Deep learning for inverse problems in quantum mechanics

Victor Lantz, Najmeh Abiri, Gillis Carlsson, Mats-Erik Pistol

Research output: Journal Article or Conference Article in JournalJournal articleResearchpeer-review

Abstract

Inverse problems are important in quantum mechanics and involve such questions as finding which potential give a certain spectrum or which arrangement of atoms give certain properties to a molecule or solid. Inverse problems are typically very hard to solve and tend to be very compute intense. We here show that neural networks can easily solve inverse problems in quantum mechanics. It is known that a neural network can compute the spectrum of a given potential, a result which we reproduce. We find that the (much harder) inverse problem of computing the correct potential that gives a prescribed spectrum is equally easy for a neural network. We extend previous work where neural networks were used to find the electronic many-particle density given a potential by considering the inverse problem. That is, we show that neural networks can compute the potential that gives a prescribed many-electron density.
Original languageEnglish
JournalInternational Journal of Quantum Chemistry
ISSN0020-7608
DOIs
Publication statusPublished - 31 Dec 2020
Externally publishedYes

Keywords

  • quantum mechanics
  • Inverse problems
  • density functional theory
  • deep learning

Fingerprint

Dive into the research topics of 'Deep learning for inverse problems in quantum mechanics'. Together they form a unique fingerprint.

Cite this