| scholarly article | Q13442814 |
| P819 | ADS bibcode | 2017NatCo...8..872B |
| P818 | arXiv ID | 1609.02815 |
| P356 | DOI | 10.1038/S41467-017-00839-3 |
| P932 | PMC publication ID | 5636838 |
| P698 | PubMed publication ID | 29021555 |
| P50 | author | Klaus-Robert Müller | Q26709023 |
| Li Li | Q57956637 | ||
| Leslie Vogt | Q89098228 | ||
| Kieron Burke | Q89362891 | ||
| P2093 | author name string | Mark E Tuckerman | |
| Felix Brockherde | |||
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| P275 | copyright license | Creative Commons Attribution 4.0 International | Q20007257 |
| P6216 | copyright status | copyrighted | Q50423863 |
| P4510 | describes a project that uses | machine learning | Q2539 |
| P433 | issue | 1 | |
| P407 | language of work or name | English | Q1860 |
| P921 | main subject | machine learning | Q2539 |
| P304 | page(s) | 872 | |
| P577 | publication date | 2017-10-11 | |
| P1433 | published in | Nature Communications | Q573880 |
| P1476 | title | Bypassing the Kohn-Sham equations with machine learning | |
| P478 | volume | 8 |
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