Density of states prediction for materials discovery via contrastive learning from probabilistic emb...
Density of states prediction for materials discovery via contrastive learning from probabilistic embeddings
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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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Machine learning for materials discovery has largely focused on predicting an individual scalar rather than multiple related properties, where spectral properties are an important example. Fundamental spectral properties include the phonon density of states (phDOS) and the electronic density of states (eDOS), which individually or collectively are...
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Density of states prediction for materials discovery via contrastive learning from probabilistic embeddings
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TN_cdi_doaj_primary_oai_doaj_org_article_43738d28e2aa4cb8a84eb39c39e3d7fe
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_43738d28e2aa4cb8a84eb39c39e3d7fe
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ISSN
2041-1723
E-ISSN
2041-1723
DOI
10.1038/s41467-022-28543-x
