@article{144044b01c3243aa8f8936aad054e84c,
title = "Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions",
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 targeting electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry.",
author = "Sch{\"u}tt, {K. T.} and M. Gastegger and A. Tkatchenko and M{\"u}ller, {K. R.} and Maurer, {R. J.}",
note = "Funding Information: We gratefully acknowledge support by the Institute of Pure and Applied Mathematics (IPAM) at the University of California Los Angeles during a long program workshop. R.J. M. acknowledges funding through a UKRI Future Leaders Fellowship (MR/S016023/1). K.T.S. and K.R.M. acknowledge support by the Federal Ministry of Education and Research (BMBF) for the Berlin Center for Machine Learning (01IS18037A). This project has received funding from the European Unions Horizon 2020 research and innovation program under the Marie Sk{\l}odowska-Curie grant agreement No. 792572. Computing resources have been provided by the Scientific Computing Research Technology Platform of the University of Warwick, and the EPSRC-funded high end computing Materials Chemistry Consortium (EP/R029431/1). K.R.M. acknowledges partial financial support by the German Ministry for Education and Research (BMBF) under Grants 01IS14013AE, 01GQ1115 and 01GQ0850; Deutsche Forschungsgesellschaft (DFG) under Grant Math+, EXC 2046/1, Project ID 390685689 and by the Technology Promotion (IITP) grant funded by the Korea government (Nos. 2017-0-00451, 2017-0-01779). Correspondence to R.J.M., K.R.M., and A.T. Publisher Copyright: {\textcopyright} 2019, The Author(s).",
year = "2019",
month = dec,
day = "1",
doi = "10.1038/s41467-019-12875-2",
language = "English",
volume = "10",
journal = "Nature communications",
issn = "2041-1723",
publisher = "Nature Publishing Group",
number = "1",
}