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Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
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Schütt, Kristof, Gastegger, Michael, Tkatchenko, Alexandre, Müller, Klaus-Robert and Maurer, Reinhard J. (2019) Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions. Nature Communications, 10 . 5024 . doi:10.1038/s41467-019-12875-2
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WRAP-unifying-machine-learning-quantum-chemistry-deep-neural-network-molecular-wavefunctions-Maurer-2019.pdf - Accepted Version - Requires a PDF viewer. Download (4Mb) | Preview |
Official URL: http://www.doi.org/10.1038/s41467-019-12875-2
Abstract
Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force-field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of molecular structures for target electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry.
| Item Type: | Journal Article | ||||||||
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| Subjects: | Q Science > QD Chemistry | ||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Chemistry | ||||||||
| Journal or Publication Title: | Nature Communications | ||||||||
| Publisher: | Nature Publishing Group | ||||||||
| ISSN: | 2041-1723 | ||||||||
| Official Date: | 2019 | ||||||||
| Dates: |
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| Volume: | 10 | ||||||||
| Article Number: | 5024 | ||||||||
| DOI: | 10.1038/s41467-019-12875-2 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Open Access | ||||||||
| Funder: | UKRI, EPSRC, Federal Ministry of Education and Research Germany (BMBF), EU Horizon 2020 / Marie Sklodowska-Curie Program, Deutsche Forschungsgesellschaft (DFG) | ||||||||
| Grant number: | MR/S016023/1 | ||||||||
| Open Access Version: |
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