Corpus ID: 202120723

Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks

@article{Pfau2019AbInitioSO,
  title={Ab-Initio Solution of the Many-Electron Schr{\"o}dinger Equation with Deep Neural Networks},
  author={David Pfau and James S. Spencer and Alexander G. de G. Matthews and W. Matthew C. Foulkes},
  journal={ArXiv},
  year={2019},
  volume={abs/1909.02487}
}
  • David Pfau, James S. Spencer, +1 author W. Matthew C. Foulkes
  • Published 2019
  • Chemistry, Physics, Computer Science
  • ArXiv
  • Given access to accurate solutions of the many-electron Schr\"odinger equation, nearly all chemistry could be derived from first principles. Exact wavefunctions of interesting chemical systems are out of reach because they are NP-hard to compute in general, but approximations can be found using polynomially-scaling algorithms. The key challenge for many of these algorithms is the choice of wavefunction approximation, or Ansatz, which must trade off between efficiency and accuracy. Neural… CONTINUE READING

    Citations

    Publications citing this paper.
    SHOWING 1-10 OF 21 CITATIONS

    Deep neural network solution of the electronic Schrödinger equation

    VIEW 2 EXCERPTS
    CITES BACKGROUND & METHODS

    Machine Learning for Quantum Matter

    VIEW 2 EXCERPTS
    CITES BACKGROUND

    Universal approximation of symmetric and anti-symmetric functions

    VIEW 1 EXCERPT
    CITES BACKGROUND

    FILTER CITATIONS BY YEAR

    2019
    2020

    CITATION STATISTICS

    • 2 Highly Influenced Citations

    • Averaged 11 Citations per year from 2019 through 2020