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

K. T. Schütt, M. Gastegger, A. Tkatchenko, K. R. Müller, R. J. Maurer

Research output: Contribution to journalArticle

17 Citations (Scopus)

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.

Original languageEnglish
Article number5024
JournalNature communications
Volume10
Issue number1
DOIs
Publication statusPublished - 2019 Dec 1

ASJC Scopus subject areas

  • Chemistry(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Physics and Astronomy(all)

Fingerprint Dive into the research topics of 'Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions'. Together they form a unique fingerprint.

  • Cite this