Identifying domains of applicability of machine learning models for materials science
Machine learning models insufficient for certain screening tasks can still provide valuable predictions in specific sub-domains of the considered materials. Here, the authors introduce a diagnostic tool to detect regions of low expected model error as demonstrated for the case of transparent conduct...
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Nature Portfolio
2020
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oai:doaj.org-article:9eb779b7ce2346bab2bcf81c05b1f1a92021-12-02T19:09:54ZIdentifying domains of applicability of machine learning models for materials science10.1038/s41467-020-17112-92041-1723https://doaj.org/article/9eb779b7ce2346bab2bcf81c05b1f1a92020-09-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-17112-9https://doaj.org/toc/2041-1723Machine learning models insufficient for certain screening tasks can still provide valuable predictions in specific sub-domains of the considered materials. Here, the authors introduce a diagnostic tool to detect regions of low expected model error as demonstrated for the case of transparent conducting oxides.Christopher SuttonMario BoleyLuca M. GhiringhelliMatthias RuppJilles VreekenMatthias SchefflerNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-9 (2020) |
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Science Q Christopher Sutton Mario Boley Luca M. Ghiringhelli Matthias Rupp Jilles Vreeken Matthias Scheffler Identifying domains of applicability of machine learning models for materials science |
description |
Machine learning models insufficient for certain screening tasks can still provide valuable predictions in specific sub-domains of the considered materials. Here, the authors introduce a diagnostic tool to detect regions of low expected model error as demonstrated for the case of transparent conducting oxides. |
format |
article |
author |
Christopher Sutton Mario Boley Luca M. Ghiringhelli Matthias Rupp Jilles Vreeken Matthias Scheffler |
author_facet |
Christopher Sutton Mario Boley Luca M. Ghiringhelli Matthias Rupp Jilles Vreeken Matthias Scheffler |
author_sort |
Christopher Sutton |
title |
Identifying domains of applicability of machine learning models for materials science |
title_short |
Identifying domains of applicability of machine learning models for materials science |
title_full |
Identifying domains of applicability of machine learning models for materials science |
title_fullStr |
Identifying domains of applicability of machine learning models for materials science |
title_full_unstemmed |
Identifying domains of applicability of machine learning models for materials science |
title_sort |
identifying domains of applicability of machine learning models for materials science |
publisher |
Nature Portfolio |
publishDate |
2020 |
url |
https://doaj.org/article/9eb779b7ce2346bab2bcf81c05b1f1a9 |
work_keys_str_mv |
AT christophersutton identifyingdomainsofapplicabilityofmachinelearningmodelsformaterialsscience AT marioboley identifyingdomainsofapplicabilityofmachinelearningmodelsformaterialsscience AT lucamghiringhelli identifyingdomainsofapplicabilityofmachinelearningmodelsformaterialsscience AT matthiasrupp identifyingdomainsofapplicabilityofmachinelearningmodelsformaterialsscience AT jillesvreeken identifyingdomainsofapplicabilityofmachinelearningmodelsformaterialsscience AT matthiasscheffler identifyingdomainsofapplicabilityofmachinelearningmodelsformaterialsscience |
_version_ |
1718377105599758336 |