mlsanityData to 7 Oct 2026

Pairing-based graph neural network for simulating quantum materials

arXiv 2311.02143 ↗First posted 3 Nov 2023cond-mat.str-el cond-mat.dis-nn cs.LGcited by 9

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Abstract (arXiv)

We develop a pairing-based graph neural network for simulating quantum many-body systems. Our architecture augments a BCS-type geminal wavefunction with a generalized pair amplitude parameterized by a graph neural network. Variational Monte Carlo with our neural network simultaneously provides an accurate, flexible, and scalable method for simulating many-electron systems. We apply this method to two-dimensional semiconductor electron-hole bilayers and obtain accurate results on a variety of interaction-induced phases, including the exciton Bose-Einstein condensate, electron-hole superconductor, and bilayer Wigner crystal. Our study demonstrates the potential of physically-motivated neural network wavefunctions for quantum materials simulations.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 19 of its 59 references that are in the map. First posted on arXiv 3 Nov 2023.

Builds on

19 of 59 references.

References · 19

  1. 2017Self-Normalizing Neural Networkscited by 591
  2. 2019Ab-Initio Solution of the Many-Electron Schr"odinger Equation with Deep Neural Networkscited by 195
  3. 2019Deep neural network solution of the electronic Schr"odinger equationcited by 124
  4. 2019Deep autoregressive models for the efficient variational simulation of many-body quantum systemscited by 64
  5. 2018Quantum Entanglement in Deep Learning Architecturescited by 49
  6. 2017Neural-Network Quantum States, String-Bond States, and Chiral Topological Statescited by 31
  7. 2020Gauge equivariant neural networks for quantum lattice gauge theoriescited by 27
  8. 2022Discovering Quantum Phase Transitions with Fermionic Neural Networkscited by 25
  9. 2021Solving the electronic Schr"odinger equation for multiple nuclear geometries with weight-sharing deep neural networkscited by 22
  10. 2021Neural tensor contractions and the expressive power of deep neural quantum statescited by 21
  11. 2022Electronic excited states in deep variational Monte Carlocited by 21
  12. 2021Ab-initio study of interacting fermions at finite temperature with neural canonical transformationcited by 16
7 more
  1. 2020Phases of two-dimensional spinless lattice fermions with first-quantized deep neural-network quantum statescited by 15
  2. 2023Neural Wave Functions for Superfluidscited by 12
  3. 2022Gauge Equivariant Neural Networks for 2+1D U(1) Gauge Theory Simulations in Hamiltonian Formulationcited by 11
  4. 2022Simulating 2+1D Lattice Quantum Electrodynamics at Finite Density with Neural Flow Wavefunctionscited by 10
  5. 2021Spacetime Neural Network for High Dimensional Quantum Dynamicscited by 9
  6. 2022Deep Variational Free Energy Approach to Dense Hydrogencited by 6
  7. 2023Artificial intelligence for artificial materials: moir'e atomcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 5.

9 papers.

  1. 2024Ab-initio variational wave functions for the time-dependent many-electron Schr"odinger equationcited by 7
  2. 2025Is attention all you need to solve the correlated electron problem?cited by 2
  3. 2026First-Principles AI finds crystallization of fractional quantum Hall liquidscited by 2
  4. 2026QERNEL: a Scalable Large Electron Modelcited by 2
  5. 2025Reduced Density Matrices Through Machine Learningcited by 1
  6. 2026Low-variance estimators overcome the phase-gradient bottleneck in complex-valued neural quantum statescited by 1
  7. 2026Mechanistic Interpretability and Causal Feature Steering of Neural Quantum States via Sparse Autoencoderscited by 1
  8. 2025Revisiting the Broken Symmetry Phase of Solid Hydrogen: A Neural Network Variational Monte Carlo Studycited by 0
  9. 2026Representability-Aware Neural Networks for Reduced Density Matrices: Application to Fractional Chern Insulatorscited by 0