From the 1 of 6 linked papers with an AI index.
6 papers
Accurate Self-Attention Wavefunctions at Large Scale
Filippo Gaggioli, Sam Azadi, Liang Fu
The authors apply self‑attention neural network variational wavefunctions to the two‑dimensional homogeneous electron gas with up to 169 particles, achieving energies lower than st…
Quantum Electron Quasicrystal
Pierre-Antoine Graham, Filippo Gaggioli, Liang Fu
The strongly correlated phases of the homogeneous electron gas constitute the vocabulary of many-body condensed matter physics and find a natural realization in semiconductors. In…
QERNEL: a Scalable Large Electron Model
Khachatur Nazaryan, Liang Fu
We introduce QERNEL, a foundational neural wavefunction that variationally solves families of parameterized many-electron Hamiltonians and captures their ground states throughout p…
Solving and visualizing fractional quantum Hall wavefunctions with neural network
Yi Teng, David D. Dai, Liang Fu
We introduce an attention-based fermionic neural network (FNN) to variationally solve the problem of two-dimensional Coulomb electron gas in magnetic fields, a canonical platform f…
Electron bubbles in highly excited states of the lowest Landau level
David D. Dai, Liang Fu
We study the entire energy spectrum of an electron droplet in the lowest Landau level. By exact diagonalization calculations, we find highly excited states in the middle of the spe…
Simulating moiré quantum matter with neural network
Di Luo, David D. Dai, Liang Fu
Moiré materials provide an ideal platform for exploring quantum phases of matter. However, solving the many-electron problem in moiré systems is challenging due to strong correla…