Complexity of many-body interactions in transition metals via machine-learned force fields from the...
Complexity of many-body interactions in transition metals via machine-learned force fields from the TM23 data set
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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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This work examines challenges associated with the accuracy of machine-learned force fields (MLFFs) for bulk solid and liquid phases of
d
-block elements. In exhaustive detail, we contrast the performance of force, energy, and stress predictions across the transition metals for two leading MLFF models: a kernel-based atomic cluster expansion m...
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Complexity of many-body interactions in transition metals via machine-learned force fields from the TM23 data set
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TN_cdi_doaj_primary_oai_doaj_org_article_a68504f7e9f24d84b4a565a76a1b91db
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_a68504f7e9f24d84b4a565a76a1b91db
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ISSN
2057-3960
E-ISSN
2057-3960
DOI
10.1038/s41524-024-01264-z
