Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-10104
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Main Title: Hamiltonian datasets: "Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions""
Author(s): Schütt, Kristof T.
Gastegger, Michael
Tkatchenko, Alexandre
Müller, Klaus-Robert
Maurer, Reinhard J.
Type: Generic Research Data
References: https://doi.org/10.1038/s41467-019-12875-2
Language Code: en
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.
URI: https://depositonce.tu-berlin.de/handle/11303/11216
http://dx.doi.org/10.14279/depositonce-10104
Issue Date: 25-Sep-2019
Date Available: 27-May-2020
DDC Class: 500 Naturwissenschaften und Mathematik
Subject(s): machine learning
hamiltonians
SchNOrb
quantum chemistry
Sponsor/Funder: EC/H2020/792572/EU/Machine Learning for Catalytic Carbon Dioxide Activation/MachineCat
BMBF, 01IS18037A, Verbundprojekt BIFOLD-BZML: Berlin Institute for the Foundations of Learning and Data
UKRI, MR/S016023/1, Computational prediction of hot-electron chemistry: Towards electronic control of catalysis
EPSRC, EP/R029431/1, High End Computing materials chemistry consortium
License: https://choosealicense.com/licenses/mit/
Appears in Collections:FG Maschinelles Lernen » Research Data

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