descript...
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Machine learned features from density of states for accurate adsorption energy prediction
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TN_cdi_doaj_primary_oai_doaj_org_article_db4b29d6fa904989b00edcedcb18059e
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_db4b29d6fa904989b00edcedcb18059e
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ISSN
2041-1723
E-ISSN
2041-1723
DOI
10.1038/s41467-020-20342-6
