Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

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…

• UR
  UK Research and Innovation

  Award: MR/S016023/1
• UO
  University of Warwick
• DF
  Deutsche Forschungsgemeinschaft

  Awards: 01GQ0850, EXC 2046, Grant Math+, EXC 2046/1, Project ID 390685689, 01GQ1115, 01IS14013A-E, 390685689, Math+, EXC 2046/1, Project ID 390685689, EXC 2046/1
• BF
  Bundesministerium für Bildung und Forschung

  Awards: 01IS14013A-E, 01GQ0850, 01IS14013A, 01IS18037A, 01GQ1115, 390685689
• UO
  University of California, Los Angeles
• EA
  Engineering and Physical Sciences Research Council

  Awards: EP/R029431, EP/R029431/1, EP/R029431/1, MR/S016023/1
• IF
  Institute for Information and Communications Technology Promotion

  Awards: 2017-0-00451, 2017-0-01779