Machine learning for improved density functional theory thermodynamics
Machine learning for improved density functional theory thermodynamics
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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
The predictive accuracy of density functional theory (DFT) for alloy formation enthalpies is often limited by intrinsic energy resolution errors, particularly in ternary phase stability calculations. In this work, we present a machine learning (ML) approach to systematically correct these errors, improving the reliability of first-principles predic...
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Full title
Machine learning for improved density functional theory thermodynamics
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TN_cdi_doaj_primary_oai_doaj_org_article_36067d9bcafc46b1bfaceaf2c2531374
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_36067d9bcafc46b1bfaceaf2c2531374
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ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-025-02088-7
