Universal fragment descriptors for predicting properties of inorganic crystals
Universal fragment descriptors for predicting properties of inorganic crystals
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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
Although historically materials discovery has been driven by a laborious trial-and-error process, knowledge-driven materials design can now be enabled by the rational combination of Machine Learning methods and materials databases. Here, data from the AFLOW repository for
ab initio
calculations is combined with Quantitative Materials Structur...
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Universal fragment descriptors for predicting properties of inorganic crystals
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TN_cdi_doaj_primary_oai_doaj_org_article_e51aabf9bad84fe6a8a7bf233cac04c5
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_e51aabf9bad84fe6a8a7bf233cac04c5
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ISSN
2041-1723
E-ISSN
2041-1723
DOI
10.1038/ncomms15679
