63 citations · 114 across the 20 of their papers we have counts for
5 papers · 1 filter
Taming Landau level mixing in fractional quantum Hall states with deep learning
Yubing Qian, Tongzhou Zhao, Jianxiao Zhang +3
Strong correlation brings a rich array of emergent phenomena, as well as a daunting challenge to theoretical physics study. In condensed matter physics, the fractional quantum Hall…
Probing quantum critical phase from neural network wavefunction
Haoxiang Chen, Weiluo Ren, Xiang Li +1
One-dimensional (1D) systems and models provide a versatile platform for emergent phenomena induced by strong electron correlation. In this work, we extend the newly developed real…
Deep learning quantum Monte Carlo for solids
Yubing Qian, Xiang Li, Zhe Li +2
Deep learning has deeply changed the paradigms of many research fields. At the heart of chemical and physical sciences is the accurate ab initio calculation of many-body wavefuncti…
Emergent Wigner phases in moiré superlattice from deep learning
Xiang Li, Yubing Qian, Weiluo Ren +2
Moiré superlattice designed in stacked van der Waals material provides a dynamic platform for hosting exotic and emergent condensed matter phenomena. However, the relevance of stro…
Symmetry enforced solution of the many-body Schrödinger equation with deep neural network
Zhe Li, Zixiang Lu, Ruichen Li +5
The integration of deep neural networks with the Variational Monte Carlo (VMC) method has marked a significant advancement in solving the Schrödinger equation. In this work, we enf…