Simulating moir'e quantum matter with neural network

Moir'e materials provide an ideal platform for exploring quantum phases of matter. However, solving the many-electron problem in moir'e systems is challenging due to strong correlation effects. We introduce a powerful variational representation of quantum states, many-body neural Bloch wavefunction, to solve many-electron problems in moir'e materials accurately and efficiently. Applying our method to the semiconductor heterobilayer WSe2/WS2 , we obtain a generalized Wigner crystal at filling factor n = 1/3, a Mott insulator n = 1, and a correlated insulator with local magnetic moments and antiferromagnetic spin correlation at n = 2. Our neural network approach improves the simulation accuracy of strongly interacting moir'e materials and paves the way for discovery of new quantum phases with variational learning principle in a unified framework.