15 citations · 24 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…