15 citations · 24 across the 8 of their papers we have counts for
8 papers
Accurate Self-Attention Wavefunctions at Large Scale
Filippo Gaggioli, Sam Azadi, Liang Fu
Self-attention neural networks provide powerful variational wavefunctions that surpass the expressivity of traditional variational ansatze. This expressivity, however, comes with i…
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 correlati…
Electrically controlled interlayer trion fluid in electron-hole bilayers
Ruishi Qi, Qize Li, Zuocheng Zhang +12
The combination of repulsive and attractive Coulomb interactions in a quantum electron(e)-hole(h) fluid can give rise to novel correlated phases of multiparticle charge complexes s…
Pairing-based graph neural network for simulating quantum materials
Di Luo, David D. Dai, Liang Fu
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 amplitud…