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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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Official URL: http://www.doi.org/10.1038/s41467-019-12875-2

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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
Subjects: Q Science > QD Chemistry
Divisions: Faculty of Science > Chemistry
Journal or Publication Title: Nature Communications
Publisher: Nature Publishing Group
ISSN: 2041-1723
Official Date: 2019
Dates:
DateEvent
2019Published
15 November 2019Available
19 September 2019Accepted
Date of first compliant deposit: 1 November 2019
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
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