Retrospective on a decade of machine learning for chemical discovery
Standfirst Over the last decade, we have witnessed the emergence of ever more machine learning applications in all aspects of the chemical sciences. Here, we highlight specific achievements of machine learning models in the field of computational chemistry by considering selected studies of electron...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2020-09-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-020-18556-9 |
| _version_ | 1818846080984416256 |
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| author | O. Anatole von Lilienfeld Kieron Burke |
| author_facet | O. Anatole von Lilienfeld Kieron Burke |
| author_sort | O. Anatole von Lilienfeld |
| collection | DOAJ |
| description | Standfirst Over the last decade, we have witnessed the emergence of ever more machine learning applications in all aspects of the chemical sciences. Here, we highlight specific achievements of machine learning models in the field of computational chemistry by considering selected studies of electronic structure, interatomic potentials, and chemical compound space in chronological order. |
| first_indexed | 2024-12-19T05:39:52Z |
| format | Article |
| id | doaj.art-df8a833a0e9a45df941e56a6116b39e1 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-12-19T05:39:52Z |
| publishDate | 2020-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-df8a833a0e9a45df941e56a6116b39e12022-12-21T20:34:03ZengNature PortfolioNature Communications2041-17232020-09-011111410.1038/s41467-020-18556-9Retrospective on a decade of machine learning for chemical discoveryO. Anatole von Lilienfeld0Kieron Burke1Faculty of Physics, University of ViennaDepartments of Chemistry and Physics, University of California, IrvineStandfirst Over the last decade, we have witnessed the emergence of ever more machine learning applications in all aspects of the chemical sciences. Here, we highlight specific achievements of machine learning models in the field of computational chemistry by considering selected studies of electronic structure, interatomic potentials, and chemical compound space in chronological order.https://doi.org/10.1038/s41467-020-18556-9 |
| spellingShingle | O. Anatole von Lilienfeld Kieron Burke Retrospective on a decade of machine learning for chemical discovery Nature Communications |
| title | Retrospective on a decade of machine learning for chemical discovery |
| title_full | Retrospective on a decade of machine learning for chemical discovery |
| title_fullStr | Retrospective on a decade of machine learning for chemical discovery |
| title_full_unstemmed | Retrospective on a decade of machine learning for chemical discovery |
| title_short | Retrospective on a decade of machine learning for chemical discovery |
| title_sort | retrospective on a decade of machine learning for chemical discovery |
| url | https://doi.org/10.1038/s41467-020-18556-9 |
| work_keys_str_mv | AT oanatolevonlilienfeld retrospectiveonadecadeofmachinelearningforchemicaldiscovery AT kieronburke retrospectiveonadecadeofmachinelearningforchemicaldiscovery |