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Honeycomb Crystal in a Mass-Asymmetric Electron-Hole Bilayer

arXiv 2610.17030 ↗First posted 12 Oct 2026cond-mat.str-el cond-mat.mtrl-sci cs.LGcited by 0

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Abstract (arXiv)

A two-dimensional electron gas in a uniform neutralizing background forms a triangular Wigner crystal at low density. In a strongly mass-asymmetric electron-hole bilayer, localized heavy holes instead provide a nonuniform positive background that supports an electron crystal at densities two orders of magnitude higher than for a homogeneous electron gas in the same material. Here we show that correlated mobile electrons can overturn the preference for triangular order, stabilizing a honeycomb electron-hole crystal. Using the powerful real-space neural-network variational Monte Carlo in the Born-Oppenheimer limit with fixed holes, we find that increasing the density drives a transition from a triangular electron-hole crystal to a honeycomb crystal, which eventually melts into itinerant electrons coexisting with the honeycomb or the triangular hole lattice at fixed interlayer separation. Relative to triangular order, honeycomb order emerges when gains in electronic kinetic energy and electron-hole attraction outweigh its higher intralayer Coulomb repulsion, a threshold never reached in the rigid point-dipole model. Moreover, the honeycomb hole lattice supports paramagnetic, antiferromagnetic, and stripe-ordered spin states. Complex polarization indicates a metal-insulator transition, while the states on the honeycomb hole lattice are consistent with a stripe-ordered Dirac semimetal and paramagnetic or antiferromagnetic Mott insulators via mapping to the half-filled Hubbard model. We further propose a BLG/hBN/WS2-WSe2 heterostructure as a platform for the predicted honeycomb crystal, at an experimentally feasible density n~1011~cm-2.

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References · 10

  1. 2019Ab-Initio Solution of the Many-Electron Schr"odinger Equation with Deep Neural Networkscited by 200
  2. 2019Deep neural network solution of the electronic Schr"odinger equationcited by 127
  3. 2022A Self-Attention Ansatz for Ab-initio Quantum Chemistrycited by 49
  4. 2022Discovering Quantum Phase Transitions with Fermionic Neural Networkscited by 26
  5. 2023Accurate Computation of Quantum Excited States with Neural Networkscited by 17
  6. 2025An ab initio foundation model of wavefunctions that accurately describes chemical bond breakingcited by 14
  7. 2023Neural Wave Functions for Superfluidscited by 13
  8. 2023Pairing-based graph neural network for simulating quantum materialscited by 10
  9. 2022Deep Variational Free Energy Approach to Dense Hydrogencited by 7
  10. 2025Revisiting the Broken Symmetry Phase of Solid Hydrogen: A Neural Network Variational Monte Carlo Studycited by 1

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