Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
Machine learning models can accurately predict atomistic chemical properties but do not provide access to the molecular electronic structure. Here the authors use a deep learning approach to predict the quantum mechanical wavefunction at high efficiency from which other ground-state properties can b...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2019-11-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-019-12875-2 |
| _version_ | 1818342612963164160 |
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| author | K. T. Schütt M. Gastegger A. Tkatchenko K.-R. Müller R. J. Maurer |
| author_facet | K. T. Schütt M. Gastegger A. Tkatchenko K.-R. Müller R. J. Maurer |
| author_sort | K. T. Schütt |
| collection | DOAJ |
| description | Machine learning models can accurately predict atomistic chemical properties but do not provide access to the molecular electronic structure. Here the authors use a deep learning approach to predict the quantum mechanical wavefunction at high efficiency from which other ground-state properties can be derived. |
| first_indexed | 2024-12-13T16:17:28Z |
| format | Article |
| id | doaj.art-23a7395375c64d40967fed2e3eac6dc0 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-12-13T16:17:28Z |
| publishDate | 2019-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-23a7395375c64d40967fed2e3eac6dc02022-12-21T23:38:49ZengNature PortfolioNature Communications2041-17232019-11-0110111010.1038/s41467-019-12875-2Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctionsK. T. Schütt0M. Gastegger1A. Tkatchenko2K.-R. Müller3R. J. Maurer4Machine Learning Group, Technische Universität BerlinMachine Learning Group, Technische Universität BerlinPhysics and Materials Science Research Unit, University of LuxembourgMachine Learning Group, Technische Universität BerlinDepartment of Chemistry, University of WarwickMachine learning models can accurately predict atomistic chemical properties but do not provide access to the molecular electronic structure. Here the authors use a deep learning approach to predict the quantum mechanical wavefunction at high efficiency from which other ground-state properties can be derived.https://doi.org/10.1038/s41467-019-12875-2 |
| spellingShingle | K. T. Schütt M. Gastegger A. Tkatchenko K.-R. Müller R. J. Maurer Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions Nature Communications |
| title | Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions |
| title_full | Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions |
| title_fullStr | Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions |
| title_full_unstemmed | Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions |
| title_short | Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions |
| title_sort | unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions |
| url | https://doi.org/10.1038/s41467-019-12875-2 |
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