4 citations · 8 across the 5 of their papers we have counts for
5 papers
Solving fractional electron states in twisted MoTe with deep neural network
Di Luo, Timothy Zaklama, Liang Fu
The emergence of moiré materials, such as twisted transition-metal dichalcogenides (TMDs), has created a fertile ground for discovering novel quantum phases of matter. However, sol…
Structure factor and topological bound of twisted bilayer semiconductors at fractional fillings
Timothy Zaklama, Di Luo, Liang Fu
The structure factor is a useful observable for probing charge density correlations in real materials, and its long-wavelength behavior encapsulated by ``quantum weight'' has recen…
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…
Artificial intelligence for artificial materials: moiré atom
Di Luo, Aidan P. Reddy, Trithep Devakul +1
Moiré engineering in atomically thin van der Waals heterostructures creates artificial quantum materials with designer properties. We solve the many-body problem of interacting ele…