QERNEL is a single conditioned neural wavefunction that variationally solves families of many-electron Hamiltonians in moiré heterobilayers and identifies the quantum liquid-crystal phase transition.
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A general-purpose self-attention Fermi neural network finds chiral p_x ± ip_y superconductivity in an attractive Fermi gas via unbiased energy minimization.
Neural quantum states yield Born-Oppenheimer and non-Born-Oppenheimer energies for high-pressure atomic hydrogen that match or beat prior projector Monte Carlo results up to 128 atoms while avoiding symmetry assumptions and mass-scale issues.
citing papers explorer
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QERNEL: a Scalable Large Electron Model
QERNEL is a single conditioned neural wavefunction that variationally solves families of many-electron Hamiltonians in moiré heterobilayers and identifies the quantum liquid-crystal phase transition.
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Attention is all you need to solve chiral superconductivity
A general-purpose self-attention Fermi neural network finds chiral p_x ± ip_y superconductivity in an attractive Fermi gas via unbiased energy minimization.
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Neural Wave Functions for High-Pressure Atomic Hydrogen
Neural quantum states yield Born-Oppenheimer and non-Born-Oppenheimer energies for high-pressure atomic hydrogen that match or beat prior projector Monte Carlo results up to 128 atoms while avoiding symmetry assumptions and mass-scale issues.
- WF-Bench: A Benchmark for Neural Network WaveFunction Expressivity and Scaling Laws