期刊
NATURE COMMUNICATIONS
卷 10, 期 -, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41467-019-12875-2
关键词
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资金
- Institute of Pure and Applied Mathematics (IPAM) at the University of California Los Angeles
- UKRI Future Leaders Fellowship [MR/S016023/1]
- Federal Ministry of Education and Research (BMBF) [01IS18037A]
- European Unions Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant [792572]
- EPSRC [EP/R029431/1]
- German Ministry for Education and Research (BMBF) [01IS14013A-E, 01GQ1115, 01GQ0850]
- Deutsche Forschungsgesellschaft (DFG) [EXC 2046/1, 390685689]
- Technology Promotion (IITP) - Korea government [2017-0-00451, 2017-0-01779]
- Scientific Computing Research Technology Platform of the University of Warwick
- EPSRC [EP/R029431/1] Funding Source: UKRI
- UKRI [MR/S016023/1] Funding Source: UKRI
- Marie Curie Actions (MSCA) [792572] Funding Source: Marie Curie Actions (MSCA)
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 targeting electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry.
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