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Discovering Quantum Phase Transitions with Fermionic Neural Networks

Gino Cassella, Halvard Sutterud, Sam Azadi, N. D. Drummond, David Pfau, James S. Spencer, and W. M. C. Foulkes
Phys. Rev. Lett. 130, 036401 – Published 20 January 2023
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Abstract

Deep neural networks have been very successful as highly accurate wave function Ansätze for variational Monte Carlo calculations of molecular ground states. We present an extension of one such Ansatz, FermiNet, to calculations of the ground states of periodic Hamiltonians, and study the homogeneous electron gas. FermiNet calculations of the ground-state energies of small electron gas systems are in excellent agreement with previous initiator full configuration interaction quantum Monte Carlo and diffusion Monte Carlo calculations. We investigate the spin-polarized homogeneous electron gas and demonstrate that the same neural network architecture is capable of accurately representing both the delocalized Fermi liquid state and the localized Wigner crystal state. The network converges on the translationally invariant ground state at high density and spontaneously breaks the symmetry to produce the crystalline ground state at low density, despite being given no a priori knowledge that a phase transition exists.

  • Figure
  • Received 28 April 2022
  • Accepted 18 November 2022

DOI:https://doi.org/10.1103/PhysRevLett.130.036401

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

Published by the American Physical Society

Physics Subject Headings (PhySH)

Condensed Matter, Materials & Applied Physics

Authors & Affiliations

Gino Cassella1,*, Halvard Sutterud1, Sam Azadi4, N. D. Drummond3, David Pfau2,1, James S. Spencer2, and W. M. C. Foulkes1

  • 1Department of Physics, Imperial College London, London SW7 2AZ, United Kingdom
  • 2DeepMind, London N1C 4DJ, United Kingdom
  • 3Department of Physics, Lancaster University, Lancaster LA1 4YB, United Kingdom
  • 4Department of Physics, University of Oxford, Oxford OX1 3PU, United Kingdom

  • *g.cassella20@imperial.ac.uk

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Vol. 130, Iss. 3 — 20 January 2023

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